evaluating poverty reduction policies in ldcs the contribution of micro-simulation techniques

« Microsimulation as a tool for ex ante evaluation of public policies », Madrid, Spain, 15-16 November 2004

Evaluating poverty reduction policiesin LDCs

The contribution of micro-simulation techniques

Anne-Sophie Robilliard

IRD/DIAL


Introduction : the context of policy evaluation in LDCs

• New political demand– Transition between the structural adjustment policies implemented in the

80s and new policies designed to reduce more effectively poverty– Millenium Development Goals (MDG) forged by the member countries

of the United Nations– Poverty Reduction Strategy Papers (PRSPs) are the cornerstone of

concessional lending by Bretton-Woods Institutions

• Two traditional strands of policy evaluation– « micro » & « ex post »

• impact evaluation that rely on micro data and econometric techniques• experimental methods in order to properly define control groups• Safety nets, workfare programs (PROGRESA, etc)

– « macro » & « ex ante » • CGE models i.e. simulation models that rely on counterfactual analysis• Representative household groups• Trade policies, structural adjustements, fiscal policies, macro shocks


Issues raised by ex post policy evaluation

• Strictly speaking, ex post evaluation seeks to check retrospectively whether the objectives of a policy have been met, with a positive approach.

• This comes close to a pharmacological type of question, such as: « is the drug effective? »

• Problem : experimental or pseudo-experimental evaluations cannot be applied to a great number of policies because most of the time it is impossible to form a control group– This is the case for all non targeted policies such as devaluations

– This is also the case for targeted policies that have strong macro-economic impacts


Issues raised by the analysis of the distributive effects of macroeconomic shocks

• Most existing studies of the distributive effects of macroeconomic shocks rely either on– the comparison of the distribution before and after the shock,– counterfactuals based on macro models with some disaggregation of

the household sector.

• The before-after approach has well-known drawbacks– difficult to isolate what is due to the macroeconomic shock and what is

due to other causes,– does not permit analyzing counterfactuals.

• Deriving the overall income distribution and poverty measures in the standard CGE approach requires – relying on a household classification into “representative” groups, and – making assumptions about the within-group income distribution and its

evolution under the shock.


Modeling Income Distribution in Applied CGE models

• Standard approach– disaggregate the household account into relevant socioeconomic groups

• “distribution matrix”: payments of factors to households• structure of consumption for each group

– ignore within group heterogeneity– associate groups and poverty

• Elaborated approach – specify an income distribution function for each group

• log normal (Adelman and Robinson, 1978)• beta law (Decaluwe et al., 2000)

– assume that the within-group variance of income is fixed– compute poverty and inequality indicators based on that assumption

• Microsimulation for policy evaluation in LDCs :– tries to input more micro information into macro models using household surveys– Starting point : lift the representative household groups assumption


What do we want to capture?

Factor marketsFactor market functioning

Segmentation Wage determination

Macroeconomic

Environment

Households

Structural featuresBinding macro constraintsGeneral Equilibrium effects

HeterogeneityHuman and physical

capitalDemographic

CompositionPreferencesAccess to Markets


Standard structure of microsimulation models

Simulation of a socio-economic

process

Population with all the characteristics

Population after the changes

Aggregation of

individual situations


Possible applications

• Impact of macroeconomic shocks or policies on income distribution and poverty:– structural adjustment, – terms of trade, – devaluation, – fiscal reforms

• Impact of poverty reduction policies:– food subsidies– schooling subsidies– public spending on health and/or education– public employment programs

• Impact of demographic changes (demographic transition) on income distribution and poverty


What is a Microsimulation Model?

• “The common denominators [of microsimulation models] are two, namely, that they all deal with behavior (decision) units at the micro (firm, household, etc.) levels and that they all aggregate up to large parts or all of the national economy.” Bergmann, Eliasson, and Orcutt, 1980.

• “... instead of aggregating observations within a household survey into a few household groups in conformity with the requirements of CGE-type models, our aim should be to work directly with all the individual observations of the survey. By doing so, we hope to achieve full consistency between macroeconomic reasoning and standard poverty evaluation.” Bourguignon, 1999.


