Presented by Luke Okafor and Elizabeth Rivard

University of Warsaw November 19, 2007

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CONDITIONAL CASH TRANSFERS, SCHOOLING AND CHILD LABOR:

MICRO-SIMULATING BOLSA ESCOLA

ByFRANÇOIS BOURGUIGNON,

FRANCISCO H. G. FERREIRA PHILLIPPE G. LEITE

Presented by Luke Okafor and Elizabeth Rivard


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OUTLINE

INTRODUCTION BOLSA ESCOLA PROGRAMME METHODOLOGY APPRAISAL OF THE STUDY SUGGESTIONS FOR IMPROVEMENT CRITIQUE BY SCHWARTZMAN CONCLUSIONS


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INTRODUCTION

Cash transfers targeted to poor people with conditions The Brazilian National Bolsa Escola is a kind of

redistributive programme with features of:

-Means-test

-The behavioral conditionality

-Eligibility criteria Evaluation of the kind of programme could be:

-Ex-post approaches

-Ex-ante methods


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BOLSA ESCOLA PROGRAMME Bolsa Escola Programme was created by law in

April 2001 Eligibility for participation in the programme

-Households with monetary income below 90 Reasi (R$) per month-with children aged 6 to 15-85 % school attendance

Goals of the programme:-Reduction of current levels of poverty and

inequality-Provision of incentives for reduction of future

poverty


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The decision of how the child’s time allocation is made within the household ignored

The decision to send the child to school is last to made

The issue of various siblings in same household ignored

The composition of the household is exogenous

ASSUMPTIONS OF THE STUDY


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METHODOLOGY

The occupational choice variable Si will be modeled using the standard utility-maximizing interpretation of the multinomial Logit framework,

Si = k iff Sk(Ai, Xi, Hi; Y-i + yik) + vik > Sj(Ai, Xi, Hi; Y-i + yij) + vij for j ≠k (1)

Collapse non-income explanatory variables into a single vector Zi and linearize

Ui(j) = Sj(Ai, Xi, Hi; Y-i + yij) + vji = Zi.γj + (Y-i + yij)αj + vij

(2)


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METHODOLOGY

The observed marketing earning of the child denoted by wi. Assuming the standard Becker-Mincerian human capital model, writes:

Log wi = Xi .δ + m*Ind(Si=1) + ui (3)

Xi set of individual characteristics

Ui random error terms

Ind(Si=1) indicator function

Based on (3) the child’s contribution to the household income yij under the various alternatives j


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METHODOLOGYYi0 = Kwi; yi1 = MKwi; yi2 =Dyio = DKwi with M = Exp (m)

where it is assumed that yij values the output or potential

market earnings

Wi is decomposed into in the proportions of k,

1-M and 1-D

Replacing (4)in (2) leads to

Ui(j) = Sj(Ai, Xi, Hi; Y-i + yij) + vji = Zi.γj + Y-i αj + β.wi + vij with: β0 = α0 K, β1= α1 MK; β2= α2 Dk (5)


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METHODOLOGY This is the final model simulated by the authors. If the coefficients of α, β, γ, wi and Vij, then the child’s occupational choice type selected by the household I is

K* = Arg Max [ Ui (j)] (6) Equation (5) is the benchmark case. If the Bolsa Escola

programme entitled all the children going to school a transfer of T, then, 5 is replaced by

Ui(j) = Zi.γj + (Y-i + BEij).αj + β.wi + vij with: βEi0 = 0 and BEi1=BEi2 =T (7)


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APPRAISAL OF THE STUDY

The individual effects could be correlated with schooling choice and the correlation between the composite error terms could make the OLS to be biased

The validation of the simulated model on survey data alone may lead to biased results eg sample bias, age effects etc

Calibrations based on the simulations afterwards may be biased as well


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APPRAISAL OF THE STUDY CONTD

The eligibility condition created problem of additionality: attendance and learning, a different kind of social exclusion, and length of the programme

Target assistencialist bias

Table 1: Bolsa Escola in Recife and Belo Horizonte


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APPRAISAL OF THE STUDY CONTD

Influence of unemployment in the family Influence of gender on the decision making process The problem associated with undeclared income The scored-based proxy for permanent may be too far

from average truth


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SUGGESTIONS FOR IMPROVEMENT

The use of simulation and CGE may give room for taking care of changing economic conditions in the long run

The labour supply model: the intra-labour choice allocations could be incorporated into the study

Validation and calibration of the model should be based on data from participants and non-participants in the Bolsa Escola programmme and the Survey


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CONCLUSIONS-ORIGINAL PAPER

Take the size of the family into account when determining which families are eligible

Monitor school attendence rather than school enrollment

The assumption that in poor families, children (ages 6-13) do not go to school because they have to work and little money incentive could change this situation may be too simplistic.


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CONCLUSIONS-ORIGINAL PAPER

Education focus of the programme (may have missed target)

Target transfer to the age with the highest risk (age 14 and above)


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CRITIQUE BY SCHWARTZMAN

Patterns of attendance not related to the stipend; limited government monitoring of attendance


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Missing school for work was not widespread


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Having the stipend decreased the chances of a child working for those aged 5-6 and 14-17, but not those aged 7-13


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Children receiving the stipend actually worked more than those who did not; child labor is mostly rural, ages 15-17


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The least poor do not always receive the stipend: majority of the poor are urban, but programme focuses on rural poor

Of the 12.8 million children in families at the lowest fifth income quintile, 35% live in rural areas, but receive 40% of the stipends. Among the rural poor, 39% receive the stipend; among the urban poor, only 30%


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CONCLUSIONS-SCHWARTZMAN

Increase stipend with age (14 and older receive more) Increase overall quality of schools Better targeting, implementation and monitoring

Presented by Luke Okafor and Elizabeth Rivard

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