11

Analyzing the Macroeconomic and Distributional Impact of an Oil Price Shock – the Case of South Africa

Presentation Evaluating the Poverty Impacts of Economy-Wide Policies

Delfin S. Go

April 28-30, 2009Poverty and Inequality Analysis Course

PREM Learning Week

22

Why worry about economy-wide impact analysis or Why worry about economy-wide impact analysis or a “new” economic framework?a “new” economic framework?

Before 2008: Mid-1990s to 2007Before 2008: Mid-1990s to 2007• External environment favorableExternal environment favorable

– Word demand strongWord demand strong– Commodity prices rising Commodity prices rising – Donor assistance/debt relief significantDonor assistance/debt relief significant

• Shifts in growth for developing countriesShifts in growth for developing countries– Emerging economies – China, India etc.Emerging economies – China, India etc.– Africa grew growing in tandem with the worldAfrica grew growing in tandem with the world

• Demands from policy –Demands from policy –– Poverty reduction strategy papers (PRSPs)Poverty reduction strategy papers (PRSPs)– Service delivery issuesService delivery issues– Public expenditure and budget managementPublic expenditure and budget management

• Demands on modeling – emphasis on micro aspects & povertyDemands on modeling – emphasis on micro aspects & poverty– Pulic expenditure tracking and benefit incidence analysisPulic expenditure tracking and benefit incidence analysis– Macro-micro linksMacro-micro links– Introducing government spending and links to MDGs (e.g. MAMS)Introducing government spending and links to MDGs (e.g. MAMS)

33

Why worry about economy-wide impact analysis or Why worry about economy-wide impact analysis or a “new” economic framework?a “new” economic framework?

2008 to present2008 to present

• Severe External ShocksSevere External Shocks– Early 2008 – High oil and food price increases Early 2008 – High oil and food price increases

– Late 2008 – Financial crisis in US and EuropeLate 2008 – Financial crisis in US and Europe

• Terms of trade shocks – 2009 and beyond (?)Terms of trade shocks – 2009 and beyond (?)– Falling commodity prices and export demandFalling commodity prices and export demand– Falling exogenous flows – capital flows, remittances etc.Falling exogenous flows – capital flows, remittances etc.– Numerical impact on the economy and povertyNumerical impact on the economy and poverty

44

Why worry about economy-wide impact analysis or a “new” Why worry about economy-wide impact analysis or a “new” economic framework?economic framework?

Demands from policyDemands from policy

• Impact of external price or terms of trade shocksImpact of external price or terms of trade shocks• Interactions and feedbacks regarding the shocks, the economy, and policyInteractions and feedbacks regarding the shocks, the economy, and policy• Impact on poor households Impact on poor households

Demands on modelingDemands on modeling• Direct and indirect effects importantDirect and indirect effects important

– First generation models – input-output & SAMFirst generation models – input-output & SAM• Multipliers tell the importance of linkages and what sectors will be affected mostMultipliers tell the importance of linkages and what sectors will be affected most• Estimates of the numerical impact importantEstimates of the numerical impact important

• Introducing feedbacksIntroducing feedbacks– Flexible prices - interaction of supply/demandFlexible prices - interaction of supply/demand– Policy – effects of macro-fiscal adjustmentPolicy – effects of macro-fiscal adjustment– Substitution possibilities – economic behaviorSubstitution possibilities – economic behavior

• CGE models – external price shocks and adjustment storyCGE models – external price shocks and adjustment story– Popular during seventies – oil price shocksPopular during seventies – oil price shocks– Eighties – trade reform, debt issues and structural adjustmentEighties – trade reform, debt issues and structural adjustment

• Micro aspects - Impact on poverty and MDGs still importantMicro aspects - Impact on poverty and MDGs still important

Purpose of lecturePurpose of lecture • Describe a macro-micro model to South AfricaDescribe a macro-micro model to South Africa• Illustrate the impact of an oil price shockIllustrate the impact of an oil price shock

55

Modeling challengesModeling challenges- linking macro and micro and retaining rich - linking macro and micro and retaining rich

structure structure - providing 2 way feedbacks- providing 2 way feedbacks

MacroMacro-PoliciesPolicies-ShocksShocks

Meso DisaggregatedMeso Disaggregated- More sectors, - More sectors,

- Factors of production - Factors of production CGE models?CGE models?