Elements for a typology

• accounting vs. behavioral

• reduced form vs. structural models

• partial vs. general equilibrium

• static vs. dynamic

• sequential vs. integrated


4 types

• Micro-accounting & disaggregated CGE models– Structure of income– Structure of consumption

• Sequential approach & reduced form models– Reduced-form micro-econometric model of household

income generation

• Integrated approach & structural models– Structural specification of household decisions

• Dynamic models


Characterization of existing applications

Nb Reference Country or region

Micro-economic part

Population

structure

Macro-economic part

Macro-micro link

1st level micro-simulation (micro-accounting and disaggregated CGE models)

[1] World Bank (2001) (RMSM-X+LP for PRSPs)

Several developing countries

Expansion of household incomes by socio-economic groups according to factor returns given by the macro-part

Static Static, financing gap model

Sequential

[2] Devarajan and Go (2001) (123-PRSP)

Zambia, but easily transferable to any other country

Expansion of household incomes by decile observed in a survey according to factor returns given by the macro-part

Static Static, multi-sector CGE model

Sequential

[3] Agénor, Izquierdo and Foffack (2002) (IMMPA)

Hypothetical data base, but transferable to any country

Expansion of household incomes by socio-economic groups observed in a survey according to factor returns given by the macro-part

Static Dynamic, multi-sector CGE model

Sequential

Source: Cogneau, Grimm and Robilliard (2002).


Nb Reference Country or

region Micro-economic part Population

structure Macro-economic part Macro-

micro link

2nd level micro-simulation (sequential approach and reduced models)

[4] Bourguignon, Fournier and Gurgand (1999, 2001), Bourguignon, Ferreira and Lustig (2001), Bourguignon, Ferreira and Leite (2001), Grimm (2001)

Several Asian and Latin-American middle income countries, Côte d’Ivoire

Reduced form household income generating model

Use of two or more empirically obs. pop. structures

Empirically based hypotheses on the evolution of returns on the labour market and labour supply behavior

No

[5] Ferreira and Leite (2002) Ceará (Brazil) Same as in [4] plus reduced form schooling and household size models

Static, but with varying education and household size distributions

Hypotheses on the evolution of the return to education

No

[6] Robilliard, Bourguignon and Robinson (2001), Bourguignon, Robilliard, and Robinson (2002)

Indonesia Same as in [4] Static Static, multi-sector CGE model Sequential

3rd level micro-simulation (dynamic models)

[7] Cogneau and Grimm (2002), Grimm (2002)

Côte d’Ivoire Structural household income generating model plus dynamic demographic model

Dynamic Hypotheses on the evolution of the return to education and labor demand

No

4th level micro-simulation (integrated approach and structural models)

[8] Cogneau (2001) Antananarivo (Madagascar)

Structural household income generating model

Static Static, three sector CGE model Integrated

[9] Cogneau and Robilliard (2001) Madagascar Structural household income generating model

Static Static, three sector CGE model Integrated

Source: Cogneau, Grimm and Robilliard (2002).


Sequential Framework with Reduced-form

Micro-simulation Module

Integrated Framework with Structural

Micro-simulation Module

Equations:

- Factors markets balance- Goods markets balance

Output:

- Macro aggregates- Production and prices- Factor employment quantities and wages

Equations:

- Factor demand- Goods supply

Output:

- Macro aggregates- Production and prices

iterations

iterations

Micro Module (Household survey)

Equations:- Reduced form occupational choice model- Wage and profit equations- Income generation equation

Equations:- Structural wage & labour supply model- Consumption demand system- Income generation equation

Output: Income distribution

Link variables:- Factor employmentquantities and wages- Consumer prices

‘Downward’ link variables:

- Prices- Wages‘Upward’ link

variables:-Aggregate

labour supply- Aggregatedemand for

goods

- Macro closures - Macro closures* Savings-Investment balance* Government budget balance* Current Account balance

Macro Module (CGE type model)


“Integrating Theory and Measurement” (Bergmann, Eliasson, and Orcutt, 1980)

• Construction and validation of macroeconomic models– SAM production– CGE modeling

• Development of microeconomic models of household behavior– Data base setting up– Events and behaviors modeling

• Model Validation– Structural validity

• specification of variables and relationships theoreticaly plausible?• initial parameters consistent with theory?

– Operational validity : is the model able to answer the question raised ?– Empirical validity : are the base simulation results consistent with empirical

observations ?

