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FINAL DRAFT

FINANCIAL PROGRAMMING AND POLICY: THE CASE OF KENYA

By

Christopher K. Kiptoo

(MEFMI Candidate Fellow)

Supervisor

Dr. Anna Lennblad

A Technical Paper Submitted to the Macroeconomic and Financial Sector Management Institute of Eastern and Southern Africa (MEFMI) in Partial Fulfilment of the Requirements

of the MEFMI Fellowship Development Program

OCTOBER 2006

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ACKNOWLEDGMENTS

A number of people and institutions have helped me in one-way or another in the course of undertaking this technical paper. First, I would like to express my gratitude and thanks to my supervisor to Dr. Anna Lennblad for her technical guidance and constructive contribution and encouragement right from the beginning to the finalisation of this paper.

Second, I would like to extend my sincere thanks to MEFMI for admitting me to the Fellowship Program and Funding all the activities under the Customized Training Plan that included the production of this technical paper. Third, I thank the management of the Central Bank of Kenya for having allowed me to pursue the program to it successful completion. Fourth, I owe a lot of thanks to a number of staff members of the Economics Department of the Central Bank of Kenya who were very cooperative in providing the data and other relevant information used in this paper.

Finally, the completion of this study could not have been a success without the help and support of my dear wife Priscilla and my children Brian, Melvin and Mona. Notwithstanding all these support from various people and institutions, I take full responsibility for the views expressed in this paper together with any errors that may be found in it.

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TABLE OF CONTENTS

ACKNOWLEDGMENTS ii

TABLE OF CONTENTS iii

ABSTRACT vi

LIST OF ABBREVIATIONS viii

LIST OF FIGURES xii

LIST OF TABLES xiii

LIST OF BOXES xiv

1.0 BACKROUND OF THE KENYAN ECONOMY 1

1.1 Historical Background 1

1.2 Kenya’s Economic Structure 2

1.2.1 The Domestic Economy 2

1.2.2 The External Sector 3

1.3 Overview of Economic Developments in Kenya: 1963-2005 6

1.3.1 The 1963-1972 Period - A Decade of Economic Expansion 7

1.3.2 The 1973-82 Period – A Decade of Uneven Economic Growth and High Inflation 8

1.3.3 The 1983-1992 Period – A Decade of Rising Inflation and Declining Growth 9

1.3.4 The 1993-2005 Period –Another Era of Mixed Economic Performance 11

1.4 Conclusion 13

2.0 THEORETICAL UNDERPINNINGS OF FINANCIAL PROGRAMMING 15

2.1. Introduction 15

2.2 Theoretical Foundations 15

2.3 Evolution in the Design of Financial Programs 18

3.0 CONSISTENCY CHECKS OF MACROECONOMIC ACCOUNT LINKS 20

3.1 Consistency Checks and Derivations 20

3.1.1 Inter-Account Consistency Checks and Derivations 20

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3.1.2 Data Consistency Checks Within Accounts 57

3.1.2.1 Data Consistency Checks Within the National Accounts 57

3.1.2.2 Data Consistency Checks Within the Balance of Payments 58

3.1.2.3 Data Consistency Checks Within the Statement of government Operations 58

3.2.1.4 Data Consistency checks within the Depository Corporations Survey 59

3.2 Discussions of Results of the Consistency Checks 61

3.2.1 Discussions of Results of the Inter-Account Consistency Checks 61

3.2.2 Discussions of Results from the Flow of Funds 66

4.0 BEHAVIORAL RELATIONSHIPS FOR FORECASTING 76

4.1 The Basic Framework 76

4.2 Steps for Forecasting in Financial Programming 77

4.3 Forecasting Individual Accounts 81

4.3.1 Forecasting National Accounts: Output, Expenditure and Prices 81

4.3.2 Procedure Used in Forecasting Kenya’s Fiscal Accounts 97

4.3.3 Forecasting the Balance of payments 101

4.3.4 Forecasting Monetary Aggregates 110

4.4 Discussions of Results Obtained from Baseline Forecasting 117

5.0 SUMMARRY, CONCLUSIONS AND RECOMMENDATIONS.........................................121

5.1 Summary 121

5.2 Conclusions 122

5.3 Recommendations 124

REFERENCE ................................................................................................................................127

APPENDICES ...............................................................................................................................131

Appendix 1: The Results of Inter-Account Consistency Checks (in absolute terms) 131

Appendix 2: Sequence of Kenya’s National accounts (Kshs Million) 133

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Appendix 3: Kenya’s Statement of Government Operations (in Kshs million) 137

Appendix 4: Regression Results of Exports Supply Function 139

Appendix 5: Regression Results of Imports Demand Function 144

Appendix 6: Kenya’s Balance of Payments (Kshs Million) 149

Appendix 7: Depository Corporations Survey (In Millions Kshs) 152

Appendix 8: Regression Results of Money Demand Function 154

Appendix 9: Selected Historical and Forecasted Economic Indicators for Kenya (2001-2008) 160

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ABSTRACT

The objective of this technical paper was to develop a framework that integrates all the four macroeconomic accounts, with internal consistency checks based on the financial programming framework. To achieve this objective, a number of data consistency checks were constructed within the financial programming framework (FPF) and the economic implications for each of them given. The manner in which the links in the consistency checks came about was derived statistically. The same framework was used to develop a forecasting scenario. The FPF was therefore used in this paper as a general approach to inform and tie together the various sectors in a consistent manner, while incorporating the Kenyan-specific factors. In this way, not only did financial programming serve as an ex ante consistency check on important macroeconomic aggregates but also provided an ex post monitoring tool.

The results of the consistency checks undertaken in chapter three revealed that virtually all the inter-account consistency checks, which were 41, in total failed to hold. To be more specific, all the ten inter-account consistency checks between the national accounts and balance of payments failed to reveal consistency except in the case of exports of services. Similarly, all the eight inter-account consistency checks between the statement of government operations and balance of payments failed to hold. In the same vein, all the three inter-account consistency checks between the statement of government operations and the depository corporations survey failed to reveal consistent results. Finally, all the thirteen inter-account consistency checks between the deposit corporations survey and the balance of payments did not reveal consistent results except in the case of monetary Gold. The results from the use of flow of funds, which was used an ultimate consistency check also produced results that were mixed. While all vertical consistency checks in the flow of funds failed, some horizontal checks failed to hold.

Based on these results, the paper concluded that there is still a lot of work to be done in Kenya to render the data from different sources consistent. In this respect, the paper made a number of recommendations. Among them was that the relevant institutions should comprehensively examine each specific item in all of the four macroeconomic accounts with a view to making sure that the compilers of such data capture all transactions by all institutional sectors as required.

The financial programming exercise carried out in chapter four involved making projections of developments in the Kenyan economy for the period 2006-08 based on the assumption that existing policies remain unchanged. The results of this baseline scenario provided a benchmark for assessing the impact of the policy package included in a program scenario. Its principal aim was to show whether existing problems were likely to remain broadly constant, to be resolved without explicit intervention by the authorities, or to worsen over time. The conclusion drawn from these

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results was that unless an active program is put in place for Kenya, the country’s economic situation, though impressive from the domestic front, may be undermined by the developments in the external sector and to a small extent the fiscal front.

The paper therefore recommended that an active program should be put in place to address the external sector as well as the fiscal problems that are likely to worsen over time in Kenya. The paper also recommended that the program should address the country’s fiscal problems that also seem to likely to worsen overtime if the current existing fiscal policies remain unchanged. The objective of the program should therefore be to achieve sustainable current account balance, non-inflationary growth as well as fiscal discipline.

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LIST OF ABBREVIATIONS

AGOA African Growth and Opportunities Act

AML Anti-Money Laundering

BB Bonds and Bills

BoP Balance of Payments

CBK Central Bank of Kenya

CBS Central Bank Survey

CE Compensation of Employees

CEg Compensation of Employees Payable by Government

CFKg Consumption of Fixed Capital by government

CFT Combating Financing of Terrorism

COMESA Common Market for Eastern and Southern Africa

CurAcBal Current Account Balance

Dg Government Deposits

DCS Depository Corporations Survey

DI Disposable Income

DIg Disposable Income of Government

EAC East African Community

EPSR Economic and Public Sector Reform

ESAF Enhanced Structural Adjustment Facility

EU European Union

FAcb Foreign Assets held by the Commercial Banks

FAma Foreign Assets held by the Monetary Authority

FE Foreign Exchange

FEma Foreign Exchange by Monetary Authority

FC Final Consumption

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FCg Final Consumption by Government

FinAcBal Financial Account Balance

FinAcBalg Final Account Balance of Government

FLcb Foreign Liabilities held by the Commercial Banks

FLma Foreign Liabilities held by the Commercial Banks

FPF Financial Programming Framework

GDP Gross Domestic Product

GFKF Gross Fixed Capital Formation

GFKFg Gross Fixed Capital Formation of Government

GKF Gross Capital Formation

GNI Gross National Income

GNDI Gross National Disposable Income

G Government

IC Intermediate Consumption

ICg Intermediate Consumption by Government

IDA International Development Association

IMF International Monetary Fund

KA Capital Assets

KAcBal Capital Account Balance

KEMSA Kenya Medical Supplies Agency

KenGen Kenya Electricity Generating Company

KPLC Kenya Power and Lighting Company

Lg Loans and Advances to Government

M Imports of Goods and Services

MEFMI Macroeconomic and Financial Sector Management Institute of Eastern and Southern Africa

MG Monetary Gold

MIT Massachusetts Institute of Technology

NA National Accounts

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nADA Net Additions to Deposits abroad vis-a-vis Non-Residents

nANPNFA Net Acquisition of Non-Produced, Non-Financial Assets

nCurTr Net Current Transfers from Non-Residents

nBrg Net Borrowing vis-a-vis Non-Residents by Government

NBER National Bureau of Economic Research

nCTr Net Current Transfers from Abroad

nCTrg Net Current Transfers vis-à-vis Non-Residents by Government

NG Non-Government

nKTr Net Capital Grants vis-a-vis Non-Residents

nKTr g Net Capital Grants vis-a-vis Non-Residents by Government

nPIg Net Property Income vis-avis Non-Residents by Government

nNPNFAg Disposal/Acquisition on Non-Produced Non-Financial Asset by Government

nRFSg Net Receipts of Foreign Securities vis-a-vis Non-Residents by Government

NFAma Net Foreign Assets of Monetary Authority

NFAcb Net Foreign Assets of Commercial Banks

nI Net Income from Abroad

NLg Net Lending of Government

ODC Other Depository Corporations Survey

OP Output

OPg Output by Government

OS Operating Surplus

OSgg Gross Operating Surplus of Government

OSng Net Operating Surplus of Government

OTPg Other Taxes on Production Payable by Government

PRGF Poverty Reduction and Growth Facility

RA Reserve Assets

RPF Reserve Position in Fund

S Savings

Sg Savings by Government

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SB Social Benefits

SC Social Contributions

SCGSg Sales of Current Goods and Services by Government

SDR Special Drawing Right

SGO Statement of Government Operations

TIW Taxes on Income and Wealth

TMRP Tourism Marketing Recovery Programme

TP Taxes on Products

TPI Taxes on Production and Imports

TTF Tourism Trust Fund

VA Value Added

VAgg Gross Value Added by Government

VAng

Net Value Added by Government

X Exports of Goods and Services

∆ Change

∆Inv Net changes in inventories,

% Percent

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LIST OF FIGURES

Name of Chart Page

Chart 1.1: Composition of Kenya's Exports During 1967-72 Period (%) 4




Chart 1.5: Composition of Kenya's Imports During 1967-72 (%) 5




Chart 1.9: Kenya’s Real GDP Growth Rates During 1963-2004 Period (%) 7

Chart 1.10: Kenya’s Inflation Rates During 1963-2004 Period (%) 8

Chart 4.1: Inflation Rates By Region (%) 95

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LIST OF TABLES

Table 1.1: Kenya's Composition of GDP by Origin at Current Prices, 1963-2003 (Percent) ........................2

Table 3.1: Inter-Account Consistency Checks: National Accounts (Na) And Statement Of Government Operations ...........................................................................................................................25

Table 3.2: Inter-Account Consistency Checks: National Accounts (NA) Versus Balance of Payments and Balance of Payments (BOP)..................................................................................................33

Table 3.3: Inter-Account Consistency Checks: Statement of Government Operations (SOG) and Balance of payments (SGO)...................................................................................................................40

Table 3.4: Inter-Account Consistency Checks: Statement of Government Operations (SGO) and the Depository Corporations Survey (DCS) .................................................................................46

Table 3.5: Inter-Account Consistency Checks: Deposit Corporations Survey (DCS) and (Balance of Payments)............................................................................................................................49

Table 3.6: The Results of Inter-Account Consistency Checks ..................................................................62

Table 3.7: Kenya’s Flow of Funds for 2004 (in Kenya shillings, Millions) ...............................................69

Table 4.1: Forecasting of Activities for the Period 2006-08 .....................................................................83

Table 4.2: Gross Domestic Product by Activity (Percentage Change).......................................................88

Table 4.3: Proxy Bases for Forecasting Revenues ...................................................................................97

Table 4.4: Transactions Affecting Kenya’s Net Worth (% GDP)..............................................................98

Table 4.5: Proxy Bases for Forecasting Expenses .................................................................................100

Table 4.6: Forecasting of Balance of Payments Items............................................................................102

Table 4.7: Forecasting of Monetary Aggregates in the Depository Corporation Survey ...........................115

Table 4.8: Selected Historical and Forecasted Economic Indicators for Kenya (2001-2008) ....................118

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LIST OF BOXES

Box Name Page

Box 4.1: The Export Supply Function 105

Box 4.2: Import Demand Function 106

Box 4.3: Money Demand Function for Kenya 115

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1.0 BACKROUND OF THE KENYAN ECONOMY

1.1 Historical Background

Kenya is a former British colony that gained independence in 1963 and became a republic in 1964. It has an area of 582,646 sq. km and a population of about 32 million, up from the 28.7 million and 15.3 million recorded in the 1999 and 1979 national census, respectively. Kenya’s population is distributed in a very uneven way throughout the country, with the majority dwelling in the Highlands, where the climate is mild. The population density hardly reaches 2 inhabitants per sq km in the arid and semiarid areas found in the north and northeast parts of the country, whereas in the rich and fertile western the rate rises to 120 inhabitants per sq km. Urban population is nearly 25% of the total and is concentrated in a few large cities, mainly in Nairobi, Mombasa, Nakuru, Eldoret and Kisumu.

Since the late 1970s, Kenya’s social indicators have been mixed in terms of performance. Over this period, for instance, contraceptive prevalence has doubled, and the total fertility rate has fallen from 8.0 children per woman to about half that number. Current estimates on fertility range from 3.1 to 5 births per woman, making Kenya among the countries with the lowest birth rate in Subsaharan Africa. Life expectancy, on the other hand, has fallen to about 48.3. Estimates place the death rate at 16.3 deaths per 1,000 people and the infant mortality rate at about 79 per 1,000 live births. The age structure of the population is very young, with 42 percent of the population under age 15, and only 2.9 percent being 65 or older. The median age is estimated to be about 18.6 years. Kenya’ literacy rate is estimated at 80 percent, with the female rate being about 10 points lower than that of the male. HIV/AIDS is one of the threats to economic recovery. It is estimated that about 4 million people (equivalent to about 12.5 percent of the population) is infected with HIV, with about 80-90% of this figure estimated to be in the 15-49 age group.

From independence in 1963 until the elections held in December 2002, Kenya had two Presidents, Jomo Kenyatta (who ruled for 15 years from 1963 to 1978) and Daniel Arap Moi (who ruled for 24 years from 1979 to 2002). Both were from the party Kenya Africa National Union (KANU), which ruled Kenya until when it lost to National Alliance Rainbow Coalition (NARC) in the elections, held in December 2002, with Mwai Kibaki becoming Kenya's third President. Kenya’s legislature is a single chamber National Assembly with 224 members, 210 of whom are directly elected every five years in single-seat constituencies; 12 members are nominated by parties on a pro rata basis; and 2 ex-officio members (the Speaker and the Attorney General). The president who is also directly elected for five years holds executive power.

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1.2 Kenya’s Economic Structure

1.2.1 The Domestic Economy

Kenya's economy can be divided into three sectors, namely: the primary sector, the secondary sector and the tertiary sector. The primary sector is composed of agriculture, forestry, mining and quarrying activities. Kenya’s economy has traditionally been based on the performance of the primary sector where the agriculture activity plays a prominent role. The primary sector’s contribution to GDP has, however, been on a declining trend over the decades as shown in Table 1.1 below. In particular, Agriculture’s weight in GDP decreased from 34.9% in the first decade of independence (1963-72) to 31.3% and 24.8% in the third (1983-92) and fourth (1993-2003) decade, respectively. Its dominant role in Kenya’s economy is still supported by the fact that about 75-80% of the employed population works in agriculture. 50% of income earnings from exports also come from this activity.. Mining and quarrying, which is the other major component of Kenya’s primary sector, has also declined as a proportion of GDP from 0.4% in the first decade of independence (1963-72) to 0.2% in the fourth decade (1993-2003).

Like the primary sector, Kenya’s secondary sector has similarly declined in performance as a share of the country’s GDP. This development has been mainly in the utilities and construction sub-sectors as the manufacturing activity has grown, albeit marginally (Table 1.1). The share of manufacturing activity in the country’s GDP has grown slowly to account for 11.5% in the period 1993-2003 compared to 11.1% in the first decade (1963-72). It employs 10% of the population. The major industrial plants are located around the big cities, mainly Nairobi, Mombasa and Kisumu. The main industries are food (crop processing and canning), beverages, tobacco, chemicals, petroleum derivatives, metals, textiles, leather, rubber, construction materials (cement, clay, glass), motorcar assembly and pharmaceutical products. Other consumer goods that are manufactured are plastics, furniture, batteries and soap.

Kenya’s tertiary sector has registered significant growth as a percent of GDP since independence. During the first decade of independence, the share of the tertiary sector as percent of GDP was 45.1%. The share, however, rose such that by the fourth decade covering 1993-2003, it stood at 57.8% of the country’s GDP. The improvement in the country’s tertiary sector as percent of GDP was mainly in the following activities: trade, restaurants, hotels as well as services from financial institutions as shown in Table 1.1 below.

Table 1.1: Kenya's Composition of GDP by Origin at Current Prices, 1963-2003 (Percent)

Origin 1963-72 1973-82 1983-92 1993-2003

Primary sector 35.3 34.5 31.6 25.0

Agriculture, forestry, and fishing 34.9 34.3 31.3 24.8

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Table 1.1: Kenya's Composition of GDP by Origin at Current Prices, 1963-2003 (Percent)

Origin 1963-72 1973-82 1983-92 1993-2003

Mining and quarrying 0.4 0.3 0.2 0.2

Secondary sector 19.6 20.6 18.8 17.2

Manufacturing 11.1 12.6 11.7 11.5

Construction 4.8 6.0 5.6 4.5

Utilities 3.6 2.0 1.5 1.2

Tertiary sector 45.1 44.8 49.6 57.8

Trade, restaurants, and hotels 9.8 11.0 11.5 20.5

Transport, storage, and communications 7.6 5.7 6.7 7.7

Financial institutions 3.6 5.7 7.8 11.0

Ownership of dwellings 5.8 6.4 8.1 5.6

Other services 18.3 16.1 15.6 13.0

GDP at factor cost 100.0 100.0 100.0 100.0

Sources: Government of Kenya, Statistical Abstract and Economic Survey, various issues

1.2.2 The External Sector

During the first two decades of independence, Kenya's exports were limited to a narrow product range, mainly coffee, tea, and pyrethrum (see Charts 1.1 and 1.2). This situation left the country highly vulnerable to external vagaries such as changes in world prices. Over the last two decades, however, the government's efforts to diversify the export base resulted in the growth of non-traditional exports, especially horticulture commodities (e.g. cut flowers, fresh fruits and vegetables) as shown in Charts 1.3 and 1.4 below.

Coffee exports, which use to be the leading foreign exchange earner in the first decade, declined such that by the fourth decade it had been relegated very far away from the top list of Kenya’s foreign exchange earners, only contributing about 9%. The latest data (for year 2005) shows that Kenya’s leading exports are tea and horticulture that accounted for 17.4% and 13.4% of total exports, respectively. Coffee accounts for 4% of total exports. Kenya’s other significant exports are petroleum products, fish, cement, pyrethrum, and sisal. The main destination countries for Kenya’s merchandise exports in 2005 were: Uganda (17.5%), United Kingdom (9.6%), Tanzania (8.2%), the Netherlands (7.5%), Pakistan (5.8%), the Democratic Republic of Congo (4.0%), Egypt (3.6%), and Rwanda (3.0%). Total exports to African countries accounted for 49.3% of Kenya’s total merchandise exports.

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Chart 1.1: Composition of Kenya's Exports During 1967-72 Period (%)

Unroasted Coffee21%

Tea13%

Petroleum products

16%

Pyrethrum3%

Horticulture5%

Cement3%

Soda Ash2%

other37%


Unroasted Coffee

28%

Tea14%

Petroleum

products25%

Pyrethrum

2%

Horticulture7%

Cement3%

Soda Ash1%

other20%


Unroasted

Coffee20%

Tea24%

Petroleum

products13%

Pyrethrum

2%

Horticulture8%

Cement1%

Soda Ash2%

other30%


Unroasted Coffee

9%

Tea24%

Petroleum products

6%

Pyrethrum1%

Horticulture3%

Cement1%

Soda Ash1%

other55%

Source: Own Calculations from Data obtained from Statistical Abstracts and Economic Surveys

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Kenya’s imports are primarily industrial inputs including crude materials, petroleum and refined petroleum products, edible oils and fats, agricultural and transport machinery and manufactured goods. As indicated in charts 1.5, 1.6, 1.7 and 1.8, the composition of Kenya’s imports has changed over the last decade. Significant changes have particularly occurred in the imports of manufactured goods, with the decline being attributed to the growth of the manufacturing sector that led to import substitution of the manufactured products. The major sources of these imports are mainly Asia and Europe. In 2005, Kenya’s main sources of imports where: United Arab Emirates (13.4 percent), United Kingdom (12.1 percent), United States of America (9.1 percent), South Africa (9.1 percent), Saudi Arabia (5.9 percent), India (5.2 percent), Japan (5.0 percent), China (4.3 percent), Germany (3.4 percent) and France (3.0 percent) in 2005.

Chart 1.5: Kenya's Composition of Imports During 1967-73 (%)

Food & live animals

7%

Beverages & Tobacco

1%

Crude Materials

3%

Mineral Fuels10%

Chemicals10%

Manufactured goods

24%

Machinery & Transport

eqp.32%

Others13%


Food & live animals

4%

Bev. & Tobacco

1%

Crude Materials

2%

Mineral Fuels30%

Chemicals11%

Manufactures15%

Mach. & Transport

eqp.29%

Others8%

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Food & live

animals6%

Beverages &

Tobacco0%

Crude Material

s3%Mineral

Fuels21%

Chemicals

17%Manufac

tured goods14%

Machinery &

Transport eqp.29%

Others10%


Food & live

animals8%

Beverages &

Tobacco1%

Crude Materials

3%Mineral Fuels20%

Chemicals

15%

Manufactured

goods14%

Machinery &

Transport eqp.26%

Others13%

Source: Own Calculations from Data obtained from Statistical Abstracts and Economic Surveys

1.3 Overview of Economic Developments in Kenya: 1963-2005

Kenya’s post-independence economic history can be divided into four periods, each constituting a decade. The first two periods, namely 1963-72 and 1973-82 is characterized by strong economic performance and huge gains in social outcomes. The other two periods (1983-92 and 1993-2004) is typified by slow or negative growth, mounting macroeconomic imbalances and significant losses in social welfare, notably rising poverty and falling life expectancy. Failures to sustain economic reforms and increased role of politics over policy are at the heart of this structural break.

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1.3.1 The 1963-1972 Period - A Decade of Economic Expansion

The first decade of Kenya’s independence covering the period 1963-72 was one of economic transformation. During this decade, Kenya not only experienced rapid expansion of smallholder cultivation but also introduction of high-value crops including tea and coffee and adoption of high-yielding maize varieties and the development of dairy farming, all of which led to rapid increases in agricultural output. The country also experienced rapid increases in industrial output, mainly due to import substitution activities. An expanding export services to neighboring countries as well as emerging tourism sector led to improved balance of payments position in Kenya. The country, for instance, had a balance of payments situation in which both the overall and basic balances were in surplus. This was also reflected in the continuous increase in the Central Bank reserve holdings from US$ 51.5 million in 1966 to US$ 205.2 million in 1970. The surplus was accompanied by a high real growth rate of 6.5% per year over the period 1963 to 1972 (Chart 1.9).

Chart 1.9: Kenya' GDP Growth Rate rates (1963-2004)

-2

0

2

4

6

8

10

12

14

16

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Year

Per

cent

(%)

REAL GDP growth rate (1982 prices) REAL GDP growth rate (2001 prices)

Such a rapid rate of economic growth meant that real per capita incomes had increased significantly since independence, despite the country having had one of the highest rates of population growth in the world. This success was also attributed to a number of factors including the existence of a politically stable atmosphere necessary for a high level of private investment. Kenya's savings performance over the period under review was good. Gross domestic savings, as a proportion of GDP, averaged around 19-20 percent in most years between 1964 and 1972. As a result of the successful fiscal policies of government, public

First Decade

Second Decade

Third Decade

Fourth Decade

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sector savings rose to over 23 percent of total savings by 1971. Additionally, the rate of inflation remained low, averaging 2.8 per cent per annum (Chart 1.10).

1.3.2 The 1973-82 Period – A Decade of Uneven Economic Growth and High Inflation

Overall, growth throughout this decade was uneven, fluctuating between 1.5% in 1974 and 8.6% in 1977, with the latter principally resulting from sharp improvement in the external; terms of trade. On average, growth during this decade averaged 4.5% (Chart 1.9). Like many Sub-Saharan African countries, Kenya was beset with unprecedented succession of external shocks, such as changes in the level and variability of terms of trade compounded by increases in oil prices, rising and variable international interest rates and sharp changes in the availability of foreign financing by the end of 1973. The economy was particularly hurt severely by the world oil price shock of 1973 and two poor harvests in 1974-75 period.

These problems were compounded by the inefficiencies and distortions brought about by the pursuit of inward development strategies such as the controls in the country’s financial system that became apparent and were exacerbated by the emergence of severe macroeconomic difficulties during this period. In the domain of international trade and payments, these problems showed up in the form of growing balance of payments deficits1, which became increasingly difficult to finance. In the internal economy, the same problem emerged as an increasing government budget deficit. The financing of the budget deficit was in many cases

1 The country’s terms of trade deteriorated further owing to adverse world conditions while the volume of exports

declined, thus causing deterioration in Bop. Other episodes of BOP crises experienced in Kenya were in 1975 and 1979.

Chart 1.10: Infation (annual average)

-10

0

10

20

30

40

50

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Year

Per

cent

(%)

Infation

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inflationary. For instance, inflation in 1975 accelerated to an annual average of 15.5 per cent compared with 4 per cent in 1974.

During the next four years up to 1982, balance of payments position worsened recording large deficits while inflation accelerated back to double-digit figures (Chart 1.10). GDP growth rate also dropped to low levels, averaging 3.2 per cent per annum – a rather relatively low level compared to earlier years. The lower growth was attributed to, among other factors; the effects of the second oil price shock experienced in 1979 and a military coup attempt in August 1982. Also contributing to declining economic growth was the stance of fiscal policy in 1979-82 that proved to be highly expansionary, mainly due to a sharp increase in Government expenditure. In fiscal years 1979-82, for instance, the budget deficit averaged about 8 per cent of GDP per annum and was principally financed by borrowing from the banking system. These large budget deficits and the consequent large borrowing from the banking system marked the beginning of a challenging period for the Central Bank of Kenya (CBK) in managing monetary policy. While the use of monetary policy before 1979 yielded reasonable results, monetary policy in the period after 1979 proved less effective largely because of unsupportive fiscal policy. The attempts in 1980/81 to liberalise imports, devalue the shilling exchange rate and raise the interest rates did not help much in improving the economic growth rate.

It is important to note that during this decade, the Executive Board of the IMF approved an extended arrangement and three standby arrangements for Kenya. The extended arrangement was approved in July 1975 while the three standby arrangements were approved in August 1979, October 1980 and January 1982, respectively. Each of these programs was, however, interrupted, primarily because the performance criterion on net credit to government was not observed. This is reflected in the uneven economic growth that was observed during this decade.

1.3.3 The 1983-1992 Period – A Decade of Rising Inflation and Declining Growth

Real GDP growth during this decade was mixed but on average was 4.0%, a level relatively lower than that registered in the previous two decades. A severe drought experienced in 1983-84 adversely affected growth and forced government to resort to food imports to avert famine. Thus, economic performance in the first three years of the decade under review remained below 3% in spite of the Executive Board of the IMF having approved two standby arrangements for Kenya in March 1983 and in February 1985. Each of these programs was also interrupted following the failure by the government of Kenya to observe the performance criterion, particularly on net credit to government as required. The continued decline in economic performance culminated in the drawing up of Sessional Paper No. 1 of 1986 on Economic Management for Renewed Growth by the end of the first half of 1980s. This policy document, which was supported by the IMF and the World Bank, set to renew the economic recovery and

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growth through a process of economic liberalisation. It set off the process of undertaking far reaching institutional and structural reforms in the economy. The change from inward looking import substitution policy regime to outward oriented strategy led to higher earnings of foreign exchange and in turn helped to reduce the balance of payments deficits.

Owing to the undertaking of these reforms as spelled out in the Sessional Paper No. 1 of 1986, real GDP growth rebounded; averaging 5.6% per annum during 1986-90 in spite of continued deterioration in terms of trade. Kenya, however, experienced sharply worsened economic conditions from late 1991 to early 1993. Economic growth decelerated from 4.4% in 1990 to 0.4 percent in 1992 (Chart 1.9), inflation accelerated from 20% in 1990 to 34% in 1992 (Chart 1.10) and external payments arrears emerged for the first time in Kenya’s history. Several factors accounted for this crisis. First, the economy was faced with a series of exogenous shocks that included drought owing to irregular rainfalls, a large influx of refuges from neighbouring countries faced with internal civil strive and unfavourable export prices in world commodity markets. Second, protracted and unsettled social and political conditions in the period leading up to the first multiparty elections in December 1992 impacted on economic policy making. Third, balance of payments assistance from bilateral donors was suspended in late 1991, reflecting donor’s concerns over weak economic policy implementation as well as the lack of progress in the introduction of multiparty democracy. Fourth, several major economic policy reversals occurred, notably in liberalization of the external sector and the maize market and this undermined the public’s confidence in the government’s economic management.

Widespread mismanagement of the financial system also contributed to the economic crisis in the early 1990s. The mismanagement involved access to central bank credit by four commercial banks through irregular means. This included access to large overdrafts with CBK as well as access to the export pre-shipment finance facility for financing of fictitious gold exports (they were not reflected in the customs statistics). The commercial banks also persistently failed to meet prudential requirements in addition to the statutory cash ratio. Thus, excessive liquidity expansion, which mainly went to finance the presidential, parliamentary and civic elections held in December 1992, became a major destabilizing force in the economy. These problems compounded the perennial difficulties relating to the central government budget and public enterprise performance. Serious problems were therefore encountered over the period not only in the implementation of macroeconomic policies, particularly monetary and to a lesser extent fiscal polices but also structural reforms particularly those pertaining to trade and exchange rate regimes as well as maize marketing. Weak export performance together with the cessation of balance of payments support brought about severe foreign exchange shortages and a major compression in imports also compounded the problem.

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1.3.4 The 1993-2005 Period –Another Era of Mixed Economic Performance

As a result of further economic reforms undertaken in early 1990s, Kenya’s economy moved onto a road to recovery. GDP grew by about 3 percent in 1994 and by an estimated 5 percent in 1995 up from 0.4 percent in 1992 and 0.1 percent in 1993. Fiscal deficit was sharply reduced from over 11 percent of GDP in 1992/93 to 2.6 percent in 1994/95. Expansion in money supply, though still high, was also reduced substantially. Inflation was also reduced substantially - annual inflation declined steadily from a peak of 62 percent in January 1994 to 6.9 percent in December 1995. However, after a small surplus in 1993, the current account balance (excluding official transfers) deteriorated to a deficit of 0.4 percent of GDP in 1994, and further to an estimated 4.2 percent in 1995. Kenya's real GDP grew at 5% in 1995. The budget deficit on commitment basis and excluding grants was reduced from 2.3% of GDP in 1994/95 to 1.4% of GDP in 1995/96 fiscal year, much lower than the programmed target of 1.9% of GDP. Although the conduct of monetary policy was complicated by large financial inflows and upward pressure on the exchange rate, success was achieved in moderating the growth in broad money (M3) and private sector credit significantly by end 1995. With the removal of all controls on trade, exchange rate and liberalization of the current account, the terms of trade increased to 100 percent in 1994. This was a marked improvement from the previous downward trend.

In order to consolidate further the gains made in the previous three years, the Kenya Government, in conjunction with the IMF and World Bank, developed a Policy Framework Paper in February 1996 that spelled out economic reform program to be implemented within the next three years. In support of the program, the Executive Board of the IMF approved a three-year loan under the Enhanced Structural Adjustment Facility (ESAF) amounting to SDR 149.55 (equivalent to 75 percent of Kenya’s quota then) on April 26, 1996. The program aimed at accelerating real GDP growth to 5 percent; maintaining inflation at 5 percent and sharply reducing the external current account deficit.

Despite the significant policy shifts supported by the IMF and World Bank and accompanying improved economic conditions witnessed in the first half of 1990s, economic trends in the second half of 1990s were disappointing, with GDP growth declining and falling substantially below the population growth rate, estimated at 2.4%. Real GDP grew by an estimated 4.2 percent in 1996, somewhat lower than programmed target of 5%. GDP growth rates assumed declining trends from 2.3% in 1997, 1.8% in 1998 and 1.4% in 1999 and drifting to negative 0.3% in 2000 -the lowest level since independence as shown in chart 1.9. The weak performance of the economy was attributed to a combination of factors including governance and management problems in the agricultural co-operative institutions that adversely affected production of most cash crops; poor transport and communications infrastructure devastated by

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the El Nino rains of 1997 and 1998 and which continued to impose heavy costs on businesses; reduced effective demand particularly for manufactures; and reduced domestic investment and savings due to declining investor confidence due to, among other factors, the withholding of donor funding, following the lapse2 in July 1997of the IMF’s ESAF supported programme.

In September 1998, the World Bank’s Board endorsed a new three-year country assistance programme for Kenya. Subsequently, the Kenya government undertook a series of governance and public sector reforms that paved the way for a resumption of a “base case” lending from the World Bank, through International Development Association (IDA). Under this program, the Bank’s Board approved an Economic and Public Sector Reform (EPSR) in three tranches for a total of US$ 150 million on August 1, 2000. This was immediately followed by an approval of three-year arrangement under the Poverty Reduction and Growth Facility (PRGF) by the IMF Executive Board on August 4, 2000 amounting to Special Drawing Rights (SDR) 150 million. On October 18, 2000, the Board also approved a modification of the program that significantly increased the fiscal deficit allowable to take account of the effects of drought and augmentation of access of SDR 40 million. In spite of this support from Brettonwood Institutions, economic activity in 2000 remained weak. The severe drought experienced during this year caused real GDP to contract by 0.3 percent. Kenya, however, managed to achieve some modest economic growth in the period 2001-05 despite the difficult external environment it continued to face.

Responding to the various policy measures undertaken by the new Government since taking over power in January 2003, the economy recovered, with real GDP expanding by 2.8% in 2003 from 1.1 percent in 2002, 1.2 percent in 2001 and negative 0.2 percent in 2000 (chart 1.9). The main sources of growth were agriculture and the manufacturing activities. The agricultural activity is estimated to have grown by 1.3% in 2003 compared with 0.9% growth in 2002, with coffee and horticulture recording strong growth in output. Growth in the manufacturing sector was estimated at 1.2% in 2003 compared with 1.0% in 2002. The manufacturing sector continued to be supported by tax incentives introduced over the previous two financial years as well as the more liberal trade policies that increased access to external markets particularly in East African Community (EAC), Common Market for Eastern and southern Africa

2 The IMF suspended the three-year loan for Kenya under the enhanced structural adjustment facility (ESAF)

signed on April 26, 1996 for an amount equivalent to SDR 149.55 million (about $216 million). This loan was meant to support the Government's economic reform program for 1996-98, which was built on the policy framework paper published by the Kenyan Authorities on February 16, 1996. The first annual loan in an amount equivalent to SDR 49.85 million (about $72 million) was disbursed in two equal semi-annual instalments, the first of which was made available on May 15, 1996. The World Bank also put a $90-million structural adjustment credit on hold.

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(COMESA), and United States through African Growth and Opportunities Act (AGOA). Major services such as transport, telecommunications, tourism and financial services also registered notable improvements in 2003.

The economy grew by 4.3% in 2004, up from 2.8% in 2003 in spite of the high cost of production inherent in steady rise in wages, crude oil prices and structural bottlenecks associated with inadequate infrastructure (Chart 1.9). The growth was mainly reflected in tourism and transport and communications sectors, which grew by 15.1% and 9.7%, respectively. Manufacturing, trade, and building and construction activities, also performed well, having expanded by 4.1%, 9.5% and 3.5% respectively in 2004. Growth in the export sector, mainly in horticulture (13.2%) and tea (10.5%), as well as significant expansion of credit to productive sectors at affordable interest rates in the local money market, and faster growth in the world economy also supported economic recovery during the year. Appropriate fiscal and monetary policies pursued resulted in a conducive macroeconomic environment for investment, particularly affordable interest rates and a stable exchange rate.

Real GDP growth rose for the third consecutive year in 2005, to reach an estimated 5.8 percent. Strong performance in the tourism, construction and telecommunications sectors stimulated activity, which was underpinned by robust private sector credit growth stemming from the implementation of modest reforms and the lagged effect of a sharp fall in interest rates in 2003. A jump in imports stemming from higher oil prices and aircraft purchases caused the current account deficit to leap to 8.3 percent of GDP in 2005, from 2.3 percent of GDP one year earlier. Nonetheless, large short-term financial inflows generated a balance of payments surplus and foreign exchange reserves rose by 18 percent over 2005 to end the year at $1.8 billion (equivalent to three months of imports of goods and non-factor services).