Micro Micro - Household dataHousehold data-Micro simulationMicro simulation

-Incidence analysisIncidence analysis-Service deliveryService delivery

66

Macro-micro Modeling IssuesMacro-micro Modeling Issues

• The difficulty of full integration of 2 very different The difficulty of full integration of 2 very different approaches –approaches –

– On the top side - aggregative macro/econometric modelsOn the top side - aggregative macro/econometric models– on the bottom - very micro and large household data sets on the bottom - very micro and large household data sets

(benefit-incidence analysis, poverty analysis, PETS etc.)(benefit-incidence analysis, poverty analysis, PETS etc.)- Top-down causality: macro to microTop-down causality: macro to micro

• Remaining problemsRemaining problems– Introducing dynamics and feedbacksIntroducing dynamics and feedbacks– Determinants of long-term growth (?)Determinants of long-term growth (?)– Firm and investment climate (?)Firm and investment climate (?)

• Trade-offs:Trade-offs:– Simplicity Simplicity vsvs sophistication sophistication– More contents More contents vsvs black boxes black boxes

77

Ex-ante Tools: Ex-ante Tools:

CGE/Macro ModelsCGE/Macro Models

AggregateAggregate DisaggregateDisaggregate

Poverty/Poverty/

Micro-Micro-

simulationsimulation

SimpleSimple 123 Model & extensions-123 Model & extensions-

Macro+PAMS etc.Macro+PAMS etc.

(many countries)(many countries)

Input-Ouput & SAMInput-Ouput & SAM

(many countries)(many countries)

Disaggregated CGE Disaggregated CGE model –model –India, South India, South Africa etc.Africa etc.

ComplexComplex Occupational Choice Occupational Choice (Indonesia, South Africa (Indonesia, South Africa etc.)etc.)

CGE+micro simulations CGE+micro simulations --Madagascar, Brazil, Madagascar, Brazil, South Africa, etc.South Africa, etc.

88

The case of South Africa – part of a TAThe case of South Africa – part of a TA1.1. A disaggregated CGE model A disaggregated CGE model

(now operating at South Africa National Treasury)(now operating at South Africa National Treasury)

– SAM , 2001 and subsequent updates (C. de Mewre)SAM , 2001 and subsequent updates (C. de Mewre)

– Dimensions of the CGE modelDimensions of the CGE model• 43 production sectors: agriculture, 30 industries, 12 services43 production sectors: agriculture, 30 industries, 12 services• 15 labor categories - by skill, formal/informal workers, and self-15 labor categories - by skill, formal/informal workers, and self-

employed by broad sectorsemployed by broad sectors• Over 10 household groups Over 10 household groups • Separate tax tables: import tariffs, consumption taxes (value added, Separate tax tables: import tariffs, consumption taxes (value added,

excise, fuel), production taxes, income taxesexcise, fuel), production taxes, income taxes

– Documentation:Documentation:• Application to VAT issues by Delfin S. Go, Marna Kearney, Karen Application to VAT issues by Delfin S. Go, Marna Kearney, Karen

Thierfelder, and Sherman Robinson Thierfelder, and Sherman Robinson

– Other features:Other features:• Rich in economic structure and behavioral contents for policy analysisRich in economic structure and behavioral contents for policy analysis• But limited heterogeneity in individuals/households for analysis of But limited heterogeneity in individuals/households for analysis of

poverty, income distribution or more detailed social impactspoverty, income distribution or more detailed social impacts

99

TA for South Africa TA for South Africa

2. Micro-simulation framework2. Micro-simulation framework

– Micro data: cMicro data: combined IES and LFS data for 2000ombined IES and LFS data for 2000– 26,265 households26,265 households

– 103,732 individuals103,732 individuals

– Econometric estimations (Korman 2005) :Econometric estimations (Korman 2005) :• Wage functions for formal wage earners by skills by sectorWage functions for formal wage earners by skills by sector