• Micro-Macro modeling: macroeconomic model based on real microeconomic data

– Reconciling household surveys and National Accounts data (Robilliard and Robinson, 2000)


Base and alternative simulations

• Base Simulation : uses variables and parameters observed or most likely

• Alternative simulations :– price changes (returns on labor market, devaluation,

consumption prices, schooling costs, ...)– alternative policies, macroeconomic shocks– preferences changes (occupational choices, human capital

investment, ….) – changes in socio-demographic characteristics (population

ageing, migration, household structure changes, …)


Analyzing results

• Growth : household income evolution (by socio-economic group)

• Poverty : poverty rates (incidence, severity, inequlity among the poor, poverty profile)

• Inequality : Lorenz Curves, Gini coefficient, Atkinson indices, entropie measures

• Other indicators : schooling, health, migration, nutrition…..


Econometrics as a tool for building microsimulation models

Example : modeling household income

• occupational choices

• wage and profit equations

• non observable heterogeneity (individual fixed effects)

• aggregation of income sources within each household


Occupational Choices

• Selection of the population « at risk »

• Binary choice: activity vs. inactivity --> probit, logit

• Multiple choice : inactivity, wage work, agricultural self employment, non agricultural self employment --> multivariate probit, multinomial logit

• Ex. Multinomial Logit

Individual i utility associated with activity j can be written:

If agent i chooses j, then: jkUU ikij

ijijjij vxU


Occupational Choices

• Assumption on the terms of error: the are independent and identically distributed according to a Weibull distribution

• The probability that agent i chooses activity j, can be written :

• The multinomial logit implicitly assumes that irrelevant alternatives are independent (IIA). This means that agent i’s choice will remain unchanged if one of the irrelevant alternatives is modified.

• STATA procedure: « mlogit »

ijv

J

j

x

x

iij

ijj

e

ejY

1

)Pr(


Wage and profit equations

• How does the labor market function? Segmentation or perfect integration? One income function or many sectoral ones?

• Choice of the dependant variable? Average wage? Last month wage? How to distinguish profit and turnover in the agricultural sector? With or without family workers payment?

• Mincer-type wage equation with log individual earnings regressed on years of schooling and years of work experience and its square

iii ux')wln(


Wage and profit equations

• Profit function

with z : family work ; x : head’s education and experience

• Family work might be endogenous Exogeneity test, if rejected then instrument, which means find variables that determine the

quantity of travail, but are independent from profit (for instance, household composition)

STATA procedure: « ivreg »

ii'xi

'zi uxz)Pln(


Non Observable Heterogeneity - taking into account the residual

• Observables can only explain part of the variance of wages or profit

• Omitting the residual (the unexplained part of the variance) is equivalent to omitting part of the variance and hence part of the inequality between individuals

• If the residual is negative (positive) the agent is less (better) paid than the average of wage workers presenting identical characteristics

• How can we model that residual ?


The Indonesia Model

Bourguignon, Robilliard and Robinson (2002)


Background

• The social impact of the financial crisis that hit Indonesia in 1997 has been the subject of ongoing research.

• Recent data analyses suggest that the shock, while not as bad as once predicted, has led to important adjustments in labor markets (Manning, 2000) and an 66.8% increase in the poverty head-count ratio (Suryahadi et al., 2000)

• The study attempts to quantify these adjustments and their effects on poverty and inequality.


Characteristics of the sequential micro-macro model for Indonesia

• MACRO– Full Static Computable General

Equilibrium

– Software: GAMS

– Data: Social Accounting Matrix for 1995

– 38 sectors

• 5 agricultural

• 15 informal

• 18 formal

– 15 factors of production

• 8 types of labor

• 7 types of capital

– marketing margins

– self consumption

• MICRO– Reduced Form Occupational Choice

Model– Software: STATA– Data: Savings-Investment module of

SUSENAS 1996– 9,800 households– 33,400 individuals aged 10 years and

older – 8 labor segments– 4 occupational choices at the individual

level• inactive• wage worker• self employed• wage worker & self employed


Macro-level module (Extended CGE-type model)- Occupational structure: L- Price variables: p- Wage and earnings: w- All other variables in macro module: Y

Micro-simulation module (Household survey)- Socio demographic characteristics: Si

- Occupational/labor-supply choice: li = O(Si,)- Income: yi = E(Si,).li

Consistency with macro. Find changes in parameters and such that:li = L and Mean E(Si,) = w

Outcome = change in distribution of income conditionally on characteristics S.