1.4 Conclusion

Based on the above discussions, it is clear that Kenya has over the last four decades faced macroeconomic disequilibria. Like most African countries, Kenya had to undertake macroeconomic stabilization and structural adjustment based on agreed IMF-supported adjustments programs in an effort to bring about internal and external balance. While macroeconomic stability has generally been achieved in Kenya following the undertaking of these reforms, that stability has, however, been fragile particularly when viewed against the backdrop of generally low economic growth rates.

The most daunting economic challenges that the country continues to face today are the creation of enough employment opportunities to absorb the large number of new entrants into the labour market and the alleviation of poverty. More than two million Kenyans are currently unemployed and at least ten million people are living below the poverty line (defined as

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earnings of less than US$ 1 per day). A large proportion of Kenya's population does not have access to safe water for domestic use, adequate health facilities and health provision, and minimum requirement of food and essential-non-food commodities. Above all, income inequality between the poor and the rich in our country remains high.

The challenge therefore that faces Kenya is to design a financial program that will enable the country achieve the desired growth rates of at least 7 percent per annum and hopefully overcome the problems of high poverty levels and unemployment it currently faces. Ability to undertake appropriate financial programming and policy advice is therefore critical. This technical paper develops a framework that integrates all the four accounts and with internal consistency checks. It is an attempt to develop Kenya’s own financial programming model and in this way take command of the technical analysis and thus be in a position to negotiate with the Fund in more pro-active terms. It will also lead to establishment of the foundation for a deeper commitment to prudent macroeconomic policy management in Kenya.

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2.0 THEORETICAL UNDERPINNINGS OF FINANCIAL PROGRAMMING

2.1. Introduction

This chapter outlines the theoretical framework underpinning the discussions of the next two chapters of this paper, namely; chapters three and four. In summary, chapter three presents a number of data consistency checks that have been constructed within the financial programming framework and gives the economic implications for each of them. It also derives statistically how the links in the consistency checks have come about. Chapter four, on the other hand, uses the financial programming framework (FPF)3 to develop a forecasting scenario. The purpose of this chapter therefore is to provide the theoretical foundations of the FPF.

The discussions in Chapter I brought out clearly that Kenya has over the last four decades faced a number of macroeconomic disequilibria and thus had to undertake macroeconomic stabilization and structural adjustment during these periods. The adjustments were based on the agreed IMF-supported programs aimed at bringing about internal and external balance. Policy targets for these programs had to be derived from the standard financial programming framework. The following section provides the theoretical framework that underpins the FPF.

2.2 Theoretical Foundations

The theoretical foundations of financial programming are based on the model designed in 1957 by J. J. Polak4 in his article entitled: Monetary Analysis of Income Formation and Payments Problems. Cast within the framework of a small open economy with a fixed exchange rate system, the model assumes that balance of payments problems were purely a monetary phenomenon, implying that money supply was an endogenous variable influenced by surpluses and deficits in the balance of payments.

Since the 1950s, the Polak model has been the centrepiece of the analysis leading to IMF conditionality—the policy actions that a borrowing country must take to have access to IMF credit. It thus forms the cornerstone of Fund programs. In general, Fund-supported programs are packages of policy measures which, combined with approved financing, are intended to achieve certain economic objectives that include: reducing inflation, promoting growth and poverty reduction, and reducing vulnerability to future balance of payments problems or financial crises. A program is thus defined by its broad objectives, the analytical framework

3 The FPF is not a macroeconomic model per se, but rather a set of accounting identities constructed on

spreadsheets that link the accounts of the major sectors of an economy in an internally consistent manner.

4 The work of Polak (1957) were modified in the 1960s and 1970s by Walter Robichek (1967, 1971)

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linking these objectives to policies, and the formulation and implementation of individual macroeconomic and structural policies.

The FPF attributes balance of payments (BoP) disequilibria to excessive credit expansion. The framework therefore considers domestic credit ceilings and a predetermined exchange rate as the key policy instruments to achieve the desired BoP target. The variables are autonomous changes in exports and the creation of domestic bank credit (Polak, 1957).

The model has four building blocks, two of which are behavioural in nature while the other two are accounting identities. The first is the demand-for-money function, which is behavioural in nature. In its simplest form, the model assumes that the demand for money is proportional to income. In this case, a change in a country's money supply (M) is assumed to be proportional to the change in its income (Y) by a factor k. In an equation form, this relationship is expressed as:

tdt YkM ∆=∆ ……………………………………………………………………………….(1)

Equation 1 shows that there is a constant proportional relationship between nominal income Y, and the money-stock M. The coefficient of proportionality, k, is the inverse of the velocity of circulation of money (Y/M); thus, k = M/Y. Going by the functional form of equation 1, it implies that the average and marginal income velocities are equal, and constant5. In this case therefore, any increase in nominal income will equal the increase in the money stock times the velocity of circulation of money.

The second building block is also a behavioral relationship, namely, the demand for imports (M) expressed as a function of a country's income (Y). This is expressed in an equation form as follows:

tt mYM = …………………………………………………………………………………(2)

where m is the country's marginal propensity to import. Equation 2 shows that there is a constant proportional relationship between imports at time t, and nominal income at time t via the marginal propensity to import out of income. Like equation 1, the lack of a constant in the equation implies that the average and marginal import propensities are the same. It is also assumed that 0 < m < 1 i.e. an increase in nominal income leads to a proportional increase in imports.

5 The constancy of the velocity of circulation of money is a crucial assumption in monetarist models of the

economy.

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The third building block is an accounting identity expressing the change in the money stock as the sum of changes in its international and domestic components. This is expressed in an equation form as follows:

ttst NDCNFAM ∆+∆=∆ ……………………………………………………………….(3)

where ∆ stM is the change in the money supply, ∆ NFAt is the change in the net foreign

reserves of the banking system (expressed in domestic currency) while ∆ NDCt is the change in the domestic credit of the banking system. The monetary identity represented by equation 3 is the most important identity in FPF. This identity could apply to the central bank, in which case M is high-powered money, or it could apply to the entire banking or financial system, in which case M is broad money, implying M is derived from the deposit corporations survey (i.e. consolidated balance sheet of the banking system including the Central Bank). The identity states that the change in assets in any form (i.e., the increase in the stock of international reserves of the monetary system expressed in domestic currency, plus the increase in domestic credit) is eventually equal to the change in liabilities ( ∆ Ms,), implying increase in the nominal supply of money.

Combining equation 1 and 2 yields the equilibrium in the money market, which in equation form is represented as:

st

dt MM ∆=∆ …………………..………………………………………………………..…(4)

where dtM is the money demand at time t while s

tM∆ is the money supply also at time t. This

means at equilibrium, change in the demand for money is equal to the change in supply of money in the money market.

The final building block is another accounting identity expressing the change in foreign reserves (R) as being equal to exports (X) minus imports, plus net financial inflows of the non-bank sector (K).

tttt KMXNFA +−= …………………….………………………………………………….(5)

Equation (5) is the definitional equation underlying the balance of payments. It states that the change in net foreign assets is equal to exports of goods and services minus imports of goods and services plus net financial inflows. Both equations 3 & 5 are accounting identities that have to be satisfied over any defined time-period. The four equations (i.e. 1, 2, 3 and 5) form the basis of the Polak model, that itself is the basis of the FPF. It is clear from the four equations that the Polak model has four endogenous variables and three exogenous variables. The endogenous variables (with corresponding symbols) are (i) the stock of money (M) (ii) nominal income (Y) (iii) imports of goods & services (M) and (iv) net foreign assets (NFA). The

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exogenous variables (with corresponding symbols) are (i) exports of goods and services (X); (ii) financial inflows (K) and (ii) net domestic credit (NDC).

From the above equations, it is clear that the FPF not only assumes a one for one relationship from the identity between the policy variable and the outcome variable but also assumes that the other variables in the identity are exogenous with respect to the policy variable. Thus, the FPF is a dynamic model as the variables in the model include macroeconomic targets. These are what the policy maker aims to achieve, such as output and inflation. It also includes policy instruments, which are the variables that the policy maker aims to change, such as money growth rates and levels of government spending, in order to attain the desired changes in the target variables. The specification of the FPF rests on both economic theory (in particular, Keynesian and Monetarist theory) and the analysis of historical data. The multiplier represents the Keynesian elements, the marginal propensity to expenditure being equal to 1 and the monetarist elements are represented by the speed of money in circulation. Data on the macroeconomic variables are interfaced with the model in order to generate statistical results that give more refined information regarding the behaviour of the economy, such that the impact of policy changes can be reliably measured.

2.3 Evolution in the Design of Financial Programs

The theoretical foundations of financial programming have remained generally unchanged since the 1970s. However, the conception and the structure of financial programs (also called adjustment programs) have gradually evolved and expanded since 1970s, partly reflecting institutional and structural changes in most developing countries that often sought support from the IMF and World Bank. Significant events that have occurred in the world economy have also necessitated the changes in the approach to program design.

While structural measures were rarely an element in Fund-supported programs until the 1980s, almost all programs included some element of structural conditionality6 by the 1990s. The expansion of structural conditionality was also reflected in increasing numbers of performance criteria, structural benchmarks, and prior actions. The increasing structural content of Fund-supported programs has, however, prompted legitimate concerns. In particular, the Fund has 6 Conditionality is the link between the approval or continuation of the Fund's financing and the implementation of

specified elements of economic policy by the country receiving this financing. It is a salient aspect of the Fund's involvement with its member countries. This link arises from the fact that the Fund's financing and policy adjustments by the country are intended to be two sides of a common response to external imbalances. Conditionality is intended to ensure that these two components are provided together. In other words, it provides safeguards to the Fund to ensure that successive tranches of financing are delivered only if key policies are on track, and assurances to the country that it will continue to receive the Fund's financing provided that it continues to implement the policies envisaged. IMF, “Conditionality in Fund Supported Programs: Policy Issues”, 2001:PP1-20

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been criticised for overstepping its mandate and core area of expertise, using its financial leverage to promote an extensive policy agenda and short-circuiting national decision-making processes. Moreover, the expansion of conditionality has raised issues regarding its effectiveness. Arguments have been put forward that most conditionality have been too comprehensive and these have undermined the ability of most countries to not only implement the necessary reforms but also Marshall the necessary political support for a multitude of policy changes advocated in the programs. Finally, there have been concerns that overly pervasive conditionality have tended to detract countries from implementing desirable policies owing to failure by the authorities' to take ownership of the program and instead viewing them as imposed by the IMF.

Concerns have also been raised that the FPP framework has, by and large, ignored the existence of country-specific structural features and adverse external shocks. Easterly7 tested the financial programming model and reported large statistical discrepancies in the accounting identities, concluding that such identities do not make a macro model. The assumption of constant velocity failed in the data and velocity was found to be non-stationary. Easterly found the programming approach as flawed, because it does not take into account the endogeneity of virtually all the variables in each macroeconomic identity, the instability of its simple behavioural assumptions, and the large statistical discrepancies in all the identities. To address this problem, he advocated for additional structural features to describe the macro economy so as to minimize the statistical discrepancies.

Finally, it is worth noting that the mutual dependence of instruments and targets within the FPF means that the modelling process is usually iterative and often quite complex. A key concern is ensuring consistency of the macroeconomic framework and coherence of the policy stance across instruments to meet program objectives. Financial programming is used as a general approach to inform and tie together the various sectors in a consistent manner, while incorporating country-specific factors. In this fashion, not only does financial programming serve as an ex ante consistency check on important financial aggregates, it also provides an ex post monitoring tool. This is clearly seen in the next two chapters of this paper.

7 Easterly, W., “The Effect of IMF and World Bank Programs on Poverty”, 2000:P26

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3.0 CONSISTENCY CHECKS OF MACROECONOMIC ACCOUNT LINKS

This chapter presents a number of data consistency checks that have been constructed in this technical paper. The chapter is structured in two parts. Part one contains the consistency checks and gives the economic implications for each of them. It also derives statistically how the links in the consistency checks have come about. Part two uses the Kenyan data as an example to check the efficacy of the consistency framework developed in part one of this chapter.

3.1 Consistency Checks and Derivations

This part is divided into two sections. Section one provides the inter-account consistency checks and gives the economic implications for each of them. Section two presents a number of data consistency checks that must hold within each account.

3.1.1 Inter-Account Consistency Checks and Derivations

The purpose of this section is to develop consistency checks that ensure that data are consistent within and across the accounts and as reliable as possible. This is achieved as follows: First, there is set of inter-account identities that must hold. For example, exports in the national accounts must be equal to exports in the balance of payments. Some of these are very simple, but some are slightly more complicated. For example, final consumption (FC) by government in the national accounts must equal compensation of employees (CE) plus intermediate consumption (IC) plus consumption of fixed capital (CFK) less sales of current goods and services (SCGS) in the government account. Second, there is a set of control mechanism within each account. For example disposable income (DI) by financial corporations and non-financial corporations must equal to savings (S) of each corporations.

Third, we will check for data gaps in the collection strategy. The best way to do this is to comprehensively examine each specific item in all of the accounts to investigate whether it is likely that we have captured all transactions by all sectors that might be involved in that transactions category. For example, in the balance of payments the starting point would be exports and imports. The main source of data comes from customs. But the customs data by definition does not capture smuggling and it might also not capture donor-financed imports. Furthermore, it does not capture repair of capital assets by non-residents nor does it capture repairs done by residents on capital assets by non-residents. One would therefore have to check whether the compilers of balance of payments and national accounts for that matter have made an effort to include such flows in the data.

The next item is income. In Kenya, the credit side for compensation of employees shows zero in each time period. This cannot be true given the substantial presence of international organizations mainly the United Nations and the large number of embassies in Nairobi. All of

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these bodies employ a considerable number of Kenyan residents some of whom are professional even though most are working as non-professionals. This type of examination should be done for each item. It should be part of the financial programming exercise because the ultimate goal of financial programming is to forecast and analyse data. Unless the dataset is complete, the analysis and forecasting loose a lot in value or meaningfulness.

As final step, we will subject the whole dataset to a flow of funds, which is the ultimate data consistency check. The flow of funds is based on the following main principles: First, all credit items are treated as positive values while all debit entries are treated as negative values. In this way, consistency checks ensure that the total value of each and every account must be equal to zero. Secondly, all prorated values are checked so as to ensure that they are equal the total.

The following sections provides the list of inter-account consistency checks for the national accounts (NA), statement of government operations (SOG), balance of payments (BOP) and Depository corporations survey (DCS) followed immediately by explanations and where applicable derivations of each consistency checks. Underpinning the whole consistency checks framework is the fact that all transactions are either of a current, capital or financial nature. Additionally, the double-entry system is used to ensure that the sum of all the transactions within any account is equal zero. Thus, the sum of the current account must be equal to the sum of the negative of the capital and financial account. Similarly, the sum of the current and capital account must be equal to the negative of the financial account.

It is imperative to note that the national accounts, the balance of payments and the statement of government operations are transactions accounts while the depository corporation survey is a stock account. Transactions are defined as acts in which two units engage in an economic exchange on a voluntary basis8. Acquisitions, disposals, borrowing, wages, interest, output and consumption are examples of such transactions. Relevant transactions in national accounts, the balance of payments and the statement of government operations are; consumption of fixed capital, withdrawals from inventories and migrants’ transfers, respectively. Although most transactions take place between two units, there are situations in which a single unit acts in two different capacities. Under this circumstance, it is analytically useful to treat this act as a transaction. Since such a transaction is internal to one economic unit, it is referred to as an internal transaction9. It is also important to note that transactions are one of three flow

8 IMF, “Balance of Payments Manual”, 1993:P6

9 Ibid “Government Finance Statistics Manual”2001: PP 23-24.

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categories. The other flow categories are; holding gains/losses10 and, other changes in volumes of assets. The two latter flow categories are recorded and organized in separate accounts.

A stock account, on the other hand, shows the value of an asset or a liability at a given point in time. Capital assets like machinery and housing as well as financial assets like loans and deposits are examples of stocks. Thus, a stock refers to a position or a value at one point in time while flow refers to an action or is an effect of an event during a time period. Given that a change between the closing balance and the opening balance is equivalent to the total flow, it is normally inappropriate to use stock accounts as source data for transaction accounts. This is because the total flow, not just the transaction is captured. This is particularly relevant for all positions in foreign exchange and for all those that are traded in a secondary market.

3.1.1.1 Inter-Account Consistency Checks: National Accounts and Statement Of Government Operations

When the statement of government operations is being compared with the national accounts, there are certain things that must be taken into account:

• The statement of government operations is consolidated as far as government transactions are concerned while the national accounts are not.

• Own-account production is reflected as capital formation both in the government account and in the national accounts. However, own-account production is also reflected as cost of production (i.e. compensation of employees plus intermediate consumption) in the national accounts but not in the statement of government operations.

• Government account in practise are compiled on cash basis while the national accounts are compiled to a very large extent on accrual basis

(a) Consolidation of Government Transactions

If statistics are done correctly, the statement of government operations should be a consolidated account. Thus, all transactions between government units are netted or eliminated. However, in the national accounts, all transactions involving two government units should be shown. A

10“Holding gains/losses (also referred to as capital gains/losses or valuation changes) are defined as a change in the monetary value of assets and liabilities in the recording period, due to fluctuations in the exchange rate and/or changes in the domestic and foreign prices of assets/liabilities. Other changes in volumes of assets on the other hand are defined as changes, which are due neither to transactions nor to unrealised holding gains/losses. Examples include writing-off bad debts, theft and, involuntary seizure of assets without compensation”. Lennblad (2005), “Stocks versus Flows and Flow Categories”

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central government, for instance, may give a transfer to a municipality. The value of that transfer should be shown in the national accounts but not in the statement of government operations due to the fact that it is netted out in the process of consolidation. Another example is when the central government purchases a service such as electricity from a municipality. While that transaction does show in national accounts, it is reflected as zero in the statement of government operations owing to the consolidation process.

In general, therefore, one will get higher values for the gross flows in the national accounts than in the statement of government operations. This statement is mainly relevant in the use of goods and services (i.e. intermediate consumption), sales as well as transfers. It is, nevertheless, not relevant for compensation of employees, transactions with non-residents or transactions with banks. This is because the other party to the transaction, by definition, is a unit outside the government sector. This means that when data is being compared across the two sets of accounts, one should approach the relevant officers in the ministry of finance to find out if they consolidate the statement of government operations as well as relevant staff in the national statistical office to establish if they do adjustments in order to show the total value of gross flows and not only the consolidated flows. If no adjustments are made, data should be identical across the two sets of accounts. However, if adjustments are made, transfers, sales and intermediate consumption should be somewhat larger in the national accounts.

(b) Own-Account Production

In a situation where the compilation of statistics is done correctly, gross fixed capital formation recorded in the statement of government operations should be the same as that recorded in the national accounts. In this case, own-account production is reflected as capital formation both in the government account and in the national accounts. However, own-account production is reflected in compensation of employees and intermediate consumption (the sum of the two equals cost of production) in the national accounts, but not in the government account.

For instance, in the statement of government operations, an improvement on an existing capital asset or the acquisition of a new capital asset, such as a bridge or building, when done on own account, should not be recorded as the cost of production, but only as gross fixed capital formation. In the national accounts, on the other hand, all three entries should be made, namely: compensation of employees; intermediate consumption, and; gross fixed capital formation. Thus, if data compilations are done correctly, the compensation of employees and intermediate consumption (i.e. cost of production) recorded in the statement of government operations would be smaller than that recorded in the national accounts. It is therefore recommended that one should contact the ministry of finance and the national statistical office to ascertain the nature of the compilation methods. If the statistical office makes no adjustments, data should be identical. Otherwise, national accounts data should relatively be bigger.

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(c) Cash Versus Accrual Accounting

Government accounts in practise are compiled on cash basis while the other accounts are compiled to a very large extent on accrual basis. When accounts are compiled on cash basis, only transactions that give rise to a cash flow are included in the accounts. Thus, the time of recording corresponds to the time the cash payment is made or received. Accrual accounting, on the other hand, means that the timing of the entry should reflect the time of the underlying economic event or process. Accordingly, the transaction should be recorded when the economic benefit associated with an event is flowing from or to the entity.

In practise, government accounts are compiled on cash basis while the other accounts are compiled to a very large extent on accrual basis. The prevailing norm in all internationally accepted guidelines for macroeconomic accounting is that transactions should be recorded on accrual basis. Many statistical offices particularly in Africa, Middle East and other developing countries, however, often do not make an effort to make adjustments to the statement of government operations to be based on accrual accounting. Due to the failure to make these adjustments, that which is recorded in the statement of government operations is often not equal to what is recorded in the other accounts.

Some transactions could have different meaning when compiled on cash or accrual basis. For example, interest payments could have an accrued component and arrears component. If done correctly, both the accrued and the arrears components would be shown in the national accounts and the balance of payments. However, if the recording of transactions of government operations is done strictly on cash basis, neither the accrued component nor the arrears component of interest payments would be shown in the statement of government operations.

In the case of grants, the balance of payments and national accounts, if done correctly should show the total values of the grant11 including technical assistance and transfers in kind. However if the statement of government operations is done correctly on cash basis, technical assistance and transfers in kind are not shown. This is also the case for arrears in loans repayment. If done correctly, the arrears of loans repayments would be shown in the balance of payments account but would not be reflected in the statement of government operations. The same also applies for debt rescheduling and debt forgiveness. In fact, no entries are made for debt rescheduling and forgiveness in a cash account.

The financial programmer must therefore contact people responsible for compilation of balance of payments and statement of government operations and find out the methodology of

11 Grants are also called transfers in both the balance of payments and the national accounts.

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compilation of these five transactions categories, namely: interest payments, transfers, arrears debt rescheduling and debt forgiveness. If no adjustments are made, data should be identical across the two sets of accounts. However, if adjustments are made, these transactions should be somewhat larger in the balance of payments and national accounts compared to the statement of government operations.

Table 3.1 below provides eight inter-account consistency checks between the national accounts and the statement of government operations.

Table 3.1: Inter-Account Consistency Checks: National Accounts (Na) And Statement Of Government Operations

National Accounts (NA) Statement of Government Operations (SOG)

1 Sg nCTrg

2 OPg CEg+ ICg + CFKg +OTP

3 FCg OPg - SCGSg

4 VAgg CEg+CFKg

5 VAgn CEg

6 OSgg CFKg

7 OSng 0

8 DIg Sg+FCg

9 GFKFg GFKFg

10 nLg Sg +KTr g +GFKFg

11 FinAcBalg -FinAcBal g

Where

Sg = Government Savings;

nCTrg = Balances of current account transactions of government;

OPg = Output of government;

CEg = Compensation of employees payable by government;

ICg = Intermediate consumption by government;

CFKg = Consumption of fixed capital by government;

OTPg = Other taxes on production payable by government;

FCg = Final Consumption by government;

SCGSg = Sales of current goods and services by government;

VAng = Net Value Added by government;

VAng = Net Value Added by government;

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OSgg = Gross operating surplus of government;

OSng = Net operating surplus of government;

DIg = Disposable Income of government;

GFKFg = Gross Fixed capital Formation of government;

nLg = Net Lending of government; and

FinAcBalg =Final Account Balance of government.

(i) Government Savings

Government savings is defined as government disposable income less final consumption by government.

Sg =DIg - FCg……………………………………………………………………………………..Eqn (6)

Alternatively, government savings can be expressed as the balance between the current revenue items and the current expense items. Thus, if compilation methods are the same, Government savings as recorded in national accounts (NA) should be equal to the balance of the current transactions of government as recorded in the statement of operations of government (SOG). In an equation form, this is expressed as follows:

Sg=CTrg…………………………………………………………………………………………Eqn (7)

Savings can be defined as the amount available for acquisition of capital and net financial assets by government. This is true because the current account transactions are equal to the negative of the sum of capital and financial account transactions.

In general, savings is a balancing item, defined as gross disposable national income less final consumption in the whole economy. It therefore constitutes the balancing item of all current transactions in an economy. If accounts are compiled strictly in accordance with international standards, then compilation methods should differ and the identity in equation 7 would not hold exactly. However, the reality in most statistical offices in Africa show that compilers of National Accounts and the Statement of Government Operations use the same compilation methods. In practice, therefore, there is normally no valid reason for having slightly different values across the two accounts, implying that equation 7 should hold.

(ii) Output by Government

In general, output is the value of goods and services produced by a productive unit. Output of goods and services produced by government is, however, calculated from the cost side due to the fact that government has certain characteristics that distinguish them from other sectors. For

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instance, government engages in non-market production - meaning that the bulk of that production is provided at below economically significant prices12 As a result; output of goods and services produced by government is calculated from the cost side.

Thus, if compilation methods are the same, the output of government as recorded in national accounts should be equal to the compensation of employees payable by government plus intermediate consumption by government plus consumption of fixed capital13 by government as well as other taxes on production by government as recorded in the statement of government operations (SOG). In an equation form, this is expressed as follows:

OPg = CEg +ICg +CFKg+(OTP) g………………………………………………………………Eqn (8)

where OPg is the output of government, CEg is compensation of employees payable by government, ICg is intermediate consumption by government, CFKg is consumption of fixed capital and OTPg is other taxes on production by government. However, OTPg in equation 8 is always zero for the government. Consequently, the output by government is written as follows:

OPg = CEg +ICg +CFKg………………………………………………………………………..Eqn (9)

Thus, the output of government is measured as the sum of compensation of employees payable by government, intermediate consumption by government and consumption of fixed capital.

(iii) Final Consumption by Government

The final consumption by government as recorded in national accounts is derived as the output of government less sales of current goods by government as recorded in the statement of government operations. This is expressed as follows in an equation form:

FCg= OPg - SCGSg………………………………………………………………………………Eqn (10)

where FCg is the final consumption by government, OPg is the output of government as derived in equation 9 and SCGS is sales of current goods by government. An alternative way of measuring SCGS is the purchases of households of products from government. The two values

12Economically significant prices are those that are sufficiently high to significantly influence economic decisions of either the buyer or the supplier or both (Ibid, 2005:1. An economically significant price is also referred to as a market price. Government mainly engages in non-market production and this means that the bulk of the services it provides to members of the community are either free of charge (e.g. defence, security, legal and administrative service) or at a price below the prevailing market price (e.g. health and education services).

13Consumption of fixed capital used to be called depreciation. It measures the decline in the value of capital asset due to obsolescence and wear and tear. It is not called depreciation because the latter is used in business accounting and is different from consumption of fixed capital in that it is calculated such that it minimizes taxation, thus not reflecting the actual decline in the value of fixed assets.

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should be approximately equal at least if compilation methods are the same. Substituting for OPg as derived in equation 9, equation 10 becomes:

FCg= CEg +ICg +CFKg - SCGSg………………………………………………………………Eqn (11)

Thus, final consumption expenditure by government is defined as the sum of compensation of employees plus use of goods and services plus consumption of fixed capital minus sales of goods and services.

Final consumption expenditure by government can be viewed as an implicit subsidy given to households by government because the latter provides goods and services at a value below the cost of production. Consider, for instance, the government paying a salary of 100 units to doctors and nurses working in hospital. It also purchases medicines and electricity for 50 units. It charges 30 units to its citizens seeking medical treatment at government hospitals.

Based on equation 6, the CE=100 units, IC=50 units, CFK=0 units and SCGS=30 units. This is shown in an equation form as follows:

FCg= 100 units +50 units + 0 units - 30 units = 120 units………………………………Eqn (12)

Equation 7 indicates that the government would be paying an implicit subsidy of 120 units. In this case, final consumption (FC) by government would be 120 units and this would be the implicit subsidy given by government to its citizen seeking health services. Final consumption expenditure by government is thus not the purchases of goods and services even though this is often thought to be the case. Substituting the information given to equation 3 on government output above yields the following results:

OPg = CEg +ICg +CFKg <=> 150 units = 100 units+50 units+0……………………….….(Eqn 13)

From the supply use equation, the following must hold:

OP + TP + M = FCg + FCh + IC + GKF + X, ……………………………………….. Eqn (14)

where TP stands for taxes on production, M for imports and X for exports.

This identity can be re-written as:

FCg = (OP + TP + M) – (IC + GKF + X) – FCh. ……………………………………..Eqn (15)

Thus, substituting for the information obtained above:

FCg = (150 units + 0 units + 0 units) – (50 units + 0 units + 0 units) – 30 units = 120 units…………………………………………………………………………………....Eqn (16)

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This shows that for the identity that supply must be equal to use, final consumption of government must be equal to 120 units.

(iv) Value Added by Government

For all units that are productive, value added is the output of goods and services produced less the goods and services that have been used in the production process. Government value added is calculated exactly the same way. The Gross value Added by government is derived as follows:

VAgg= OPg - ICg - ………………………………………………………………………………Eqn (17)

where VAgg is gross value added, OPg is the output of government as derived in Equation 9 and

ICg is intermediate consumption by government. Substituting OPg (derived in Equation 9), the following equation is obtained:

VAgg = CEg +ICg +CFKg - ICg - ………………………………………………….…………Eqn (18)

Following the cancellations of intermediate consumption from equation 11, we get the following equation:

VAgg = CEg +CFKg ……………………………………………………………………….Eqn (19)

CEg is compensation of employees payable by government and CFKg denotes the consumption of fixed capital. On a net basis, however, the value added by government excludes consumption of fixed capital and is therefore stated as follows:

VAgn = CEg ……………………………………………………………………………………..Eqn (20)

where VAgn is net Value Added while CEg is compensation of employees payable by

government.

The sum of the values added of all resident producers is equal to GDP by definition. This definition has far reaching implications from a statistical point of view! It means that the government’s contribution to real growth rate14 of the economy is determined to a very large extent by the number of employees. This relationship between changes in the number of employees in the government sector and the real growth rate of the economy within an accounting period is very important when numbers are analysed and forecasted. It implies that for every accounting period that the government adds to its number of employees, the real

14 The economic growth rate of any country is calculated as a change in the volume component of value added, meaning that all variables consist of both the price and volume components.

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growth rate of the economy will, by definition, be positively affected while the reverse is true when the number of employees is reduced. But this only holds within the current accounting period since it does not take into account the accompanying efficiency issues. If the increase in the number of employees is not matched by an improvement in the efficiency of both existing and the additional workers, then in the medium to long run, the economic growth rate will be negatively affected.

(v) Operating Surplus

The operating surplus is a balancing item and it signifies the return to the factor of production called capital. Broadly speaking, it would be close to the profit generated by market producers before they pay income tax, profit tax or interest and record any holding gains/losses. It is the output less cost of production of market producers.

The operating surplus of government as recorded in the national accounts is derived as the equivalent of value added by government less compensation of employees as well as taxes on production and imports as recorded in the statement of government operations. In an equation form, this is expressed as follows:

OSg= VAgg -CEg –TPIg …………………………………………………………………………Eqn(21)

where OSg is the operating surplus of government recorded in national accounts while VAgg is

the gross value added by government, CEg is compensation of employees payable by government and TPIg is taxes on production and imports payable by government, all recorded in the statement of government operations.

Substituting for VAgg as derived in equation 17, the following identity is obtained:

OSg= CEg +CFKg -CEg –TPIg …………………………………………………………………Eqn(22)

Again, CEg cancels out in equation 22, and the following identity is therefore obtained:

OSg= CFKg –TPIg ………………………………………………………………………………..Eqn(23)

But we know that TPIg are not relevant for government because it is either receivable or payable to government and not the other way round. Thus, TPIg = 0 and equation 23 is therefore expressed as:

OSgg= CFKg ……………………………………………………………………………………..Eqn(24)

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Where the superscript OSgg refers to gross operating surplus of government. It is necessary to

remove the consumption of fixed capital from the gross operating surplus to obtain the net operating surplus. This gives rise to the following equation:

OSng= 0 ……………………………………………………………………………………………Eqn(25)

Where OSng refers to the net operating surplus of government. Equation 16 is true since

government is not market producer and cannot therefore have an operating surplus since its output is equal to cost of production.

(vi) Disposable Income of Government

The disposable income of government as recorded in national accounts must equal to government savings plus final consumption of government as recorded in the statement of government operations. This is expressed in an equation form as follows:

DIg = Sg + FCg………………………………………………………………………………….Eqn (26)

Where DIg is the disposable income of government, Sg is the government savings and FCg is the final consumption of government.

Disposable income is a balancing item. It is defined as income resulting from production and ownership of financial and non-produced, non-financial assets as well as income resulting from transfers. Stated differently, disposable income by government is all income available to government including revenue from taxes, property income, administrative fees, fines, penalties, forfeits as well as social contributions and grants. However, in the case of the ownership of financial and non-produced, non-financial assets as well as transfers, such income is expressed as a net item. The government uses such income to meet some of its recurrent expenditures, namely payments of salaries, wages and social contributions to its workers as well as payments for goods and services. It also uses the income to finance development expenditures such as the building of roads and bridges for its citizens.

(vii) Gross Fixed Capital Formation of Government

The gross fixed capital formation of government as recorded in national accounts should always be equal to the gross fixed capital formation as recorded in the statement of government operations. This is expressed in an equation form as follows:

(GFKFg)na = (GFKFg)sog ………………………………………………………………….Eqn (27)

Where GFKFg is the Gross Fixed Capital Formation of government, na is national accounts while sog is the statement of government operations.

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An example of gross fixed capital formation of government is the acquisition of a new fleet of vehicles or computer hardware and software to the various departments of government. Another example is when government builds a road or a bridge. It is important to note that the acquisitions and/or disposals of land and sub-soil assets by government are not part of gross fixed capital formation of government.

(viii) Net lending to Government

In general, net lending is defined as savings plus capital transfers less net acquisition of capital assets for a given unit, sector or country. This is further emphasised by the IMF’s Government Finance Statistics (GFS) Manual of 2001, which states in Paragraph 4.1715 that:

“ Net lending (+) /borrowing (-) is a summary measure indicating the extent to which government is either putting financial resources at the disposal of other sectors in the economy or utilizing the financial resources generated by other sectors. It may therefore be viewed as an indicator of the financial impact of government operations on the rest of the economy.”

Net lending therefore measures the amount of financial resources that each unit, sector or country can put at the disposal for use by other units, sectors or countries. Net lending to the government sector as recorded in national accounts is equal to the government savings plus net capital transfers to government less gross fixed capital formation by government as recorded in the statement of government operations. This is expressed in an equation form as follows:

NLg=Sg +KTrg -GFKFg16……………………………………………………………………Eqn (28)

where NLg = Net Lending, Sg is government savings, KTrg is net capital transfers to government and GFKFg is the fixed capital formation by government.

If net lending is negative, it indicates that the sector must have recourse to financial resources from other sectors. The reverse is also true. An example of negative net lending to government is when the government borrows and issues government paper, mainly in the form of bonds and bills from the domestic market. In this scenario, the balance reflects the impact of government’s 15IMF, “Government Finance Statistics Manual” 2001:P39

16 GFKF is acquisitions of assets by government. Given that increases in assets are recorded as negatives in the accounting system,, it represents a negative (i.e. debit) item. From a technical perspective, therefore, savings plus net capital transfers plus GFKF equals to net lending. Thus, net lending can be written as:

NLg=Sg +KTrg+GFKFg………………………………………………………………………………………….…(29).

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operations on monetary and financial factors in the economy. Net lending is therefore a very important determining factor of the change in public debt/GDP ratio. An example of positive net lending to government is when the government generates a budget surplus, which means that the government would stop borrowing from the domestic market and instead use the generated surplus to repay outstanding domestic debt.

If compilation methods are the same, net lending to government as recorded in national accounts is also equal to the negative of the financial account balance of the statement of government operations. This is true because net lending is the balancing item of all current and capital transactions. This is written in equation form as:

NLg= - FinAcBalg………………….…………………………………………………….……Eqn (30)

where NLg is the net lending to government while FinAcBal g is the financial account balance of government. This explains why net lending measures the financial impact of government operations.

3.1.1.2 Inter-Account Consistency Checks: National Accounts Versus Balance of Payments

Table 3.2 below presents five inter-account consistency checks between the national accounts and the balance of payments. A brief description of each of these consistency checks is also provided immediately thereafter. It is worth noting that the rest of the world (ROW) account in the national accounts is compiled from the perspective of a non-resident while the balance of payments is compiled from the perspective of a resident. Thus, the signs of all transactions included in the accounts are opposite in the two accounts. However, the coverage in both accounts is identical.

Table 3.2: Inter-Account Consistency Checks: National Accounts (NA) Versus Balance of Payments and Balance of Payments (BOP)

National Accounts (NA) Balance of Payments (BOP)

1 -(X – M) (in ROW account) (X – M) (in merchandise account of BOP)

2 - nI (in ROW account) nI (in income account of BOP)

2 - CTr (in ROW account) CTr (in current transfer account of BOP)

4 S-GKF CAB

5 Net Lending (NL) -FinAcBal (including the reserve assets)

Where

X = Exports;

M = Imports;

nI = Net Income from abroad

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CTr = Net current transfers from abroad and;

FinAcBal = Financial account balance.

(i) Exports and Imports

Exports and imports consist of trade in goods and services17 between residents and non-residents valued at the exporter's customs frontier (i.e. f.o.b.). Exports of goods and services are recorded as credit items in the balance of payments and as debit items in the national accounts. Imports of goods and services on the other hand are recorded as debit entries in the balance of payments and as credit entries in the national accounts. Thus, the trade balance (i.e. exports – imports) recorded in both the national accounts and the balance of payments should be equal but with opposite signs. In an equation form, this is expressed as follows:

(X - M)na = - (X - M)bop ……………………………………………………..……...…Eqn (31)

where na refers to national accounts while bop refers balance of payments

If the Kenyan government, for instance, receives technical assistance from abroad, then data on that assistance must be treated in the same way both in the national accounts and balance of payments. More specifically, such assistance should be recorded as an import of service both in the national accounts and balance of payments. Similarly, smuggling and timing adjustments should be treated in exactly the same way in both accounting systems. Note however that signs of entries for imports are different in the two accounts, with the national accounts showing credit (plus) and the balance of payments indicating a debit (minus).