• Earning functions for self-employed and informal workers by Earning functions for self-employed and informal workers by broad sector – primary, secondary and tertiary sectors broad sector – primary, secondary and tertiary sectors

• Occupational-choice (multinomial-logit) functions similar labor Occupational-choice (multinomial-logit) functions similar labor categoriescategories

– Features:Features:• Full heterogeneity of households/individuals/labor force Full heterogeneity of households/individuals/labor force

estimated through cross-section data estimated through cross-section data

• Limited detail in structure of productive sector for policy Limited detail in structure of productive sector for policy analysisanalysis

1010

Distribution of Employment by Sector and Distribution of Employment by Sector and OccupationOccupation

Agriculture Industry Services Total

Formal Sector Workers Low -Skill 6.0 2.9 5.7 14.6

Semi -Skill 6.2 8.7 16.5 31.3 High- Skill 0.7 1.3 9.6 11.6

Informal Sector Workers 2.7 2.5 13.9 19.2 Self Employees 9.1 2.8 11.5 23.4

Total 24.6 18.2 57.2 100.0

1111

Micro-simulation model for South AfricaMicro-simulation model for South Africa

mmimigmimigmi kivxwLog ...,1. )()(

mmmfmfmmfm NZyLog .. )()()(

mmm

k

imimim PyISyIWwY

m

/.. 01

K

kkmkm psP

1

.

Wage and self-employment earnings of each household

by primary, secondary, and tertiary sectors

by low, medium and high skill for formal workers

1212

Micro-simulation model for South Africa Micro-simulation model for South Africa

Next, each individual chooses the occupation that gives the highest utility (table 6)

Occupational status or choices are: • Self-employment by sector (3)

• Informal wage employment by sector (3)

• Formal wage employment by skill and sector (9)

• Unemployed and inactive (Base Group)

Multinomial Logit Probabilites (P) calculated using:

15

1

)exp(1

)exp()(

jij

ij

x

x

i jYP

15

1

)(exp1

1)0(

jij x

iYP

1313

Some observations Some observations from the wage equationsfrom the wage equations

• Statistical Statistical – Key variables like education and experience have expected signs and Key variables like education and experience have expected signs and

are significant; are significant; – OLS regression similar to Heckman 2 stage model that corrects for OLS regression similar to Heckman 2 stage model that corrects for

problem of selection bias (exclusion of non-wage earners);problem of selection bias (exclusion of non-wage earners);

• Education has significant impact for low-skilled workersEducation has significant impact for low-skilled workers– Primary sector – 3 additional years increases wage by 5.7%;Primary sector – 3 additional years increases wage by 5.7%;– Manufacturing – 3 additional years increases wage by 2.4%;Manufacturing – 3 additional years increases wage by 2.4%;– Tertiary sector – 3 additional years increases wage by 9.6%;Tertiary sector – 3 additional years increases wage by 9.6%;

• Others Others – Union wages for low-skill higher than non-union wages by Union wages for low-skill higher than non-union wages by

60% in primary sector (mining?); 40% in manufacturing; and 60% in primary sector (mining?); 40% in manufacturing; and 62% in tertiary sector; 62% in tertiary sector;

– Urban wages are >30% higher;Urban wages are >30% higher;– Male employees are paid 9-51% higher than female Male employees are paid 9-51% higher than female

employees;employees;

1414

Macro-micro integration Macro-micro integration

• Top-down approachTop-down approach– Most common, because of different dimensions and statistical Most common, because of different dimensions and statistical

packages used for macro and micro components; packages used for macro and micro components; – Macro and micro are run separately;Macro and micro are run separately;– No sacrifice in the rich structure of the economy in the macro/CGE No sacrifice in the rich structure of the economy in the macro/CGE

modelmodel

• Full integration Full integration – Madagascar (Ann-Sophie Robillard etc. ) - macro/CGE component Madagascar (Ann-Sophie Robillard etc. ) - macro/CGE component

has few sectors;has few sectors;– U.S. (Hechman and Lochner) – overlapping generations general U.S. (Hechman and Lochner) – overlapping generations general

equilibrium model for the macro part is aggregativeequilibrium model for the macro part is aggregative

