Executive summary

Annex 3.4: Methodology USED TO perform the Long Run Impact simulations of RdO

This appendix is largely based on Ferreira and Leite (2002). In order to understand the impact of a substantial expansion of education for the eligible population aged between 18-23 and 18-29, we estimate a simple model of household income determination. This model is recursive and consists of five blocks.

1. 
   Block 1:
   

The per capita household income is given by

where denotes total income of household h and is the family size.
The total household income is the sum of labor and non-labor incomes of all household members.

2. 
   Block 2:
   

It is assumed that the non-labor incomes are exogenously determined. Depending on the individual occupational choice, labor income is or , where denotes the labor earnings of individual in sector and denotes the profits of individual in the self-employment sector. is a 0-1 participation dummy.

Both and depend on observable and unobservable characteristics, and the vectors of parameters and determine how observable characteristics affect the labor earnings or the profits, respectively.

Notice that expressions (3) are standard Mincerian earnings equations. The estimation results for both equations are reported in table A.3.4.2

3. 
   Block 3:
   

This block models the choice of occupation into wage employment, self-employment or inactive. For this purpose, using a discrete choice model (multinomial logit) we estimate the probability of choice of each occupation as a function of a set of family and personal variables. Table A.3.4.1 shows the estimation result.

4. 
   Block 4:
   

In this blocks we estimate the probability of choosing a certain number of children (0, 1, 2, 3, 4, 5, +) using a discrete choice model as in block 3. The variable used for the number of children in the estimation refers to the number of sons and daughters of the mother who are aged 14 or less and live in the household.

The per capita income of individual is affected by fertility decisions. The increase in the number of children increases the denominator in equation 1 and thus, keeping other thing constant, reduces the per capita income of all household members. Additionally, the number of children affects the labor participation decision of some household members; generally the mother’s modifying the probability of being in the labor market and thus affecting the numerator of equation (1). The estimation results are showed in table A.3.4.3.

5. 
   Block 5:
   

This block models and individual’s choice of final education attainments in terms of years of schooling. For this purpose we use an ordered probit model (OPM). Table A.3.4.4 shows the estimation results.

Changes in the years of schooling of individual could affect the per capita income of the family by several ways. The fertility decisions are likely to depend of the level of education of individual , hence the occupational choice are likely to depend on the number of children in the household and the level of education of individual . Finally, the labor earnings are likely to depend on the occupational choice and the level of education of individual .
To assess the reduction observed in both short run and long run poverty induced by the accumulation of human capital due to the CCT conditionalities, we simulate the impact of an increase to 10 in the average of years of schooling of the population selected to participate in the program. We estimate this rise implementing the computer algorithm proposed by Ferreira and Leite which take into account through the OPM estimated that the educational attainment is distributed jointly with age, gender and spatial location. See Bourguignon, Ferreira and Leite (2002) and Ferreira and Leite (2002) for more detailed statistical discussion of this kind of counterfactual analysis.

Table A.3.4.1: The Estimated Occupational Choice - Multinomial logit Population between 18-64 years old Dependent Variable: Occupational Category

Source: Own estimation based on ENV 2003 data. Note 1: Out of labor force is the reference category Note 2: t values in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%

Table A.3.4.2: The Estimated Fertility Choice - Multinomial Logit Female Population between 18-64 years old - Dependent Variable: Number of Children in the HH. aged 17 and less

Source: Own estimation based on ENV 2003 data. Note 1: 4 or + number of children in the HH. is the reference category Note 2: t values in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%

Table A.3.4.3: Earnings Equation - OLS Model Worker Population between 18-64 years old - Dependent Variable: Log Labor Income
Source: Own estimation based on ENV 2003 data. Note 1: t values in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%
Table A.3.4.4: Ordered Probit Model for Years of Schooling Worker Population between 18-64 Years Old Dependent Variable: Years of Schooling
Source: Own estimation based on ENV 2003 data. Note 1: t values in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%