(ii) Net Income from abroad

Income covers two types of transactions between residents and non-residents, namely: compensation of employees and investment income. Compensation of employees comprises wages, salaries and other benefits (in cash and in kind) earned by individuals in economies other than those in which they are residents for work performed for and paid by residents of those economies18. Investment income, on the other hand, covers income derived from a resident’s ownership of foreign financial assets19. Interest and dividends are examples of income from financial assets. In particular, interest income is associated with ownership of

17 Including sales/purchases, barter and gifts

18 IMF, “Balance of Payments Manual”, 1993: Paragraph 269

19 Ibid., Paragraph 274

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interest-bearing financial instruments such as: loans extended to non-resident units, bank deposits held abroad and; bills as well as bonds purchased from non-resident productive unit(s). Dividends on the other hand are income earned from distribution of profits from shares owned by a non-resident in an enterprise unit located abroad or domestically20. Rent is income that arises from ownership of land, sub-soil assets and other non-produced, non-financial assets. It is important to note that unrealised holding gains/losses (also referred to as capital gains/losses or valuation changes) are excluded21 from this category. Holding gains/losses emanate from two sources, namely exchange rate changes and domestic and foreign price movements in assets or liabilities.

Income receivable from abroad is recorded as a credit item in the balance of payments and as a debit item in the national accounts while the income payable abroad is recorded as a debit item in the balance of payments and as a credit item in the national accounts. The debit and credit items recorded in both national accounts and balance of payments must therefore be equal but with opposite signs. This is expressed in an equation form as follows:

nIna = -nIbop……………………………………………………………………….…………….Eqn (32)

where nI =net income from abroad.

(iii) Net current transfers from abroad

Net current transfers from abroad are defined as the net transfers in cash or in kind between resident and non-resident units. The government of Kenya could, for instance, receive cash from the German government without giving anything in return. This is example of cash transfers between governments for purposes of financing current expenditures. Another example of transfer between governments is when the Kenyan government provides gifts like food, clothing and some medical supplies to the government of Sudan when the latter faces a natural disaster like famine, floods or earthquakes. Under this circumstance, the Kenya government would be receiving nothing in return from the transfer given to Government of Sudan. Workers’ remittances by migrants employed in new economies are also considered part of current transfers. Thus current transfers from abroad are defined as the net transfers in cash or in kind between resident and non-resident units, whether productive or not.

20 Earnings from ownership of shares in a company operating in Kenya’s export processing zones (EPZ) is a good

example of dividends earned from distribution of profits from shares owned by non-resident in an enterprise located in a domestic economy.

21 Holdings/losses should not be recorded in either the statement of government operations, balance of payments or main sequence of national accounts even though they do affect stock positions They are recorded in a separate account referred to as the revaluation account.

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In general, current transfers receivable from non-resident units are recorded as a debit entry in the national accounts and as a credit entry in the balance of payments. Similarly, the current transfers payable to non-resident units are recorded as credit items in the national accounts and as a debit item in the balance of payments. On a net basis, therefore, the net current transfers recorded in both the national accounts and balance of payments should always be equal but with opposite signs. This is expressed in an equation form as follows:

nCTrna = - nCTrbop……………………………………………………………………………..Eqn (33)

where nCTr = Net current transfers from abroad, na is national accounts and bop is balance of payments.

(iv) Savings Investment Gap

The starting point in this derivation is the identity between output produced and the disposition of that output in the national income and product accounts. The supply of goods and services in a given year is equal to the domestically produced output plus imports of goods and services. In an equation form, this is written as:

GDP+M = FC+GKF+X…………………………………………………….……….Eqn (34)

Where:

GDP = Gross domestic product

M = Imports of goods and non-factor services

GKF = Gross fixed capital formation

X = Exports of goods and non-factor services

Rearranging this identity results in the following equation:

GDP = FC+GKF+X-M………………………………………………….……………………Eqn (35)

If we add net factor income (nI) from abroad to both sides of equation 35, we obtain:

GDP +nI= FC+GKF+(X-M +nI) …………………………………………………………..Eqn (36)

The left hand side of equation 36 now equals to gross national income (GNI):

GNI= FC+GKF+(X-M +nI) ………………………………………………………………..Eqn (37)

If we add net current transfer (nCTr) from abroad to both sides of equation 37, we obtain:

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GNI+nCTr= FC+GKF+(X-M +nI +nCTr) ……………………………………………….Eqn (38)

The left hand side of equation 38 now equals gross national disposable income while the terms in brackets in the right hand side now gives the current account balance (CAB) of the balance of payments. Thus, equation 38 can be rewritten as follows:

GNDI=FC+GKF+CAB……………………………………………………………..………..Eqn (39)

By subtracting FC and GKF from both sides of equation 39, the following is obtained:

GNDI- (FC-GKF)=CAB…………….………………………………………….…………….Eqn (40)

Equation 40 indicates that a current account deficit can result from excess spending in the entire economy or from excess spending in one or more of its sectors. By observing that gross national disposable income (GNDI) minus final consumption (FC) equals to savings (S), equation 40 can be re-written as:

S-GKF=CAB……………………………………………………….……………..……………Eqn (41)

The interrelationship between internal and external sectors of an economy can be seen in greater details by distinguishing the government and non-government sectors. The government sector consists of “all government units and all non-market Non-Profit Institutions (NPIs that are controlled and mainly financed by government units” IMF, 2001: 10. The non-government sectors on the other hand refer to all non- government units and all non-market Non-profit Institutions (NPIs) that are not controlled and mainly financed by government units. Thus, gross fixed capital formation of the entire economy can be disaggregated into gross fixed capital formation of the government sector (GKFg) and gross fixed capital formation of the non-government sector (GKFng). Following the disaggregation, equation 41 can therefore be written as follows:

(Sg-GKFg)+ (Sng-GKFng)=CAB………………………………………………………………Eqn (42)

Where:

Sg-GKFg= savings and gross fixed capital formation behaviour of the government and

Sng-GKFng= savings and gross fixed capital formation behaviour of the non-government sector.

Equation 42 above indicates that the current account balance is a mirror image of the savings and gross capital formation behaviour of the domestic economy. The equation shows that a current account deficit is related to deficits in both government and other sectors. It also shows that such current account deficits are the result of either very low savings owing to high final

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consumption or very high gross capital formation. Current account deficits can also result if the rate of domestic absorption is higher than the rate of generation of disposable income. The starting point in addressing the problem of current account deficits is to diagnose the root causes of such developments. Such causes could the result of low savings or a boom in gross capital formation within the economy.

(v) Net Lending

As already explained in section 3.1.1.1 (viii), net lending is obtained by subtracting purchases of capital goods (fixed assets as well as non-produced, non-financial assets) from savings obtained in the use of disposable income. In the balance of payments, net lending equals the sum of the current and capital account balances. In an equation form, this can be written as follows:

(S-GKF+nKTr-nAnPnFa)na =(CAB+KAB)bop………………………………………….…..Eqn (43)

where nKTr stands for net capital transfers; nAnPnFa stands for net acquisition of non-produced, non-financial assets (mainly land and sub-soils) and KAB stands for capital account in the balance of payments.

The left hand sight of equation 43 represents net lending in the national accounts22. The right hand sight represents the balance of payments less the financial account transactions in the balance of payments, implying that net lending is also equal to the negative of the financial account in the balance of the balance of payments. Thus, net lending as recorded in national accounts should always be equal to net lending in the balance of payments. This is provided in an equation form as follows:

NLna = NLbop…………………………………………………………………………………….Eqn (44)

Where:

NLna = Net lending as recorded in national accounts

NLbop = Net lending as recorded in balance of payments

From the perspective of the whole economy, net lending, as indicated in section 3.1.2 (viii), measures the financial resources that the residents can put at the disposal for use by non-

22 While the transactions in the statement of government operations have slightly different names than in the

balance of payments, the idea is exactly the same. Thus, the balance of capital transfers receivable and payable as well as acquisitions less disposals of non-produced non-financial asset in ROW equals the current and capital account balance in the balance of payments

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residents. Thus, net lending recorded in national accounts not only includes net lending to government but also that to the other domestic sectors.

In a closed economy, net lending must be zero. This is because the total expenditure incurred on capital assets by one resident unit is only financed from the savings made by other resident entities but not from non-resident entities. Stated differently, financial resources made available from one resident entity must stem from a reduction in financial assets or increased financial liabilities of another resident entity. Thus, the increase in financial assets by one entity is exactly offset by the decrease in financial assets or increase in financial liabilities of another resident entity such that the total effect on the domestic net financial asset position is zero.

If net lending is negative, it indicates that the sector must have recourse to financial resources from other sectors. The reverse is also true. Net lending to the government sector as recorded in the national accounts is equal to the government savings plus net capital transfers to government less gross fixed capital formation by government as recorded in the statement of government operations. Similarly, net lending to the other sectors as recorded in national accounts is equal to the savings of these other sectors plus net capital transfers to the private sector less gross fixed capital formation by the private sectors. Thus for the entire economy, that which is left after total expenditure on capital assets has been deducted from savings must be used to accumulate financial assets or reduce financial liabilities. This means that if savings do not cover total expenditure on capital assets, then financial resources must be made available from another entity. This means a reduction in financial assets or increased financial liabilities of that entity. The reverse is also true.

As explained above, from the whole country’s perspective, net lending is tantamount to a financial exchange with non-residents. An example of such net lending when the amount is positive is the accumulation of foreign notes and coins under the mattresses by people who either have no confidence in the banking system or live far away from the banks such as the Kenyan Masaai herdsmen. Another example of positive net lending vis-à-vis non-residents is when, say, a Kenyan household buys shares from the Tanzania Breweries Company Limited. An example of negative net lending vis-à-vis non-residents is when a household builds or buys a house within the accounting period and needs to borrow from a bank abroad. Similarly positive net lending occurs when, for instance, a Kenyan household purchases consumption goods in excess of its income and must therefore use financial resources such as credit card liabilities from a bank abroad. In both cases, that borrowing will be recorded in national accounts as positive net lending by the household sector.

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3.1.1.3 Inter-Account Consistency Checks: Statement of Government Operations and Balance of payments

Table 3.3 below provides eight inter-account consistency checks between the Statement of Government Operations (SGO) and Balance of payments (BOP). A brief description of each of these consistency checks is also provided immediately thereafter. Table 3.3: Inter-Account Consistency Checks: Statement of Government Operations

(SOG) and Balance of payments (SGO)

SGO Item BOP Item

1 Compensation of employees (CE)g vis-à-vis non-residents

Compensation of employees payable by government (CE)g

2 Net Property Income (nPI)g vis-avis non-residents

Net Investment income (nPI)g by government

3 Net current transfers (nCTr)g vis-à-vis non-residents

Net current transfers (nCTr)g by government

4 Net capital transfers vis-a-vis non-residents (nKTr) g

Net capital transfers by government (nKTr)g

5 Disposal/acquisition on non-produced non-financial asset e.g. land vis-a-vis non-residents (nNPNFA) g

Disposal/acquisition on non-produced non-financial asset (nNPNFA)g such as land

6 Net borrowing vis-a-vis non-residents (nB)g Net borrowing by government (nB)g

7 Net Receipts of Foreign securities vis-à-vis non-residents (nRFS)g

Portfolio investment by government (nRFS)g

8 Net additions to deposits abroad vis-à-vis non-residents (nADA)g

Net additions to deposits of government (nADA)g

where

CEg Compensation of employees vis-à-vis non-residents by Government

nPIg Net Property Income vis-avis non-residents by Government

nCTrg Net current transfers vis-à-vis non-residents by Government

nKTr g Net capital grants vis-a-vis non-residents by Government

nNPNFAg Disposal/acquisition on non-produced non-financial asset by Government

nBg Net borrowing vis-a-vis non-residents by Government

nRFSg Net Receipts of Foreign securities vis-a-vis non-residents by Government

nADA Net additions to deposits abroad vis-a-vis non-residents by Government

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(i) Compensation of employees

Compensation of employees consists of two main components, namely; wages and salaries; and social contributions paid to households for providing labour to a productive unit. This decomposition is identical both in the statement of government operations and balance of payments. In both accounts, compensation of employees basically represents the income receivable by resident employees from non-resident productive units and/or the income payable by resident productive units to non-resident employees.

The net amount recorded, as income receivable by resident employees from non-resident productive units is exactly the same in the balance of payments as it is in statement of government operations. In an equation form, this can be written as follows:

CEsgo = CEbop…………………………………………………………………………………Eqn(45)

Where:

CEsgo is compensation of employees as recorded in the statement of government operations while CEbop compensation of employees as recorded in the balance of payments.

A good example of compensation of employees in Kenya is the amount of wages and salaries and other benefits in cash or in kind earned by Kenyans employed in the various embassies in Nairobi as well as by the other citizens working in such embassies and United Nations (UN) bodies. This is because embassies and the UN are considered extraterritorial to the economies in which they are located. The compensation received by staff of employed from the host country (in this case Kenya) is classified as that paid by non-residents to residents. This amount should be exactly the same in the national accounts and balance of payments. It is important to mention that payment for short-term consultancy services from non-residents entities is not compensation of employees even though it is often thought of that way. It instead refers to imports of a service. This would be captured in the rest of the world account of the national accounts as imports and in the statement of government operations as use of goods and services.

(ii) Net Property Income

Property income is defined as the income payable on financial assets as well as non-produced, non-financial assets located abroad. Both residents and non-residents can earn income on assets located abroad. On a net basis, therefore, property income is the income receivable from financial assets as well as non-produced, non-financial assets located abroad less income payable to non-residents from financial assets as well as non-produced, non-financial assets located in the domestic economy. The debit and credit items recorded in both statement of

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government operations and balance of payments should therefore be equal. This is expressed in an equation form as follows:

PIsgo = PIbop…………………………………………………………………………………Eqn(46)

Rent is a good example of income earned from a non-produced, non-financial asset such as land. Interest and dividends on the other hand are examples of property income earned on financial assets such as loans and shares, respectively.

(iii) Net Current Transfers

As indicated in section 3.1.1.1 (iii), current transfers are transfers in cash or in kind between a resident and a non-resident. The current transfers receivable from non-resident units are recorded as credit items in the statement of government operations and the balance of payments. On the other hand, the current transfers payable to non-resident units are recorded as debit entries in the statement of government operations and the balance of payments. On a net basis, the net current transfers recorded in both the statement of government operations and balance of payments should always be equal. This is expressed in an equation form as follows:

nCTrsog= nCTrbop………………………………………………………………………………Eqn(47)

Examples of current transfers between a resident and a non-resident are gift items including: food, clothing, and medical supplies such as those given to Kenya in 1998 following the terrorist bomb blast in Nairobi. Other examples include those gifts including food, clothing, and medical supplies associated with relief efforts during periods of natural disasters such as the recent “Tsunami” floods and earthquakes recently experienced in many East Asian countries. Technical assistance received/offered by one country to another is also classified as current transfers.

(iv) Net capital grants

A capital transfer between a resident unit and a non-resident unit can be in kind or in cash. If in cash, the transfer is linked to or conditional on the acquisition or disposal of a fixed asset by one or both parties to the transaction. If in kind, the transfer can either be of ownership of a fixed asset or the forgiveness of debt’s financial liability without anything in return to the creditor.

Capital transfers receivable by a resident unit from a non-resident unit are recorded as credit items in the statement of government operations and the balance of payments. Similarly, capital transfers payable by a resident unit to a non-resident unit are recorded as debit entries in the statement of government operations and the balance of payments. Thus, the net capital grants

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vis-à-vis non-residents recorded in the statement of government operations should be equal to that recorded in the balance of payments. In an equation form, this is expressed as follows:

nKTrsog = nKTrbop……………………………………………………………………………Eqn (48)

An example of a capital transfer is when, say, the Kenya government receives debt forgiveness from bilateral creditors to the tune of US$ 100m. This amount would be reflected both in the balance of payments and statement of government operations as a reduction in liability. Another example of a capital transfer is when the Kenya government, for instance, receives capital equipment such as caterpillars, tractors and other machinery (from a foreign government such as the US government) for use in road construction or X-ray machines and other equipment from, say, the Government of Japan for use in government hospitals.

(v) Disposal / Acquisition on Non-Produced Non-Financial Asset

As already mentioned in section 3.1.1.1, acquisition/ disposal of non-produced non-financial asset comprises the acquisition or disposal of non-produced non-financial assets such as land and subsoil assets, as well as the acquisition/ disposal of non-produced non-financial, intangible assets such as patents, copyrights and trademark.

The disposal of non-produced non-financial assets such as land by a resident to a non-resident is treated as a credit entry in the statement of government operations and the balance of payments. The reverse is true for the acquisition of non-produced non-financial asset such as land by a resident from a non-resident. Thus, what is recorded in the balance of payments as the net disposal/acquisition of non-produced non-financial should exactly be the same as the amount recorded as net disposal/acquisition acquisition of non-produced non-financial in statement of government operations. This is expressed in an equation form as follows:

nNPNFAsog= nNPNFAbop ……………………………………………………………………Eqn(49)

An example of acquisition / disposal of non-produced non-financial asset is the sale of land by the Kenyan government to the US government immediately after the 1998 bomb blast. Another example is the acquisition of “Nandos” - the fast food South African franchise- by Kenyan residents in 1999. The recordings of both of transactions should exactly be the same in Kenya’s statement of government operations as well as the balance of payments data.

It is noteworthy to mention that for land and sub-soil assets (which constitutes most transactions recorded under this item), entries are only made in the balance of payments when the government is involved. When non-government units (sometimes erroneously referred to as private sector units) are involved, however, acquisitions of land are recorded as direct investment (occasionally as portfolio investment) in the balance of payments.

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(vi) Net Borrowing vis-à-vis Non-residents

All borrowing by residents from non-residents is considered an increase in liabilities and a credit item both in the statement of government operations and the balance of payments. Thus both long-term and short-term loans received are recorded as credit items in both accounts. On the other hand, both long-term and short-term loan repayments are recorded as debit items in both the statement of government operations and the balance of payments.

Unrealised holding gains/losses for deposits are not recorded in either the statement of government operations or the balance of payments even though they do affect stock positions. This implies that the treatment for holding gains is identical in both accounts such that the equality rather than the approximate sign is relevant. Thus, the net borrowing by resident units from non-resident units as recorded in the statement of government operations is equal to that recorded in the balance of payments. This is expressed in an equation form as follows:

nBrsog= nBrbop………… ………………………………………………………………………Eqn (50)

where nBr means net borrowing. (vii) Net Receipts of Foreign securities

In the statement of government operations and the balance of payments, an increase in the amount of foreign securities vis-à-vis non-residents are recorded as credit items. Similarly, a decrease in the amount of foreign securities vis-à-vis non-residents is recorded as debit items in the statement of government operations. On a net basis, the receipt of foreign securities vis-à-vis non-residents recorded in both the statement of government operations and the balance of payments should be equal. This is expressed in an equation form as follows:

nRFSsog= nRFSbop……………………………………………………………………………Eqn (51)

where nRFS means net borrowing in form of foreign securities vis-à-vis non-residents.

(viii) Net additions to deposits abroad

Deposits are mainly23 claims on the central bank and other depository corporations. Deposits held by non-residents form part of the foreign liabilities of the depository corporations sector and are a component of net foreign assets in the depository corporations survey. The net foreign assets held by commercial banks, includes all assets and liabilities held by commercial banks

23 In some cases, government and other institutional units also accept deposits; in this case the deposit must be

represented by evidence thereof .

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vis-à-vis non-residents, irrespective of currency of denomination, but with some exceptions made depending on how it affects the aggregate demand and hence the price level of the country.

If deposits held by non-residents in domestic commercial banks are used in local transactions, then they do affect the overall price level of the country and thus should be excluded from the net foreign assets category and instead included as part of money, otherwise they should be included as net foreign assets held by commercial banks. Thus, the treatment of non-resident deposits should depend on the specific situation in each country. It is very important that the financial programmer is aware of the way non-resident deposits are classified in the depository corporation survey before embarking on this consistency check exercise. For instance, net additions to deposits abroad vis-à-vis non-residents recorded in the statement of government operations should always be equal to those recorded in the balance of payments. This is expressed in an equation form as follows:

nADAsog= nADAbop……………………………………………………………………..……Eqn(52)

Where nADA is the net additions to deposits abroad. Again, financial programmers must make sure that they understand exactly how non-resident deposits have been classified in the depository corporation survey and the balance of payments such that before making comparisons, they are sure that the total value of such deposits have been captured is made.

3.1.1.4 Inter-Account Consistency Checks: Statement of Government Operations and the Depository Corporations Survey

The depository corporations survey (DCS) is linked to the statement of government operations (SGO) through the government’s net indebtedness to the banking system. Table 3.4 below therefore provides the inter-account consistency checks between the statement of government operations and the depository corporations survey. A brief description of each of these consistency checks is also provided immediately thereafter.

It deserves mention that the content of the data in the statement of government operations and the depository corporations survey is not the same. In the statement of government operations financial assets and liabilities are shown as transactions, while in the depository corporations survey they appear as stock values. As mentioned in the introductory part of this chapter, a financial programmer must be careful when using changes in the stock data from the depository corporations survey as a source of data in a transaction account, e.g. the statement of government operations. Changes in the stock positions of financial assets and liabilities are flows, of which transactions are just one sub-category.

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If a financial programmer uses the depository corporation survey as source data for the balance of payments, he/she might get exactly the same values and not the approximated ones as suggested by the equations in this section. However, for the values to be exactly the same, the time periods used for the comparisons must be identical. For example, if annual data are used in the depository corporation survey for the comparison, but shorter time periods have been used to derive the transaction data in the balance of payments, the values will, by definition, not be the same. In this document, we strongly suggest that shorter time periods be used when transaction data are estimated. This explains the approximation sign.

Another reason for using an approximation sign is that we want to make it clear that the there is actually a better way of obtaining the data than using the depository corporation survey, namely, as already mentioned in the text, to forego the depository corporation survey and to obtain the transactions data from the foreign exchange department or any other department responsible for keeping such data. If the latter is done, then the values will not be the same, hence the approximation sign. Furthermore, we strongly encourage that the depository corporation survey should never be used as source data for monetary gold, SDR and reserve position in the Fund. Therefore, when one looks at the total values of reserve assets and the net foreign assets of the central bank, one must have the approximation sign since the values from the depository corporation survey and balance of payments cannot be the same.

It is worth mentioning also that for financial assets and liabilities in domestic currency, the problems of holding gains/losses are normally not experienced since the exchange rate changes that cause such holding gains/losses do not arise. However, in countries where trading financial assets and liabilities is traded in secondary markets, problems of holding gains/losses do arise from domestic price movements which results in the changes in the value of such financial assets and liabilities. For financial assets and liabilities in foreign currency, the problem of holding gains/losses is often quite big both the changes in domestic price movements as well as changes in exchange rates affect the value of such financial assets and liabilities.

Table 3.4: Inter-Account Consistency Checks: Statement of Government Operations (SGO) and the Depository Corporations Survey (DCS)

SGO Item DCS Item

1 D (vis-à-vis domestic banks) D (changes in stock positions between two periods)

2 L (vis-à-vis domestic banks) L (changes in stock positions between two periods,) including advances to government

3 BB (vis-à-vis domestic banks) BB (changes in stock positions between two periods), issued by government

Where:

D = Government Deposits

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L = Loans and advances to government

BB = Bonds and bills vis-à-vis domestic banks

(i) Government Deposits

Most government units hold a variety of deposits as assets including those in foreign currency. They also can hold some deposits as liabilities. For the deposits denominated in domestic currency, what is recorded as the transactions in government deposits in the statement of government operations in a given time period is exactly the same as what is recorded as the change in the stock position of government deposits in the depository corporations survey. However, for deposits denominated in foreign currencies, the situation is slightly more problematic, because holding gains/losses will occur unless the exchange rate is perfectly fixed. It is important to note that comparing data of deposits from a stock account such as the depository corporations survey with data from a transaction account such as the statement of government operations is only permissible for deposits in domestic currency. For other deposits, adjustments must be made to reduce or eliminate the holding gains/losses.

In an equation form, the equivalence between the depository corporations survey and the statement of government operations is shown as follows:

nDsgo= ∆Ddcs…………………………………….…………………………………………eqn (53)

where nD = net government deposits denominated in domestic currency vis-à-vis domestic banks as recorded in statement of government operations while ∆D government deposits denominated in domestic currency vis-à-vis domestic banks as recorded in the depository corporations survey.

(ii) Loans vis-à-vis Domestic Banks

The transactions in the loans recorded in the statement of government operations as having been extended to the government sector from the domestic banks (i.e. depository corporations sector) must be equal to the change in loans recorded in the depository corporations survey as having been extended to the government sector from the domestic banks. Similarly, the change in the loans recorded in the statement of government operations, as having been repaid by the government sector from the depository corporations sector must be equal to the change in loans recorded in the deposit corporations survey as having been repaid by the government sector to the deposit corporations sector. In countries where trading financial assets and liabilities is traded in secondary markets, problems of holding gains/losses do arise from domestic price movements which results in the changes in the value of such financial assets and liabilities. For loans, however, the problem of holding gains/losses is often not common although it could occur particularly in situations where such lending is in foreign currency.

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Overall, the net loan transactions extended to government from the deposit taking institutions as recorded in the statement of government operations should always be equal to the net transaction in loans recorded in the deposit corporations survey as having been extended to the government sector from the domestic banks. This can be shown in an equation form as follows:

nLsgo=∆nLdcs………………………………………………………………………………Eqn (54)

where nL is the net loans to government from the depository corporations sector.

(iii) Bonds and Bills vis-à-vis Domestic Banks

The change in net government borrowing from the domestic banks in the form of bills and bonds as recorded in the statement of government operations must be the same as what is recorded in the depository corporations survey as the change in net government borrowing from the domestic banks in the form of bills and bonds. In countries like Kenya where trading of bonds and bills in the secondary markets is not well developed, the problem of holding gains/losses for these financial instruments is not common. The problem is, however, serious in countries with relatively more sophisticated secondary markets.

On a net basis, therefore, the net government borrowing from the domestic banks in the form of bills and bonds as recorded in the statement of government operations and the net government borrowing from the domestic banks in the form of bills and bonds depository corporations survey is shown in an equation form as follows:

(∆nBB)sgo=(∆nBBdcs)………………………………………………………………………Eqn (55)

where nBB is the net amount of government borrowing in form of bills and bonds from the domestic banking system.

3.1.1.5 Inter-Account Consistency Checks: DCS and BOP

Table 3.5 below provides thee inter-account consistency checks between the balance of payments and the depository corporations survey. A brief description of each of these consistency checks is also provided immediately thereafter.

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Table 3.5: Inter-Account Consistency Checks: Deposit Corporations Survey (DCS) and (Balance of Payments)

DCS BOP

1 1.1 ∆MGcbs

1.2 ∆SDRcbs

1.3 ∆RPFcbs

1.1MGbop

1.2 SDRbop

1.3 RPFbop

2 ∆FEcbs FEbop

3 ∆RAcbs (1+2) RAbop (1+2)

4 ∆FEcbs FEbop

5 ∆FLcbs FLbop

6 ∆NFAcbs (4+5) NFAbop (4+5)

7 ∆FAods FAbop

8 ∆FLods FLbop

9 ∆NFAods (7-8) NFAbop (7-8)

10 ∆NFAdcs (6+9) NFAbop (6+9)

Where:

MGcbs = Monetary Gold,

SDRcbs = Special drawing right,

RPFcbs = Reserve Position in Fund,

FE cbs = Foreign Exchange held by the monetary authority

RAcbs = reserve assets held by the monetary authority,

FA cbs = Foreign assets held by the monetary authority

FAods = Foreign assets held by the commercial banks

FLcbs = Foreign liabilities held by the monetary authority

FLods = Foreign liabilities held by the commercial banks

(NFAma)cbs = Net foreign Assets of monetary authority

(NFAcb)ods = Net foreign assets of commercial banks

(NFA)dcs = Net foreign assets of the Depository Corporations

∆ = Change

cbs = Central Bank Survey

odc = Other Depository Corporations Survey

dcs = Depository Corporations Survey

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(i) Monetary Gold, Special Drawing Right and Reserve Position in the Fund

Monetary gold is gold held by a central bank. The amount of monetary gold recorded in the depository corporations survey in a given period is not the same as the amount of monetary gold recorded in the balance of payments. This is because while the depository corporations survey records stocks of various financial liabilities and assets (including monetary gold), the balance of payments records such items as flows, more specifically as transactions. In an equation form, this is shown as:

(∆MG)cbs ≈ (MG)bop ……………………………………………………………….………Eqn (56)

The SDR are international reserves created by the IMF to supplement foreign exchange reserve positions of member countries. As in the case of monetary gold, the amount of SDR recorded in the depository corporations survey in a given period is not the same as the amount of in monetary gold recorded in the balance of payments. This is also due to the fact that while the depository corporations survey records the various financial liabilities and assets (including the SDR) as stocks, the balance of payments records such items as transactions. This is expressed in an equation form as:

(∆SDR)cbs ≈ (SDR)bop ……………………………………………………………….………Eqn (57)

The reserve position in the Fund reflects the amount in convertible currency the country has deposited at the IMF plus the Fund’s net use of the country’s currency24. When a country draws on the reserve tranche, it recoups part or all of the hard currency it originally had deposited at the IMF. In return, it must make an equivalent amount in its own currency available to the IMF, implying that the contingent liability increases. As in the case of monetary gold and SDR, the amount of reserve position in the Fund recorded in the depository corporations survey in a given period is not the same as the amount of reserve position in the Fund recorded in the balance of payments. This is because the survey records the various financial liabilities and assets as stocks while the latter survey records such items as transactions as indicated in the equation below:

24 When a country becomes a member of the IMF, it is required to make 25 per cent of the quota available to the

Fund in a convertible currency and the remaining 75 per cent in a currency of its own choice, normally its own currency. In practice, however, the 75 per cent component is not paid out. If it has paid up the full amount of the 25 per cent payable in convertible currency, not drawn upon its reserve tranche and the IMF has not borrowed from the country, the reserve position corresponds to the 25 per cent portion of the quota that is payable in convertible currency. This amount also corresponds to the country’s reserve tranche at the Fund. When the reserve position equals the reserve tranche, the country is free to draw the total amount under the reserve position without any conditions or charges imposed by the IMF. This is because, in fact, the country owns these funds. Lennblad, “Balance of Payments: Below-the-Line Items”, 2005:P4

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(∆RTF)cbs ≈ (RTF)bop ……………………………………………………………….………Eqn (58)

It is worth repeating that the depository corporations survey should not be used as source data for any of these three items (i.e. the monetary gold, SDR and reserve position in the fund). The reason is that there are very few transactions in these items, implying that in most accounting periods the entire flow can be explained by holding gains/losses only. Thus, it is much better to approach the department responsible for keeping records of these items and find out if there have been any transactions in either of them. If yes, the magnitude of the transaction should be ascertained. If no, a zero should be entered in the balance of payments under the relevant items. In this respect, there should be no direct link between the depository corporations survey and the balance of payments for any of these three items.

(ii) Convertible Foreign Exchange Reserves

The foreign exchange reserves held as reserves held at the central bank as convertible currency are recorded as transactions in the balance of payments and as stocks in the depository corporations survey. Thus, in order to compare the foreign exchange recorded in both the balance of payments and the depository corporations survey, the stock positions recorded in the latter survey must be converted into transactions. This way, the amount of foreign exchange held at the central bank as convertible currency reserves will be approximately equal to what is recorded in the balance of payments as the amount of foreign exchange transactions held as held at the central bank as convertible currency reserves. In an equation form, this is shown as:

(∆CFE)cbs ≈ (CFE)bop ……………………………………………………………….………Eqn (59)

Where (∆CFE)cbs is convertible central bank foreign exchange reserves as recorded in the central bank survey while (CFE)bop is the convertible central bank foreign exchange recorded in the balance of payments.

The best way of getting relatively better data on transactions of convertible foreign exchange reserves is to approach the department responsible for keeping track of these accounts at the central bank and ask them to provide data on transactions rather than stocks. Such data may, however, not be available, as most computer systems in central banks of most developing countries do not capture such detailed information. The second option for getting the data is to take the flows in foreign exchange and convert them into the country’s currency using the period considered. More reliable data is obtained the shorter the time period considered. Thus, quarterly conversions of foreign exchange are better than annual conversions. Of course, this methodology will not yield perfect transaction data, but it is better than applying period-end exchange rates to stock positions.

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(iii) Reserve Assets

When the convertible foreign exchange reserves held at the central bank are added together with monetary gold, SDR and the reserve position in Fund, the result is reserve assets. Thus, when what is on the left hand sight of equations 56 to 59 are added together, what results is the reserve assets (∆RAcbs) as reported in the central bank survey. In an equation form, this is shown as:

∆MGcbs +∆SDRcbs +∆RFPcbs +∆CFE cbs = ∆RAcbs…………………………….…….……Eqn (60)

Similarly, when what is on the right hand side of equations 56 to 58 are added together, what results is the reserve assets (RAbop) as reported in the balance of payments. This is also shown in equation form as:

MGbop +SDRbop +RFPbop +CFEbop = RAbop……………………………………………….…Eqn (61)

Thus, when equations 60 and 61 are combined, it means that transactions in reserve assets between two periods as recorded in the balance of payments should approximately be equal to the change of stock positions of reserve assets over the same time as recorded in the depository corporations survey. This is expressed in an equation form as follows:

∆RAdcs≈RAbop…………………………………………………………………………………Eqn (62)

where ∆RAdcs are changes in reserve assets as recorded in the depository corporations survey while RAbop are reserve assets as recorded in the balance of payments. The approximate sign is a reminder that the flow value in the depository corporation survey includes holding gains/losses.

It is important to note that in certain situations, securities other than shares as well as financial derivatives can also be classified as part of reserve assets. In order to be part of reserve assets, securities other than shares must possess the following characteristics:

(a) Must be denominated in convertible currency in order to be part of reserve assets

(b) Must be easily tradable in the secondary market

(c) If not tradable in the secondary market, they must be of a short term nature, less than a year - perhaps much shorter than a year, and

(d) The risk level must be low, for instance, it must have a good rating by the international rating agencies probably (e.g. +AAA).

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For financial derivatives to qualify to be part of reserve assets, they must possess the following characteristics:

(a) Must have a low risk profile - if very risky, they should be excluded on that consideration from reserve assets but this depends on the technical expertise of people working in the central bank

(b) Must not be overly complex. If they are too complex, they should also be excluded on that consideration from reserve assets. The degree of complexity depends on the level of financial expertise available in-house in each central bank to determine

(c) Tradability in secondary market -they must be highly tradable in secondary market without restrictions or penalties.

If both the securities other than shares and financial derivatives possess the above characteristics, they should be captured as part of reserve assets in depository corporation survey and the balance of payments and therefore reflected in equation 62. If they lack these characteristics, they will be classified together with other assets of the central bank such as loans, shares and other equity that do not form part of reserve assets.

(iv) Non-Convertible Foreign Exchange Reserves

The foreign exchange reserves held as reserves held at the central bank as non-convertible currency are also recorded as transactions in the balance of payments and as stocks in the depository corporations survey. If the stock positions of the foreign exchange held as reserves as non-convertible currency held at the central bank as recorded in the depository corporations survey must be converted into transactions flows, then what is obtained is approximately the same as what is recorded in the balance of payments as the amount of foreign exchange transactions held as held at the central bank as non-convertible currency reserves. In an equation form, this is shown as:

(∆NCFEn)cbs ≈ (NCFE)bop ……………………….……………………………………. Eqn (63)

Where (∆NCFE)cbs= The change in foreign exchange reserves held as reserves held at the central bank as non-convertible currency as indicated in the depository corporations survey and (NCFE)bop = The transactions in foreign exchange reserves held as reserves held at the central bank as non-convertible currency as recorded in the balance of payments.

Like in the case of convertible foreign exchange reserves, more reliable data on foreign exchange reserves held at the central bank as non-convertible currency may be obtained from the department responsible for keeping track of such data. In the event that such data is

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unavailable, then an effort should be made to use flows in the non-convertible foreign exchange and convert them into local currency using quarterly rather than annual conversions of foreign exchange. Since convertible foreign exchange reserves are not at the effective disposal of and available for use by the monetary authority for financing purposes in a balance of payments, they should not be part of reserves assets but should appear as part of the sum of various categories above the line, mainly currency and deposits This is true because if a country suffers from a speculative attack on its currency, there is very little the monetary authorities can do with such assets.

(vi) Foreign Exchange Liabilities of the Central Bank

The foreign exchange liabilities held by central bank are obtained by adding together deposits, securities other than shares, loans, financial derivatives and any other financial liability. These liabilities are recorded as stock values in the depository corporations survey while they are recorded as transactions values in the balance of payments. Thus, to be able to compare the two values, the stock values of total foreign exchange liabilities of the central bank are converted to transactions value. This is obtained by subtracting the stock position of the total liabilities to non-residents by central bank of one period from that of another period as explained in the foreign exchange assets position of central bank. The resultant figure approximately equals to transactions values in the balance of payments. In an equation form, this is shown as:

(∆FL)cbs ≈ (FL)bop………………………….……………………………………. Eqn (64)

Where (∆FL)cbs = change in total foreign exchange liabilities on non-residents by central bank as recorded in the depository corporations survey and (FL)bop = change in total liabilities on non-residents by central bank as recorded in the balance of payments. Again, the approximate sign is a reminder that the flow value in the depository corporation survey includes holding gains/losses.

(vii) Net Foreign Exchange Assets Held by Central Bank

Net foreign assets are obtained by subtracting total central bank liabilities to non-residents from the total claims by central bank on non-residents. The central bank claims on non-residents is obtained by adding together reserve assets (composed of monetary gold, SDR holdings, reserve position in the Fund and foreign currency deposits) as indicated in sections (i) to (v) above. Thus, the changes in net foreign assets recorded in the depository corporations survey, should approximately equal to the transactions recorded in the balance of payments. In an equation form, this is shown as:

(∆NFAma)cbs ≈ (NFAma)bop …………………………………………………………… Eqn (65)

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Where (∆NFAma)cbs = Change in net foreign assets held by monetary authority/central bank as indicated in the depository corporations survey and (NFAma)bop = value of net foreign assets as indicated in the balance of payments.

If the changes in net foreign assets recorded in the depository corporations survey are not approximately equal to the transactions recorded in the balance of payments, then the differences should be investigated. They are probably due to errors and omissions, in which case they must be investigated and corrected. They could also be due to strong exchange rate changes, in which case there are acceptable.

(viii) Gross Foreign Exchange Assets of Commercial Banks

The claims on non-residents by commercial banks are obtained by adding together foreign currency reserves, deposits, securities other than shares, loans and financial derivatives. Since both the claims on non-residents and total liabilities on non-residents by commercial banks are recorded as stocks values, the net foreign assets reported in the depository corporation survey are not exactly the same as those reported in the balance of payments since the latter reports transactions values.