• Recursive dynamics and feedbacks for South AfricaRecursive dynamics and feedbacks for South Africa– Retain the rich structure of the economy in the CGE model (e.g. 49 Retain the rich structure of the economy in the CGE model (e.g. 49

sectors)sectors)– Upward Feedback through the structure of labor and householdsUpward Feedback through the structure of labor and households

1515

The feedback mechanismThe feedback mechanism

CGE model

Linkage AggregatedVariables

-prices, wages, employment levels

Household income micro-simulation model

Feedback variables for next period–labor

supplies, household distribution by groups

ttt LDwp ,, 11, ttLS

1616

Factor AccumulationFactor Accumulation

• New labor supplies are calculated for t+1 from the micro New labor supplies are calculated for t+1 from the micro simulationsimulation

o Aggregation consistent with CGE labor market categories and Aggregation consistent with CGE labor market categories and storystory

o Simple demographic growth rates (as a start)Simple demographic growth rates (as a start)

• Aggregate/sector investment determined in the CGE model (no Aggregate/sector investment determined in the CGE model (no micro-firm story)micro-firm story)

The recursive feedback mechanism The recursive feedback mechanism from micro to macrofrom micro to macro

1717

The Impact of a Large Oil Price ShockThe Impact of a Large Oil Price Shock

Two ScenariosTwo Scenarios

• 125% increase in the world import price of oil 125% increase in the world import price of oil (similar to the shock during 2003-06; to about $65/bbl)(similar to the shock during 2003-06; to about $65/bbl)

• The same oil price shock plus spill-over effects:The same oil price shock plus spill-over effects:– 30% increase in the world import price of basic chemicals, plus 30% increase in the world import price of basic chemicals, plus

– 6% increase in the world import price of all other goods.6% increase in the world import price of all other goods.

– In the long run all factors are fully mobile across all sectors .In the long run all factors are fully mobile across all sectors .

– In the short run, capital is activity specific and labor is mobile within In the short run, capital is activity specific and labor is mobile within subsets of activities: agriculture, industry, and services.subsets of activities: agriculture, industry, and services.

1818

Macroeconomic ResultsMacroeconomic Results

Real variables (percent change)

Oil Price Shock Oil & General Price Shock

Real exchange rate 16.2 22.4 Total absorption -5.6 -7.8 Exports 7.7 9.1 Imports -6.2 -10.3 Household consumption -6.5 -8.8 Total investment -7.0 -10.8 GDP (at market prices) -1.8 -2.5 Total employment -2.1 -2.7 CPI 1.9 2.7

1919

Output Adjustment in the Industry ActivitiesOutput Adjustment in the Industry Activities

Oil Price ShockOil Price Shock

-8

-6

-4

-2

0

2

4

6

8

10

Tra

nsport E

quip

Leath

er a

nd P

roducts

Wood a

nd P

roducts

Machin

ery

and E

quip

ment

Textile

s

Pro

f. & S

ci. E

quip

Gla

ss a

nd G

lass P

roducts

Furn

iture

Moto

r Vehic

les

Paper a

nd P

roducts

Basic

Iron a

nd S

teel

Wearin

g A

ppare

l

Meta

l Pro

ducts

Com

munic

atio

n E

quip

Basic

Non-fe

rrous M

eta

ls

Oth

er In

dustrie

s

Pla

stic

Pro

ducts

Bev &

Tobacco

Refin

ed P

etro

leum

Footw

ear

Ele

ctric

al M

achin

ery

Man-M

ade F

ibers

Rubber P

roducts

Food

Non-m

eta

llic M

inera

ls

Prin

ting, P

ublis

hin

g, M

edia

Basic

Chem

icals

Perc

en

t ch

an

ge

2020

Output adjustment in the Industry ActivitiesOutput adjustment in the Industry Activities