Link variables: L, w, p

The Sequential Framework


• Assume the CGE model will deliver

where i is a segment, F indicates formal sectors (wage work), andI indicates informal sectors (self employment).

• Let aki + bk.zp + ukp be the utility for individual p of being in occupational group k (1 for wage work and 2 for self employment)

where zp is a set of observed individual characteristics,

ukp summarizes the effects of unobservables,

aki is a constant, and

bk is a vector of coefficients.

; ; ;i F i I iF ai I ai F I

a a

E L E L w y


• Let earning and profit functions write:

where xp is a set of individual characteristics,

m designates households,

Zm is a set of household characteristics,

Nm is the number of family workers,

i, , and are sets of estimated coefficients,

and are constants, and

and vp summarize the effect of unobservables.

Log

Log

ip i i p ip

m m m m

w x v

Y Z N


Let ( ) be the logical indicator function taking the value 1 if the expressions within brackets are true and zero otherwise.

The problem can be written: find (a1i,a2i,i,) such that

for all segments i.

1 1 1 1 1 1 2 2 2

2 2 2 2 2 2 1 1 1

1 1 1 1 1 1 2 2 2

0;

0;

exp 0;

exp 0

ii p p i p p i p p F

p i

ii p p i p p i p p I

p i

ii i p ip i p p i p p i p p F

p i

m m m m Im

a b z u a b z u a b z u E

a b z u a b z u a b z u E

x v a b z u a b z u a z b u w

Z N N y

2 2 2 2 2 2 1 1 1where 0;m i p p i p p i p pp m

N a b z u a b z u a b z u


Household incomes are then derived from the occupational choice functions and the earning and profit functions through:

and OIm is non labor income (transfers, imputed rents,...etc).

1 1 1 1 1 1 2 2 2

2 2 2 2 2 2 1 1 1

exp 0;

exp 0

where 0;

m i i p ip i p p i p p i p pp i

m m m m m

m i p p i p p i p pp m

I x v a b z u a b z u a b z u

Z N N OI

N a b z u a b z u a b z u


Econometric Estimations

• Earning functions for all segments:

• Occupational choice model for heads, spouses and others:

U0 = a0 + b0.zp + u0

p inactivity work

U1 = a1 + b1.zp + u1

p wage work

U2 = a2 + b2.zp + u2

p self-employment

U3 = a3 + b3.zp + u3

p both

• Profit functions for agricultural, informal and mixed activities:

Log ip i i p ipw x v

Log m m m mY Z N


Solution Algorithm

Consider a set of nonlinear functions fi(P1,...,Pn), which yield the following problem: f(P) = 0

where P is the vector of variables and f is the vector of functions

Any iteration procedure for solving this set of equations can be written as P(k+1) = P(k) + (k)d(k)

where k refers to the iteration, d(k) is a direction vector, and (k) is a scalar giving the step of the size to be taken in direction d(k).

In the Newton-Raphson procedure, the step size is equal to one and the direction vector writes: -D-1,

where D is the matrix of derivatives of the functions P

i

ijj

fD

P


Explanatory variables for individual level equations occupational choice model head spouse other

earning functions

tearn_d1 household head wage x x h2j1 dummy for household head occupational choice (=1 if “has two

jobs”) x x

h2jb x scyrp schooling x x x x scyrp2 schooling squared x x x x exp experience x x x x exp2 experience squared x x x x havevec dummy for vehicule x x x houstat dummy for housing status x x x oinctot non labor income x x x namn number of adult male with no education x x x namp number of adult male with primary education x x x nams number of adult male with secondary education x x x namh number of adult male with high education x x x nafn number of adult female with no education x x x nafp number of adult female with primary education x x x nafs number of adult female with secondary education x x x nafh number of adult female with high education x x x nemn number of elderly male with no education x x x nemp number of elderly male with primary education x x x nefn number of elderly female with no education x x x nefp number of elderly female with primary education x x x ncl2 number of children aged less than two x x x nc2_4 number of children aged 2 to 4 x x x nc5_9 number of children aged 5 to 9 x x x nc10_18 number of children aged 10 to 18 x x x dl0_05 dummy land less than 0.5 hectare x x x dl05_1 dummy land 0.5 to 1 hectare x x x dl1_2 dummy land 1 to 2 hectares x x x dlm2 dummy land more than 2 hectares x x x jakarta dummy for Jakarta (urban regressions only) x javoutjk dummy java out of jakarta x x x x