Thus, to be able to compare the values of net foreign assets from the two surveys, the stock values of net foreign assets as reported in the depository corporation survey are converted to transactions value. This is obtained by subtracting the stock position of the net foreign asset of one period from that of another period. Thus, one can use data from balance of payments to establish whether what is recorded as transactions in net foreign assets equals to what is recorded as the change in net foreign assets in the depository corporations survey. This is shown in equation form as follows:

(∆FA)ods ≈ (FA)bop…………………………….………………………………………… Eqn (66)

Where (∆FA)ods = change in total assets on non-residents by commercial banks as indicated in the other depository corporations survey and (FA)bop = change in total assets on non-residents by commercial banks as indicated in the balance of payments.

(ix) Foreign Exchange Liabilities of Commercial Banks

The total liabilities to non-residents by commercial banks are obtained by adding together deposits, securities other than shares, loans, financial derivatives and any other financial liability. The total liabilities on non-residents by commercial banks are recorded as stocks values in the other depository corporation survey while they are recorded in the balance of payments as transactions values.

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Thus, to be able to compare the values of total liabilities on non-residents by commercial banks from the two surveys, the stock values of total liabilities on non-residents by commercial banks as reported in the depository corporation survey are converted to transactions values. This is obtained by subtracting the stock position of the total liabilities on non-residents by commercial banks of one period from that of another. The resultant figure should approximately equal to the transactions value recorded in the balance of payments. This is shown in equation form as follows:

(∆FL)ods ≈ (FL)bop…………………………….………………………………………… Eqn (67)

Where (∆FL)ods = change in total liabilities on non-residents by commercial banks as indicated in the other depository corporations survey and (FL)bop = change in total liabilities on non-residents by commercial banks as indicated in the balance of payments.

(x) Net Foreign Assets Held by Commercial Banks

Just like net foreign assets held by central bank, net foreign assets held by commercial banks commercial banks are obtained by subtracting total liabilities to non-residents from the total claims on non-residents. Combining equations 66 and 67 yields the following equations:

(∆NFAcb)ods = (NFAcb)bop ……………………………………………………………………… Eqn (68)

Where (∆NFAcb)ods = change in net foreign assets held by commercial banks as indicated in the depository corporations survey and (NFAcb)bop = change in total liabilities on non-residents by commercial banks as indicated in the balance of payments.

(xi) Net Foreign Assets of the Banking System

Under this category, all financial assets and liabilities of the domestic banking sector vis-à-vis non-residents are recorded. It is a net item by definition. At the first level of disaggregation, a distinction is made between positions held by the central bank (equation 65) and those held by other depository corporations (equation 68)

The change in the net foreign assets of the banking system as reported in the depository corporations survey is obtained by summing up the net foreign assets of the central bank (left hand side of equation 65) and the net foreign assets of commercial banks (left hand side of equation 68) .In an equation form, this is expressed as:

(∆NFAma)cbs+(∆NFAcb)dcs=(∆NFA)dcs ………………………………………………...…… Eqn (69)

Similarly, the net foreign assets of the banking system as reported in the balance of payments is obtained by summing up the net foreign assets of the central bank (on right hand side of

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equation 65) and the net foreign assets of commercial banks (on the right hand side of equation 68). This is obtained as follows in an equation form:

(NFAma)bop+ (NFAcb)bop = (∆NFA)bop ……………………………………………………… Eqn (70)

Thus, to be able to compare the values of net foreign assets on non-residents by banking system from the two surveys, the stock values of total assets and liabilities on non-residents by commercial banks as reported in the depository corporation survey are converted to transactions values. This is obtained by subtracting the stock position of the net foreign assets on non-residents by banking system of one period from that of another period. The resultant figure should approximately equal to the transactions value recorded in the balance of payments. This is shown in equation form as follows:

(∆NFA)dcs ≈ (NFA)bop …………………….……………………………………………. Eqn (71)

Where (∆NFA)dcs = change net foreign assets of the banking system as indicated in the depository corporations survey and (NFA)bop = net foreign assets of the banking system as indicated in the balance of payments.

3.1.2 Data Consistency Checks Within Accounts

Within the individual files, a number of data consistency checks have been constructed in this paper such that a zero value indicates that the data are consistent. This is because credit items are treated as positive values while debit entries as treated as negatives. Thus, the total value of summing up credits and debits in each and every account must equal zero. The purpose of undertaking consistency check is to ensure that data are consistent and correct within and across the accounts. This way, a financial programmer avoids making calculation mistakes. Below are the inter-account consistency checks within each of the four macroeconomic accounts.

3.1.2.1 Data Consistency Checks Within the National Accounts

i. OP+M = FC+IC+GKF+X…………………….……Eqn (72)

ii. GDP - FC -GKF –X+ M = 0………………………………………..…. Eqn (73)

iii. VA = OP+TP-IC…………………………….…. Eqn (74)

iv. OS = VA-CE-TPI……..………………….…… Eqn (75

v. GNI = OS + CE + TPI + nPI………….………Eqn (76)

vi. GNDI = GNI + nCTr……………………….……. Eqn (77)

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vii. S = GNDI-FC………………..……….…. …..Eqn (78)

viii. NL = S (CAB) + nKTr – GKF – nADNPNFA ………..…….Eqn (79)

ix. nNDI = GNDI – CFK…….………………………….…. Eqn (80)

x. GNI – GDP = nCE (from BoP) + nInvInc (from BoP) ……….…….. Eqn (81)

xi. NL = - FinAcBal………………………………………….…….. Eqn (82)

xii. FC = FCng + FCg……………………………... Eqn (83)

xiii. GCF = GCFng + GCFg and……………………. Eqn (84)

xiv. GNDI = GNDIng + GNDIg…………………..…. .Eqn (85)

xv. GNDIng + GNDIg = FCng + FCg + GCFng + GCFg + CurAcBal…. .Eqn (86)

xvi. CAB = (GNDIg-FCg-GCFg) + (GNDIng - FCng - GCFng) ……Eqn (87)

xvii. CAB = (Sg - GCFg) + (Sng - GCFng) ……………..……………. .Eqn (88)

3.1.2.2 Data Consistency Checks Within the Balance of Payments

i. (X-M) + nI+ nTr nCTr = CAB…………………………. …Eqn (89)

ii. CAB+KA+FACB (exc. RA) = Overall BOP balance………. …Eqn (90)

iii. CAB+KA+FACB (inc. RA) = 0……………………………. ……Eqn (91)

iv. CAB+KAB = -FACB ………..…………. …….Eqn (92)

v. KAB+FACB = KFAB……………………. …….Eqn (93)

vi. CAB + KFAB = Overall BOP …………… ……..Eqn (94)

vii. CAB + KFAB = 0 (where FACB inc. RA) ……..Eqn (95)

viii. Overall BOP = -Financing Items………… ……..Eqn (96)

ix. Financing Items = ∆RA + EF……………. ………….Eqn (97)

3.1.2.3 Data Consistency Checks Within the Statement of government Operations

i. TR = TT + SC + OR……………. ………………………………Eqn (98)

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ii. TT = TIPCG+TPW+TP+TGS+TITT+OT……………..........Eqn (99)

iii. SC = SSC + OSC……………. …………………………..........Eqn (100)

iv. TG = Gfg+ Gio+ Gogu……………. ……………………………..Eqn (101)

v. OR = PI + SG&S +FPF +VT&OG + M&UR………...........Eqn (102)

vi. ∆NW = NFA + FA – FL……………. …………………………..Eqn (103)

vii. ∆NW = nANFA + nAFA – nIL……………. ………………….Eqn (104)

viii. nANFA = FxA +Inv + V+ NPA……………. …………………..Eqn (105)

ix. nAFA = ADFA+AFFA + MG&S ……………. ………………Eqn (106)

x. nIL = iDL +iFL……………. ……………………….………..Eqn (107)

where TR= Total Revenue, TT= Total taxes, TIPCG =Taxes on income, profits, and capital gains, TPW= Taxes on payroll and workforce, TP= Taxes on property, TGS= Taxes on goods and services, TITT = Taxes on international trade and transactions and OT = Other taxes, SC = Social security contributions, OSC= Other social contributions, TG = Total grants, Gfg= grants from foreign governments, Gio= and Grants from international organizations, Gogu = Grants from other general government units, PI = Property income, SG&S = Sales of goods and services, FPF = Fines, penalties, and forfeits, VT&OG= Voluntary transfers other than grants, M&UR = Miscellaneous and unidentified revenue, TE = Total expenditure, CE = Compensation of employees , UG&S = Use of goods and services , I = interest, S=subsidies, Grants, SB = social benefits, OE = other expenditure, nANFA = Net acquisition of nonfinancial assets, nIL = Net incurrence of liabilities, FxA = Fixed assets, Inv = Inventories, V =Valuables, NPA = Nonproduced assets, ADFA = Acquisition of Domestic financial assets, AFFA = Acquisition of Foreign financial assets, MG&S= Monetary gold and SDRs, iFL = Incurrence of liabilities, iDL =Incurrence of Domestic Liabilities

3.2.1.4 Data Consistency checks within the Depository Corporations Survey

3.12.4.1 Data Consistency checks within the Central Bank Survey

i. NFAcbs = CoNR cbs - LoNR cbs……………. …..…………..Eqn (108)

ii. NDAcbs = CiDIcbs+ nCoCGcbs + CoScbs……………. ........Eqn (109)

iii. HHcbs = NFAcbs + NDAcbs……………. …………………Eqn (110)

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iv. HHcbs = CiCcbs + LtDCcbs+ DiBMcbs+ SoScbs………….Eqn (111)

where NFAcbs = net foreign assets of central bank, CoNR cbs = Claims on non-residents LoNRcbs = Liabilities to non-residents, NFAcbs = foreign assets of central bank, CiDIcbs = Claims on other depository corporations, nCoCGcbs =Net claims on central government, CoScbs= Claims on other sectors, NDAcbs = net domestic assets, HH = High powered/reserve money of central bank, CiC = currency in circulation, BR = bank reserves, OIN = Other items net, nDCBb net domestic credit to commercial banks, nDCp net domestic credit to private sector and cbs is central bank survey. CiCcbs = Currency in circulation, LtDCcbs= Liabilities to other depository corporations, DiBMcbs= Deposits included in broad money, SoScbs = Securities other than shares, included in broad money

3.1.2.4.2 Data Consistency Checks Within Other Depository Corporations Survey

i. NFAods = CoNR ods - LoNR ods……………. ……………..Eqn (112)

ii. NDA ods = CiDIods + nCoCCods + nCoCGods + CoSods……Eqn (113)

iii. RAods = NFAods + NDAods……………. ………………...Eqn (114)

iv. DCods = LtCBods + DiBMods + SoSi ods + DeBMods+ SoSeods + Lods + FDods + TCAods+ SoEods+ OINods… ……………………..Eqn (115)

where NFAods = net foreign assets of central bank, CoNRods = Claims on non-residents LoNRods = Liabilities to non-residents, NFAods = foreign assets of central bank, CiDI ods = Claims on other depository corporations, RAods is total reserve assets, nCoCGods =Net claims on central government, CoSiods = Claims on other sectors, NDAods = net domestic assets, HH = High powered/reserve money of central bank, LtCB = Liabilities to central bank, DiBMods =Deposits included in broad money, SoSiods = Securities other than shares, included in broad money, DeBMods =Deposits excluded from broad money, SoSeods =Securities other than shares, excluded from broad money.

3.1.2.4.3 Data Consistency checks within the Depository Corporations Survey

i. NFAdcs = NFAcbs + NFAodc……………. ……………….Eqn (116)

ii. NDAdcs = NDAcbs+ NDA odc ……………. ……………..Eqn (117)

iii. BMLdcs = NFAdcs+NDAdcs……………. ………………..Eqn (118)

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where NFAdcs = net foreign assets of depository corporations survey (dcs), NDAods = net domestic assets of depository corporations survey (dcs), and BMLdcs = broad money liabilities in the depository corporations survey.

3.2 Discussions of Results of the Consistency Checks

Part 3.1 of this chapter developed consistency checks that ensure that data are consistent within and across the accounts and as reliable as possible. This part uses the consistency framework to check the consistency of the Kenyan dataset. The first check is the inter-account identities that are supposed to hold as indicated in part 3.1. Table 3.1 below provides the results of 41 inter-account consistency checks for the national accounts (NA), statement of government operations (SOG), balance of payments (BOP) and Depository Corporations Survey (DCS) all expressed in percent of data of the relevant variable from one of the two accounts in which consistency is being checked. The details of how these percentages were derived is explained below.

3.2.1 Discussions of Results of the Inter-Account Consistency Checks

In the inter-account consistency checks between NA and SGO which had seven consistency checks shown in Table 3.6 below, the relevant data from the NA accounts was used as a denominator to express the inconsistent data into percentage terms. For instance, in row number 1 in Table 3.6, the government savings as recorded in NA were not consistent with the balance of the current transactions of government as recorded in the statement of operations of government (SOG). That inconsistency was expressed in percent terms using data from the national accounts as follows:

( ) ( )[ ]( ) 100*

NAg

sgogNAg

S

CTrS −………………………………………………………….(Eqn 119)

where ( )NAgS is government savings as recorded in NA while ( )

sgogCTr is balance of the current

transactions of government as recorded in the statement of operations of government (SOG).

Following the same approach, the relevant data from BOP was used as the denominator to express the data inconsistencies in percentage terms for the inter-account consistency checks between the NA and BOP as well as that between DCS and BOP. Finally, relevant data from DCS was used as the denominator to express the data inconsistencies in percentage terms for the inter-account consistency checks between the SGO and BOP.

Table 3.6 below contains the results of these inter-account consistency checks in percentage terms as stated above. The results of the inter-account consistency checks in absolute terms are, however, shown in Appendix 1. It is important to note that the results shown below could be different if the denominators for calculating the discrepancy in accordance with equation 119

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above are changed. For example, in 2004, exports of goods as recorded in BoP were worth Kshs 206. 827 billion while exports of goods recoded in the NA in 2004 were worth Kshs 237.961 billion. If the financial programmer uses the BOP as the source data for the denominator, then the discrepancy is 15.1% as indicated in Table 3.6 below. However, if the NA is used as the source data for the denominator, the discrepancy would be 13.1%, which is 2|% lower than what is shown in table 3.6.

Table 3.6: The Results of Inter-Account Consistency Checks

2000 2001 2002 2003 2004

A: Inter-Account Consistency Checks: National Accounts (Na) And Statement Of Government Operations

1 Sg=CTrg 30% -107% 129% -6% 25%

2 OPg = CEg +ICg +CFKg 20% 21% 24% 23% 17%

3 FCg= CEg +ICg +CFKg - SCGSg -17% -12% -17% -13% -4%

4 VAgg= OPg - ICg 23% 22% 27% 30% 22%

5 VAgn = CEg 23% 22% 27% 30% 22%

6 OSgg= CFKg 100% 100% 100% 100% 100%

7 NLg=Sg +KTrg -GFKFg -969% -835% -584% -328% -287%

B: Inter-Account Consistency Checks: National Accounts Versus Balance of Payments (BOP)

1 (X – M)na = - (X – M)bop (4-7) -1.2% 15.2% 8.5% 9.2% -13.3%

2 Xg -2.5% 4.1% -1.0% -5.2% -15.1%

3 Xs 0.0% 0.0% 0.0% 0.0% 3.2%

4 X (2+3) -2.5% 4.1% -1.0% -5.2% -11.8%

5 Mg -1.3% -11.1% -9.5% -14.4% -13.9%

6 Ms 0.0% 0.0% 0.0% 0.0% 15.4%

7 M (5+6) -1.3% -11.1% -9.5% -14.4% 1.5%

8 nIna = -nIbop 19.6% -0.4% -0.2% -0.5% -26.7%

9 nCTrna = - nCTrbop 0.0% -31.5% -46.8% -47.6% 33.5%

10 S-GKF=CAB -78.6% -85.3% -33.7% -212.6% -72.9%

C: Inter-Account Consistency Checks: Statement of Government Operations and Balance of payments

1 CEsog = CEbop - - - - -

2 PIsog = PIbop - - - - -

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Table 3.6: The Results of Inter-Account Consistency Checks

2000 2001 2002 2003 2004

3 nCTrsog= nCTrbop - - - - -

4 nKTrsog = nKTrbop 94% -796.8% -6.2% 128.1% 199.7%

5 nNPNFAsog= nNPNFAbop -93% -100.0% -16.5% -41.4% -76.2%

6 NBsog= nBrbop - - - - -

7 nRFSsog= nRFSbop 73% 27.2% 155.4% 111.0% -153.3%

8 NADAsog= nADAbop -100% -100% -100% -100% -100%

D: Inter-Account Consistency Checks: Statement of Government Operations and the Depository Corporations Survey

1 Dsgo= DDdcs 100% 100% 100% 100% 100%

2 nLsgo=DnLdcs 100% 100% 100% 100% 100%

3 DnBBsgo=DnBBdcs - - - - -

E: Inter-Account Consistency Checks: Deposit Corporations Survey (DCS ) and Balance of Payments (BOP)

1 ∆MGcbs = MGbop 100% 100% 100% 100% 100%

2 ∆SDRcbs = SDRbop 100% 100% 100% 100% 100%

3 ∆RPFcbs = RPFbop -160% 227% 254% 212% 179%

4 ∆FEcbs≈FEbop 38% -7% 114% -4% 38%

5 ∆FLcbs≈FLbop 100% 100% 100% 100% 100%

6 (∆RA)cbs ≈ (RA)bop (1+2+3+4) 78% 420% 568% 408% 417%

7 (∆NFA)cbs ≈ NFA)bop (6-5) -22% 320% 468% 308% 317%

8 ∆FEods≈FEbop -16% 34% 7% 30% 126%

9 ∆FLods≈FLbop 100% 100% 100% 100% 100%

10 (∆NFA)ods ≈ NFA)bop (8-9) 84% 134% 107% 130% 226%

11 ∆FEdcs≈Febop (4+8) 63% 454% 575% 438% 543%

12 ∆FLdcs≈Flbop (5+9) 200% 200% 200% 200% 200%

13 (∆NFA)dcs ≈ NFA)bop (11-12) -137% 254% 375% 238% 343%

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The results in Table 3.6 above clearly show that there is a lot of work to be done in Kenya to render the data from different sources more consistent. Virtually all the inter-account checks for historical data were inconsistent as seen in Table 3.6. For example, the seven inter-account consistency checks between NA and SGO, revealed inconsistent results. The possible reason for this discrepancy could be that while the compilation methods were all in accordance with the latest international manuals, the data recorded for NA were based on calendar year basis while that of SGO was based on fiscal year basis. Thus, to get the latter into the calendar year basis, two consecutive fiscal year figures of government savings were, for instance, added together and divided by two. The results may not necessarily reflect the actual developments on calendar year basis. If monthly data statement of government operation was, however, available, then the discrepancy may be reduced if not eliminated in all the seven inter-account checks.

In the ten inter-account consistency checks between the NA and BOP, all the checks failed to reveal consistency except in the case of exports of services as indicated in Table 3.6 above. However, the percentage of discrepancy in this inter-account consistency check were relatively lower than those obtained in other inter-account consistency checks undertaken in this study. One of the possible reasons for the failure to achieve the inter-account consistency between NA and BOP could be attributed to the differences in sources and coverage of data for both accounts. The BOP compilers, for instance, often make adjustments on the general merchandise exports by adding together domestic exports (f.o.b.) and re-exports and then subtracting from the total the following three items: newspapers & periodicals, exports of precious articles and electricity exports. The compilers of NA do not often do these adjustments. For this reason, what is recorded as merchandise exports in BOP is often higher than what is recorded in NA. Similarly, BOP compilers often make adjustments on general merchandise imports by adding the military imports as well as electricity imports to the imports (c.i.f.) and then subtracting from the total the following items: monetary imports, cinematographic films; newspaper & periodicals and aircraft leases. Since the compilers of NA do often not make these adjustments, what is recorded as general merchandise imports in BOP is often higher than what is recorded in NA, hence the discrepancy shown in Table 3.6 above.

Another observation that relates to extent of coverage of the trade data is that most BOP data is obtained from the Customs Department of the Kenya Revenue Authority (KRA). The customs data by definition does not capture smuggling and it might also not capture donor-financed imports. Furthermore, it does not capture repair of capital assets by non-residents nor does it capture repairs done by residents on capital assets by non-residents. It appears that the compilers of BOP and NA in Kenya do not make efforts to include such flows in the data. This is a serious omission both in the BoP and NA that needs to be urgently addressed. Another item that requires attention is that of income. The credit side for compensation of employees in Kenya’s BOP data shows zero in each time period. This cannot be true given the substantial

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presence of international organizations mainly the United Nations and the large number of embassies in Nairobi. All of these bodies employ a considerable number of Kenyan residents some of whom are professional even though most are working as non-professionals.

One important result from the inter-account consistency checks between NA and BOP is that concerning the identity (S-GKF) = CurAcBal, which identity shows the interrelationship between internal and external sectors of an economy. In particular, the identity shows that the current account balance is a mirror image of the savings and gross capital formation behaviour of the domestic economy. As shown in the inter-account consistency checks between NA and BOP in Table 3.6, the results obtained are very poor. Yet, this is a very important analytical identity. As shown in the same table, the percentages of inconsistency are generally very high. They imply that the data obtained from the national accounts on the savings less gross capital formation of the domestic economy have historically been far much less than what is recorded as the current account deficits the balance of payments. Such big inconsistencies for such an important analytical variable in macroeconomics are not acceptable. The results show that there is poor coordination between the compilers of BOP and those of the NA.

Table 3.6 above also indicates that the eight inter-account consistency checks between the SGO and BOP failed to hold. In both the SGO and BOP, data on compensation of employees (CE)g vis-à-vis non-residents and net Property Income (nPI)g vis-avis non-residents were not recorded and hence the extent of data inconsistency could not be ascertained. The same also applied to the net disposal/acquisition on non-produced non-financial asset (e.g. land) vis-a-vis non-residents (nNPNFA)g. The discrepancy in the data recorded in the BOP and NA for both the net current transfers (nCTr)g vis-à-vis non-residents as well as the net capital transfers vis-à-vis non-residents (nKTr) is attributed to the fact that the compilers of both accounts use data from different sources, with lower numbers being recorded in the SGO compared with BOP. Finally, the discrepancy in the data for net borrowing vis-a-vis non-residents (nB)g, net receipts of foreign securities vis-à-vis non-residents (nRFS)g and net additions to deposits abroad vis-à-vis non-residents (nADA)g is attributed to the fact that no data is recorded for these items in the SGO while it is data recorded in the BOP.

Table 3.6 above also indicates that the three inter-account consistency checks between the SGO and the DCS failed to reveal consistent results. This was largely because whereas the compliers of DCS have reported data for government deposits (D) vis-à-vis domestic banks, loans and advances to government (L) - vis-à-vis domestic banks and Bonds and bills (BB) vis-à-vis domestic banks, no such data was reported by the compliers of SGO. Finally, the thirteen inter-account consistency checks between the DCS and the BOP did not reveal consistent results except in the case of monetary Gold. Again, the reason for this inconsistency is that the

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compilers of BOP failed to record most items including monetary gold, special drawing rights, and foreign liabilities whereas the compliers of DCS actually recorded data on them.

3.2.2 Discussions of Results from the Flow of Funds

As a final step, we subjected the whole dataset to a flow of funds, which is the ultimate data consistency check in this paper.25 This involved creating a new sheet within the Excel file and linking the relevant data to the four macroeconomic accounts, namely: the national income and product accounts; the balance of payments; the government operations statistics; and the monetary accounts. Thus, the flow of funds contains no additional data and constitutes a powerful consistency check on all the data in the individual accounts.

As shown in Table 3.7, the consistency checks are constructed to reflect the fact that the sum of each row and column in the flow of funds is zero. Two reasons explain why the rows are equal zero. First, for transactions where there are no entries in the rest of the world account, the sum of the entries in the institutional cells must equal the entry in the cell belonging to the column for the total economy. Therefore, a subtraction of the total from the individual entries must yield a zero value. If not, then it shows that there is data inconsistency. Secondly, for transactions where there are entries in the rest of the world account, the value in the column for the total economy must equal the value of the transaction with the non-residents. Subtracting the total from the entry in the rest of the world account must also equal zero.

The reason why the columns should equal zero is explained by the fact that the value of net lending equals the value of the sum of net decreases in financial assets and net increases in financial liabilities. Thus, net lending summarises all the entries made higher up in the flow of funds table, implying that net lending is equal to savings plus net capital transfers. Savings on the other hand are equal disposable income less final consumption. Each separate column in the flow of funds table must therefore be equal zero and this means that internal data consistency in each individual institutional sector holds.

It deserves mention that the accounting relationships among the various accounts, summarised in the flow of funds in Table 3.7, highlights the fact that any sector that spends beyond its income must be financed by the savings of other sectors. Excess spending by the entire economy is reflected in the item called net lending, which by definition is equal to zero in a closed economy. This is because what one sector lends to another sector is exactly equal to

25 “A flow of funds table presents the amount of income acquired by each sector, and the way it was spent during a specified period of time, usually one year. It then shows how the surplus of each sector was disposed off or how deficit was financed. The upper part of flow of funds table shows real transactions among the sectors while below the line, the table presents flow of financial resources” Nyella J.J., “Financial Programming: The Case of Tanzania”, 2003:P26.

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what the second sector borrows from the first, thus cancelling out when the transactions are summed up. The results of the flow of funds consistency checks shown in Table 3.2 confirms that Kenya’s data is consistent with respect to this item.

As shown in Table 3.7, four horizontal checks were constructed in the order in which they appear from left. The first horizontal check was for the individual components within the national accounts such as gross national income, namely compensation of employees, operating surplus and taxes on production and imports are all-consistent. The second horizontal check was for relevant variables in the rest of the world (ROW) sub-account of NA and BOP while the third horizontal check was for relevant variables in the SGO and BOP. The fourth and final horizontal check was for relevant variables in the NA and SGO.

The results of the first horizontal check showed that the individual components of the gross national income, namely; compensation of employees, operating surplus and taxes on production and imports were all consistent.4.2 However, the consistency checks failed to hold in the gross national disposable income following the lack of disaggregated data by institutional sectors for the property income and current transfers. For the same reasons, consistency checks was not achieved in the net acquisition of capital assets. It is worth mentioning that the data on taxes on production and imports as well as taxes on income and wealth in the national accounts were also found to be inconsistent with those in the statement of government operations as shown in Table 3.2. This paper therefore recommends that the numbers from these two sources should be crosschecked to ascertain the correct the numbers from the data providers.

The results of the second horizontal consistency check were very much similar to those found in the ten inter-account consistency checks between the NA and BOP in Table 3.6 above. Some of the checks failed because there was no data available for the relevant variable. For instance, data for the compensation of employees receivable from non-residents or payable to non-residents was lacking in both NA and BOP. This cannot be the case given the substantial presence of international organizations (mainly the United Nations and the large number of embassies) in Nairobi. All of these bodies employ a considerable number of Kenyan residents. Both the NA and BOP should therefore be capturing these transactions. On the other hand property income receivable from non-residents or payable to non-residents that is recorded in the NA were found to be inconsistent with those recorded in the BOP.

The third and fourth horizontal consistency checks were for relevant variables in the SGO and BOP on the one hand and NA and SGO. These checks hand also failed to produce consistent results. In the latter case, for instance, data on the final consumption by government as recorded in the national accounts was found to be inconsistent with that recorded in the SGO. Ideally, this number should be calculated from the SGO in accordance with the following formula: Compensation of employees paid by government plus use of goods and services less sales of

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goods and services but excluding sales of capital assets. However, Table 3.2 indicates that the results obtained differs from that provided in the national accounts. Ideally, the source of the data should be the government account and not the national accounts for this variable. This paper therefore recommends that the Kenya’ Central Bureau of Statistics should be contacted in order to eliminate not only this inconsistency but also the others mentioned above as well.

A vertical consistency check shown in Table 3.7 was designed to check whether what is above the line was equal to what was below the line in the flow of funds constructed for Kenya. One of the above the line item was the disposable income, which was broken down into its main components – primary income and transfers. The other entries were final consumption, savings, gross capital formation, net lending and financial transactions. The below the line items were the financial transactions, which were sub-divided into two main categories: domestic and external. The vertical consistency check was therefore designed to reveal whether the above the line items were equal to the below the line items. The results in Table 3.7 indicate that the vertical consistency check failed to produce consistent results.

Based on these results and those found earlier in Table 3.6, it is concluded that there is need to check for data gaps in the collection strategy for Kenya’s macroeconomic data. This is because virtually all consistency checks constructed failed to show consistent results. The best way to do this is to comprehensively examine each specific item in all of the accounts to investigate whether it is likely that the compilers have captured all transactions by all sectors that might be involved in that transactions category. The results in both tables above clearly reveal that there is a lot of work to be done in Kenya to render the data from different sources more consistent. Sorting out such data inconsistencies is big job that would involve a lot of players. Such an effort would, however, be beyond the scope of this technical paper.

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Table 3.7: Kenya’s Flow of Funds for 2004(Kenya Shillings, Millions)

Total

Domestic G NFC FC NPISH HH

Horizontal

Check ROF BOP G

Horizontal

Check

(BOP/ROW)

Horizontal

Check

(SGO/ROW)

Horizontal

Check

(SGO/NA)

Gross national disposable income 1,335,572 161,809 186,998 15,560 0 912,780 58,426 413,791 251,982

Net national disposable income 1,214,577 138,801 186,998 15,560 0 912,780 -39,561 255,865 117,064

Gross national income 1,269,804 161,809 186,998 15,560 0 912,780 -7,342 113,192 -48,616

Compensation of employees receivable by resident households 489,801 489,801 0 0

of which: receivable from non-residents 0 0 - - - 0 -

Compensation payable to non-residents 0 - - - 0 -

Taxes on production and on imports receivable 142,148 142,148 152,860 10,712

Subsidies on production payable -200 -200 -10,188 -9,988

Gross operating surplus/mixed income 645,397 19,860 186,998 15,560 0 422,979 0 0 -19,860

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Total


Horizontal

Check ROF BOP G

Horizontal

Check

(BOP/ROW)

Horizontal

Check

(SGO/ROW)

Horizontal

Check

(SGO/NA)

Property income, receivable 4,703 0 0 0 0 0 4,703 -4,703 3,564 5,066 -1,139 5,066 1,502

of which: receivable from residents 0 - - - - - -

of which: receivable from non-residents 4,703 - - - - - - -4,703 3,564 -1,139 - -3,564

Property income, payable -12,045 0 0 0 0 0 -12,045 12,045 -13,581 -34,546 -1,536 -34,546 -20,964

Payable to non-residents -12,045 -12,045 12,045 -13,581 -8,111 -1,536 -8,111 5,471

Corresponding to accumulation of interest arrears vis-a-vis non-residents 0 0 -1,257 -1,257 0 1,257

Payable to residents 0 0 -26,435 0 -26,435 -

Corresponding to accumulation of interest arrears vis-a-vis residents 0 - - - - - - - - - -

Current transfers (net) 65,768 0 0 0 0 0 65,768 0 0 0

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Total


Horizontal

Check ROF BOP G

Horizontal

Check

(BOP/ROW)

Horizontal

Check

(SGO/ROW)

Horizontal

Check

(SGO/NA)

Taxes on income and wealth, receivable 0 0 0 89,206 0 89,206 -

Taxes on income and wealth, payable 0 0

Social benefits received 0 0 0

Social benefits paid 0 0 -924 0 -924 -

Resident current transfers, receivable 0 0 240 0 240 -

Resident current transfers, payable 0 0 -11,897 0 -11,897 -

Non-resident current transfers, receivable 65,968 65,968 -65,968 82,711 5,342 16,743 5,342 -77,369

Non-resident current transfers, payable -200 -200 200 -5,028 -1,147 -4,828 3,882 3,882

0

Final consumption -

1,171,212 -216,563 -954,649 0 -208,439 0 8,124 -

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Total


Horizontal

Check ROF BOP G

Horizontal

Check

(BOP/ROW)

Horizontal

Check

(SGO/ROW)

Horizontal

Check

(SGO/NA)

Of which Final Consumption of Government 0 0 0 -

0

Gross savings 105,934 -54,754 186,998 15,560 0 -41,869 0 205,352 -163,483 260,106 205,352

Net Savings 82,926 -77,762 186,998 15,560 0 -41,869 0 205,352 -163,483 283,114 205,352

0

Capital transfers, net 13,133 0 0 0 0 0 13,133 0 0 2,732 0 2,732 2,732

Capital transfers, receivable 13,133 13,133 10,223 0 10,223 10,223

Capital transfers, payable 0 0 -7,491 0 -7,491 -7,491

0

Acquisition of capital assets, net 329,243 -12,339 0 0 0 0 341,582 0 0 -27,007 0 -14,668 -27,007

Gross fixed capital formation 208,248 -35,347 243,595 -25,711 0 9,636 -25,711

Consumption of fixed capital 120,995 23,008 97,987 0 0 -23,008 0

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Total


Horizontal

Check ROF BOP G

Horizontal

Check

(BOP/ROW)

Horizontal

Check

(SGO/ROW)

Horizontal

Check

(SGO/NA)

Changes in inventories 0 0 0 0 0 0

Acquisition of land, net 0 0 0 0 0 0 0 0 0 -1,296 0 -1,296 -1,296

Acquisition of land 0 0 -1,296 0 -1,296 -1,296

Disposal of land 0 0 0 0 0 0

0 0

Net lending 93,595 -67,093 186,998 15,560 0 -41,869 0 181,076 0 248,170 181,076

0

Financing -14,721 -84,360 13,746 21,492 -1,437 -42,282 78,121 0 0 -55,853 0 28,507 -55,853

Internal financing -47 -80,958 21,942 24,567 -1,437 35,838 0 -42,281 0 38,677 -42,281

Change in money from DCS 0 -1,586 -14,944 60,249 -1,437 -42,282 0 -12,328 0 -10,742 -12,328

Change in other liabilities of banks -47 -8,181 9,893 -1,760 0 0 8,181 0

Change in net domestic assets of banks 0 -30,267 36,886 -37,396 30,777 0 -29,953 0 314 -29,953

Change in other 0 -40,925 -8,179 49,103 0 0 40,925 0

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Total


Horizontal

Check ROF BOP G

Horizontal

Check

(BOP/ROW)

Horizontal

Check

(SGO/ROW)

Horizontal

Check

(SGO/NA)

items net of banks

Change in government liabilities vis-à-vis non-financial sectors 0 0 0 0 0

0

External financing -14,674 -3,402 -8,197 -3,075 0 0 0 0 0 -13,572 0 -10,170 -13,572

Change in above-the-line external position of gov., net -13,572 -13,572 0 17,901 0 31,473 17,901

Change in financial sector (but excluding central bank) external assets, net 0 0 0 0 0 0 0

Change in external assets held by other sectors, net -8,197 -8,197 0 0 0 0

Errors and omissions 0 0 0 0 0

Change in reserve assets -3,075 -3,075 0 0 0 0

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Total


Horizontal

Check ROF BOP G

Horizontal

Check

(BOP/ROW)

Horizontal

Check

(SGO/ROW)

Horizontal

Check

(SGO/NA)

Exceptional financing 10,170 10,170 0 0 0 0 0 0 0 0 -10,170 0

Change in stock of principal arrears 7,183 7,183 0 0 -7,183 0

of which: due to the interest component 1,257 1,257 0 0 -1,257 0

Use of Fund credit -768 -768 0 0 768 0

Debt forgiveness 0 0 0 0 0 0

Other exceptional financing 2,498 2,498 0 0 -2,498 0

Vertical check 754 -151,453 200,744 37,052 -1,437 -84,151 0 -303,660 552,940 -126,640 -39,926 -166,866 24,813

Source: Own Derivations

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4.0 BEHAVIORAL RELATIONSHIPS FOR FORECASTING

This Chapter confines itself to a search for behavioural relationships that can support financial programming in Kenya. Hence, it uses the framework constructed in chapter III to derive several forecasting scenarios that are consistent. As the reader will recall, the framework constructed in Chapter III presented data consistency checks and gave the economic implications for each of them. The chapter also derived statistically how the links in the consistency checks have come using the Kenyan data. This chapter seeks to bring an understanding of the behavioural relationships among the key variables so as to provide the policy makers with the necessary information for making the best choices among the alternative forecasting scenarios.

4.1 The Basic Framework

The starting point in any financial programming exercise is to make projections of developments in the economy for the programme year based on an assumption that existing policies remain unchanged. These projections are called baseline scenario and provides a benchmark for assessing the impact of the policy package included in the program scenario. Its principal aim is to show whether existing problems are likely to remain broadly constant, to be resolved without explicit intervention by the authorities, or to worsen over time. Thus, it gives the general policy direction. A baseline scenario does not include explicit targets.

A program scenario, on the other hand, is based on an explicit policy package designed to achieve a desired set of objectives. The starting point is to evaluate the economic problems prevailing. An understanding of the county’s economic, institutional and socio-political structures, recent developments, and available instruments is essential to forecasting and policy analysis. Policymakers must identify and quantify a coordinated set of policy instruments. An iterative approach is required to produce an internally consistent program. A financial programme therefore includes a data consistency framework.

Preparing a financial program thus requires assessing economic problems in the light of national objectives in order to determine the nature and size of any needed adjustment. In this case, the nature of economic imbalance should first be identified. Second, the source of such imbalance should be established. Finally, the seriousness of imbalance should be ascertained. The overall objective of doing this is to come up with specific targets that will lead to elimination of the imbalance in the economy. Under a program scenario therefore, objectives are quantified and preliminary targets set before developing policy packages. The objective could, for instance, be to achieve sustainable current account deficits and non-inflationary growth. Examples of targets could be the overall balance of payments position and the monetary base. The determination of specific targets sets the stage for the development of the

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appropriate policy package. Output and employment are more elusive targets, as governments are not in a position to directly determine the growth of their economies. Thus, output and employment are set as intermediate targets within a financial programme.

It worth mentioning that financial programming is not the same as economic modelling. The main difference between them is that the former yields unique solutions while the latter yields multiple solutions. The main focus of financial programming is to ensure consistency of data. The more behavioural equations are in a financial programming framework, the closer the financial programmer gets to economic modelling and the more he/she restricts the number of solutions. Economic modelling is a single or multiple equation system but financial programming involves a considerable component of judgement based on a very stringent accounting framework that ensures consistency of data across and within accounts.