Oil & General Price ShockOil & General Price Shock

-6

-4

-2

0

2

4

6

8

10

Tra

nsport E

quip

Leath

er a

nd P

roducts

Wood a

nd P

roducts

Pro

f. & S

ci. E

quip

Gla

ss a

nd G

lass P

roducts

Machin

ery

and E

quip

ment

Textile

s

Furn

iture

Wearin

g A

ppare

l

Basic

Iron a

nd S

teel

Paper a

nd P

roducts

Meta

l Pro

ducts

Moto

r Vehic

les

Basic

Non-fe

rrous M

eta

ls

Com

munic

atio

n E

quip

Footw

ear

Oth

er In

dustrie

s

Bev &

Tobacco

Refin

ed P

etro

leum

Pla

stic

Pro

ducts

Ele

ctric

al M

achin

ery

Food

Man-M

ade F

ibers

Rubber P

roducts

Non-m

eta

llic M

inera

ls

Prin

ting, P

ublis

hin

g, M

edia

Basic

Chem

icals

Perc

en

t ch

an

ge

2121

Output Adjustment in the Service ActivitiesOutput Adjustment in the Service Activities

Oil Price ShockOil Price Shock

-7

-6

-5

-4

-3

-2

-1

0

1

2

Cate

rin

g a

nd A

ccom

odatio

n

Whole

sale

and R

eta

il Trade

Ele

ctic

ity &

Gas

Fin

ancia

l Servic

es

Governm

ent S

ervic

es

Com

unic

atio

n

Busin

ess S

ervic

es

Wate

r S

upply

Transporta

tion a

nd S

torage

Oth

er P

roducers

Constr

uctio

n a

nd C

ivil E

ng.

Health

and P

ersonal S

ervic

es

Percen

t c

han

ge

2222

Output adjustment in the Service ActivitiesOutput adjustment in the Service Activities

Oil & General Price ShockOil & General Price Shock

-10

-8

-6

-4

-2

0

2

Cate

rin

g a

nd A

ccom

odatio

n

Whole

sale

and R

eta

il Trade

Ele

ctic

ity &

Gas

Fin

ancia

l Servic

es

Governm

ent S

ervic

es

Com

unic

atio

n

Busin

ess S

ervic

es

Transporta

tion a

nd S

torage

Wate

r S

upply

Oth

er P

roducers

Health

and P

ersonal S

ervic

es

Constr

uctio

n a

nd C

ivil E

ng.

Percen

t c

han

ge

2323

Employment Changes (percent change)Employment Changes (percent change) Oil Price

Shock Oil & General Price Shock

Agriculture Formal high-skilled workers 3.5 4.9 Formal semi-skilled workers -1.5 -1.4 Formal low-skilled workers 1.0 1.9 Self employed 3.6 5.2 Informal workers 8.0 11.8 Industry Formal high-skilled workers 2.1 2.5 Formal semi-skilled workers -1.7 -2.6 Formal low-skilled workers 0.5 0.1 Self employed 2.6 2.8 Informal workers 4.2 5.2 Services Formal high-skilled workers -0.5 -0.7 Formal semi-skilled workers -8.6 -11.4 Formal low-skilled workers -8.4 -11.6 Self employed -3.4 -4.8 Informal workers -2.3 -3.3 Economy-wide employment Total -2.1 -2.7 Formal semi-skilled workers -5.3 -7.0 Formal low-skilled workers -2.8 -3.7

2424

Wage Changes (percent change)Wage Changes (percent change)

Oil Price Shock

Oil & General Price Shock

Real Formal high-skilled workers -11.3 -15.2 Formal semi-skilled workers 0.0 0.0 Formal low-skilled workers 0.0 0.0 Self employed -10.5 -13.8 Informal workers -9.6 -12.8 Nominal Formal high-skilled workers -9.6 -13.0 Formal semi-skilled workers 1.9 2.7 Formal low-skilled workers 1.9 2.7 Self employed -8.8 -11.6 Informal workers -7.9 -10.5

