Unpacking the ‘Black Box’ of Public Expenditure Statistics

Lessons from a Diagnostic Analysis of Agricultural Sector Public Expenditures

Agriculture Public Expenditure Workshop8. October 2014, Golden Peacock Hotel

Lilongwe, Malawi

Objective of this Research Programme

• Several major policy initiatives requiring measurement and tracking of public expenditures in support of the agriculture sector (e.g. CAADP, country strategies, IDPs)

• However, how to measure the quantity of agricultural expenditures?• Inconsistencies: Different reports and databases report different

figures (for the same country and year)• Non-transparent aggregates: Not always clear what “ingredients

went into the soup”

• This research programme seeks to offer approaches for country analysts to quantify agPEs in a consistent and transparent way

• 4 country cases: Ghana, Kenya, Malawi, Mozambique

Tracking Aggregate Ag. PE over Time– Growth of Funds or of Coverage?

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CAADP guideline: 10 % ag spending share

• Data in earlier years didn’t include public expenditures related to cocoa, debt servicing; subsequently included

• Most recent data started including local government funds, and feeder roads

Ghana

What the Research Programme Is Not

• What this IFPRI programme is not primarily about:• Econometric analysis of the returns to and impact of public

expenditures in agriculture

other longstanding research on this in IFPRI and elsewhere• A database or dataset of public expenditures in agriculture

several initiatives have generated such datasets (especially cross-country), by IFPRI, IMF, FAO, OECD, etc.

• Descriptive review of trends and patterns of agricultural expenditures

other well established work on this through World Bank AgPERs, and through other initiatives (however, complementary, and Malawi AgPER to be presented here)

Unpacking the ‘Black Box’ of Public Expenditures in Agriculture:

A Case Study of Mozambique

Tewodaj Mogues, Senior Research Fellow, IFPRI, Washington DC

(Collaboration w/ Leonardo Caceres, Francisco Fernandes, Mariam Umarji)

Agriculture Public Expenditure Workshop8. October 2014, Golden Peacock Hotel

Lilongwe, Malawi

World Bank AgPER:PE in Ag. 20073,281 MMT2,773 MMT (excluding OILL)

Different figures in different reports

IMF Article IV Consultation: PE in Ag. & Rural Dev’t 20072,067 MMT

Different figures in different reports

Step 1:

Decide on the scope of expenditures to fall under “agriculture”

Proposal to undertake a “DIY” approach, using the existing government public accounts

Step 2:

Understand nature of expenditure data along the budget process

Understanding expenditure data along the budget process

Step 3:

Understand the types and quality of the classification systems in use

A multiplicity of classification systems: Too much of a good thing?

Functional classification: COFOG (IMF GFSM 2001)

COFOG Level 1

Functional classification: COFOG (IMF GFSM 2001)

COFOG Level 2 COFOG Level 3 No int’l coding for Level 4

Not

Functional classification: COFOG (IMF GFSM 2001)

Functional classification: COFOG (IMF GFSM 2001)

Functional classification: COFOG (IMF GFSM 2001)

Functional classification: Great in principle … but limited usefulness as practiced

Mozambique applies Level 4 codes

Not used in the budget data, but only in the execution and actual expenditure data

Administrative classification: Detailed, and of relevance to government for its operations

However, changing coding system over time, and no dedicated codes for units within a ministry

Programmatic classification

Useful, though not a comprehensive system

Step 4:

Reconstruction of agricultural public expenditures using the most appropriate

classification system(s)

Illustration of reconstruction using administrative and programmatic classification

Illustration of reconstruction using administrative and programmatic classification

Illustration of reconstruction using administrative and programmatic classification

Step 5:

Accounting for domestic and donor spending that is not captured in the public accounts

Comparison with international and national expenditure data sources (2010)

Comparison with international and national expenditure data sources (2011)

Some key take-aways

• Analysts and others wanting to obtain time-consistent information on how much PE is going to agriculture need to work with the appropriate classification and coding systems of government accounts

• The administrative system of classification tends to be most versatile for reconstruction of agPE

• But coding system should be more detailed, follow a clear logic, and be consistent over time

• IDPs should be aware of capacity constraints, and asking for too many classification systems reduces their quality

Unpacking the ‘Black Box’ of Public Expenditures in Agriculture:

A Case Study of Mozambique

Presented by: Tewodaj Mogues, Senior Research Fellow, IFPRI, Washington DC

Agriculture Public Expenditure Workshop8. October 2014, Golden Peacock Hotel

Lilongwe, Malawi