4.2 Steps for Forecasting in Financial Programming

Step I: Make a Forecast of the Real GDP Growth

The first step in a financial programming exercise is to project the real GDP growth rate of the economy. In the short to medium term, growth in potential output of a country often reflects changes in the extent to which labour and capital are fully employed in that economy. In particular, short run determination of output reflects the explicit consideration of the interaction of demand and supply. Such consideration of the interaction of supply and demand factors requires a properly constructed model26. An example of such a production function is:

( )TLKfQ ,,= ……………………………………………………………………Eqn(120)

Where Q = real output, K= capital, L= labour and T= technology

An estimate of this production function requires adequate data on labour and the capital stock. In case there is neither the data nor the model to support this approach exists, forecasts of output in the financial programming exercise should essentially rely on ad hoc procedures. In this case, the financial programmer should project real GDP growth for each activity in percentage terms such that a growth rate for the GDP at basic prices is obtained. Care should be taken to ensure that the growth rate for each individual activity at basic prices is sensible. Each individual growth rate must take into account past developments and expected events.

26 IMF, “Financial Programming and Policies: The Case of Turkey”, 1996:P85

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Step 2:Make a Projection of Inflation Rate

The second step in financial programming is to project the inflation rate. As discussed in Chapter II, the financial programming model normally takes output and price as exogenous variables. In particular, output is forecasted exogenously while price is set as a target. The other program variables are determined such that they conform with forecasted output and targeted price. This implies that under this framework, inflation should be taken as an exogenous variable. In particular, the financial programmer should set the projected price level on the basis of the expected or targeted rate of inflation.

Step 3: Make a Forecast of the Nominal GDP

The third step in financial programming exercise is to make a forecast of the nominal GDP using the results of steps 1 and 2. This involves combining the projected inflation obtained from Step 2 with the projected real GDP obtained in step 1 to obtain the projected nominal GDP.

Step 4: Make Projections on Money Demand

The fourth step of the exercise of financial programming is to project the amount of money that will be demanded in the economy given the projected nominal GDP. The starting point could be to assume that velocity of money will remain constant. The path actually used in the projected framework, however, depends on a number of factors such as the behaviour of velocity of money over the recent past. Alternatively, a financial programmer could develop a money demand function and estimate it using econometric techniques. If the estimated money demand function is found to be stable, then the model can be used to estimate money demand. The choice between the use of a money demand function or assumption of constant velocity depends on what the financial programmer finds appropriate.

Step 5: Make Projections on Money Supply

Depending on the approach taken to estimate money demand, the fifth step in the exercise of financial programming is to estimate the amount of money to be supplied by the monetary authority. If the approach of constant velocity is taken as will most often be the case for developing countries such as Kenya, then the projected values for nominal GDP obtained in step 4 and the velocity of money determined in step 5 should be used to determine the projected path of the money supply using the following standard identity:

M x V = P x Y…………………………………………………………………………Eqn(121)where M is the money supply, V is velocity, P is the price level (consumer price index or the deflator), and Y is real Gross Domestic Product. It is clear from this framework (equation

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121) that the money supply expansion is endogenous. Alternatively, instead of projecting the money supply using the above equation (121), this identity can be used to project prices. In that case the price evolution is endogenous and the money supply expansion exogenous. In the latter case, the evolution of the money supply is normally derived from the projected time path of the monetary base.

Step 6: Decide on the Source of Data for Items Appearing in More Than One Account

The sixth step in the financial programming exercise is to make a decision on the source of data for items appearing in more than one account and link them to the various accounts in accordance with macroeconomic identities. A significant number of items such as borrowing from non-residents by government may appear in more than one account. These items should not only be listed but more importantly a decision must be made regarding which account the projection should be made and in which account or accounts the projected values for that variable should be taken as given.

Step 7: Determine the Residuals For Each Account.

The seventh step in financial programming exercise is to determine the residuals for each account. Choosing residuals is an important part of financial programming exercise, as it sets out the purpose of the entire forecasting scenario in the financial programming framework. For example, the programmer might want to examine the impact of a given set of policy actions on gross fixed capital formation by non-government sectors. In that case, this variable will be the main residual in the national accounts and the sector policy actions must also be specified in line with the chosen objective. However, if the purpose is of a more general nature, it is more appropriate to choose the variable of the largest magnitude in the GDP-by-expenditure equation, namely, final consumption of households. This would also be appropriate given that final consumption by households is often not based on primary data27.

A financial programmer should bear in mind that residuals should not be items on which there is a lot of primary information for the projected time period. In addition, a residual should yield a result in the forecasting exercise. Thus, a residual should be a variable of some magnitude and with some analytical importance. As a starting point for the financial programme for Kenya, a general baseline scenario has been constructed in Excel spreadsheets and its details are provided in section 4.3 below, which deals with forecasting of individual accounts for Kenya.

27 Lennblad, 2005:2 “Forecasting within a Financial Programming Framework”

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Step 8: Make a Projection of Remaining Variables in the Macroeconomic Accounts

The eighth step in financial programming exercise is to project the remaining variables in the national accounts, depository corporation survey, balance of payments and government account. This is done primarily on the basis of the projected paths for the money supply and GDP as described in the preceding steps. The financial programmer should bear in mind that if there is too much detail in terms of remaining variables in each of the above four accounts, the whole financial programming framework easily becomes unwieldy and intractable28. On the other hand, if there is not enough detail of other remaining variables in each account, the results may not be useful indicators of economic reality. The financial programmer must therefore consider carefully the level of disaggregation of each remaining variable.

Step 9: Determine the Future Evolution of Monetary Base

The second but last step of financial programming exercise involves determining the future evolution of the high-powered money, also referred to as monetary base. In this case, the projected path of the money supply is used in conjunction with a money multiplier to derive the monetary base. Thus, the behaviour of the monetary aggregates of the deposit corporation survey is linked to reserve money through the money multiplier as indicated in the following equation:

mmRMM = ………………………………………………………. …………..Eqn (122)

where M is the projected money supply.

Step 10: Perform Iterative Exercises Until Good Results Are Obtained

The last step of financial programming exercise is to not only ensure that the evolution of the residual in each account is meaningful and sensible but that the projected paths of all variables that appear in more than one account make sense in all of these accounts. If not, the financial programmer should go back to all variables for which no reasonable time path has been found and repeat the exercise for as many times as may be necessary. This is the iterative and final part of the forecasting exercise within a financial programme.

28 Lennblad, ib id:P3

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4.3 Forecasting Individual Accounts

As a starting point for the financial programme for Kenya, a general baseline scenario has been constructed in Excel spreadsheets. In order to ensure consistency in the all macroeconomic accounting, inter-account links in the excel spreadsheets were constructed before the forecasting of the individual accounts was undertaken. In this case, four sheets for each account were created with the first part of each sheet having historical data and the second part having a forecasting part. A fifth sheet of the inter-account consistency check for historical data was created but was, however, not relevant for forecasting. It is in the first four sheets where the inter-account links for the forecasting framework were entered. By entering data for the four accounts, an implicit choice of the level of detail was made in accordance with the steps. Financial programmers should always bear in mind that structuring the accounts well for doing this is not totally obvious! While it is important that a financial programmer chooses an appropriate degree of detail in each account, he/she should not be bogged into much detail – just have enough to make meaningful forecast and this was a matter of judgment. Below are the descriptions of how the individual accounts for Kenya were made within the context of the ten steps enumerated in section 4.2 above.

4.3.1 Forecasting National Accounts: Output, Expenditure and Prices

As indicated in chapter two, the preparation of a financial program requires forecasts to be made for output, and the composition of expenditures and prices. These forecasts provide a major input into the determination of other aggregates within the financial program. Below are the details of how these forecasts were made for the case of Kenya:

4.3.1.1 Forecast of Kenya’s Real GDP

An estimate of the production function stated in section 4.2 (equation 120) requires adequate data on labour and the capital stock. In the case of Kenya, however, neither the data nor the model to support this approach existed. Consequently, forecasts of Kenya’s output in the financial programming exercise were not undertaken. Instead, value added29 of the various economic activities were estimated and summed up to get real GDP30 for the period under review. This essentially relied on ad hoc procedures and iterative approaches. Of course this is 29 GDP is also defined as the sum of the value of output of the various economic activities less the sum of the

intermediate consumption of the various economic activities.

30Kenya’s National Accounts data were revised in 2005 and are now calculated in accordance with the 1993 UN Standard of National Accounts (SNA) instead of the 1968 SNA. The base year has been changed from 1982 to 2001 and sectoral coverage broadened. Under the new measure, the economy was 5 percent larger than previously estimated. In turn, growth rates in recent years were revised up, with that for 2004 increased from 2.6 percent to 4.3 percent.

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not the best approach but it is a generally accepted approach in circumstances where the required data is lacking.

In the forecasting exercise carried out in this paper, assumptions concerning the real GDP growth rates for each economic activity at constant prices were first made. These assumptions took into account a number of factors including recent behaviour of the relevant activity, the policies that are currently in place, and the expected exogenous factors such as weather and changes of prices of concerned commodity in the world markets. The growth rates were then applied to the numbers actually obtained in previous year to determine the forecasted value added for each activity of that year. Subsequently, the projected values for each activity were added together to get the total projected value added in the economy. Kenya’s economic prospects are mixed over the forecasting period (2006-2008). The implementation of a number of structural reforms over the recent past including infrastructure enhancements particularly in the telecommunications sector31 as well as the trade opportunities within the regional countries has generated a momentum in growth that is expected to be maintained over the forecasting period. This growth momentum is expected to be supported by the recovery of the country from the drought during the second half of 2006 that, in turn, is expected to lead to a reduction in overall inflation to single digit levels32. Nonetheless, Kenya’s economy faces a number of weaknesses that include vulnerability to exogenous shocks, widespread poverty and unemployment levels, governance related problems including serious allegations of high-level corruption. Above all, the political situation in the country is fluid as political bickering in the ruling National Rainbow Coalition (NARC) as well as other political parties such as the opposition party KANU continues.

Since Kenya’s private sector has grown accustomed to political instability and the often stop-go relationship with the donor community (often reflected in disruption of donor inflows), the political factors are therefore not expected to significantly impact the performance of the economy over the forecasting period as indicated in Table 4.2 below. Given, however, that the political commitment to tackle the weaknesses enumerated above are not strong enough, economic performance is forecast to remain below potential during the period 2006-08. The

31 Telecommunications was the fastest growing sector in 2005 owing to the rapid rollout of mobile phone

technology. The number of mobile phone subscribers jumped from about 2 million in 2004 to about 5 million in 2005.

32 The effects of drought led to higher food prices, which pushed the inflation rate to nearly 20 percent by end of 2005. The implementation of a tight monetary policy and the appreciation of the Kenya shilling, however, kept the underlying inflation at below the 5 percent.

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results of the projections of the national accounts are indicated in Appendix A1. Provided hereunder, however, are the details of how the forecasts were made for each of Kenya’s main activities:

Table 4.1: Forecasting of Activities for the Period 2006-08

Industry Approach Used to Forecast Growth Rate

1 Agriculture and Forestry Forecasts for agriculture and forestry activity relied on ad hoc and iterative procedures:

• The forecasts took into account the latest developments and expectations regarding the agricultural and forestry activity. The 3% growth in value added of that activity achieved in 2005 was, for instance, taken into account in deriving projections for 2006 and beyond..

• The expected recovery of the country from the drought during the second half of 2006 was taken into account in order to derive the forecasts.

• The developments in commodity prices were also taken into account in forecasting. For instance, the surging international coffee prices as well as the positive effects of the recent reforms undertaken to streamline the coffee marketing system were considered to likely to improve coffee out and hence the overall activity of agriculture and forestry. Strong performance coffee as well as horticulture, tea, sugar and maize are expected to drive the performance in this sector.

• Finally, other factors such as the amount of area cultivated, new agro-based manufacturing plants and activities put in place, the expanded capacity and the ongoing structural reforms were taken into account in the forecasting scenario.

2 Manufacturing • The latest developments and expectations regarding manufacturing activity were considered in the forecasting exercise. These included:

the recent drought and the expected recovery of the country from the drought during the second half of 2006.

The recent pick up in manufacturing in response to a reduction in tariffs in neighbouring countries

• The expected slowdown in agricultural output in 2006 was factored in as likely to be reflected in decline in the output of manufacturing sector, which is dominated by agro-processing industries. Assuming normal weather conditions, agricultural production and therefore agro-processing industries is expected to recover in 2007.

• Government plans to put in place additional generating capacity, the costs of which will be passed on to consumers are expected to boost the manufacturing sector.

• Finally, new manufacturing plants and activities, expanded capacity and ongoing structural reforms were also considered.

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3 Electricity & Water Supply • The latest developments and expectations regarding the electricity & water supply activity were considered in the forecasting of the growth rates of this activity shown in Table 4.2. These included recent weather changes such as the drought that hit the country in late 1995 and expected recovery of the country from the drought during the second half of 2006.

• The current obstacles to the growth of this activity including frequent outages and high costs were also taken into consideration in the forecasting Although it seems that the rains in 2006 will be sufficient to prevent much disruption to the hydroelectric power output, the government is nevertheless putting in place additional generating capacity, the costs of which will be passed on to consumers.

• Tax and other concessions planned to encourage investment in hydroelectricity and in the geothermal energy, in which Kenya is a pioneer, were also taken into account in the projections.

4 Construction

The latest developments and expectations regarding the construction sector were considered in order to arrive at the growth rates shown in Table 4.2. These included:

• The impressive performance in 2005 as a result of donor-funded projects and the use of Kenyan contractors for development work in Southern Sudan.

• Building and construction that remained buoyant in 2005 following relatively lower interest rates that led to increased demand for mortgages

• The budgetary allocation for roads construction and rehabilitation in the last few years and government plans to increase the share of resources going to this sector from 15.6 percent of total spending in 2004/05 to 20.5 percent by 2007/08.

• The ongoing government policy to complete stalled projects and the expectations of more business opportunities in construction work in Southern Sudan in the coming years.

5 Hotels and Restaurants The latest developments and expectations regarding the hotels and restaurants activity were considered in the forecasting scenario in order to arrive at the growth rates indicated in Table 4.2 below. These included the following:

• Kenya’s impressive performance of tourism sector in 2005 in both revenue and tourist arrivals.

• The intensified marketing undertaken by the Kenya Tourism Board with the support of donors such as European Union (EU) to promote the sector

• The effects of external factors such as the bird flu and the tsunami experienced in Asia. For instance, the visitors deterred from travelling to Asia due to the effects of bird flu and the tsunami were factored in as likely to boost Kenya’s tourism industry.

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• Security concerns stemming from high crime rates and the threat of terrorist activity including that U.S. government advisory against travel to Kenya by its citizens.

The fact that local industry bodies in Kenya have reported hotels and lodges throughout the country strong bookings and the fact that the operations of tour operator and handing agents indicated the need for additional staff and tour vehicles were additional factors that were considered in forecasting of this activity. The pace of expansion of hotel room occupancy reaches capacity in peak season and this was also taken into account.

6 Transport and Communication

The latest developments and expectations regarding the transport and communication sector were considered in order to get the forecasted growth rates of this activity that are indicated in Table 4.2 below. These included:

• The move by the Kenya government in collaboration with donor countries to give high priority to the rehabilitation of the road infrastructure as a key part of the country’s development strategy in the medium term.

• The serious under-investment and corruption in contracts that have left the road network in a poor state of repair

• The years of mismanagement that have left the rail network in a dilapidated condition and the 25-year concession for a South African company to invest a minimum of $6 million per annum over a period of five years upon taking operational control of the railway expected to commence in July 2006 were taken into account in the forecasting.

• Plans by the government to finance Telecommunications through proceeds of the sale of a 9 percent stake in mobile operator Safaricom, a joint venture between Telkom Kenya (60 percent) and Vodafone (40 percent) were also factored in the forecasts.

The fact that telecommunications was the fastest growing sector in 2005 owing to the rapid rollout of mobile phone technology was recognized in the projections. The number of mobile phone subscribers jumped from 2.2 million in August 2004 to 5.2 million by the end of 2005 according to official reports from Kenya’s ministry of finance.

•

7 Financial Intermediation The latest developments and expectations regarding the financial sector were considered to arrive at the growth rates shown in Table 4.2. These included:

• Economic environment in recent years , the levels of interest rates and the stance of monetary policy:

Commercial bank lending has jumped in recent years because of

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the improved economic environment, lower interest rates and reduction in the reserve requirement to 6%.

• Plans that are underway to introduce credit bureau, which should improve access to finance for small and medium-sized enterprises and middle and lower-income borrowers.

• Past and present efforts by the government in implementing a number of measures to strengthen the financial system and create a predictable environment for private sector development. Recent and prospective reforms include:

Setting up the Bank Restructuring and Privatisation Unit in the Ministry of Finance to develop and implement restructuring reforms for state owned banks;

Submission to Parliament for enactment of the Micro Finance and SACCO Bills;

Building capacity to fight Money Laundering and Combating Financing of Terrorism (CFT), including:

Gazetting of the National Task Force on Anti-Money Laundering (AML) and CFT in 2003;drafting of the AML and Proceeds of Crimes Bill; and

Drafting the Suppression of Terrorism Bill (2003) to criminalize the financing of terrorism; and

Modernizing of the financial system including drafting of a specific Bill on Electronic Money Transfer,

Amendment of the Banking Act and Central Bank Act to transfer all regulatory and supervisory role from Ministry of Finance to Central Bank of Kenya, and introduction of a new regulation tightening loan provisioning and classification.

8 Education Given that the value added of Education in real terms is equal to the number of employees, the forecasting of this activity took into account Government’s employment and wage policies. The Government’s wage policy that aims at reducing the ratio off the wage bill--to-GDP from 7.8 percent in 2004//05 to 7.3 percent by 2008//09 was factored. The wage projections in the medium term was however revised to allow for recruitment of teachers following the demand for additional teachers triggered by the recent introduction of free education.

9 Health and Social Work As in the education activity, the value added of health and social workmeasured in constant prices took into account the number of employees planned to be in place during the forecasting period. The Government’s wage policy that aims at reducing the ratio of the wage bill--to-GDP from 7.8 percent in 2004//05 to 7.3 percent by 2008//09 was factored in the projections. The wage projections in the medium term were, however, revised to allow for recruitment of medical personnel in line with Government’s aim of reaching the optimum level off personnel for the health sector and to move toward achieving

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the MDGs.

10 Fishing Forecasts took into account recent behaviour of the activity, the policies that are currently in place, and the expected exogenous factors such as weather changes already enumerated for the other activities

11 Mining and Quarrying, Forecasts took into account recent behaviour of the activity, the policies that are currently in place, and the expected exogenous factors such changes of prices of concerned commodities in the world markets

12 Wholesale and Retail Trade, Repairs,

Forecasts took into account recent behaviour of the relevant activity

13 Real Estate, Renting and Business Services,

Forecasts took into account recent behaviour of the relevant activity

14 Public Administration and Defence

Given that the value added of public administration in real terms is equal to the number of employees, the forecasting of this activity took into account Government’s employment and wage policies that aims at reducing the ratio off the wage bill--to-GDP civil service current took into account Government’s wage policy aimed to reducing the ratio off the wage bill--to-GDP from 7.8 percent in 2004//05 to 7.3 percent by 2008//09.

15 Other Community, Social and Personal Services

Forecasts took into account recent behaviour of the relevant sectoral activity and the policies that are currently in place

16 Private Households with Employed Persons

Forecasts took into account recent behaviour of the relevant sectoral activity

17 Less: Financial Services Indirectly Measured

Forecasts took into account recent behaviour of the relevant sectoral activity

18 All Industries at Basic Prices

Obtained by summing up the growth rates of the above activities (i.e. 1+2+3+….+17)

19 Taxes Less Subsidies on Products

Linked to the forecasts made in the statement of government operations

20 GDP at Market Prices Obtained by summing row 18 and 19.

A detailed explanation of some of the sectoral activities shown in Table 4.2 are provided here under:

Agriculture

As indicated in chapter one, Kenya's economy has traditionally been based on the performance of the agricultural activity. Over the period 2002-2005, the share of the activity in the GDP averaged 23.9% of GDP (Table 4.2). The activity, however, registered mixed performance over this period. It is estimated to have grown by 1.3% in 2004 compared to negative 3.1% and 2.6%, in 2002 and 2003, respectively.

Notwithstanding the effects of drought experienced in a number of parts in the country, the output of the activity is estimated to have risen by 3 percent in 2005, with the main growth

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coming from export crops. In particular, production of horticultural products rose by 12 percent, reflecting the fact that most of them were not affected by the poor rains experienced during the year as they depend on irrigation. Favourable weather conditions in the main tea producing areas and an increase in domestic processing capacity stimulated another bumper harvest in 2005. Coffee production, however, continued to decline as farmers switched out of the crop owing to excessive regulation.

The short to medium term prospects of the sector will be determined by the recovery from the recent drought. While seasonal rains have begun, the effects of the drought will most likely remain for a while before they are over. For these reasons, growth in the agricultural is anticipated to be sluggish in 2006. Early indications show that maize production is expected to increase while tea output is expected to fall to around 15 percent in 2006. Coffee production, on the other end, is expected to rise, albeit marginally as farmers respond to surging international prices as well as the positive effects of the recent reforms undertaken to streamline the marketing system. The positive effects of the passage of the coffee act in 2005 including allowing farmers to bypass the coffee auctions and negotiate directly with buyers in addition to saving farmers the costs of the fees associated with auction floor are also expected to be felt in the short to medium term. Further strong growth in horticultural production is expected as a result of ongoing investment. Assuming normal rains will prevail, the agricultural activity is expected to improve in the period 2007-08 as indicated in Table 4.2 below:

Table 4.2: Gross Domestic Product by Activity (Percentage Change)

Activity % Share of GDP in

2004

2000 2001 2002 2003 2004 2005 2006 2007 2008

Agriculture and forestry 23.64 -1.2 10.6 -3.1 2.6 1.4 3.0 1.5 2.2 2.7

Fishing 0.49 -5.1 -18.3 -21.6 -6.9 4.1 4.0 4.2 4.2 4.4

Mining and quarrying 0.50 2.4 11.1 1.8 2.9 2.3 2.5 2.7 2.8 2.9

Manufacturing 9.91 0.7 0.3 0.6 4.9 4.1 3.0 2.4 3.0 3.1

Electricity & water supply 1.83 -7.5 5.0 21.3 14.7 2.1 1.9 3.0 3.2 3.4

Construction 3.55 -5.4 3.8 -2.6 1.7 3.5 4.0 4.2 4.4 4.5

Wholesale and retail trade, repairs 10.11

-7.5 5.0 21.3 14.7 2.1 1.9 3.0 3.2 3.4

Hotels and restaurants 1.06 -12.9 5.4 29.6 18.3 1.4 1.2 2.0 2.1 2.2

Transport and communication 10.55

6.2 4.1 3.6 5.2 4.3 3.9 6.1 6.5 6.9

Financial intermediation 3.81 -3.8 -9.6 -1.8 1.7 1.5 2.0 2.2 2.3 2.4

Real estate, renting and business services 5.68

2.6 2.8 3.0 2.3 2.7 2.0 2.3 2.6 2.9

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Table 4.2: Gross Domestic Product by Activity (Percentage Change)

Activity % Share of GDP in

2004

2000 2001 2002 2003 2004 2005 2006 2007 2008

Public administration and defense 4.49

10.0 -3.4 4.7 -20.3 15.1 15.3 15.6 15.0 15.0

Education 7.77 0.5 3.2 2.0 6.5 1.4 3.0 3.5 4.0 4.2

Health and social work 2.51 4.4 1.8 3.4 2.8 3.8 2.8 3.0 3.2 3.3

Other community, social and personal services 3.81

1.8 4.0 2.7 -0.2 3.3 3.0 1.5 3.0 5.0

Private households with employed persons 0.41

2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0

Less: Financial services indirectly measured -0.71

-1.1 -5.7 -9.4 -3.3 4.7 -10.0 3.0 3.6 4.0

All industries at basic prices 89.40

0.3 4.9 0.5 2.9 3.8 3.8 3.4 3.9 4.3

Taxes less subsidies on products 10.60

3.0 0.0 -0.5 1.3 8.5 18.5 11.9 13.6 16.1

GDP at market prices 100.00 0.6 4.4 0.4 2.8 4.3 5.4 4.4 5.2 6.0

Tourism

Kenya’s tourism sector registered an impressive performance in 2005 in both revenue and tourist arrivals. Tourist arrivals rose by 25 percent in 2005 to stand at over 830,000. The total revenue generated by the sector was 48.9 billion shillings (equivalent to US$ million), an increase of 27.0% over 200433, with UK and Germany continuing to be the leading source markets for tourists to Kenya. The impressive performance of Kenya’s tourism sector in 2005 is attributed mainly to two factors:

• Intensified marketing undertaken by the Kenya Tourism Board to promote the sector. A total of Ksh 500 million (equivalent to US$ 7 million), was used in the Tourism Marketing Recovery Programme, (TMRP) which was jointly funded by EU Tourism Trust Fund (TTF) and the Government of Kenya in 2004. The Europe wide campaign, conducted from late 2003 to March 2004, targeted the European public with images of Kenya through advertising, an online campaign and through feature stories in the media.

33 The country's tourism sector had been deteriorating following bomb attacks on the US embassy in Nairobi in

1998 and a hotel near the coastal town Mombasa in 2002. It has however recovered following aggressive marketing campaign in its main markets in Europe and in new ones in Asia, leading to tourism's best performance in 15 years in 2004.

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• Inflow of visitors deterred from travelling to Asia by bird flu and the tsunami. Although security concerns stemming from high crime rates and the threat of terrorist activity remain, and the U.S. government maintains an advisory on travel to Kenya, official efforts to improve security for tourists appear to be having an effect, with arrivals from North America surging by 47 percent in 2005. The EU through TTF has supported KTB’s international marketing effort over the years. It has committed an additional Euro 3 million in 2005/06 to support the industry. The KTB is determined to strengthen the image and perception of Kenya and to make it a leading tourist destination of choice. The marketing focus in 2006 will continue to reflect the diversity of Kenya’s tourism portfolio34.

Due to these efforts to improve Kenya’s tourism sector, local industry bodies in Kenya report hotels and lodges throughout the country are experiencing strong bookings and that tour operator and handing agents are employing more staff and requiring additional tour vehicles are needed to cope with the increased business. In particular, tourist bookings for 2006 point to continued double-digit growth in arrivals and the pace of expansion is likely to slow in 2007 as room occupancy reaches capacity in peak season. Since there are physical limits on the amount of accommodation that can be built in the main “safari” areas, industry bodies are seeking to diversify the packages on offer to holidaymakers to spread them more evenly through the year and the country. It will take some time for these efforts to show results, particularly as infrastructure improvements are necessary to open up other areas of the country.

Construction

Construction activity performed strongly in 2005 as a result of donor-funded projects and the use of Kenyan contractors for development work in Southern Sudan. Building and construction remained buoyant in 2005 with increased demand for mortgages, supported by relatively lower interest rates. Cement consumption, which is a key indicator in building and construction activities, increased by 10.9 percent in 2005. In addition, the increase in budgetary allocation for roads construction and rehabilitation in the last few years and the ongoing completion of stalled projects has given major boost to building and construction. Government plans to increase the share of resources going to this sector from 15.6 percent of total spending in 2004/05 to 20.5 percent by 2007/08. Kenyan contractors are expected to be at the forefront of construction and development work in Southern Sudan in the coming years.

34 National Parks cover one tenth of the country, and most tourists come Kenya to go on “safar”i and see the

wildlife. The most famous games park in Kenya is the Masai Mara. It has lions, cheetahs, baboons, buffalo and the annual wildebeest migration, which is a stunning sight. There are many beautiful beaches to be found on the Indian Ocean coast of Kenya. The beaches are of white coral sand, with warm water and many coral reefs making this an ideal place for swimming, snorkelling and diving.

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Transport and Communications

Kenya’s road network carries more than 80 percent of passenger and freight traffic in the country. However, serious under-investment and allegations of corruption in contracts have left the road network in a poor state of repair, hence the reason why growth has been below potential. The government and donor countries have prioritized the rehabilitation of the road infrastructure as a key part of the country’s development strategy in the medium term. In particular, the World Bank will spend US$207 million approved in April 2004 to support the Northern Corridor Transport Improvement (NCTI) project, 80 percent of which will be spent on roads. Other funds will come from private capital offset by toll charges, as well as donations from the European Union and the United States. Rail, and air transport are other significant in Kenya, which are in need of stepped-up investment for better maintenance and expansion. Years of mismanagement have left the rail network in a dilapidated condition. Under the terms of the 25-year concession, the South African company will invest a minimum of $6 million per annum over a period of five years upon taking operational control of the railway expected to commence in July 2006.

Telecommunications was the fastest growing sector in 2005 owing to the rapid rollout of mobile phone technology. The number of mobile phone subscribers rose by 56.9 percent from 4.3 million in 2004 to 5.6 million by the end of 2005. This growth was however slower than the 66.3 percent recorded in 2004. Greater private sector participation in the railways and telecommunications sector are expected to support economic activity. Considerable internal restructuring is needed in advance of the Telkom Kenya sale. The government plans to finance this from proceeds of the sale of a 9 percent stake in mobile operator Safaricom, a joint venture between Telkom Kenya (60 percent) and Vodafone (40 percent). The transaction with potentially the largest economic impact is the concession awarded to a South African group in October 2005 to operate Kenya Railways.

Manufacturing

Manufacturing growth increased by 5 percent in 2005 from 4.5 percent in 2004. The pick up in growth was reflected in output of all major industries, with Tobacco, food and textile industries being the leading ones. This development was in response to a reduction in tariffs in neighbouring countries, a stable macroeconomic environment that prevailed during the year, improved access to credit and the increase in export demand particularly from the East African Community (EAC) and the Common Market for Eastern and Southern Africa (COMESA) although infrastructure, telecommunication and security deficiencies remained major constraints for many industries. The expected slowdown in agricultural output in 2006 will be reflected in the manufacturing sector, which is dominated by agro-processing. Although it seems that the rains will be sufficient to prevent much disruption to the hydroelectric power

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output, the government is nevertheless putting in place additional generating capacity, the costs of which will be passed on to consumers. Assuming normal weather conditions, agricultural production and therefore agro-processing industries will recover in 2007.

Financial Intermediation

Banking sector profits recorded double-digit growth in 2005 due to an increase in interest income from loans and advances. Commercial bank lending has jumped in recent years because of the improved economic environment, lower interest rates and a sharp reduction in the cash reserve requirement in mid-2003. The lending environment remains difficult, however, so banks tend to invest heavily in government paper. Plans are in place for the introduction of a credit bureau, which should improve access to finance for small- and medium-sized enterprises and middle- and lower-income borrowers.

The government has implemented a number of measures to strengthen the financial system and create a predictable environment for private sector development. Recent and prospective reforms include: Setting up the Bank Restructuring and Privatisation Unit in the Ministry of Finance to develop and implement restructuring reforms for state owned banks; Submission to Parliament for enactment of the Micro Finance and SACCO Bills; Building capacity to fight Money Laundering and Combating Financing of Terrorism (CFT), including: gazetting of the National Task Force on Anti-Money Laundering (AML) and CFT in 2003;drafting of the AML and Proceeds of Crimes Bill; and drafting the Suppression of Terrorism Bill (2003) to criminalize the financing of terrorism; and Modernizing of the financial system including drafting of a specific Bill on Electronic Money Transfer, amendment of the Banking Act and Central Bank Act to transfer all regulatory and supervisory role from Ministry of Finance to Central Bank of Kenya, and introducing a new regulation tightening loan provisioning and classification.

With capacity in Southern Sudan destroyed by over 40 years of civil war and with NGOs and oil companies investing heavily, the role for Kenyan providers of financial and other services is also set to expand. A gradual process of donor-assisted financial reform has improved key banking sector ratios. A World Bank financial sector reform credit is under discussion that would meet the fiscal costs for further reform within public sector banks. In addition, the government is issuing bonds worth Kshs 22 billion in the current fiscal year for the restructuring of National Bank of Kenya and Consolidated Bank. The financial position of the country’s leading private banks, the largest of which are subsidiaries of foreign banks, is far better. Some smaller private banks have suffered financial problems in recent years and there is considerable scope for consolidation, given that many of the 39 private banks currently operating have very small branch networks.

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Electricity

Total electricity generation grew by 6.8 percent from 5,194.5 Gwh in 2004 to 5,547.0 Gwh in 2005, owing mainly to improved electricity production from thermol oil which expanded by 45.1 percent. Electricity generation from hydro sources declined by 4.1 percent in 2005 compared to a decline of 7.7 percent in 2004. The largest share of Kenya’s electricity supply comes from hydroelectric stations at dams along the upper Tana River, as well as the Turkwel Gorge Dam in the west. A petroleum fired plant on the coast, geothermal facilities at Olkaria (situated near Nairobi), and electricity imported from Uganda make up the rest of the supply. Kenya’s installed capacity stood at 1,1156.6 megawatts in 2005 compared to 1,198.1 megawatts in 2004 and 1,142.2 megawatts a year between 2001 and 2003. The state-owned Kenya Electricity Generating Company (KenGen), established in 1997 under the name of Kenya Power Company, handles the generation of electricity, while the Kenya Power and Lighting Company (KPLC), which is slated for privatization, handles transmission and distribution. Shortfalls of electricity occur periodically, when drought reduces water flow.

In 1997 and 2000, for example, drought prompted severe power rationing, with economically damaging 12-hour blackouts. Frequent outages, as well as high cost, remain serious obstacles to economic activity. Tax and other concessions are planned to encourage investment in hydroelectricity and in the geothermal energy, in which Kenya is a pioneer. Although it seems that the rains will be sufficient to prevent much disruption to the hydroelectric power output, the government is nevertheless putting in place additional generating capacity, the costs of which will be passed on to consumers.

Health

Total health expenditure as a percentage of total government expenditure has been increasing in line with policy to raise the level to 12% by 2010. The increase in allocation off resources to the health sector should facilitate a shift in government’s focus from curative to preventive care and help to expand immunization coverage. It will also lead to expanded coverage of preventive health services (e.g. full immunization of all children), as well as increase the proportion of the population protecting themselves against HIV/AIDS and other communicable diseases whose pace off implementation will take into account the availability off resources.

The Ministry of Health has initiated a number of reforms to increase the efficiency and effectiveness of the combined investments of the government and other stakeholders. Among these reforms are: the development of the National Health Sector Strategic Plan 2005-2010; establishment of District Health Management Boards and Health Management Teams to facilitate better use of resources and to strengthen supervisory capacity, staff rationalization undertaken to ensure the distribution of skilled staff as well as the recruitment of essential

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health staff; Restructuring of Kenya Medical Supplies Agency (KEMSA) to improve drug supply; implementation of HIV/AIDS programme including the establishment of VCT centres; establishment of a community HIV/AIDS initiative accounts, overhaul of the drugs procurement and distribution system. Once the necessary health care infrastructure and staff are in place, Government plans to phase-in the National Social Health Insurance Fund (NSHIF) taking into account the availability of resources.

Following the increased allocation of funding together with the implementation of the above reforms, the health sector is expected to register significant growth during the period 2006-2008 as indicated in Table 4.1.

Education

Kenya’ education sector has registered immense growth since independence. The number of secondary schools increased from 300 in 1963 to 4,112 by 2005. The sector has recorded success with the introduction of Universal Free Primary Education in 2003 leading to a rise in enrolments from 5.9 million in 2002 to 7.6 million in 2005. Education recurrent budget has risen from 33.4% of the total government recurrent budget in 2001/02 to 34% in 2004/05. The number of secondary schools increased from 300 in 1963 to 4,112 by 2005.

The overall goal of the Government is to achieve Education For All. In this respect, the Government is committed to continuing to fund the free primary education program, while at the same time rehabilitating secondary school classrooms and laboratories and providing bursaries to poor bright students.. It is expected that full implementation off Universal Primary Education ((UPE)) program will lead to growth balance between boys and girls. To resuscitate and re--invigorated the Youth Polytechnics and Vocational Training Centres, the government will ensure that resources for rehabilitation and provision of modern training equipment are provided.Some of the reforms implemented in the education sector include: The development of Sessional Paper No. 1 of 2005 as the blue print for providing policy direction on quality education and training in the 21st century and the Sector Wide Approach (SWAP) where Kenya Education Sector Support Programme (KESSP) 2005-2006 provide a framework to enhanced investment in education and training. The KESSP has been positively evaluated by Development Partners. In addition, a National Strategy for technical and vocational education and training has been developed and is expected to lead to the rehabilitation of physical facilities and equipment and making sure that Vocational and Technical Institutions are appropriately equipped by 2010. Taking into account these measures, growth in Kenya’s education sector is poised to improve in the period 2006-2008 as already shown in Table 4.1 above.

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4.3.1.2 Projections on Expenditure

In the system of national accounts, expenditure is divided into two parts, namely: the domestic demand and the foreign demand. The domestic demand is composed of the final consumption, gross fixed capital formation as well as changes in inventories while foreign demand is composed of exports of goods and services as well as imports of goods and services. Final consumption is divided into that of government and that of the households. The same also applies for gross fixed capital formation consisting of machinery, equipment, and structures, and changes in inventories.

In this paper, the projections for the external demand (i.e. exports and imports) were based on the projections made in the balance of payments worksheets whose details are contained in section 4.2.3 below. On the other hand, the projections for the domestic demand were made as follows:

• The forecasts for government consumption35 and gross capital investment were based on the projections made in the statement of government operations worksheet whose details are explained in section 4.2.2 below.

• Final consumption of households and operating surplus36/mixed income were selected as residuals. Final consumption was the residual in the case of GDP by expenditure approach while the operating surplus/mixed income was the residual in the generation of income account. Final consumption was chosen as the residual not because it is the variable with the largest contribution to GDP in the GDP by expenditure expenditure but more importantly because final consumption by households is often not based on primary data. Put in other words, the final consumption was as taken a residual because there were not much primary information for the forecasting period under review The same reasons were also the basis for the choice of operating surplus/mixed income in the forecasting of items in the generation of income account. It is the item in the generation of income account in which there was not much primary information for the forecasting period under review.

35 As indicated in chapter three, final consumption by government (FCg) = Output of government (OPg) - sales of

current goods and services by government (SCGSg) where OPg = compensation of employees payable by government (CEg) plus intermediate consumption by government (ICg) plus consumption of fixed capital by government (CFKg) - sales of current goods and services by government (SCGSg).