Explanatory variables for household level equations profit functions agricultural non

agricultural mixed

probit pure farm

lnse log of number of self employed family members x x x scyrp_1 schooling x x x x scyrp2_1 schooling squared x x x x exp_1 experience x x x x exp2_1 experience squared x x x x namn number of adult male with no education x namp number of adult male with primary education x nams number of adult male with secondary education x namh number of adult male with high education x nafn number of adult female with no education x nafp number of adult female with primary education x nafs number of adult female with secondary education x nafh number of adult female with high education x nemn number of elderly male with no education x nemp number of elderly male with primary education x nefn number of elderly female with no education x nefp number of elderly female with primary education x ncl2 number of children aged less than two x nc2_4 number of children aged 2 to 4 x nc5_9 number of children aged 5 to 9 x nc10_18 number of children aged 10 to 18 x dl0_05 dummy land less than 0.5 hectare x dl05_1 dummy land 0.5 to 1 hectare x x dl1_2 dummy land 1 to 2 hectares x x dlm2 dummy land more than 2 hectares x x java dummy for java x x x jakarta dummy for Jakarta (urban regressions only) x javoutjk dummy java out of jakarta x


AGGREGATE VARIABLES INSTRUMENTS

1 pww211 % wage workers in seg 211 ( h1r211[1,30], s1r211[1,36], o1r211[1,36] ) constants of the multilogit model

2 pse211 % self employed in seg 211 ( h1r211[1,30], s1r211[1,36], o1r211[1,36] ) constants of the multilogit model

3 pwse211 % wage&self in seg 211 ( h1r211[1,30], s1r211[1,36], o1r211[1,36] ) constants of the multilogit model

4 W211 mean wage in seg 211 cw211[1,8] constant of the wage equation for seg 211













17 WAGR agricultural profit pagr[1,11] constant of the agricultural profit equation

18 WINF non-agricultural profit pinf[1,8] constant of the informal profit equation

19 WMIX mixed profit pmix[1,11] constant of the mixed profit equation

20 PAGR % of pure agricultural pbag[1,25] constant of the probit model


Policy Analysis with CGE model simulations

• Reproduce historical changes in occupational choices and real wages

• Decompose historical change

– Financial crisis

• real devaluation

• credit crunch

– El Nino drough

• Analyze alternative policy packages

– targeted safety nets

– food subsidy


CGE Simulations: Decomposing the historical shock

FINCRI + El Nino DroughtSIMALL

DEVCCF + Domestic Credit CrunchFINCRI

SIMDEV+ Foreign Credit CrunchDEVCCF

Real DevaluationSIMDEV

El Nino DroughtSIMELN

DescriptionSimulation


Decomposing the social impact of the Indonesian financial crisis

Per Capita Income (Rp. th) BASE SIMELN SIMDEV DEVCCF FINCRI SIMALL

Urban 170.9 -12.7 -7.6 -21.3 -22.9 -30.1

Rural 90.6 -10.0 -2.0 -7.1 -12.6 -21.2

All 121.1 -11.5 -5.0 -14.7 -18.1 -26.0

Theil Index (x100)

Urban 53.9 8.5 6.2 5.6 7.7 15.8

Rural 33.1 5.1 3.9 6.6 10.8 15.9

All 49.3 5.2 2.4 -1.1 2.9 9.3

Poverty Head-Count Index (P0)

Urban 4.0 78.6 47.1 129.3 165.0 279.4

Rural 12.4 43.0 13.0 36.7 63.2 113.5

All 9.2 48.9 18.6 51.9 80.0 140.9

Notes: Base values for BASE column and percent change for other simulations.SIMELN El Niño DroughtSIMDEV Real DevaluationDEVCCF Real devaluation + Foreign Credit CrunchFINCRI Real devaluation + Foreign Credit Crunch + Domestic Credit CrunchSIMALL Real devaluation + Foreign Credit Crunch + Domestic Credit Crunch + El Niño DroughtSource: Robilliard, Bourguignon and Robinson 2001.