2525

Impact of Oil Shock on Poverty Impact of Oil Shock on Poverty

and Income Distributionand Income Distribution Base Case

Simulation 1: Oil Price Shock

National Urban Rural National Urban Rural

Poverty Indicators Using Expenditure

Head Count Ratio 0.37 0.21 0.60 0.38 0.22 0.61 Poverty Gap 0.15 0.08 0.26 0.16 0.08 0.27 Poverty Severity 0.08 0.04 0.15 0.08 0.04 0.15 Using Income Head Count Ratio 0.49 0.33 0.72 0.495 0.33 0.72 Poverty Gap 0.33 0.22 0.51 0.33 0.22 0.51 Poverty Severity 0.28 0.17 0.43 0.28 0.18 0.43 Inequality Indicators Using Expenditure General Entropy(0) 0.87 0.77 0.60 0.89 0.78 0.61 General Entropy(1) 0.98 0.80 1.04 1.00 0.81 1.06 Gini coefficient 0.67 0.63 0.58 0.68 0.64 0.59 Using Income General Entropy(0) 1.22 1.02 1.14 1.23 1.03 1.15 General Entropy(1) 1.20 1.00 1.46 1.21 1.02 1.48 Gini coefficient 0.71 0.67 0.71 0.72 0.68 0.72

2626

Distribution of Gains and Losses to Distribution of Gains and Losses to

Households by DecilesHouseholds by Deciles

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

1 2 3 4 5 6 7 8 9 10

Population Deciles

% g

ain

s/lo

ss

es

pe

r c

ap

ita

% of gain/loss per capita income Zero Line

2727

Gains and Losses from Oil Price Shock for Gains and Losses from Oil Price Shock for

Low Skill HouseholdsLow Skill Households -3

0-2

0-1

00

10

% g

ain/

loss

per

cap

ita

0 20 40 60 80 100per capita consumption percentiles

% gain/loss per capita zero line

2828

Gains and Losses from Oil Price Shock for Gains and Losses from Oil Price Shock for High Skill HouseholdsHigh Skill Households

-15

-10

-50

5%

gain

/loss

per

cap

ita

0 20 40 60 80 100per capita consumption percentiles

% gain/loss per capita zero line

2929

Decomposition of the Impact on InequalityDecomposition of the Impact on Inequality

Vertical

Component Horizontal

Component Total

Gains from Consumption1 137.0 -37.0 100.0

Gains from changes in Formal Wages1 120.4 -20.4 100.0

Gains from changes in Informal Wages1 104.1 -4.1 100.0 Gains from Changes in Self Employed Income 1 77.0 23.0 100.0 Aggregate Gains1 135.0 -35.0 100.0 Aggregate Gains2 119.0 -19.0 100.0

Notes:

1. Using per capita consumption as an explanatory variable in the Lowess regression 2. Using per capita income as an explanatory variable in the Lowess regression

3030

Conclusions/further research……Conclusions/further research……

Oil shock simulations did not include offsetting factorsOil shock simulations did not include offsetting factors

The elaborate macro-micro framework has several potential policy applications;The elaborate macro-micro framework has several potential policy applications;

For the bottom-up feedback, positive and negative effects tends to cancel out with For the bottom-up feedback, positive and negative effects tends to cancel out with income classification;income classification;

But the potential is there to look at the micro aspects much more thoroughly for winners But the potential is there to look at the micro aspects much more thoroughly for winners and losers by alternative household characteristics;and losers by alternative household characteristics;

Questions/issues –Questions/issues –• Is the upward feedback with household classification worth exploring further Is the upward feedback with household classification worth exploring further

more?more?• Next applications Next applications

– impact of a wage subsidy issue (done) impact of a wage subsidy issue (done) – carbon tax (done – w/o micro simulations)carbon tax (done – w/o micro simulations)– falling export prices and exogenous flowsfalling export prices and exogenous flows– combined incidence of taxation and public expenditures in South Africacombined incidence of taxation and public expenditures in South Africa

• Applications to external shocks – what’s hapenning to the efficiency parameters (of Applications to external shocks – what’s hapenning to the efficiency parameters (of investment, public expenditures et.)investment, public expenditures et.)