36 The operating surplus of government (OSg) is derived as the equivalent of value added by government (VAgg)

less compensation of employees (CEg) less taxes on production and imports (TPIg). In an equation form, this is written as OSg= VAg

g -CEg –TPIg = CEg +CFKg -CEg –TPIg = CFKg –TPIg = CFKg since TPIg = 0 . The OSg is sometimes referred to as mixed income in situations where productive households do not maintain separate accounts, thus making it difficult to distinguish between compensation of employees and operating surplus. This is why it is sometimes referred to as operating surplus/ mixed income.

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4.3.1.3 Setting the Inflation Target

According to section 4 (1) of the Central Bank Act, “The principal object of the Bank shall be to formulate and implement monetary policy directed to achieving and maintaining stability in the general level of prices,” In accordance with this objective, Kenya’s monetary policy37 is designed in such a way that it supports a high and sustainable non-inflationary economic growth and employment as well as a viable balance of payment position while maintaining low and stable inflation (i.e. that level of inflation that can not only preserve the purchasing power of the domestic currency but also preserve the country’s international competitiveness). In this respect, the targeted inflation should be that which is equal or less than prevailing among Kenya’s trading partners. The average inflation of Kenya’s trading partners is currently about 5% (Chart 4.1). Thus, monetary policy is geared towards attaining and maintaining an inflation rate at most 5% over the period 2006-2008. On the basis of this targeted inflation for the forecasting period, the forecasted GDP at constant price was converted into forecasted GDP at current prices. This actually involved combining the projected inflation with projected real GDP to get projected nominal GDP (see Appendix 2). The projected nominal GDP was then used in some revenue categories as a proxy tax base while forecasting fiscal accounts as indicated in the next section below.

0

10203040

506070

%

P eriod

C h art 4 .1: In fla tio n R ates b y reg io n (% )

W orld Indus tria l C oun tries D eve lop ing C oun tries A frica

W orld 18 24 22 11 7 .5 5 .3 6 .9 4.2 4 .4 3.4 3 .9 3.3 3 .8

Indus tria l C ountries 2 .7 2 .6 2.4 2 .3 2 .4 1 .8 1 .3 1.8 2 .6 1.3 2 .1 1.6 2

D eve lop ing C oun tries 46 60 53 24 14 9 .8 14 7.2 6 .5 5.9 6 5.3 6

A frica 46 21 45 25 21 8 10 9 8 12 10 8 3 5 3 4 2

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

37Recent economic policy debates and discussions in Kenya, however, shows an inherent tendency to ask too much

of monetary policy. In most cases, the Central Bank has been criticised for being overzealous in the pursuit of a single-minded monetary policy geared to achieve low and stable inflation. In particular, the high level of lending interest rates as well as an appreciating shilling exchange rate and the attendant low economic growth has been attributed to the pursuit of tight monetary policy by CBK

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4.3.2 Procedure Used in Forecasting Kenya’s Fiscal Accounts

The forecasting of Kenya’s fiscal account was based on a consistent set of overall macroeconomic assumptions including value added, inflation and balance of payments. This is because fiscal revenues as well as expenditures affect and in turn are affected by the overall macroeconomic situation, particularly changes in economic activity as reflected in GDP growth, the rate of inflation, and developments in the external sector including the exchange rate behaviour. In contrast, a significant part of Kenya’s fiscal expenditures heavily depends on the discretion of the policy makers. Only a few fiscal expenditures such as interest payments do not depend on the discretion of the policy makers. On the basis of these macroeconomic assumptions, the guidelines for forecasting of Kenya’s fiscal revenue and expenditure are provided hereunder.

4.3.2.1 Forecasting Kenya’s Fiscal Revenues The forecasting of Kenya’s fiscal revenues was based on the effective tax rate approach. This approach relied on the observed data for tax revenues from a tax category. The forecast revenue is equal to forecast of tax base multiplied by the effective tax rate. The formula for deriving the effective tax rate is

TBATETR = ……………………………………………………………………….Eqn (123).

where ETR is the effective tax rate, AT is the actual tax revenue and TB is the tax base.

Due to the complexity of tax laws and data limitations in Kenya, "proxy" tax bases were used to estimate the yield of specific taxes. Thus, specific linkages were allowed in the file containing the forecasting framework as follows:

Table 4.3: Proxy Bases for Forecasting Revenues

REVENUE CATEGORY "PROXY" TAX BASE

A. Taxes (1-6) 1. Taxes on income, profits, and

capital gains Compensation of employees (for taxes Payable by individuals) and mixed income/operating surplus for taxes payable by corporations and other enterprises

2. Taxes on payroll and workforce

Compensation of employees in the generation of income account of the SNA worksheet

3. Taxes on property GDP obtained from the SNA worksheet 4. Taxes on goods and services Output at basic prices. The only exception is VAT whose proxy

tax base is final consumption in expenditure approach in the SNA worksheet

5. Taxes on international trade and transactions

Imports of goods and services in the BOP worksheet

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Table 4.3: Proxy Bases for Forecasting Revenues

REVENUE CATEGORY "PROXY" TAX BASE 6. Other taxes GDP obtained from the SNA worksheet

B. Social contributions

Compensation of employees in the generation of income account in the SNA worksheet

C. Grants

Uncertainty over future relations with donors means that grants remain broadly unchanged in previous years level . Thus grants for current period were projected as a moving average of the last recent three years

D. Other revenue (7-11) 7. Property income Mixed income obtained from the generation of income account

in the SNA worksheet 8. Sales of goods and services GDP obtained from the SNA worksheet

9.Fines, penalties, and forfeits GDP obtained from the SNA worksheet

10.Voluntary transfers other than grants

GDP obtained from the SNA worksheet

11.Miscellaneous and unidentified revenue

GDP obtained from the SNA worksheet

Table 4.4 below shows the details of the projections in percentage terms. The rest of the data is shown as Appendix 3. It is worth mentioning that in recent years, the Kenya government has had some success in adhering to its primary fiscal goal of reducing domestic debt as a percentage of GDP. In part, this was attributed to very strong growth in tax receipts and the improved efficiency of the revenue collection agency – the Kenya Revenue Authority(KRA) Growth in tax revenue in the three years from 2002/03 exceeded growth in nominal GDP by 4.5 percentage points per annum. The good performance reflected improved tax receipts due to improved tax administration measures, increased corporate profits following recovery in economic activity, and positive response to tax amnesty offered to previous defaulters. The tax amnesty, for instance, netted revenues amounting to 0.3 % of GDP.

Table 4.4: Transactions Affecting Kenya’s Net Worth (% GDP)

2001 2002 2003 2004 2005 2006 2007 2008

Revenue 20.5% 20.4% 21.1% 21.8% 22.8% 21.5% 21.6% 21.8%

Taxes 17.8% 17.6% 18.1% 18.1% 19.0% 18.2% 18.4% 18.7%

Social contributions 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.1% 0.1%

Grants 0.7% 0.9% 1.0% 1.4% 1.2% 0.7% 0.6% 0.5%

Other revenue 2.0% 1.8% 1.9% 2.3% 2.5% 2.5% 2.5% 2.5%

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Table 4.4: Transactions Affecting Kenya’s Net Worth (% GDP)

2001 2002 2003 2004 2005 2006 2007 2008

Expense -18.8% -20.1% -21.5% -23.0% -20.6% -19.9% -18.3% -16.2%

Compensation of employees -9.6% -10.1% -10.4% -10.4% -11.4% -11.4% -11.0% -10.3%

Use of goods and services -4.7% -4.9% -5.2% -6.1% -6.0% -5.5% -7.7% -10.2%

Consumption of fixed capital 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Interest -2.8% -2.6% -3.2% -2.9% -2.7% -2.7% -2.4% -2.1%

Subsidies -0.3% -0.3% -0.5% -0.7% -0.8% -0.7% -0.6% -0.5%

Grants -0.3% -0.3% -0.3% -0.8% -1.6% -1.4% -1.2% -1.1%

Social benefits -0.1% -0.1% -0.1% -0.1% -0.1% -0.1% -0.1% -0.1%

Other expense -0.4% -0.4% -0.4% -0.4% -0.4% -0.4% -0.4% -0.4%

Gross operating balance 2.4% 1.6% 1.0% 0.3% -0.2% -0.6% -1.9% -3.0%

Net operating balance 2.4% 1.6% 1.0% 0.3% -0.2% -0.6% -1.9% -3.0%

Transactions in Nonfinancial Assets (% GDP)

Net Acquisition of Nonfinancial Assets 1.0% 1.3% 2.0% 2.1% 2.1% 2.3% 2.5% 2.5%

Fixed assets 1.0% 1.3% 2.0% 2.1% 2.0% 2.1% 2.2% 2.2%

Change in inventories 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Valuables 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.1%

Nonproduced assets 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.1% 0.1%

Net lending / borrowing 1.4% 0.3% -1.0% -1.8% -2.3% -2.9% -4.4% -5.4%

4.3.2.2 Forecasting Kenya’s Fiscal Expenditures

As indicated in the preceding section, expenditure projections are also based on projected GDP, the policies in place and available resources such as the expected amount of foreign grants or raw material prices. Expenditures are driven by existing government policies and it is therefore important that a financial programmer investigates any known future changes in policies and takes them into account in the forecasting exercise.

There are two main categories of expenditures. The first category is discretionary and depends on government policies. The second category is non-discretionary and includes items such as interest payments. The challenge of this paper was to identify the discretionary elements in

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Kenya’s expenditure. The endogenous elements were forecasted using macroeconomic projections such as output and inflation, projections for recipients from donor related programs or numbers of employees in the case of wages and salaries projections. The schedule of debt service obligations (interest) and likely implementation rate (capital projects) were also used in the projections of the fiscal expenditures. The specific linkages allowed in the file containing the forecasting framework were as follows:

Table 4.5: Proxy Bases for Forecasting Expenses

Expense Category "Proxy" Expenditure Base

Compensation of employees (1-2)

1. Wages and salaries Total number of employees in addition to salary adjustments to take into account the changes in costs of living index i.e. inflation

2. Social contributions Compensation of employees in the generation of income account in SNA worksheet

Use of goods and services Obtained as a residual

Consumption of fixed capital Linked to GDP obtained from the SNA worksheet

Interest (1-3)

1. To non-residents Schedule of external debt service obligations as reflected in the BOP worksheet

2. To residents other than general government

Schedule of domestic debt service obligations as reflected in the GFS worksheet

3. To other general government units

Schedule of domestic debt service obligations as reflected in the GFS worksheet

Subsidies The subsidies were forecast to remain constant i.e. at previous year level. This assumption is in line with current policy of government not to make additional increases in subsidies.

Grants Projected outflows of grants in the current account in BOP worksheet

Social benefits Financial consumption by households in SNA worksheet

Other expense Assumed to move proportionately to projected GDP in SNA worksheet

The starting point for the government account is to use the projections made in the government budget and assess the various items against the projected GDP. According to the information contained in the most recent Budget Outlook Paper, expenditures over the recent past lagged behind due to delays caused by elaborate procurement procedures and slow absorption of donor project disbursements. This is due to wide-ranging reforms at the Kenya Revenue Authority that include the upgrading of IT systems, consolidation of internal structures, the creation of

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regional operations, greater training, and a clampdown on corruption. Weak management, bottlenecks stemming from poor infrastructure and disruptions to donor funding also prevented the government from achieving spending targets.

Preliminary fiscal plans have been unveiled for the next three years. These envisage a gradual decline in the fiscal deficit to 2.6 percent of GDP by 2008/09. The sharpest fall in the deficit is projected for 2006/07 owing to the one-off nature of spending this year related to the drought. Spending on poverty alleviation is to be increased while the government wage bill and transfers to parastatals are set to decline. Further gains in revenue collection are anticipated. The recent suspension of some donor funding is unlikely to have too much of an impact on the fiscal outturn as the government does not include bilateral budgetary support in its projections. Governance concerns mean that donors tend to have fairly tight control over the spending of moneies advanced for various projects... Based on the above information, projections were made for each expenditure category as shown in table 4.5 above. Where inconsistencies were identified, modifications were made. In general, expenditure projections were based on projected GDP, the policies in place and available resources .

As mentioned above, a significant part of fiscal expenditures heavily depend on the discretion of the policy makers. Because of such discretions in the determination of fiscal expenditures, the task of forecasting becomes very difficult for the financial programmer. This is particularly so in the case of purchases of goods and services by government. In this paper, little information was available about how much government was going to use in purchasing goods and services. In this respect, purchases of goods and services was selected as the residual item. The details of the projections of the government account are shown in Appendix A3.

4.3.3 Forecasting the Balance of payments

Forecasting the balance of payments requires the projection of each of its main components such as imports and exports of goods and service, net income and transfers from abroad and capital and financial flows. The main inputs for the forecasting of these items were based on developments in both the domestic economy and the external economy. Thus, forecasts for some BOP items depended on the performance of GDP, industrial production, agricultural supply and utilization capacity on the domestic economy. Projected developments in the world economy such as interest rates, commodity prices, exchange rates and purchasing power of trading partners were considered. The forecasts of developments in the world economy such as commodity prices would have meant making forecasts on a country by country basis and this would have been too costly. In this respect, this study used forecasts from other institutions such as the IMF’s World Economic Outlook .

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This paper also holds the “small country assumption” in the forecasting of some BOP items such as imports and exports. For imports, the assumption is that a nation is sufficiently small in the world market that changes in its demand do not affect the foreign currency price of its imports, i.e., world supply is infinitely elastic. On the export side, the small country assumption implies that the country is sufficiently small in the world market that it can sell as much as it likes without affecting the foreign currency price of its exports, i.e., world demand is infinitely elastic. As long as these conditions hold, analysis of trade volumes can focus on demand factors for imports and supply factors for exports. On the basis of these assumptions, the guidelines for the forecasting of Kenya’s balance of payments are provided hereunder in Table 4.6.

Table 4.6: Forecasting of Balance of Payments Items

Category Forecasting Approach

1. Current Account

1.1 Merchandise trade The volumes and prices for individual categories of exports of goods (X) and imports of goods (M) were forecasted separately:

• The volumes of both export and import commodities were made on the basis of recent developments as well as policies and incentives that have been put in place to boost the respective commodities.

• The small country assumption was assumed to hold for Kenya and thus the export and import prices of goods were taken as those prevailing in world market conditions. No new forecasts for Kenya’s export and import commodity prices were made. Instead, the forecasts already made by the IMF and published its World Economic Outlook publications were used.

• The values of each export (X) and import (M) commodity were obtained as a residual using the following basic identity for X and M:

( ) )Pr1(*1(*) 111 +++ ∆++= tttt iceXVolumeXValueXValueX

( ) )Pr1(*1(*) 111 +++ ∆++= tttt iceMVolumeMValueMValueM

• An alternative approach to obtaining overall export volumes and overall import volumes was got through use of estimated equations of exports and imports (equations 124 and 125, respectively). The results of the estimated equations are shown as Appendices A5 (i) and (ii)

1.2 Forecasting of Exports and Imports of Non-factor Services

Projection of exports of services were linked to performance of relevant activities such as trade, GDP and other foreseeable policies or developments:

• The value of travel of residents abroad were assumed to be positively linked to growth in domestic income and on relative price of tourist services at home and abroad. The value of travel (tourism) of non-residents to Kenya were assumed to be positively linked to growth of income levels in the countries from which travellers originate,

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relative price of tourist services and other factors that may affect travel such as security concerns including incidences of terrorism.

• The export of transportation services was assumed to grow in line with projected growth in exports of goods while the import of transportation services was assumed to grow in line with projected growth in imports of goods

• The exports and imports of other private services were assumed to grow in proportion to forecasted GDP valued at market prices

• The exports and imports of other government services were assumed to grow in proportion to forecasted GDP valued at market prices

1.3 Income • Projections of receipts and payments of investment income were done based on o historical values of s’ foreign assets and liabilities as well as new ones that are known.Projections of interest payments on external debt, which is the major debit item of this account, was made based on the amount and costs of past and current foreign borrowing. In particular, the forecasts for interest payments of government external debt was based on planned amortization as per the information obtained from the debt management departments at the central bank and the Treasury.

• The forecast of compensation of employees were linked to the size of GDP. This figure has however been zero in the past.

1.4 Transfers Since there was no reliable theory that exists that could have been used in forecasting transfers, ad hoc means were instead used in the forecasting of items in this account. This was done as follows:

• Receipts of current transfers by government were linked to the current transfers projected in the statement of government operations as budget support. Payments of current transfers on the other hand were based on projections in the statement of government operations

• Worker's remittances from abroad were assumed to grow in line with recent trend. Worker's remittances to abroad were assumed to grow in line with projected GDP valued at market prices.

• Political prospects for 2007 were factored in the projection of inflows of other private transfers. In particular, the heightened political tensions ahead of the 2007 elections are assumed to likely cause a modest decline in inflows of private transfers in the forecasting period. Historically, the outflows of other private transfers have been zero and hence were forecasted to remain zero in the period under review

2. Capital and Financial Account

2.1Capital Account • General government current transfers were linked to the statement of government operations, more specifically the grants from/to foreign governments and international organizations

• Both the credit and debit items of other private capital transfers including migrant's transfers and debt forgiveness have historical

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been zero. Thus, the same levels were assumed to prevail during the forecasting period.

• Historically, the credit items of acquisition/disposal of non-produced, non-financial assets have been zero. This was assumed to remain in the forecasting period. Similarly, the debit items of acquisition/disposal of non-produced, non-financial assets have been zero except in 2002. In this case, zero was also assumed to remain in the forecasting period

2.2 FDI investment • FDI forecasts were based on recent trends and incentives/policies being implemented to attract foreign investors. These include the passage of the privatization bill in August 2005.

• How much foreign investors are willing to invest to a particular country depends on a number of factors including: how much they have to invest, and what share they want to allocate to that country (in this case they consider the country rating).

• Some inertia on direct investment (both equity capital and reinvested earnings) in reporting economy was assumed for the forecasting period. In this case, forecasts of FDI inflows were made on the basis of the average of last three years. Similarly, forecast of FDI outflows were forecasted on the basis of the average of last three years.

2.3 Portfolio Investment Financial flows are influenced by relative rates of return adjusted for expectations including country risk.

• Portfolio inflows and outflows takes into account country prospects and known public/private sector financing needs and available domestic financing.

• Forecasts of portfolio investment assets were made as follows:

Equity Securities were assumed to grow in tandem with GDP

Debt Securities were assumed to be the average of the previous two years

Portfolio investment liabilities were assumed to remain in the same level as the previous year

2.4 Other investments • Trade Credits have traditional been zero. Thus, the same level was assumed for the forecasting period.

• Official medium & long term loan disbursements and repayments, debt rescheduling, forgiveness and accumulation of arrears were projected in accordance with information obtained from the debt management departments at the central bank and the Treasury that takes into account commitments from prior loans.

• Forecasts of for private medium & long term borrowing is based on past track record in debt service, debt ratings and confidence factors.

• Forecasts of private short term borrowing took into account prospects of trade flows, money and capital market conditions including

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expectations of interest rates and exchange rates and other confidence factors that affect a country’s prospects.

3. Net errors and omissions (NE&O)

• NE&O reflect errors in estimations and omissions of transactions. Credits and debits errors may offset each other and in this case NE&O not indicate necessary the accuracy of the BOP statistics. Conceptually it should be forecasted to be zero, but this may impose a substantial adjustment in other items. One alternative a financial programmer could take average of last few years unless there is a clear trend of capital flight, in which case it should be adjusted.

• In this study, NE&O were forecasted as the average of the previous four years

4. Reserve Assets

• Reserve assets which comprise foreign currency and securities, gold, SDR and reserve tranche position in the IMF are under the effective control of monetary authorities. In this study, Monetary Gold, Special Drawing Rights (SDR ) and Reserve Position in the Fund were linked to the forecasts of these items in the depository corporations survey

• Official foreign exchange assets were taken as the residual in the balance of payments account. As mentioned earlier, residuals should not be items on which there is a lot of primary information for the projected time period. This was the case for official foreign exchange assets and yet their levels have analytical importance. In this baseline scenario, there are no explicit targets and the performance of the current account has to be financed by the capital and financial account so that the overall balance dictates how much reserves are built or depleted. As pointed out above, the much gold, SDR and reserve tranche position in the IMF are under the effective control of monetary authorities. However, not much information is known has to how much currency and deposits will be available as they are dictated by the outcome of the balance of payments position. This explains why they were chosen as residuals.

A discussion of how each variable has behaved in the recent past is provided hereunder taking into account what is stated in Table 4.6. As indicated in the table 4.6, the projection of exports of goods were linked to performance of the activities that contribute most to exports as already covered under section 4.2.1. This involved making forecasts on volumes of commodity exports as well as world commodity prices. An important source of information for the latter was the World Economic Outlook published by the IMF.

4.3.3.1 Forecasting of Exports and Imports of Goods

The forecasts for the volumes of commodity exports were made on the basis of recent developments as well as well as policies and incentives that have been put in place to boost the sector. For instance, in 2005, Kenya’s exports grew by over 13.7%, the fourth consecutive year of double-digit growth. Higher production accounted for the improved receipts from tea and

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horticultural products in 2005, while an increase in coffee prices compensated for a small decline in production. The double-digit growth rates of exports are expected in the period 2006-2008. Export revenues, however, are expected to increase, albeit moderately in 2006 owing to the impact of drought on the tea crop and processed agricultural goods. Although coffee production is expected to rise as farmers respond to surging international prices and take advantage of positive effects of reforms taken in the marketing system, revenues are set to fall in response to global oversupply stemming from the surge in prices in recent years. Higher production is expected to boost receipts from horticultural products. Prospects for other leading crops are mixed, but majority are expected to increase.

Another alternative to the forecasting of the total volume of exports is the use of regression analysis. This approach was also attempted as indicated in Box 4.1 below.

Box 4.1: The Export Supply Function

The export supply function is derived from the assumption of profit maximization on the part of the producer. In the Goldstein and Khan (1978) and Savvides (1992) two-country models of the international trade framework, the main determinants of structural export supplies are domestic relative prices of exports, and domestic capacity utilization proxied by real GDP (Yd). Consistent with literature, we extend this theoretical framework to include the terms of trade (TOT). Consolidating the main determinants of export supplies by including domestic relative prices, terms of trade and domestic capacity utilization proxied by real GDP yields the following extended structural export supply function:

),,( TOTYPfX dxs = …………….……………………………… Eqn (124)

where Xs denotes the supply of exports, Px is the domestic relative price of exportable goods proxied by real exchange rate (RER); Yd is the domestic capacity utilization proxied by real GDP and TOT designates other exogenous determinants of export supply. Expressing equation 124 in logarithm linear form yields the following specification:

εαααα ++++= totypx dxs 3210 lnlnln …………….Eqn (125)

where ε is the error term. The details of the regression results including diagnostic tests and unit root analysis are shown in Appendix 4.

Substituting the coefficients of variables in equation 125 with the estimated values yields the following results:

totypx dxs 459.0ln205.1ln075.0534.4ln +++−= α …………………………………………………………………………………………Eqn (126)

The above results were not used in this study to forecast the volume of exports. However, if a financial programmer were interested in doing so, then the procedure for getting the forecast for the volume of exports over the period 2006-08 would be done as follows: For every observation in the forecast period (2006-08), the financial programmer would have to compute the fitted value of exports (x) using the estimated parameters in equation 126 and the corresponding values of the regressors, Px, yd and tot. In this case, the financial programmer has to make certain that he/she has valid values for the exogenous right-hand side variables for all observations in the forecast period. This would involve first making assumptions of the data value of the independent variables, which in the above model are: the real GDP (yd) and the real exchange rate (px) and the terms of trade (tot) The second step

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would involve multiplying the estimated coefficients with the data for the respective variable over the forecasting period. Finally, adding together the constant and the derived value for each variable would derive the volume of imports for each period.

The projection of imports of goods were linked to performance of the activities that contribute most to imports. This involved making forecasts on volumes of commodity exports as well as world commodity prices. The World Economic Outlook published by the IMF was the main source for world prices. The forecasts for the volumes of commodity imports were made on the basis of recent developments and current policies and incentives put in place to improve the performance of the sector. In 2005, imports increased by 18.2 percent owing to higher oil prices and aircraft purchases by Kenya Airways. Crude petroleum and refined products accounted for over 20 percent of the import bill. Improved economic performance was reflected in a significant increase in imports of capital goods. Lower imports are expected in 2006, reflecting the one-off costs of aircraft purchases in 2005 and a moderation in the pace of oil price rises, will narrow the current account deficit to 5.5 percent of GDP in 2006. The current account deficit is forecast to ease to 2.7 percent of GDP in 2007 and 2.9 percent of GDP in 2008 as a result of higher agricultural exports. As in the case of exports, this study also attempted an alternative to the forecasting of the total volume of imports as described in the preceding paragraph. This involved using regression analysis to estimate the import demand function whose details are shown in Box 4.2 below.

Box 4.2: Import Demand Function

Like in the case of export supply function, there are two primary determinants of imports (Hooper and Marquezz, 1988). The first is the income variable, which measures the economic activity, and hence the purchasing power of Kenya. This is called the income effect. The second is the price effect, which takes into account the relative price or the real exchange rate variable. i.e. when these factors are incorporated, then we get the following import-demand function:

ttdtt pym εααα +++= 210 ……………………………………………….Eqn 127)

where mt denotes the logarithm of real imports, ydt is the logarithm of domestic income, Pt denotes the logarithm of relative price, which serves as an indicator of external competitiveness and is measured as the real exchange rate (RER), and tε is the uncorrelated error term. It is expected from theory that the higher the income in Kenya, the higher the demand for imports by Kenyans. Therefore, the value for

t1α is expected to be positive. It is also expected that a decline in the relative price, which implies an appreciation of the real exchange rate (RER), lead to a higher imports while the reverse is true. The value for

2α is thus expected to be negative. Thus,

from the foregoing, the following results are expected: 01 >α , 0,2 <α .

The details of the results of the estimation of the above model are shown in Appendix 5. These results were, however, not been used to generate the forecasted volumes of imports. However, if a financial programmer wanted to do so for the period 2006-08, he/she would use the following estimated equation:

)0.6954ln(p-)ln(3245.00704.8)ln( tdtt ym += …………..….Eqn (128)

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For every observation in the forecast period, the financial programmer would have to compute the fitted value of imports (m) using the estimated parameters in equation 128 and the corresponding values of the regressors, y and P. In this case, the financial programmer has to make certain that he/she has valid values for the exogenous right-hand side variables for all observations in the forecast period. This would involve first making assumptions of the level (i.e. data) of independent variables, which in the above model are: the real GDP and the real exchange rate. The second step would involve multiplying the estimated coefficients with the data for the respective variable over the forecasting period. Finally, adding together the constant and the derived data for each variable would derive the volume of imports for each period.

4.3.3.2 Forecasting of Exports and Imports of Services

Projection of service receipts were linked to performance of relevant activities such as trade and other foreseeable policies or developments. For instance, an increase in hotel facilities and campaigns about the country as a tourist destination are expected to boost tourism receipts in the forecasting period. Similarly, the income levels in the countries from which travellers originate were considered in the projection of tourism receipts. These projections took account of the developments in the tourism sector in 2005. During this year, inflows of tourist revenue and official transfers kept the invisibles account firmly in surplus. Tourism revenue rose by only 7.1 percent. However, this is inconsistent with the 25 percent increase in arrivals as it implies a large fall in spending per tourist given. This inconsistency was been taken into account in the forecasting of tourism receipts in the period 2006-08 as explained in Table 4.1 on the forecasting of tourism.

4.3.3.3 Forecasting of the Income and Transfers Accounts

In the income account, forecasts were made for compensation of employees and investment income. The forecast of compensation of employees were assumed to grow at constant growth rates although passed values of the item have been zero. Ideally, this item should not be zero if Kenya’s BOP compilation process was being done correctly. Investment income, payments and receipts were linked to banks’ foreign assets and liabilities. The major debit item of this account is usually interest payments on external debt, which depend on the amount and costs of past and current foreign borrowing. Outflows arising from direct investment depend on past-accumulated foreign investment. The credit side of this account reflects interest inflows received in the past on lending or investment in foreign countries.

Forecasts of the government transfers were related to the current transfers projected in the government account as budget support. The heightened political tensions ahead of the 2007 elections were factored in as likely to cause a modest decline in private transfers in the forecasting period.

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4.3.3.4 Forecasting of the Financial Account

Foreign direct investment (FDI) has generally remained below 3 percent of GDP in the recent past. A key question that had to be addressed while making the forecasts was how much foreign investors will be willing to invest in Kenya during 2006-2008. One of the issues considered was the passage of the privatization bill in August 2005 that is expected to raise the prospect of a pickup in FDI inflows over the next few years. Previously, concerns over foreign ownership and unemployment have slowed privatization. In order to get the privatization bill passed, which was a key donor demand, the government is committed to ensure that, where possible, a stake in the enterprise would remain in the hands of local investors through a listing on the NSE. This paved the way for the recent KenGen IPO. Equity investment are also expected to continue to support the capital account in 2006 and beyond, with foreign participation in forthcoming IPOs boosting net portfolio inflows. Inflows of direct investment are set to rise as a result of the sale of a minority stake of Kenya Telkom. The full disposal of the government’s stake to be held in 2007 through the combination of an IPO and a sale to a strategic partner, are expected to lead to significant net inflow of equity investment.

Loan disbursements and repayments, debt rescheduling, debt forgiveness and debt accumulation of arrears were projected in accordance with information obtained from the debt management departments at the central bank and the Treasury. According to official data, the World Bank disbursed around US$30 million in 2005 through IDA, down from nearly US$80 million the previous year. This was spread over projects covering healthcare, agriculture, energy and public sector reform. No new loans were signed in 2005 but two were approved in the first quarter of 2006, for institutional reform and capacity building and trade and transportation, worth a total of US$145 million. The IMF made one disbursement, of US$74 million, under the PRGF in 2004.

In 2005, official transfers declined slightly as donors withheld some assistance over concerns related to governance. The financial account is expected to benefit from a Paris Club deal struck in January 2004. Under the deal, US$350 million of the US$484 million that was due to mature between 2004 and 2006 was rescheduled. As a result of the surplus on the balance of payments, foreign exchange reserves rose to $1.8 billion at the end 2005, up 18 percent from the end of the previous year. However, import cover declined to 3 months from 3.5 months at the end of 2004 because of the rapid growth in imports. It is assumed that there will be only one more disbursement under the current PRGF, which will limit inflows from other donors. While around $200 million per year in support for projects will be disbursed, these funds will be closely managed and are someway short of what would be expected in the absence of concerns over governance. Amortization payments are forecast to exceed disbursements in 2007, as payments next year are not covered by the most recent Paris Club deal.

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The results of the forecasting of the balance of payments transactions are contained in appendix 6. The appendix also contains historical balance of payments data.

4.3.4 Forecasting Monetary Aggregates

The forecast of the monetary aggregates is an integral part of financial programming, particularly in the case of a country such as Kenya whose monetary policy framework is that of monetary targeting38. Underpinning the forecast of monetary aggregates is the objective of the CBK to match the supply of money with the demand for money at a level consistent with its inflation target and the estimated rate of real GDP. Thus, the projections for the demand for money are made in such a way that the monetary authority (in this case CBK), can change policy instruments with a view of achieving the targets established for the stock of reserve money. The Central Bank has three main instruments through which it can affect monetary conditions in order to obtain suitable growth in the money supply, namely: the open market operations (OMO), the cash ratio requirement and rediscount rate.

The use of intermediate target (i.e. a target that lies between the tools and the goals of the central bank) is necessary since the relationship between the monetary policy instruments under the control of central bank and the final objectives of the central bank are not direct. While the final objective of the monetary authority is to attain a low and stable inflation, it does not have an instrument that it can use to control it directly.

4.3.4.1 Forecasting Monetary Aggregates Within the Depository Corporations Survey(DCS)

The DCS provides a framework for quantifying monetary developments and linkages with other sectors of the economy. The main identity of the survey is:

M= NFA + NDC + OIN……………………………………………………………Eqn(129)

An important objective of this paper was to establish the values of the above variables that are consistent with the expected outcomes for macroeconomic variables such as real GDP growth. The steps followed in this paper to forecast the monetary aggregates within the DCS were as follows:

i. Estimating Money Demand (M) consistent with the inflation target and forecasted real GDP growth;

38 Under this framework, the intermediate target is broad money (M3X) while the operating target is reserve

money.

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ii. Estimating the Net Foreign Assets (NFA);

iii. Estimating Other Items Net (OIN)39; and,

iv. Estimating Domestic Credit.

4.3.4.1.1 Estimating Money Demand (M)

According to available literature, the forecasting of the demand for money and hence the monetary aggregates can be done in two ways. The first is through the use of econometric techniques while the second is through the use of the quantity theory approach where an assumption regarding the expected movement of money velocity is made. Whichever method is used, the attainment of a stable and predictable demand for money relationship is central to formulating an effective monetary policy.

In this paper, the quantity theory approach was used to estimate a money demand function40. This approach involved expressing the quantity of money in terms of the quantity of goods and services it can buy i.e. in real money terms. In an equation form, this is shown as

kyPM

= ………………………………………………………………. ……….Eqn (130)

where M is stock of money, P is the price level, k is a constant and y is real GDP. This demand function states that the quantity of real money balances demanded is proportional to real income. Rearranging equation 130 yields the following equation:

PykiM =

……………………………………………………………. ……….Eqn (131)

which is equivalent to

PyMv = ……………………………..…………………………………. ……….Eqn (132)

where v=1/k.

Equation 132 can be rewritten as follows: 39 Other items net (OIN) are by nature a difficult variable to forecast. OIN includes capital and reserves, profits or

losses of central bank, valuation adjustments of net foreign assets and any other items that have not been classified elsewhere.

40 An estimation of money demand function using regression analysis was also attempted an is shown as Appendix 8.

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YMv = ………………………………………………………………. ……….Eqn (133)

Where Y is the nominal GDP obtained by multiplying the price level P by real GDP (y). Because velocity (v) is fixed in the short run, any change in the money supply leads to a proportionate change in nominal GDP as indicated in the following equation.

MYv = ………………………………………………………………. ……….Eqn (134)

According to data available, the money velocity in Kenya has been relatively stable over the recent past. Thus, a constant velocity of 4.58 was assumed in the forecasting of the monetary aggregates. This involved multiplying the velocity with the forecasted nominal GDP to get the monetary aggregate. In an equation form, this is indicated as follows:

vYM *= …………………………………………………………. ……….Eqn (135))

It is worth noting that underestimating the expected velocity tends to overstate the demand for money and this is therefore likely to result in a higher inflation and perhaps a worsening in the balance of payments. The reverse is true.

4.3.4.1.2 Estimating the Net Foreign Assets (NFA).

The projections made for the NFA are linked directly with the prospects of the overall balance of payments based on the existing policies and an underlying assumption that valuation changes are zero. As already indicated in the section dealing with balance of payments forecasts, the envisaged inflows and outflows in the balance of payments were used to determine the level of international reserves in the short-term. In the medium term, however, desired international reserves were used in order to meet the desired balance of payment objectives.

4.3.4.1.3 Estimating Other Items Net (OIN).

OIN consist of other assets and other liabilities of the depository corporations, which are not classified elsewhere in the DCS. These include: revaluation of assets, capital and reserves and the fixed assets. These items are by their very nature difficult to forecast. Changes in the capital of banks and valuation of NFA as well as foreign currency deposits due to changes in the exchange rate are some of the factors that influence the OIN. Profits of the banking system also affect the behaviour of OIN. Since a detailed knowledge of the particular items on OIN was not available for the forecasting period, this paper relied on the recent behaviour of the OIN. When expressed as a proportion of broad money, OIN have remained at about 8.3% per year over the period 2004-05. This proportion was assumed to remain for the forecasting period i.e. 2006-08.

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4.3.4.1.4 Estimating Domestic Credit.

The distribution of credit between government and nongovernmental sectors is a function of policy priorities. In this paper, the policies of the Kenya government were taken as given. Thus, the amount of projected net credit extended to government sector was dictated by the existing budgetary position in relation to the costs and availability of external and non-bank financing as reflected in the fiscal accounts whose details of projections have already been given. Consequently, the credit to non-government sectors was treated in this paper as a residual item for the DCS. As early mentioned, residuals should not be items on which there is a lot of primary information for the projected time period. In this case, not much information was available concerning the likely credit that will be extended to non-government sectors such as households, NPHIS and non-financial corporations. However, information on the budgetary position of government and hence its likely recourse to credit from the banking system was available. The latter was therefore not a choice as a residual in the government account.

4.3.4.2 Forecasting the Monetary Aggregates in the Central Bank Survey

Once the economy’s demand for money has been estimated for a given inflation and growth objective as indicated in section 4.2.4.1 above, the monetary authorities, must set the policy instruments at their disposal so as to create the amount of money their estimates suggests will be demanded. The framework for such projections is the accounts of the monetary authorities whose key identity in equation form is:

OINCPSCDBMNCGNFARM ++++= ………………………………….Eqn (136)

where RM is reserve money, NFA is net foreign assets, NCG is net credit to government, CDBM net credit to deposit money banks , CPS is credit to private sector and OIN is other items net. From the liability side of the monetary survey, the stock of RM is equal to the sum of the currency held outside (COU) the banking system, required reserves (RR) and excess reserves (ER) that banks hold.

The behaviour of the monetary aggregates of the deposit corporation survey is then linked to that above equation of reserve money. This linkage is indicated in an equation form as follows:

mmRMM = ………………………………………………………. …………..Eqn (137)

where M is the monetary aggregate, mm is the value of the money multiplier and RM is the reserve money. The money multiplier is related to the inverse of the average reserve requirement in a fractional reserve banking system although the two are not identical. A formula that also allows for variation in the proportion of currency outside banks is derived as follows;

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DDCOUM +=3 …………………………………………………………….Eqn (138)

where COU is currency outside banks, DD is demand deposits. As indicated above, stock of reserve money is equal to the sum of the currency held outside (COU) the banking system, required reserves (RR) and excess reserves (ER) that banks hold. This is reflected in an equation form as follows:

RRCOURM += +ER………………………………………………………….Eqn (139)

Combining equation 135 and 136 yields the following equation

ERRRCOUDDCOUmm++

+= …………………………………………………………….Eqn (140)

Dividing the numerator and denominator by D, the following is obtained:

errc

cmm++

+=

1 ………………………………………………………. ……….Eqn (141)

Thus the value of the multiplier is a function of the behaviour of the monetary authority, which sets the reserve requirement (r), the commercial banks that decide on the excess reserves (er) to hold and the general non-bank that depending on the structure and level of interest rates determines how much money to hold as currency (c) rather than as deposits. The money multiplier provides the link between money supply and the monetary base. For a central bank to be able to control money supply, the money multiplier has to be stable or at least predictable. The M3 multiplier ranged between 5.0 and 6.0 in the early 1990s in Kenya. It assumed a declining trend in the mid 1990s, but has stabilised between 4 and 4.5 in the recent past.