Real Devaluation + Foreign Credit Crunch + Domestic Credit Crunch + El Nino Drought% change in per capita income

-24

-22

-20

-18

-16

-14

-12

-10v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 v18 v19 v20

full

rhg


Concluding Remarks

• Overall changes in poverty are important and mainly fuelled by the negative income impact of the crisis.

• Poverty indicators show that the impact is worse on the poorest of the poor.

• Poverty rate increases in the urban sector are bigger than in the rural sector where inequality decreases.

• Nevertheless, poverty indicators remain higher in the rural sector.

• Increase of inequality in the urban sector is either offset by a decrease in the rural sector or by an decrease of inequality between the two sectors.

• Socio-demographic changes play an important role in labor market changes and slightly offset the negative impact of the crisis in the rural sector.


Concluding Remarks (cont’d)

• The CGE model does a relatively good job in reproducing changes in real wages and in occupational choices at aggregate level.

• It does a less satisfactory job in reproducing changes in occupational choices by segment => more work on labor market specification and technology is needed.

• The microsimulation module provides “reasonable” estimates of poverty and inequality changes.

• It does not take fully into account socio-demographic changes.


The Madagascar Model

Cogneau & Robilliard (2001)


Background

• Declining GDP per capita since the beginning of the 70s.

• Important contribution of the agricultural sector– 34% of GDP– 85% of active population

• Failure of agricultural policies– low investment– bias against agriculture– low productivity

• What development strategy for poverty reduction?


Characteristics of the integrated micro-macro model for Madagascar

• MACRO– Stylized CGE Framework:

endogenous prices for labor and goods markets

– Data: Social Accounting Matrix for 1995

– 3 goods• 1 agricultural• 1 informal• 1 formal

– 5 factors of production• 3 types of labor• 2 types of capital

• MICRO– Structural labor allocation model:

Endogenous occupational choice and time allocation

– Data: Enquete Permanente aupres des Menages 1993

– 4,500 households– 12,000 individuals aged 15 years

and older– 4 occupational choices at the

household level• farmer• informal wage worker• formal wage worker (rationed)• farmer & wage worker

• Software: GAUSS


Macro-level module (Extended CGE-type model)- Occupational structure: L- Price variables: p- Wage and earnings: w- All other variables in macro module: Y

Micro-simulation module (Household survey)- Socio demographic characteristics: Si

- Occupational/labor-supply choice: li = O(Si,E)- Income: yi = E(Si,w).li

E = earning rate of individual/household i in various occupations. These “personal” rates are a function of a set of standard market rates, w.

Outcome = change in distribution of income conditionally on characteristics S.

Aggregating: li = L

The Integrated Framework


Growth Shock Simulations

Simulation Description

EMBFOR Formal hiring (10 per cent)

SALFOR Increase in formal wages (10 per cent)

PGFAGRI Increase in total factor productivity in agricultural sector (10 per cent)

PGFALIM Increase in total factor productivity in agricultural foodstuffs sector (10 per cent)


BASE EMBFOR SALFOR PGFAGRI PGFALIM

Equilibrium partial general partial general partial general partial general

Per Capita Income (Francs Malagasy thousands)

Urban 1 628 5.4 4.2 6.7 5.9 -0.2 1.9 -0.2 1.6

Rural 605 4.0 5.8 1.9 3.5 1.1 5.0 0.9 4.2

All 863 4.7 5.0 4.2 4.7 0.5 3.5 0.4 3.0

Theil Index (x100)

Urban 90.9 1.6 -1.0 3.0 2.0 0.2 -0.8 0.3 -0.0

Rural 51.0 4.7 5.9 3.3 3.1 -2.3 0.3 0.2 9.4

All 81.6 3.0 0.8 4.6 3.1 -1.2 -1.5 -0.2 2.0

Poverty Head-Count Index (P0)

Urban 43.4 -3.9 -3.3 -2.5 -2.1 2.9 -2.6 2.9 -1.8

Rural 74.9 -1.2 -2.4 -0.3 -1.5 -2.9 -3.9 -1.9 -2.0

All 67.0 -1.7 -2.6 -0.7 -1.6 -2.0 -3.7 -1.2 -2.0

Notes: Base values for BASE column and percent change for other simulations.EMBFOR Formal hiring (10 per cent)SALFOR Increase in formal wages (10 per cent)PGFAGRI Increase in total factor productivity in agricultural sector (10 per cent)PGFALIM Increase in total factor productivity in agricultural foodstuffs sector (10 per cent)Source: Cogneau and Robilliard 2001.