In this paper, the forecasting of the monetary aggregates in the monetary survey were undertaken as follows:

i. Estimation of the value of reserve money was done by use of the money multiplier derived as indicated above;

ii. Estimation of change in net foreign assets was taken as consistent with the projections made in the balance of payments;

iii. The estimate of demand of currency by the public was taken from the forecast of the DCS. In this survey, an assumption was made that the proportion of the currency outside banks to broad money supply during the forecasting period would remain constant;

iv. Estimation of credit to government was done taking into account the previous size of the fiscal deficit and alternative sources of financing including the central bank;

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v. Estimation of net credit to non-government sector was taken as a residual.

The summary of the forecasting approaches for the various monetary aggregates are provided in Table 4.7 below

Table 4.7: Forecasting of Monetary Aggregates in the Depository Corporation Survey

Category of Monetary Aggregate

Approach Taken in Forecasting

Estimating Money Demand (M

Was done through the use of the quantity theory approach where an assumption regarding the expected movement of money velocity was made. It was also done through the use of econometric techniques where a stable and predictable demand for money relationship was central in the results

Estimating Other Items Net (OIN).

Since a detailed knowledge of the particular items on OIN was not available for the forecasting period, this paper relied on the recent behaviour of the OIN. When expressed as a proportion of broad money, OIN have remained at about 8.3% per year over the period 2004-05. This proportion was assumed to remain for the forecasting period

Estimating Domestic Credit

The policies of the Kenya government regarding the distribution of credit between government and nongovernmental sectors were taken as given. Thus, the amount of the forecast of net credit extended to government sector was linked to the forecasts in the statement of government operations.

The credit to non-government sectors was treated as a residual item for the depository corporation survey.

Forecasting the Monetary Aggregates in the Central Bank Survey

• Estimation of reserve money was done by use of the money multiplier derived as indicated equation 135

• Estimation of change in net foreign assets was taken as consistent with the projections made in the balance of payments

• The estimate of demand of currency by the public was taken from the forecast of the DCS where an assumption of constant proportion of the currency outside banks to broad money supply was made

• Estimation of credit to government was done taking into account the previous size of the fiscal deficit and alternative sources of financing including the central bank

• Estimation of net credit to non-government sector was taken as a residual.

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The historical and forecasted data for the DCS are shown as Appendix 7. An alternative method for establishing the money demand was also considered. This involved using regression analysis to estimate a money demand function. The details of the money demand function are indicated in Box 4.3 below.

Box 4.3: Money Demand Function for Kenya

People demand money mainly for three reasons: (i) Transactions motive (to smooth out difference between income and expenditure related to real output and prices) (ii) Precautionary motives; and (iii) portfolio motive (related to return on money and return on other assets). Thus, the nominal money demand is a function of income (GDP, personal disposable income or wealth), inflation and opportunity cost variables such as interest rates as wells the nominal:

( ) )142(............................................................................................,,, EqneiPYfM d =

Where Md = money demand, Y = income (GDP), P = expected rate of inflation (proxied by the inflation rate), r = interest rate and e is the nominal exchange rate.

The demand for real cash balances is positively related to income and negatively related to the opportunity cost of holding money. In an equation form, this is expressed as:

( ) )143(.................................................................................................,, EqneYifP

M d =

Where P

M d is real money demand, Y is income (GDP), P is price, r is interest rate and e is nominal

exchange rate as described above.

Expressing equation 141 in natural logarithms yields the following t:

( ) )144(...................................................................ln 321 EqneiYP

MLn d βββα +++=

Taking partial adjustment of actual money balances to the desired level:

)145(............................................ln11

EqnP

MPM

PM

PM

Lnt

d

tt

d

t

d

−

=

−

−−

λ

Combining equations 144 and 145 we get:

( ) )146(.....................ln)1(ln1

321 EqnP

MeiY

PM

Lnt

dd

−++++=

−

λλβλβλβλα

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By letting = λαπ =0 ,, 11 λβπ = , 22 λβπ = 33 λβπ = λπ −= 14 ), equation 146

becomes: ( ) )147(......................lnln1

43210 EqnP

MeiY

PM

Lnt

dd

++++=

−

πππππ

Equation 145 was then estimated using ordinary least square techniques. The details of the regression results and other diagnostic tests are shown in Appendix 8. These results were not used in forecasting in this paper. However, if one were to do so, the following estimated equation from Appendix 8 would be used to forecast the real money balances for the period 2006-08:

( ) )148(............ln609.0101.0049.0ln101.0596.11

EqnP

MeiY

PM

Lnt

dd

++−+=

−

The forecast for the real money balances over the period 2006-08 would therefore first involve making assumptions of data value of the independent variables, namely; the interest rates, the real GDP and the nominal exchange rate. The second step would involve multiplying the estimated coefficients with the data for the respective variable over the forecasting period. Finally adding together the constant and the derived data for each variable and converting the result into natural numbers would derive the real money balances for each period.

4.4 Discussions of Results Obtained from Baseline Forecasting

The financial programming exercise carried out in this chapter involved making projections of developments in the Kenyan economy for the period 2006-08 based on the assumption that existing policies remain unchanged. The results of this baseline scenario therefore provides a benchmark for assessing whether existing problems are likely to remain improve, broadly constant or to worsen over time.

Table 4.8 below indicates that Kenya’s macroeconomic prospects are mixed over the forecasting period of 2006-08. Whereas growth in real GDP is forecast to improve, the fiscal position as well as the current account position are poised to deteriorate over the period under review. Growth in real GDP is expected to decline to 4.4% in 2006 from 5.4% in 2005. The projected decline in GDP during 2006 is attributed to the impact of drought on agriculture and agro based manufacturing industries experienced in the last quarter of 2005 and the first quarter of 2006. As the economy recovers from this drought and has the lagged effects of modest structural reforms implemented in the recent past together with expected continued trade opportunities in the neighbouring countries, Kenya’s real GDP growth is forecast to rise in 2007 to 5.2% and to 6.0% in 2008. The details of Selected Economic Indicators are provided in Appendix 9 and Table 4.8 below.

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Table 4.8: Selected Historical and Forecasted Economic Indicators for Kenya (2001-2008)

(Annual Percentage Changes Unless Otherwise Stated)

Actual Forecasts

Macroeconomic variable 2001 2002 2003 2004 2005 2006 2007 2008

National income and prices

Real GDP 4.4 0.4 2.8 4.3 5.4 4.4 5.2 6.0

GDP deflator 100.0 100.9 107.9 115.6 126.0 135.2 146.8 161.2

Inflation(Implicit Price Deflator) 1.6 0.9 7.0 7.2 8.9 7.4 8.6 9.8

Inflation(Consumer Price Index) 0.0 0.0 0.0 0.0 5.0 5.0 5.0 5.0

External sector

Current Account (Incl. official grants) in % of GDP -0.9 1.0 -4.4 -8.0 -6.2 -6.6 -7.0 -0.9

Current Account (Excl. official grants) in % of GDP -1.2 0.6 -5.1 -8.6 -5.4 -5.2 -4.5 -1.2

Foreign Exchange Reserves (in US$ millions) 1079.9 1503.4 1519.1 1896.6 1455.5 1046.2 -43.7 1079.9

Months of Import Cover 3.3 4.2 3.3 3.1 2.3 1.6 -0.1 3.3

Monetary sector

Net foreign assets 14.0 18.4 15.8 13.3 -20.1 -21.2 -81.4 14.0

Domestic claims 7.8 7.8 13.1 5.7 19.6 20.7 28.4 7.8

Net claims on central government 12.6 16.9 -9.1 -2.3 11.6 22.3 15.6 12.6

Claims on other sectors 6.1 4.3 22.7 8.3 -12.0 23.7 38.8 6.1

Broad money liabilities, M3X 10.0 11.5 13.4 9.1 12.1 14.2 16.4 10.0

Monetary base 11.9 -1.1 23.5 3.1 13.0 15.7 15.1 11.9

Velocity of Money 2.6 2.5 2.5 2.6 2.6 2.6 2.6 2.6

Money Multiplier 4.1 4.6 4.3 4.5 4.5 4.4 4.5 4.1

Government Finance Statistics (In Percent of GDP)

Revenue 20.5 20.4 21.1 21.8 22.8 21.5 21.6 21.8

Expense -18.1 -18.8 -20.1 -21.5 -23.0 -22.1 -23.5 -24.8

Gross operating balance 2.4 1.6 1.0 0.3 -0.2 -0.6 -1.9 -3.0

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Table 4.8: Selected Historical and Forecasted Economic Indicators for Kenya (2001-2008)


Actual Forecasts

Macroeconomic variable 2001 2002 2003 2004 2005 2006 2007 2008

Net Acquisition of Non-financial Assets 1.0 1.3 2.0 2.1 2.1 2.3 2.5 2.5

Net lending / borrowing 1.4 0.3 -1.0 -1.8 -2.3 -2.9 -4.4 -5.4

Net acquisition of financial assets 1.0 1.2 0.5 0.2 0.2 0.2 0.0 0.0

Net incurrence of liabilities 0.0 0.0 8.5 7.7 6.5 5.6 10.8 10.8

The monetary program in the forecasting period (2006-08) will continue to aim at containing underlying inflation at most 5%. This is expected to be achieved through reserve money targeting, with broad money (M3) as the intermediate target, and open market operations as the main instruments. To meet this target, broad money is therefore forecast to expand by 12.1%, 14.2% and 16.4% in 2006, 2007 and 2008, respectively as shown in Table 4.8. The expansion in broad money M3 is expected to be mainly supported by an increase in net domestic assets (NDA) of the banking sector as the net foreign assets are forecast to shrink owing to expected deterioration on balance of payments. Domestic claims by depository institutions are forecast to increase during the forecasting period as shown in Table 4.8. The increase in domestic claims will be mainly reflected in the increase in credit to government as the private sector credit is expected to decline by 12% in 2006 before picking up to reach 39.1% in 2008. The credit to the private sector was the residual in the DCS. Its behaviour over the period under review is rather volatile with a significant decline of 12% in 2006 and a significant increase of 39% in 2008. These results are nonetheless not unusual. Private sector credit declined by 6% in 2001 and rose by 23% in 2004.

The baseline scenario shows that if current policies remain unchanged, Kenya’s fiscal position would worsen over the forecasting period. Both fiscal revenues and fiscal expenditures are forecast to increase in absolute terms over the forecasting period. When expressed as percent of GDP, however, fiscal revenues are expected to decline from 22.8 percent in 2005 to 21.8 percent in 2008 while fiscal expenditures are forecast to decline as percent of GDP from 23.0 percent in 2005 to in 24.8 percent 2008. Consequently, the budget deficit including grants, which is referred to as net lending in this paper is forecast to increase from negative 2.3 percent of GDP in 2005 to 4.5 percent in 2008. In the SGO, the residual was use of goods and services. This variable is forecast to increase modestly in the period under review, in tandem with growth in GDP.

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Kenya’s balance of payments position is also forecast to deteriorate over the forecasting period except 2006. The current account deficit excluding grants is expected to widen from 5.2 percent of GDP in 2006 from 8 percent in 2005. The narrowing of the current account in 2006 is mainly expected to come from the merchandise account as lower imports were projected after adjustment for the one-off costs of aircraft purchases in 2005 as well as the expected moderation in the rate of increase of oil prices. The current account deficit expressed as percent of GDP is, however, forecast to widen in 2007 and 2008 by 5.6 percent and 6.0 percent, respectively owing mainly to the widening in the trade deficits as net non-factor services and net current transfers are expected to increase. Over the medium term, the current account is forecast to widen as imports remain robust in response to a pickup in growth and private sector led capital spending. These results indicate that Kenya’s external sector position may not be sustainable over the forecasting period. The general rule of thumb is that a current account deficit that is more than 5% may not be sustainable. Thus, the high current account deficits obtained in the baseline scenario are not likely to be sustainable. This is evidenced by the huge decline in reserves over a period of three years from an equivalent of 3.1 months of import cover in 2005 to an equivalent of only 1 month of import cover in 2008. Foreign exchange reserves were a residual in the BOP forecasts. The weakening of the balance of payments is also expected to put downward pressure on the shilling, thus undermining the monetary policy in place since it will lead to inflationary pressures.

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5.0 SUMMARRY, CONCLUSIONS AND RECOMMENDATIONS

5.1 Summary

The objective of this paper was to develop a framework that integrates all the four macroeconomic accounts and with internal consistency checks based on the financial programming framework. The paper also aimed at using the financial programming framework to develop a forecasting scenario. The paper was therefore organized into five chapters.

The first chapter dealt with the background information of the Kenyan economy. Here, the paper pointed out that, like most African countries, Kenya has had to undertake a number of macroeconomic stabilization and structural adjustment measures based on agreed IMF-supported adjustments programs in an effort to eliminate the disequilibria in its economy. The paper also highlighted the fact that while macroeconomic stability has generally been achieved in Kenya, that stability has been fragile particularly when viewed against the backdrop of declining economic growth rates, reflected in high unemployment and poverty levels.

Chapter two provided the theoretical framework underpinning the financial programming framework used in this paper as a general approach to inform and tie together the various sectors in a consistent manner, while incorporating the Kenyan-specific factors. In this way, not only did financial programming serve as an ex ante consistency check on important macroeconomic aggregates but also provided an ex post monitoring tool. Chapter three developed a number of data consistency checks within the financial programming framework spelt out in chapter two. The chapter also gave the economic implications for each of the inter-account consistency checks and derived statistically how the links in the consistency checks came about. The results of the consistency checks revealed that virtually all the 41 inter-account consistency failed to hold. As a final step, the whole dataset was subjected to a flow of funds, which was the ultimate data consistency check. The results were mixed, with some consistent results being achieved for some accounts and inconsistent results for others. The conclusion drawn from this chapter was that there is still a lot of work to be done in Kenya to render the data from different sources consistent.

Chapter four used the financial programming framework to develop a forecasting scenario. As a starting point for the financial programme for Kenya, a general baseline scenario was constructed in Excel spreadsheets. In order to ensure consistency in all the macroeconomic accounts, inter-account links in the excel spreadsheets were constructed bbefore the forecasting of the individual accounts was undertaken. The principal aim of forecasting the baseline scenario was to show whether existing problems are likely to remain broadly constant, to be resolved without explicit intervention by the authorities, or to worsen over time. Thus, it gave the general policy direction.

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5.2 Conclusions

. Based on the foregoing, the conclusions drawn in this paper are of two types. The first are those related to the results found when Kenya’s macroeconomic data set was used as an example to check the efficacy of the inter-account consistency framework developed in chapter three. The second are those related to the results obtained when the same framework was used to develop a forecasting scenario for Kenya for the period 2006-2008.

We start with the first set of conclusions. The results from chapter three indicates that virtually all the 41 inter-account consistency checks for Kenya’s national accounts, statement of government operations, balance of payments and depository corporations survey failed to hold. To be more specific, all the ten inter-account consistency checks between the national accounts and balance of payments failed to reveal consistency except in the case of exports of services as indicated in Table 3.1. Similarly, all the eight inter-account consistency checks between the statement of government operations and balance of payments failed to hold. In the same vein, all the three inter-account consistency checks between the statement of government operations and the depository corporations survey failed to reveal consistent results. Finally, the thirteen inter-account consistency checks between the deposit corporations survey and the balance of payments did not reveal consistent results except in the case of monetary Gold. The results from the use of flow of funds as an ultimate consistency check also produced results that were mixed.

One important observation from this inter-account consistency checks was that concerning the identity S-GKF = CurAcBal. Large inconsistencies were obtained for this identity, implying that the data obtained from the national accounts on the savings less gross capital formation of the domestic economy have historically been far much less than what is recorded as the current account deficits the balance of payments. Such big inconsistencies for such an important analytical variable in macroeconomics are unacceptable. One can therefore conclude that there is poor coordination between the compilers of balance of payments and those of the national accounts in Kenya.

Another important observation is that some of the inter-account consistency checks failed because of lack of adequate data for the relevant variables. For instance, data for the compensation of employees receivable from non-residents or payable to non-residents was lacking in both national accounts and balance of payments. This cannot be the case given the substantial presence of international organizations (mainly the United Nations and the large number of embassies) in Nairobi. All of these bodies employ a considerable number of Kenyan residents. Both the national and balance of payments should therefore be capturing these transactions. The results also show that property income receivable from non-residents or payable to non-residents that is recorded in the Kenya’s national accounts were not consistent wit recorded in the country’s balance of payments statistics. The conclusion drawn from these

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results is that either Kenya has not fully implemented the IMF’s 1993 “Balance of Payments Manual”, or that the compilers of the balance of payments statistics have not made any effort to capture and record such transactions.

The results from flow of funds which was the ultimate consistency check showed that while the individual components of the gross national income, namely; compensation of employees, operating surplus and taxes on production and imports were all consistent, the same could not be said for the gross national disposable income following the lack of disaggregated data by institutional sectors for the property income and current transfers. For the same reasons, consistency results were not achieved in the case of net acquisition of capital assets. Similarly, final consumption by government as recorded in the national accounts was found to be inconsistent with that recorded in the SGO. Ideally, this number should be calculated from the SGO in accordance with the following formula: Compensation of employees paid by government plus use of goods and services less sales of goods and services but excluding sales of capital assets. However, Table 3.2 indicates that the data obtained from SGO differs from that provided in the national accounts.. It can therefore be concluded that Kenya has not fully implemented the 1993 “Systems of National Accounts Manual”, developed by the UN and the IMF among other bodies. Similarly, Kenya appears not to have fully implemented the IMF’s 2001 “Government Finance Statistics Manual”.

From the above discussions and the detailed results of the inter-account consistency checks for Kenya shown in Tables 3.1 and 3.2, it can be concluded that there are many data gaps in the collection strategy for Kenya’s macroeconomic data that need to be urgently sorted out. This is because virtually all consistency checks constructed failed to show consistent results.

The second set of conclusions are those related to the results of obtained framework used to develop a forecasting scenario using the Kenyan data for the period 2006-2008. The financial programming exercise carried out in chapter four of this paper involved making projections of developments in the Kenyan economy for the period 2006-08 based on the assumption that existing policies remain unchanged. The results of this baseline scenario provides a benchmark for assessing the impact of the policy package included in a program scenario. Its principal aim was to show whether existing problems were likely to remain broadly constant, to be resolved without explicit intervention by the authorities, or to worsen over time.

The results indicate that Kenya’s macroeconomic prospects are mixed over the forecasting period of 2006-08. Growth in real GDP is forecast to decline to 4.4% in 2006 from 5.4% in 2005 but is, however, expected to rise in 2007 to 5.2% and to 6.0% in 2008. These forecasts are impressive as they show that even without changing existing policies, growth in real GDP is expected to grow and this will therefore partly resolve the current problems of high unemployment and poverty levels. The monetary policy being implemented in the forecasting

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period is tight. In this respect, inflation is forecast to be at most 5% throughout the period. From a national account perspective, it can be observed that Kenya’s economic performance is going to perform far much better compared to the historical data, at least going by the current policies in place.

Another observation made based on the baseline scenario is that if current policies remain unchanged, Kenya’s fiscal position would worsen over the forecasting period. Both fiscal revenues and fiscal expenditures are forecast to increase in absolute terms over the forecasting period. When expressed as percent of GDP, fiscal revenues are expected to decline from 22.8 percent in 2005 to in 19.4 percent 2008 while fiscal expenditures which are forecast to decline as percent of GDP from 23.0 percent in 2005 to in 24.8 percent 2008. Consequently, the budget deficit including grants, which is referred to a net lending in this paper, forecast to increase from negative 2.3 percent of GDP in 2005 to 4.5 percent in 2008.

The balance of payments forecasts indicate that the current account balance excluding grants is expected to narrow 5.2 percent of GDP in 2006 from 8 percent in 2005. The current account deficit expressed as percent of GDP is, however, forecast to widen in 2007 and 2008 by 5.6 percent and 6.0 percent, respectively. These are generally very high levels of current account deficits. The general rule of thumb is that a current account deficit that is more than 5% may not be sustainable. One key observation here is that the high current account deficits obtained in the baseline scenario are not likely to be sustainable. This is evidenced by the huge decline in reserves over a period of three years from an equivalent of 3.1 months of import cover in 2005 to an equivalent of only 1 month of import cover in 2008. The weakening of the balance of payments is also expected to put downward pressure on the shilling, thus undermining the monetary policy in place since it will lead to inflationary pressures.

From the above observations, this paper draws the conclusion that unless an active program is put in place for Kenya, the country’s economic situation, which seems impressive from the domestic front, may be undermined by the developments in the external sector and to a small extent the fiscal front. These developments have the potential to cause the current high poverty levels and unemployment to worsen overtime.

5.3 Recommendations

The overall conclusion drawn in this paper based on the data consistency checks undertaken in chapter three of this paper is that there are a number of data gaps, which suggest that Kenya’s data collection strategy for macroeconomic data should be reviewed with a view to eliminating such gaps. We therefore recommend to the relevant institutions to comprehensively examine each specific item in all of the four macroeconomic accounts with a view to making sure that the compilers of such data capture all transactions by all institutional sectors as required.

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Sorting out such data inconsistencies should involve all the stakeholders. In this respect, we recommend that the Central Bureau of Statistics and the Central Bank of Kenya should take the lead in this endeavour.

The results obtained in chapter three, for instance, showed that taxes on production and imports as well as taxes on income and wealth in the national accounts were inconsistent with those in the statement of government operations. We therefore recommend that the numbers from these two sources should be cross-checked to ascertain the correct position. We also recommend that the Central Bureau of Statistics should make an effort to ascertain the reason for the data inconsistency in the final consumption by government as recorded in the NA and in the SGO. Ideally, this number should be calculated from the SGO in accordance with the following formula: Compensation of employees paid by government plus use of goods and services less sales of goods and services but excluding sales of capital assets

The results also showed a data gap in BOP data. Kenya’s balance of payments data indicate that the credit side for compensation of employees is zero in each time period. This cannot be true given the substantial presence of international organizations mainly the United Nations and the large number of embassies in Nairobi. All of these bodies employ a considerable number of Kenyan residents some of whom are professional even though most are working as non-professionals. We therefore recommend that the compilers of Kenya’s balance of payments should make an effort to capture and record these transactions.

An overall recommendation that this paper makes is that Kenya’s compilers of macroeconomic statistics should endeavour to move with speed to implement all the standard manuals of macroeconomic data. In particular, we recommend that the Kenyan authorities should make sure that the following international manuals are not only adopted but more importantly, implemented in full:

• The 1993 “Balance of Payments Manual” produced by the IMF

• The 1993 National Accounts Manual”, developed by the UN, World Bank, IMF and OECD

• The 2001 “Government Finance Statistics Manual” produced by the IMF and;

• The 2000 “Monetary and Financial Statistics Manual”, produced by the IMF

The financial programming exercise carried out in this paper involved making projections of developments in the Kenyan economy for the period 2006-08 based on the assumption that existing policies remain unchanged. The conclusion drawn from chapter four is that unless an active program is put in place for Kenya, the country’s economic situation though impressive from the domestic front may be undermined by the developments in the external sector and hence cause the current high poverty levels and unemployment to worsen further. We therefore recommend

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that an active program be put in place to address the external sector problems that are likely to worsen over time in Kenya. The program should also address the country’s fiscal problems that seem to likely to worsen overtime under the current existing policies. In this respect, an active program should be developed that shows explicit policy packages designed to achieve a desired set of objectives, namely; address balance of payments problems and the fiscal problems to a small extent. The objective of the program should be to achieve sustainable current account deficits and non-inflationary growth as well as fiscal discipline.

127

REFERENCE

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Dicks-Mireaux, L., Mecagni, M. and Schadler, S. (2000). “Evaluating the Effect of IMF Lending to Low-Income Countries,” Journal of Development Economics, Vol. 61: PP. 495-526.

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Drazen A.(2002), “Conditionality and Ownership in IMF Lending: A Political Economy Approach” IMF Staff Papers, Volume 49 Special Issue, IMF, 700 19th Street, Washington, DC 20431, U.S.A.

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Edwards, S. (1989) “The International Monetary Fund and the Developing Countries: A Critical Evaluation,” Carnegie-Rochester Conference Series on Public Policy, 31: PP. 7-68, The Netherlands: Elsevier Science Publishers, North-Holland. Goldstein, M. and Montiel, P. (1986). “Evaluating Fund Stabilization Programs with Multi- Country Data: Some Methodological Pitfalls,” IMF Staff Papers, 33, 304-344. Haque, N. and Khan, M.S, (2002). "Do IMF-Supported Programs Work? A Survey of the Cross-Country Empirical Evidence," in Khan, M. S. Nsouli, S. and Wong, C. H. (eds.), Macroeconomic Management: Programs and Policies, IMF, Washington D.C.. Hooper, P.S. & Kohlhagen, W. (1978), “The Effect of Exchange Rate Uncertainty on the Prices and Volume of International Trade.” Journal of International Economics, Vol. 8: PP. 483-511.November.

IMF (2004), “The Design of Fund Supported programs – Overview”, IMF, Policy Development and Review Department, 700 19th Street, Washington, DC 20431, U.S.A.

_____ (2001), “Conditionality in Fund Supported Programs: Policy Issues” IMF, Policy Development and Review Department, 700 19th Street, Washington, DC 20431, U.S.A.

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_____ (2001), “Government Finance Statistics Manual”, IMF, Statistics Services, 700 19th Street, Washington, DC 20431, U.S.A.

_____(2000), “Monetary and Financial Statistics Manual”, IMF, Statistics Services, 700 19th Street, Washington, DC 20431, U.S.A.

_____(1996), “Financial Programming and Policy: The Case of Sri Lanka”, IMF Institute, IMF, 700 19th Street, Washington, DC 20431, U.S.A.

_____(1993), “Balance of Payments Manual”, IMF, Statistics Services, 700 19th Street, Washington, DC 20431, U.S.A.

_____(1993), “Systems of National Accounts Manual”, International Monetary Fund, Statistics services, 700 19th Street, Washington, DC 20431, U.S.A.

_____(1987) “Theoretical Aspects of the Design of Fund-Supported Adjustment Programs”, Occasional Paper Number 55 September 1987.

Lennbald, A. (2005), “Flow of Funds within a Financial Programming Framework”, Unpublished Paper. _____ (2005), “Balance of Payments: Below-the-Line Items”, Unpublished Paper. _____(2005), “Money Multiplier and Velocity”, Unpublished Paper. _____ (2005), “Flow of Funds within a Financial Programming Framework”, Unpublished Paper. _____ (2005), “Forecasting within a Financial Programming Framework”, Unpublished Paper. Nyella, J.J., (2003), “Financial Programming: The Case of Tanzania”, A Technical Paper Submitted to Macroeconomic and Financial Management Institute of Eastern and Southern Africa (MEFMI) in partial fulfIlment of the Requirements of the MEFMI Fellowship Program Killick, T. (1995), “IMF Programmes in Developing Countries: Design and Impact” London and New York: Routledge. Mussa, M. and Savastano, M. (1999) "The IMF Approach to Economic Stabilization," NBER. Macroeconomics Annual 1999, Cambridge, Mass: Massachusetts Institute of Technology (MIT) Press.

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131

APPENDICES

Appendix 1: The Results of Inter-Account Consistency Checks (in absolute terms)

2000 2001 2002 2003 2004

A: Inter-Account Consistency Checks: National Accounts (Na) And Statement Of Government Operations

1 Sg=CTrg -3,134 -20,095 11,850 -2,631 66,671

2 OPg = CEg +ICg +CFKg 40,929 51,031 56,205 45,756 -24,933

3 FCg= CEg +ICg +CFKg - SCGSg 19,869 28,607 25,889 8,124 -24,902

4 VAgg= OPg - ICg 29,423 39,612 50,836 41,006 -21,197

5 VAgn = CEg 29,423 39,612 50,836 41,006 -21,197

6 OSgg= CFKg 16,873 17,915 20,388 23,008 26,416

7 NLg=Sg +KTrg -GFKFg 44,940 46,523 42,302 38,711 -20,895

B: Inter-Account Consistency Checks: National Accounts Versus Balance of Payments (BOP)

1 (X - M)na = - (X – M)bop (4-7) -22,031 -25,332 -48,640 -65,988 69,103

2 Xg 6,121 -1,743 -9,611 -31,134 1,258

3 Xs 1 0 1 3,954 0

4 X (2+3) 6,122 -1,744 -9,610 -27,179 1,258

5 Mg 28,153 23,588 39,031 50,274 -67,845

6 Ms 0 0 0 -11,465 0

7 M (5+6) 28,153 23,588 39,031 38,809 -67,845

8 nIna = -nIbop 34 25 32 2,675 0

9 nCTrna = - nCTrbop -62,975 -49,629 -61,985 -65,768 -77,682

10 S-GKF=CAB 23,497 3,287 -23,377 35,260 85,225

C: Inter-Account Consistency Checks: Statement of Government Operations and Balance of payments

1 CEsog = CEbop - - - - -

2 PIsog = PIbop - - - - -

3 nCTrsog= nCTrbop 46,614 63,863 63,536 85,385 77,661

4 nKTrsog = nKTrbop -4,043 -1,053 -5,131 -8,763 -10,628

5 nNPNFAsog= nNPNFAbop 0 217 540 1,296 1,427

6 NBsog= nBrbop 2,337 17,040 16,870 2,975 2,824

7 nRFSsog= nRFSbop 12,273 -8,313 -4,222 3,225 0

8 NADAsog= nADAbop 6,840 -12,273 8,313 4,222 -3,225

132

Appendix 1: The Results of Inter-Account Consistency Checks (in absolute terms)

2000 2001 2002 2003 2004

D: Inter-Account Consistency Checks: Statement of Government Operations and the Depository Corporations Survey

1 Dsgo= DDdcs 245.44 -385.16 1394.40 148.71 -736.90

2 nLsgo=DnLdcs 1133.74 -1040.38 7460.62 -695.16 5132.39

3 DnBBsgo=DnBBdcs #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!

E: Inter-Account Consistency Checks: Deposit Corporations Survey (DCS ) and Balance of Payments (BOP)

1 ∆MGcbs = MGbop 3 3 2 1 1

2 ∆SDRcbs = SDRbop 58 240 -149 -125 -16

3 ∆RPFcbs = RPFbop -74 178 243 159 -320

4 ∆FEcbs≈FEbop -829 -1,169 -1,146 1,824 -8,163

5 ∆FLcbs≈FLbop 2,368 626 -2,643 1,435 -3,227

6 (∆RA)cbs ≈ (RA)bop (1+2+3+4) -843 -748 -1,050 1,860 -8,498

7 (∆NFA)cbs ≈ NFA)bop (6-5) -3,211 -1,374 1,594 424 -5,270

8 ∆FEods≈FEbop -3510 955 -3637 20452 -4475

9 ∆FLods≈FLbop 810 -87 254 -2010 3401

10 (∆NFA)ods ≈ NFA)bop (8-9) -2700 868 -3383 18443 -1074

11 ∆FEdcs≈Febop (4+8) -4,353 207 -4,686 22,312 -12,973

12 ∆FLdcs≈Flbop (5+9) 3177 539 -2389 -574 174

13 (∆NFA)dcs ≈ NFA)bop (11-12) -7,530 -331 -2,297 22,887 -13,147

133

Appendix 2: Sequence of Kenya’s National accounts (Kshs Million)

Historical Data Projections

2001 2002 2003 2004 2005 2006 2007 2008

Production Approach

Output at basic prices 1,744,528 1,900,365 2,154,461 2,473,542 2,773,378 3,166,809 1,744,528 1,900,365

Of which government 212,920 244,323 268,437 247,614 307,800 385,565 212,920 244,323

Taxes less subsidies on products 116,460 125,585 135,334 277,047 316,165 371,777 116,460 125,585

Output at market price 1,860,988 2,025,950 2,289,795 2,750,589 3,089,543 3,538,586 1,860,988 2,025,950

Intermediate consumption -822,223 -884,170 -1,012,649 -1,131,900 -1,291,290 -1,517,418 -822,223 -884,170

Of which government -65,497 -75,050 -81,305 -80,291 -126,683 -191,527 -65,497 -75,050

GDP at market prices 1,038,764 1,141,780 1,277,146 1,466,294 1,644,035 1,877,257 1,038,764 1,141,780

Expenditure Approach

Final consumption -988,216 -

1,071,484 -1,171,212 -1,405,618 -1,529,614 -1,739,608 -2,008,624 -988,216

Final consumption by government -173,297 -202,937 -216,563 -231,263 -289,467 -364,631 -425,217 -173,297

Final consumption by households and NPISH -814,919 -868,547 -954,649 -1,174,355 -1,240,147 -1,374,977 -1,583,407 -814,919

Gross fixed capital formation -177,781 -179,248 -208,248 -239,090 -268,072 -306,101 -356,298 -177,781

Gross fixed capital formation by government -20,043 -29,453 -35,347 -33,907 -41,320 -48,666 -23,663 -20,043

Gross fixed capital formation by households and NPISH -157,738 -149,795 -172,901 -205,184 -226,752 -257,434 -332,635 -157,738

Dwellings, other buildings and structures -84,914 -94,930 -107,504 -127,576 -140,987 -160,064 -206,821 -84,914

Transport equipments -50,086 -37,129 -38,090 -45,202 -49,954 -56,713 -73,280 -50,086

Machinery and equipment -41,614 -46,016 -61,414 -72,881 -80,542 -91,440 -118,151 -41,614

Cultivated assets -1,120 -1,142 -1,169 -1,387 -1,533 -1,740 -2,248 -1,120

Intangible fixed assets -48 -32 -71 -84 -93 -106 -137 -48

Change in inventories 8,404 -19,519 -24,596 -28,239 -31,662 -36,153 -42,082 8,404

Exports of goods and services -252,207 -281,394 -357,243 -378,904 -398,509 -417,057 -447,357 -252,207

Exports of goods -172,030 -193,829 -237,961 -236,444 -243,796 -247,633 -257,406 -172,030

134



2001 2002 2003 2004 2005 2006 2007 2008

Exports of services -80,178 -87,565 -119,282 -142,459 -154,713 -169,424 -189,951 -80,178

Imports of goods and services 326,340 360,960 473,982 545,639 539,067 570,557 609,769 326,340

Imports of goods (c.i.f) 272,348 310,013 411,086 460,082 447,809 469,243 495,332 272,348

Imports of services 53,992 50,947 62,896 85,557 91,257 101,314 114,437 53,992

GDP - Expenditure side 1,083,460 1,190,685 1,287,318 1,477,973 1,657,128 1,892,209 2,202,509 1,083,460

Discrepancy b/w expenditure & production approaches -44,696 -48,905 -10,172 -11,678 -13,094 -14,951 -17,403 -44,696

Generation of Income Account

GDP at market price 1,038,764 1,141,780 1,277,146 1,466,294 1,644,035 1,877,257 2,185,106 1,038,764

Taxes on production and imports -121,197 -131,948 -142,148 -265,372 -302,833 -356,086 -430,257 -121,197

Taxes on products -116,460 -125,585 -135,334 -252,651 288,316 -339,016 409,631 -116,460

Other taxes on production -4,737 -6,363 -6,814 -12,721 -14,517 -17,070 -20,626 -4,737

Subsidies 200 200 200 9,459 10,188 10,188 10,188 200

Subsidies on products 0 0 0 0 0 0 0 0

Other subsidies on production 200 200 200 9,459 10,188 10,188 10,188 200

GDP at factor cost 917,768 1,010,032 1,135,198 1,210,381 1,351,390 1,531,359 1,765,037 917,768

Compensation of employees -391,424 -432,700 -489,801 -522,240 -583,081 -660,732 -761,556 -391,424

of which: paid by government 0 0 0 0 0 0 0 0

Mixed income/Operating surplus -526,344 -577,332 -645,397 -678,682 -758,121 -860,440 -993,293 -526,344

of which: government 22,858 26,159 28,621 0 0 0 0 22,858

Allocation of Primary Income Account

Resources

Operating surplus and mixed income, gross -526,344 -577,332 -645,397 -678,682 -758,121 -860,440 -993,293 -526,344


of which: received by non-residents

135



2001 2002 2003 2004 2005 2006 2007 2008

Taxes on production and imports, net of subsidies -120,997 -131,748 -141,948 -255,913 -292,645 -345,898 -420,069 -120,997

Taxes on production and imports -121,197 -131,948 -142,148 -265,372 -302,833 -356,086 -430,257 -121,197

Less subsidies 200 200 200 9,459 10,188 10,188 10,188 200

Primary incomes receivable from the rest of the world 2,786 4,526 4,703 5,536 4,550 5,043 4,796 2,786

Uses

Primary incomes payable to rest of the world -14,036 -11,222 -12,045 -13,730 -13,655 -13,693 -13,674 -14,036

Gross national income (GNI) 1,027,515 1,135,084 1,269,804 1,448,641 1,624,741 1,858,419 2,166,040 1,027,515

Check GNI from top = GNI from this account 44,696 48,905 10,172 21,138 23,282 25,139 27,591 44,696

Secondary Distribution of Income Account

Resources

Gross national income (GNI) 1,016,395 1,027,515 1,135,084 1,269,804 1,448,641 1,624,741 1,858,419 2,166,040

Current transfers receivable from the rest of the world 1,079,370 1,077,144 1,197,069 1,335,572 1,526,323 1,694,690 1,932,235 2,237,922

Uses

Current transfers payable from the rest of the world -126 -151 -177 -200 -5,028 -4,200 -4,614 -4,407

Gross national disposable income (GNDI), gross 1,079,370 1,077,144 1,197,069 1,335,572 1,526,323 1,694,690 1,932,235 2,237,922

Check GNDI from top = GNDI from this account 24,824 44,696 48,905 10,172 21,138 23,282 25,139 27,591

Disposable income and saving

Gross national disposable income 1,079,370 1,077,144 1,197,069 1,335,572 1,526,323 1,694,690 1,932,235 2,237,922

Net national disposable income 984,986 978,706 1,087,206 1,214,577 1,387,409 1,538,936 1,754,387 2,030,909

Of which government 161,699 171,629 205,915 224,582 228,267 291,231 367,729 429,934

Final consumption expenditure -971,805 -988,216 -1,071,484 -1,171,212 -1,405,618 -1,529,614 -1,739,608 -2,008,624

136



2001 2002 2003 2004 2005 2006 2007 2008

Private -812,269 -814,919 -868,547 -954,649 -1,174,355 -1,240,147 -1,374,977 -1,583,407

General government -159,536 -173,297 -202,937 -216,563 -231,263 -289,467 -364,631 -425,217

Discrepancy on GDP

Saving, net 13,181 -9,510 15,722 43,365 -18,209 9,322 14,778 22,286

General government 2,164 -1,668 2,978 8,018 -8,436 -30,323 -54,331 -58,443

All Other Sectors 11,017 -7,842 12,745 35,347 -9,773 39,645 69,110 80,728

Financing of Capital Formation

Saving, net 13,181 -9,510 15,722 43,365 -18,209 9,322 14,778 22,286

Capital transfers, receivable from abroad 4043 6467 12381.27 13133 7,807 6,773 7,147 5,875

Capital transfers, payable from abroad 0 0 0 0 -25 -7,491 -7,491 -7,491

Total Gross Capital Formation 17,224 -3,043 28,104 56,498 -10,426 8,604 14,434 20,669

Gross fixed capital formation -185,186 -177,781 -179,248 -208,248 -239,090 -268,072 -306,101 -356,298

Consumption of fixed capital 94,384 98,438 109,863 120,995 138,914 155,753 177,848 207,013

Changes in inventories -11,596 8,404 -19,519 -24,596 -28,239 -31,662 -36,153 -42,082

Net lending (+) / Net borrowing(-) 8,015 -27,498 -20,958 -9,145 15,916 17,845 20,377 23,718

General Government -15,208 -15,244 -14,094 -14,196 25,312 10,236 -7,680 -4,465

Private 23,223 -12,254 -6,864 5,050 -9,396 7,609 28,057 28,183

Total 0 0 0 0 0 0 0 0

Source: Central Bureau of Statistics

137

Appendix 3: Kenya’s Statement of Government Operations (in Kshs million)

Transactions affecting net worth: 2001 2002 2003 2004 2005 2006 2007 2008

Revenue 208,805 219,474 248,920 291,512 315820.9 354624.1 410088.9 470915.3

Taxes 180,494 187,958 207,114 242,066 267575.1 301766.4 351215.5 404125.1

Social contributions 437 442 230 1,828 1,923 2,148 2,437 2,814

Grants 9,642 10,883 15,583 15,565 10,012 10,012 10,012 10,012

Other revenue 18,232 20,192 25,993 32,053 36,311 40,698 46,424 53,964

Expense -192,490 -208,889 -245,047 -294,143 -324,257 -386,033 -465,582 -530,604


Use of goods and services -50,696 -54,078 -69,681 -76,555 -80,291 -126,683 -191,527 -243,432

Consumption of fixed capital 0 0 0 0 0 0 0 0

Interest -27,138 -32,764 -33,244 -34,546 -38,888 -40,131 -40,309 -39,853

Subsidies -3,102 -5,489 -8,403 -10,188 -10,188 -10,188 -10,188 -10,188

Grants -2,931 -3,315 -9,655 -20,535 -20,545 -20,540 -20,543 -20,542

Social benefits -811 -830 -743 -924 -972 -1,086 -1,232 -1,423

Other expense -4,432 -4,602 -4,883 -5,269 -6,050 -6,288 -7,745 -9,015

Gross operating balance 16,315 10,585 3,873 -2,631 -8,436 -31,409 -55,494 -59,688

Net operating balance 16,315 10,585 3,873 -2,631 -8,436 -31,409 -55,494 -59,688

Transactions in nonfinancial assets:

Net Acquisition of Nonfinancial Assets 13,418 20,694 24,335 27,147 33,748 40,559 46,651 53,978

Fixed assets 13,418 20,551 23,795 25,851 30,894 36,431 41,903 48,484

Change in inventories 0 0 0 0 0 0 0 0

Valuables 0 0 0 0 1,427 2,064 2,374 2,747

Nonproduced assets 0 143 540 1,296 1,427 2,064 2,374 2,747

Net lending / borrowing 2,897 -10,108 -20,463 -29,778 -42,184 -71,968 -102,144 -113,667

Transactions in financial assets and liabilities (financing):

138

Appendix 3: Kenya’s Statement of Government Operations (in Kshs million)

Net acquisition of financial assets 12,528 5,072 2,266 2,852 2,852 420 478 546

Domestic 12,528 5,072 2,266 2,852 2,852 420 478 546

Foreign 0 0 0 0 0 0 0 0

Monetary gold and SDRs 0 0 0 0 0 0 0 0

Net incurrence of liabilities 0 87,841 87,841 82,644 82,644 177,331 201,883 230,865

Domestic 0 44,921 44,921 55,927 55,927 120,002 136,617 156,230

Foreign 0 42,920 42,920 26,718 26,718 57,328 65,266 74,636

139

Appendix 4: Regression Results of Exports Supply Function

Dependent Variable: LN_EXP_01

Method: Least Squares

Sample: 1967 2005

Included observations: 39

Variable Coefficient Std. Error t-Statistic Prob.

LN_RGDPK_01 1.204655 0.077612 15.52143 0.0000

LN_TOT_01 0.458568 0.182090 2.518365 0.0165

LN_RER_01 0.075055 0.034412 2.181090 0.0360

C -4.533999 1.255949 -3.610019 0.0009

R-squared 0.940886 Mean dependent var 4.351754

Adjusted R-squared 0.935820 S.D. dependent var 0.572142

S.E. of regression 0.144946 Akaike info criterion -0.928000

Sum squared resid 0.735324 Schwarz criterion -0.757378

Log likelihood 22.09600 F-statistic 185.6934

Durbin-Watson stat 1.254934 Prob(F-statistic) 0.000000

6.8

7.0

7.2

7.4

7.6

7.8

8.0

70 75 80 85 90 95 00 05

LN_REXP_01

6.5

7.0

7.5

8.0

8.5

9.0

9.5

70 75 80 85 90 95 00 05

LN_RGDPK_01

140

4.2

4.4

4.6

4.8

5.0

5.2

5.4

70 75 80 85 90 95 00 05

LN_TOT_01

4.0

4.2

4.4

4.6

4.8

5.0

70 75 80 85 90 95 00 05

LN_RER1_01

Prior to the estimation of the Export Supply Function specified in equation 125, time series properties of the model variables were carried out to establish whether the variables were stationary or not. Both the ADF and Philip Peron unit root tests showed that all variables were non-stationary at levels but stationary at first differences. Thus, all the variables in the model were integrated of order one as indicated in below:

Results of Unit Root Tests for Variables used in Estimation of Money Demand Function

Variable Type of Test/Level of Test

Calculated Value

Level of Significance Critical Value

Order of Integration

1% Critical Value* -3.6171

5% Critical Value -2.9422

ADF Test Statistic at Levels

-1.196062




ADF Test Statistic at First Deference

-3.302994




PP Test Statistic at Levels

-1.404037




Log real exports

PP Test Statistic at First Difference

-6.136396


I (1)




-1.353172


Real GDP for Kenya (rgdpk)

4


-3.298135 1% Critical Value* -3.6228

141



Calculated Value



5% Critical Value -2.9446 at First Deference





-1.530449





-5.747289


I (1)




-1.869768





-4.046418





-2.216980




Log RER


-6.578822


I (1)




-1.341319




TOT


-4.527114


I (1)

142



Calculated Value






-1.281443





-7.111965


Before arriving at the above regression results, an effort was made to ensure that the residuals from the regression were not only white noise but also homoskedastic and normally distributed. Stability tests were also carried out. These residual diagnostics are indicated in the charts and tables below:

Correllogram -Q-Statistics Sample: 1967 2005 Included observations: 39

Autocorrelation Partial Correlation AC PAC Q-Stat Prob . |*** | . |*** | 1 0.360 0.360 5.4603 0.019 . |*. | . | . | 2 0.152 0.026 6.4623 0.040 . | . | . | . | 3 0.053 -0.011 6.5883 0.086 . | . | . | . | 4 0.035 0.018 6.6452 0.156 . *| . | .**| . | 5 -0.161 -0.206 7.8654 0.164 . | . | . |*. | 6 -0.032 0.102 7.9151 0.244 . *| . | .**| . | 7 -0.186 -0.213 9.6426 0.210 .**| . | . *| . | 8 -0.249 -0.147 12.828 0.118 . *| . | . |*. | 9 -0.109 0.092 13.464 0.143 .**| . | .**| . | 10 -0.238 -0.318 16.590 0.084 .**| . | . *| . | 11 -0.301 -0.112 21.770 0.026 .**| . | . *| . | 12 -0.233 -0.142 24.985 0.015 . *| . | . *| . | 13 -0.151 -0.149 26.385 0.015 . | . | . |*. | 14 -0.051 0.105 26.552 0.022 . |** | . |*. | 15 0.232 0.127 30.144 0.011 . |*. | . *| . | 16 0.084 -0.182 30.634 0.015

Correllogram Squared Residuals Sample: 1967 2005 Included observations: 39

Autocorrelation Partial Correlation AC PAC Q-Stat Prob . |** | . |** | 1 0.232 0.232 2.2682 0.132 . *| . | . *| . | 2 -0.073 -0.134 2.4977 0.287 . | . | . | . | 3 -0.055 -0.005 2.6299 0.452 . *| . | . | . | 4 -0.058 -0.057 2.7852 0.594 . | . | . |*. | 5 0.043 0.070 2.8718 0.720 . |** | . |** | 6 0.282 0.263 6.7345 0.346 . | . | . *| . | 7 -0.011 -0.161 6.7402 0.456 . | . | . | . | 8 -0.046 0.057 6.8474 0.553 . *| . | . *| . | 9 -0.123 -0.147 7.6521 0.570 . *| . | . | . | 10 -0.087 0.004 8.0741 0.622

143

. |*. | . |*. | 11 0.130 0.141 9.0382 0.618 . |** | . |*. | 12 0.262 0.120 13.112 0.361 . | . | . *| . | 13 -0.036 -0.091 13.191 0.433 . | . | . | . | 14 -0.057 -0.001 13.398 0.495 . *| . | . *| . | 15 -0.152 -0.106 14.941 0.456 . *| . | . *| . | 16 -0.165 -0.098 16.838 0.396

0

2

4

6

8

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

Series: ResidualsSample 1967 2005Observations 39

Mean 4.64E-16Median -0.016202Maximum 0.359819Minimum -0.297849Std. Dev. 0.139107Skewness 0.334653Kurtosis 2.985331

Jarque-Bera 0.728304Probability 0.694786

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 2.742335 Probability 0.079110

Obs*R-squared 5.558112 Probability 0.062097

White Heteroskedasticity Test:



ARCH Test:



-20

-10

0

10

20

75 80 85 90 95 00 05

CUSUM 5% Significance

-0.4

0.0

0.4

0.8

1.2

1.6

75 80 85 90 95 00 05

CUSUM of Squares 5% Significance

144

Appendix 5: Regression Results of Imports Demand Function

Dependent Variable: LN_RIMP_01


Sample: 1967 2005

Included observations: 39


LN_RGDPK_01 0.324489 0.029307 11.07187 0.0000

LN_RER_01 -0.691239 0.171236 -4.036776 0.0003

C 8.070415 0.695438 11.60479 0.0000






Durbin-Watson stat 1.531316 Prob (F-statistic) 0.000000

Before estimating the parameters of the import demand function specified in equation 126, the order of integration of each series was examined. This examination was necessary because there is substantial evidence in recent literature that suggest that many macroeconomic time series data often possess unit roots, which means that they exhibit a stochastic trend and thus are non-stationary processes. A random variable or random process is said to be stationary if all of its statistical parameters are independent of time. Unit root tests based on both the ADF and Philip Peron tests showed that all variables were non-stationary at levels. However, unit root tests performed at first differences showed that all variables were stationary. Thus, all the variables in the model were integrated of order one as indicated in the Table below:

7.0

7.2

7.4

7.6

7.8

8.0

8.2

70 75 80 85 90 95 00 05

LN_RIMP_01

4.0

4.2

4.4

4.6

4.8

5.0

70 75 80 85 90 95 00 05

LN_RER1_01

6.5

7.0

7.5

8.0

8.5

9.0

9.5

70 75 80 85 90 95 00 05

LN_RGDPK_01

145



Calculated Value






-1.353172





-3.298135





-1.530449




Log real imports


--5.747289


I (1)




-1.353172





-3.298135





-1.530449




Real

GDP for Kenya (rgdpk)


-5.747289


I (1)

1% Critical Value* -3.6171 Log RER ADF Test Statistic at Levels

-1.869768


146



Calculated Value







-4.046418





-2.216980





-6.578822


I (1)

Before arriving at the above regression results, an effort was made as in the case of export demand function to ensure that the residuals from the regression were not only white noise but also homoskedastic and normally distributed. Stability tests were also carried out. These residual diagnostics are indicated in the charts and tables below:

Correllogram –Q- Statistics Sample: 1967 2005 Included observations: 39

Autocorrelation Partial Correlation AC PAC Q-Stat Prob . |*. | . |*. | 1 0.195 0.195 1.5926 0.207 . |*. | . |*. | 2 0.119 0.084 2.1996 0.333 . |*. | . |*. | 3 0.104 0.069 2.6757 0.444 . |*. | . | . | 4 0.083 0.045 2.9893 0.560 . *| . | .**| . | 5 -0.154 -0.201 4.1044 0.534 . *| . | . | . | 6 -0.091 -0.053 4.5040 0.609 . *| . | . *| . | 7 -0.175 -0.142 6.0347 0.536 .**| . | .**| . | 8 -0.259 -0.191 9.5068 0.301 . *| . | . | . | 9 -0.121 0.013 10.287 0.328 . *| . | . | . | 10 -0.091 -0.038 10.745 0.378 . *| . | . | . | 11 -0.097 -0.034 11.285 0.420 . | . | . | . | 12 -0.021 0.008 11.311 0.502 . | . | . *| . | 13 -0.048 -0.123 11.453 0.573 . *| . | . *| . | 14 -0.123 -0.166 12.420 0.573 . |** | . |** | 15 0.205 0.218 15.221 0.436 . | . | . *| . | 16 0.006 -0.137 15.224 0.508

Correllogram Squared Residuals Sample: 1967 2005 Included observations: 39

147

Autocorrelation Partial Correlation AC PAC Q-Stat Prob . *| . | . *| . | 1 -0.145 -0.145 0.8804 0.348 . *| . | . *| . | 2 -0.074 -0.097 1.1147 0.573 . |*. | . |*. | 3 0.129 0.107 1.8554 0.603 . | . | . | . | 4 -0.001 0.029 1.8554 0.762 . | . | . | . | 5 -0.035 -0.014 1.9144 0.861 . | . | . *| . | 6 -0.048 -0.071 2.0259 0.917 . |*. | . |*. | 7 0.166 0.148 3.4017 0.846 . | . | . |*. | 8 0.031 0.080 3.4500 0.903 . |*. | . |*. | 9 0.117 0.180 4.1799 0.899 . *| . | . *| . | 10 -0.111 -0.109 4.8639 0.900 . |*. | . | . | 11 0.069 0.043 5.1341 0.924 . | . | . *| . | 12 -0.021 -0.059 5.1607 0.952 . *| . | . *| . | 13 -0.123 -0.081 6.0861 0.943 . | . | . *| . | 14 -0.045 -0.126 6.2154 0.961 . | . | . | . | 15 0.027 -0.015 6.2651 0.975 . | . | . | . | 16 0.009 -0.042 6.2704 0.985

0

2

4

6

8

-0.2 -0.1 0.0 0.1 0.2 0.3 0.4


Mean -1.46E-15Median 0.004866Maximum 0.351825Minimum -0.221698Std. Dev. 0.125444Skewness 0.269394Kurtosis 3.179997


Breusch-Godfrey Serial Correlation LM Test: F-statistic 0.918089 Probability 0.408952 Obs*R-squared 1.998287 Probability 0.368195 ARCH Test: F-statistic 0.809551 Probability 0.374232 Obs*R-squared 0.835733 Probability 0.360620 White Heteroskedasticity Test: F-statistic 1.072547 Probability 0.385268 Obs*R-squared 4.369718 Probability 0.358276 White Heteroskedasticity Test: F-statistic 1.204578 Probability 0.328645 Obs*R-squared 6.019357 Probability 0.304340

148

-20

-10

0

10

20

70 75 80 85 90 95 00 05


-0.4

0.0

0.4

0.8

1.2

1.6

70 75 80 85 90 95 00 05


149

Appendix 6: Kenya’s Balance of Payments (Kshs Million)

2001 2002 2003 2004 2005 2006 2007 2008

A. Current Account (Incl.off. grants) -27.5 -9.8 11.0 -48.4 -93.0 -63.3 -71.2 -81.3

Current Account (Excl.off. grants) -28.3 -11.9 6.3 -56.1 -99.8 -53.8 -54.3 -47.6

1.1 Exports of Goods 148.6 170.3 184.2 206.8 236.4 243.8 247.6 257.4

1.1.1 Coffee 6.5 7.4 8.2 9.3 9.7 9.6 9.5 9.4

1.1.2 Tea 34.3 37.5 39.3 43.6 50.8 52.4 50.4 52.9

1.1.3 Horticulture 26.3 33.7 36.8 42.9 45.5 43.3 44.4 46.4

1.1.4 Oil products 12.4 12.8 11.5 12.0 16.7 18.1 18.2 18.2

1.1.5 Reexports 22.8 23.6 21.1 22.2 30.7 33.3 33.5 33.5

1.1.6 Processed Food & Vegetables 4.2 4.7 5.4 5.4 6.8 6.3 6.2 6.3

1.1.7 Hides, skins, & leather 1.2 1.1 1.1 1.2 1.8 1.8 1.9 1.9

1.1.8 Soda Ash 2.0 2.1 2.3 2.4 3.2 3.2 3.2 3.2

1.1.9 Cement 1.0 1.3 1.7 1.7 2.7 3.0 3.3 3.8

1.1.10 Pyrethrum 1.0 0.9 1.0 1.1 1.3 1.2 1.1 1.0

1.1.11 Other Exports 36.8 45.1 56.0 64.9 67.4 71.5 76.0 80.9

1.2 Imports of Goods -254.4 -248.8 -271.0 -360.8 -460.1 -436.7 -457.3 -482.6

1.2.1 Public -7.1 -7.4 -15.6 -8.6 -11.7 -13.0 -14.9 -17.6

1.2.2 Private -247.3 -241.4 -255.4 -352.3 -448.4 -423.7 -442.5 -464.9

1.2.2.1 Food -16.2 -13.1 -14.0 -15.9 -23.8 -23.5 -24.3 -25.6

1.2.2.2 Beverage and tobacco -0.9 -1.6 -1.8 -2.0 -3.7 -3.8 -3.8 -3.9

1.2.2.3 Basic raw materials -8.0 -7.2 -7.5 -8.4 -9.0 -9.4 -9.8 -10.4

1.2.2.4 Mineral fuels -56.6 -61.8 -63.4 -67.7 -96.8 -105.6 -108.5 -111.7

1.2.2.5 Animal & vegetable oils -9.6 -16.7 -16.8 -17.7 -12.8 -13.2 -14.9 -15.6

1.2.2.6 Chemicals -37.6 -40.7 -43.9 -53.1 -62.9 -62.4 -62.3 -63.6

1.2.2.7 Manufactures -34.4 -30.4 -30.9 -35.7 -58.9 -62.2 -66.2 -71.1

1.2.2.8 Machinery & transport -47.5 -54.4 -55.4 -88.7 -134.9 -95.5 -101.5 -108.0

1.2.2.9 Other -36.5 -15.4 -21.8 -63.2 -45.6 -48.2 -51.3 -55.1

150

2. Non-Factor Services 22.2 26.2 36.6 48.9 56.9 64.2 68.9 76.4

2.1 Exports of Non-Factor Services 85.6 80.2 87.6 123.2 142.5 154.7 169.4 190.0

2.1.1 Transportation 33.8 33.6 34.5 47.4 55.6 57.4 58.3 60.6

2.1.2 Foreign Travel 24.3 21.7 25.8 38.5 43.7 49.0 56.0 65.2

2.1.3 Other Services (private) 4.3 2.7 2.9 11.3 15.6 17.5 20.0 23.3

2.1.4 Other Services (Government) 23.3 22.1 24.4 26.1 27.4 30.8 35.1 40.9

2.2 Imports of Non-Factor Services -63.3 -54.0 -50.9 -74.4 -85.6 -90.5 -100.5 -113.6

2.2.1 Transportation -24.4 -21.5 -19.0 -27.4 -31.6 -30.0 -31.4 -33.1

2.2.2 Foreign Travel -11.2 -9.9 -9.7 -8.5 -9.4 -10.5 -12.0 -13.9

2.2.3 Other Services (private) -20.3 -14.2 -14.5 -27.6 -30.8 -34.6 -39.5 -45.9

2.2.4 Other services (government) -7.4 -8.5 -7.8 -10.8 -13.8 -15.5 -17.7 -20.6

(II) Income -9.6 -11.3 -6.7 -10.0 -8.2 -9.1 -8.6 -8.9

1. Compensation of Employees 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2. Investment Income -9.6 -11.3 -6.7 -10.0 -8.2 -9.1 -8.6 -8.9

2.1 Direct Investment -3.8 -5.9 -2.5 -2.4 -2.6 -2.5 -2.6 -2.6

2.2 Portfolio Investment -0.2 -0.2 -0.2 -0.5 -0.3 -0.4 -0.3 -0.4

2.3 Other Investment -5.5 -5.1 -4.1 -7.1 -5.3 -6.2 -5.7 -5.9

(III) Current Transfers (Net) 65.9 53.3 66.1 62.2 77.7 69.9 73.8 71.9

1. General Government 0.7 2.1 4.7 7.7 6.8 -7.7 -7.7 -7.7

2. Other Sectors (Private) 65.1 51.2 61.4 54.5 70.9 79.5 90.7 105.6

B. Capital & Financial Account 14.9 9.6 45.8 19.0 57.9 42.9 70.6 12.6

(I) Capital Account 4.0 6.4 12.4 11.5 7.8 -2.8 -2.8 -2.8

1. Capital Transfers 4.0 6.5 12.4 11.5 7.8 -2.8 -2.8 -2.8

2. Acquisition/disposal of NPNFA 0.0 -0.1 0.0 0.0 0.0 0.0 0.0 0.0

(II) Financial account (Net) 10.8 3.2 33.5 7.5 50.1 45.7 73.4 15.5

1. Direct Investment 0.4 1.6 6.0 3.3 0.9 3.4 3.4 3.5

2 Portfolio Investment -0.1 -0.4 -2.9 -5.3 -2.3 -3.5 -3.7 -3.1

3 Other Investment 10.5 2.0 30.3 9.4 51.5 45.8 73.6 15.2

C Errors & Omissions 25.7 0.4 -25.4 32.5 56.3 15.9 19.8 31.1

151

D. Overall Balance 13.1 0.3 31.4 3.1 21.2 -20.4 -0.6 -68.7

E. Financing -13.1 -0.3 -31.4 -3.1 -21.2 20.4 0.6 68.7

4.1 Reserve Assets -13.1 -0.3 -31.4 -3.1 -21.2 20.4 0.6 68.7

4.1.1 Monetary Gold 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

4.1.2 Special Drawing Rights (SDR ) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

4.1.3 Reserve Position in the Fund 0.0 -0.1 -0.1 -0.1 0.1 0.1 0.0 0.0

4.1.4 Foreign Exchange -13.1 -0.1 -31.2 -3.0 -21.3 20.4 0.6 68.7

Memorandum items

Forex Reserves 83.1 83.2 114.5 117.5 138.7 118.2 117.6 48.9

Months of Import Cover 3.1 3.3 4.2 3.2 3.0 2.7 2.5 1.0

Forex Reserves (In US$ millions) 1.1 1.1 1.5 1.5 1.9 1.6 1.6 0.6


152

Appendix 7: Depository Corporations Survey (In Millions Kshs)

Historical Data Projections Monetary Aggregate 2002 2003 2004 2005 2006 2007 2008 Net foreign assets 103,555.9 122,602.8 142,025.9 160,905.0 140,483.6 139,854.8 71,182.5 Domestic claims 413,269.8 445,441.1 503,595.5 532,277.3 624,871.6 729,922.9 934,414.3 Net claims on central government 115,836.7 135,365.6 123,037.7 120,199.3 134,191.3 164,077.9 189,624.3 Claims on other sectors 297,433.1 310,075.4 380,557.8 412,077.9 350,707.0 410,165.9 570,395.8 Broad money liabilities, M3X 404,788.2 451,176.0 511,425.2 557,770.5 625,382.0 714,098.6 831,202.7 Currency outside depository corporations 53,882.2 55,489.3 62,674.6 66,252.8 74,283.8 84,821.7 98,731.5 Transferable deposits 126,289.3 173,721.6 201,026.1 215,792.2 241,949.9 276,272.9 321,578.6 Other deposits 224,603.7 221,914.3 247,676.9 275,545.5 308,946.4 352,773.5 410,624.4 Securities other than shares, included in broad money 13.0 50.8 47.6 180.0 201.8 230.4 268.2 Deposits excluded from broad money 585.3 1,195.8 940.8 1,601.5 1,795.6 2,050.3 2,386.6 Securities other than shares, excluded from broad money 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Loans 6,071.2 5,575.2 5,591.9 5,031.5 5,744.9 5,744.9 5,744.9 Financial derivatives 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Trade credit and advances 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Shares and other equity 65,701.1 73,678.8 85,071.9 82,384.8 92,371.2 105,475.0 122,771.7 Other items (net) 46,210.1 33,019.5 39,162.5 48,002.6 40,061.5 42,408.9 43,491.0 Memprandum item Monetary base 98,315.0 97,209.0 120,021.6 123,770.0 139,917.8 161,838.4 186,249.1 Of which Currency in circulation 62,525.0 63,179.0 70,962.2 76,786.7 74,283.8 84,821.7 98,731.5 Net foreign assets 14.0 18.4 15.8 13.3 -12.7 -0.4 -49.1 Domestic claims 7.8 7.8 13.1 5.7 17.4 16.8 28.0 Net claims on central government 12.6 16.9 -9.1 -2.3 11.6 22.3 15.6 Claims on other sectors 6.1 4.3 22.7 8.3 -14.9 17.0 39.1 Broad money liabilities, M3X 10.0 11.5 13.4 9.1 12.1 14.2 16.4 Currency outside depository corporations 19.0 3.0 12.9 5.7 12.1 14.2 16.4 Transferable deposits 13.1 37.6 15.7 7.3 12.1 14.2 16.4 Other deposits 6.4 -1.2 11.6 11.3 12.1 14.2 16.4 Securities other than shares, included in broad money -14.4 291.4 -6.2 277.9 12.1 14.2 16.4 Deposits excluded from broad money -0.9 104.3 -21.3 70.2 12.1 14.2 16.4 Securities other than shares, excluded from broad money - - - - - - - Loans -5.8 -8.2 0.3 -10.0 14.2 0.0 0.0 Financial derivatives - - - - - - - Trade credit and advances - - - - - - - Shares and other equity 5.8 12.1 15.5 -3.2 12.1 14.2 16.4

153

Appendix 7: Depository Corporations Survey (In Millions Kshs)

Historical Data Projections Monetary Aggregate 2002 2003 2004 2005 2006 2007 2008 Other items (net) 75.7 -28.5 18.6 22.6 -16.5 5.9 2.6 Memprandum item - - - - - - - Monetary base 11.9 -1.1 23.5 3.1 13.0 15.7 15.1 Of which Currency in circulation 17.8 1.0 12.3 8.2 -3.3 14.2 16.4

154

Appendix 8: Regression Results of Money Demand Function

Dependent Variable: LN_RMONEY_01


Sample (adjusted): 1968 2005

Included observations: 38 after adjusting endpoints


LN_RGDPK_01 0.217636 0.059922 3.631996 0.0009

TBRATE -0.010088 0.002243 -4.496852 0.0001

LN_NER_01 -0.585936 0.106515 -5.500980 0.0000

LN_RMONEY_01(-1) 0.325935 0.103377 3.152885 0.0034

C 3.235725 0.513867 6.296812 0.0000






Durbin-Watson stat 1.098658 Prob(F-statistic) 0.000000

Prior to the estimation of the money demand function specified in equation 147, time series properties of the model variables were carried out to establish whether the variables are stationary or not. The four charts below show the time movement of the model variables41 namely; real money demands, interest rates, real output for Kenya, and the nominal exchange rates. However, casual inspection may lead to misleading conclusion, and therefore further tests were done using unit root test. Unit root tests based on both the ADF and Philip Peron tests showed that all variables were non-stationary at levels. However, unit root tests performed at first differences showed that all variables were stationary. Thus, all the variables in the model were integrated of order one.

0

10

20

30

40

50

60

70 75 80 85 90 95 00 05

TBRATE

1.5

2.0

2.5

3.0

3.5

4.0

4.5

70 75 80 85 90 95 00 05

LN_NER_01

41 Data was sourced from the IMFs online international Financial Statistics (IFS) and Kenya’s Economic Surveys

in addition to Central Bank of Kenya Statistical Bulletins

155

1.5

2.0

2.5

3.0

3.5

4.0

4.5

70 75 80 85 90 95 00 05

LN_NER_01

5

6

7

8

9

70 75 80 85 90 95 00 05

LN_RMONEY_01



Calculated Value






-0.668327





-3.769472





-0.529298




Log real money


-4.649229


I (1)




-1.353172



Real GDP for Kenya (rgdpk)


-3.298135


156



Calculated Value







-1.530449





-5.747289


I (1)




-2.006004





-6.289095





-2.621591




Tbill rate


-9.508090


I (1)




-0.156812





-3.584212


Log NER


0.042545 1% Critical Value* -3.6117

I (1)

157



Calculated Value



5% Critical Value -2.9399 Levels





-4.686561


Before arriving at the above regression results, an effort was made to ensure that the residuals from the regression were not only white noise but also homoskedastic and normally distributed. Stability tests were also carried out. These residual diagnostics are indicated in the charts and tables below:

-0.2

-0.1

0.0

0.1

0.23.5

4.0

4.5

5.0

5.5

6.0

70 75 80 85 90 95 00 05

Residual Actual Fitted

Correllogram –Q- Statistics Date: 10/02/06 Time: 14:52 Sample: 1968 2005 Included observations: 38

Autocorrelation Partial Correlation AC PAC Q-Stat Prob . |*** | . |*** | 1 0.410 0.410 6.9123 0.009 . |*. | . *| . | 2 0.106 -0.075 7.3862 0.025 . *| . | . *| . | 3 -0.123 -0.168 8.0409 0.045 .**| . | . *| . | 4 -0.228 -0.133 10.369 0.035 .**| . | . *| . | 5 -0.218 -0.075 12.562 0.028 . *| . | . | . | 6 -0.115 -0.003 13.189 0.040 . *| . | . *| . | 7 -0.111 -0.123 13.797 0.055 . | . | . | . | 8 -0.055 -0.039 13.949 0.083

158

. |*. | . |*. | 9 0.091 0.109 14.385 0.109 . *| . | .**| . | 10 -0.077 -0.250 14.703 0.143 . *| . | . | . | 11 -0.088 -0.052 15.144 0.176 .**| . | .**| . | 12 -0.274 -0.304 19.527 0.077 .**| . | . | . | 13 -0.189 -0.015 21.708 0.060 . *| . | . *| . | 14 -0.158 -0.183 23.281 0.056 . *| . | .**| . | 15 -0.094 -0.209 23.871 0.067 . | . | . | . | 16 0.061 0.020 24.131 0.087

Correllogram Squared Residuals Date: 10/02/06 Time: 14:52 Sample: 1968 2005 Included observations: 38

Autocorrelation Partial Correlation AC PAC Q-Stat Prob . |** | . |** | 1 0.306 0.306 3.8364 0.050 . | . | . *| . | 2 -0.039 -0.146 3.8993 0.142 .**| . | . *| . | 3 -0.222 -0.184 6.0301 0.110 .**| . | . *| . | 4 -0.235 -0.128 8.4966 0.075 .**| . | . *| . | 5 -0.242 -0.187 11.190 0.048 . | . | . | . | 6 -0.052 0.007 11.319 0.079 . *| . | .**| . | 7 -0.092 -0.207 11.735 0.110 . *| . | . *| . | 8 -0.086 -0.149 12.105 0.147 . *| . | . *| . | 9 -0.062 -0.134 12.307 0.197 . | . | . *| . | 10 -0.002 -0.123 12.308 0.265 . |*. | . |*. | 11 0.183 0.104 14.196 0.222 . |** | . | . | 12 0.259 0.053 18.131 0.112 . | . | .**| . | 13 -0.018 -0.234 18.151 0.152 . *| . | .**| . | 14 -0.188 -0.189 20.389 0.118 . *| . | . | . | 15 -0.080 0.025 20.810 0.143 . *| . | .**| . | 16 -0.151 -0.220 22.382 0.131

0

2

4

6

8

10

-0.2 -0.1 0.0 0.1 0.2


Mean 5.05E-16Median -0.028081Maximum 0.195201Minimum -0.185623Std. Dev. 0.094484Skewness 0.358668Kurtosis 2.357788


159

Breusch-Godfrey Serial Correlation LM Test: F-statistic 4.436050 Probability 0.020218 Obs*R-squared 8.455532 Probability 0.014585 ARCH Test: F-statistic 4.102922 Probability 0.050487 Obs*R-squared 3.882270 Probability 0.048799 White Heteroskedasticity Test: F-statistic 0.629791 Probability 0.746063 Obs*R-squared 5.624734 Probability 0.689185 White Heteroskedasticity Test: F-statistic 0.501333 Probability 0.908471 Obs*R-squared 8.884776 Probability 0.838360

-20

-10

0

10

20

75 80 85 90 95 00 05


-0.4

0.0

0.4

0.8

1.2

1.6

75 80 85 90 95 00 05


160

Appendix 9: Selected Historical and Forecasted Economic Indicators for Kenya (2001-2008)


Actual Projections

2001 2002 2003 2004 2005 2006 2007 2008

National income and prices

Real GDP 4.4 0.4 2.8 4.3 5.4 4.4 5.2 6.0

Real domestic demand 9.0 -0.9 9.0 9.1 15.2 6.4 9.4 9.7

Final consumption 7.6 1.7 7.8 8.0 15.9 5.9 9.4 9.6

Final consumption by government 9.5 8.6 15.9 5.8 5.4 18.6 17.7 10.3

Final consumption by households and NPISH 7.2 0.3 6.1 8.6 18.3 3.4 7.4 9.4

Gross fixed capital formation 14.5 -4.0 0.8 14.0 11.8 9.0 9.7 10.2

Gross fixed capital formation by government 10.6 -6.4 43.5 17.3 -3.2 16.2 12.1 -31.9

Gross fixed capital formation by households and NPISH 15.0 -3.7 -4.7 13.3 14.8 7.8 9.2 18.1

Change in inventories 10.8 -1.1 10.9 11.5 14.8 7.8 9.2 18.1

GDP deflator 100.0 100.9 107.9 115.6 126.0 135.2 146.8 161.2

Inflation (Implicit Price Deflator) 1.6 0.9 7.0 7.2 8.9 7.4 8.6 9.8

Inflation (Consumer Price Index) 0.0 0.0 0.0 0.0 5.0 5.0 5.0 5.0

External sector

Export volumes 19.9 8.7 9.5 4.1 6.2 1.8 3.9 4.1

Export prices -4.9 5.9 1.2 8.5 6.1 -0.3 -1.6 -0.1

Import volumes 23.1 0.3 10.9 40.9 11.7 -3.3 6.1 7.0

Import prices 0.6 5.1 1.8 11.5 6.7 0.7 -0.6 -0.6

Terms of trade (Index) 101.6 106.3 98.2 79.6 77.8 79.1 75.6 72.8

Terms of trade (Annual % Change) -22.7 4.6 -7.6 -18.9 -2.3 1.7 -4.4 -3.7

Current Account (Incl. official grants) in % of GDP -2.7 -0.9 1.0 -4.4 -8.0 -5.2 -5.6 -6.0

Current Account (Excl. official grants) in % of GDP -2.8 -1.2 0.6 -5.1 -8.6 -4.4 -4.2 -3.5

161

Appendix 9: Selected Historical and Forecasted Economic Indicators for Kenya (2001-2008)


Actual Projections

2001 2002 2003 2004 2005 2006 2007 2008

Foreign Exchange Reserves (in US$ millions) 1057.1 1079.9 1503.4 1519.1 1896.6 1619.6 1557.7 623.4


Monetary sector

Net foreign assets 14.0 18.4 15.8 13.3 -12.7 -0.4 -49.1 14.0

Domestic claims 7.8 7.8 13.1 5.7 17.4 16.8 28.0 7.8

Net claims on central government 12.6 16.9 -9.1 -2.3 11.6 22.3 15.6 12.6

Claims on other sectors 6.1 4.3 22.7 8.3 -14.9 17.0 39.1 6.1

Broad money liabilities, M3X 10.0 11.5 13.4 9.1 12.1 14.2 16.4 10.0

Monetary base 11.9 -1.1 23.5 3.1 13.0 15.7 15.1 11.9

Velocity of Money 2.6 2.5 2.5 2.6 2.6 2.6 2.6 2.6

Money Multiplier 4.1 4.6 4.3 4.5 4.5 4.4 4.5 4.1

Government Finance Statistics (In Percent of GDP)

Revenue 20.4 21.1 21.8 22.8 21.5 21.6 21.8 20.4

Expense -18.8 -20.1 -21.5 -23.0 -22.1 -23.5 -24.8 -18.8

Gross operating balance 1.6 1.0 0.3 -0.2 -0.6 -1.9 -3.0 1.6

Net Acquisition of Nonfinancial Assets 1.3 2.0 2.1 2.1 2.3 2.5 2.5 1.3

Net lending / borrowing 0.3 -1.0 -1.8 -2.3 -2.9 -4.4 -5.4 0.3

Net acquisition of financial assets 1.2 0.5 0.2 0.2 0.2 0.0 0.0 1.2

Net incurrence of liabilities 0.0 8.5 7.7 6.5 5.6 10.8 10.8 0.0

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