Impact on poverty and income distribution of alternative development strategies in Madagascar


Conclusions

• General equilibrium mechanisms have significant redistribution effects

• The “lognormal-with-fixed-variance” assumption can yield biased results

• In order to be effective in reducing poverty, any development strategy based on the growth of the urban/formal sector has to be redistributed to the agricultural/rural households through an improvement in the agricultural terms of trade


The Cote d’Ivoire Model

Cogneau & Grimm (2002)


A Dynamic Fully Integrated Household Income Micro-Simulation Model for Côte d’Ivoire

Motivation Adjustment policies have effects in the medium and long-term,

which are very different from those in the short-term (e.g. impact of school dropouts on human capital supply)

Objective• Precise identification of the impact of demographic changes and

their interaction with labour market outcomes• Accounting for endogeneity of demo-economic behavior

(education, migration)• Generation of a series of counterfactual cross-sections and

potentially simulation of individual trajectories through time• Analysis of intertemporal trade-offs linked to poverty-reduction

strategies


Model structure1. Dynamic demo-economic module: modeling changes in the

population structure: fertility, mortality, household formation, migration, school enrolment and dropouts

2. Micro-economic module: modeling occupational choices, income formation and consumption choices

3. Macro-economic module: goods and labor markets closures

Data sources/Implementation• Extensive use of relevant household data: DHS (1994), Pop.

Census (1998), Migration survey (1993), LSMS-type surveys (1993, 1998)

• Income formation draws from recent work of Grimm (2001) using the BFG decomposition methodology for the case of Côte d’Ivoire


Alternative education strategies in Côte d’Ivoire

Simulation Description

REFSIM reference simulation, constant enrolment age pattern from 1998 on

PRIMED universal primary school for the generations enrolled in 1998 and after

HIGHED PRIMED + higher progression rates into higher education

ALPH-- HIGHED + literacy programs for adults

--CR ALPH with constant returns to education

--DR ALPH with decreasing returns to education


growth p.a. (1992 - 1998)

1998 2005 2010 2015 1997-2015

Per Capita Income (CFA Francs thousands)

REFSIM 351 457 498 546 2.61

PRIMED -0.3 -0.7 -0.6 -2.6 -0.15

HIGHED 2.0 -2.2 -0.6 -0.5 -0.02

ALPHCR -0.3 -2.0 1.4 5.3 0.30

ALPHDR -2.6 -10.5 -11.4 -15.0 -0.92

Gini Index over the distribution of household income per capita

REFSIM 0.598 0.609 0.609 0.609 0.18

PRIMED -0.2 -0.8 -0.7 -1.0 -0.06

HIGHED -0.8 0.2 1.0 1.3 0.07

ALPHCR 0.0 0.0 0.2 0.2 0.00

ALPHDR -0.7 -3.1 -2.6 -4.8 -0.28

Poverty Head-Count Index (P0) (household income per capita under 1 USD PPP)

REFSIM 0.348 0.300 0.271 0.254 -1.75

PRIMED 0.0 -2.0 -2.2 0.0 -0.01

HIGHED -0.3 -0.7 4.8 4.3 0.22

ALPHCR 1.7 -1.0 -0.7 -0.8 -0.05

ALPHDR 0.6 3.3 7.0 5.1 0.27Notes: Simulation from 1992 to 2015, income in 1000 CFA Francs 1998-Abidjan Source: Grimm 2002a.


The Road Ahead

• Improving microeconomic specifications– Intertemporal household behavior

• savings and investment in physical and human capital• demographic changes and migrations

– Intra-household allocation of resources

• Improving market functioning specifications in the rural sector– segmentation and market failures in factor markets (land,

labor, credit), – spatial and regional dimensions in markets for goods

(access to markets, transaction costs)

• Designing a “standard” framework?