Medición de la capacidad orientado a resultados

The results from estimating Equations 2.1 and 2.2 for labor supply responses of children are presented in Table 2.5. I split the sample by level of household assets and perform all estimations separately for the subsample of children who live in households with relatively low levels of assets and those with relatively high levels of assets. The reason why I split the sample in this way is because several previous studies have shown that household assets are an important determinant of child labor and school attendance. For instance, some studies have shown that child labor increases with the amount of assets possessed by the household up to a certain level (e.g. Bhalotra and Heady (2003), Dumas (2007), Del Carpio (2008), Basu, Das, and Dutta (2010), Menon (2010)). To classify children in households with low and high levels of assets, I construct an asset index using principal component analysis which includes several measures of household assets including land ownership, dwelling ownership, access to water and sanitation, access to electricity and landphone, kitchen a separate room, and dwelling all high quality materials (e.g. Filmer and Pritchett, 1998). The labor supply responses of children are measured by two variables: an indicator for whether the child is involved in paid or unpaid market or farm work (Panel A), and the total number of hours per week that the child spends performing that activity (Panel B). In columns (1) to (4), I present the Bolivida effect for the

subsample of children living in households with relatively low levels of assets (≤ 25 percentile of the asset index distribution). In columns (5) to (8), I present the Bolivida effect for the subsample of children living in households with high levels of assets (> 25 percentile of the asset index distribution). Each pair of columns presents the intent-to-treat estimates (ITT) and the analogous treatment-on-the-treated effects (TOT) of the program. All estimates are obtained using OLS methods. The treatment-on-the-treated estimates are estimated using the two-stage procedure described in Section 2.

Although most of the coefficients are imprecisely estimated, rural households with relatively low levels of assets seem to have responded to the Bolivida by increasing children’s participation in farming and agricultural activities. In particular, the Bolivida is associated with an increase in the probability that children in rural areas from households with relatively low levels of assets are involved in child labor. Children in rural households with low levels of assets who live with an eligible person are 5.53 percentage points (ITT) more likely to be engaged in child labor as compared to those who live with a near-eligible person. The F-statistic for the null that Ho: Bolivida+Bolivida*Rural=0 is 3.52 (p-value=0.08). In contrast, the Bolivida is associated with a large reduction in the probability that children in urban areas from households with relatively low levels of assets are involved in child labor (-32.97 percentage points, ITT).

Moreover, the Bolivida is not associated with an increase in the number of hours rural children from house- holds with relatively low levels spend working. The F-statistic for the null that Ho: Bolivida+Bolivida*Rural=0 is 0.04 (p-value=0.84). Yet, the Bolivida is associated with a substantive reduction in the number of hours urban children from households with relatively low levels of assets spend working. Children from urban households with relatively low levels of assets and who live with an eligible person spend on average 146.45 percent (ITT) less hours working compared to those who live with a near eligible person.

Lastly, the Bolivida is not associated with significant changes in child labor patterns (both probability of working and number of hours spent working) among children living in relatively richer households as measured by asset levels. Yet, to the extent that child labor has been shown to significantly affect school achievement (Bezerra, Kassouf, and Arends-Kuenning, 2009), the significant increase in child labor among rural children might potentially have pervasive effects among these children.

The results from estimating Equations 2.1 and 2.2 for human capital investments are summarized in Table 2.6. The Table presents OLS estimates. Children’s human capital investments are measured by two variables: an indicator for whether the child attends school on a regular basis (Panel A), and the total aggregate direct schooling expenditures in logs (Panel B). In columns (1) to (4), I present the effects of the Bolivida for the subsample of children in households with relatively low levels of assets. In columns (5) to (8), I present the Bolivida effects for the subsample of children in households with relatively high levels of assets. Each pair

of columns presents the intent-to-treat estimates and the analogous treatment-on-the-treated effects of the program.

The increase in the probability that rural children from households with relatively low levels of assets work as a result of the Bolivida does not necessarily translate into reductions in the probability that they attend school. Indeed, school attendance among both urban and rural children is largely unaffected by the Bolivida. Next, I examine whether the Bolivida might have affected human capital investments at the intensive margin. The data allow exploring whether direct schooling expenditures might have been affected by the program and, if so, the specific components affected. Panel B, in Table 2.6, presents the effects of the Bolivida on an aggregated measure of direct schooling expenditures (in logs). This aggregated measure includes annual enrollment fees, monthly school fees, transportation-to-school, uniforms, textbooks and school supplies, contributions to parent-teacher associations, contributions for top-ups to teachers’ salaries, and contributions to improve school’s infrastructure. The ITT effects are estimated using OLS, in Equation 2.1, including now as an additional covariate the mean of direct schooling expenditures in logs (at the census enumeration area level) to control for differences across regions in school choice and income levels. The TOT effects are estimated using the same two-stage least squares procedure described in Section 2 but now adding as an additional control the mean of direct schooling expenditures (in logs) at the level of census enumeration area. Overall, the Bolivida is not associated with significant changes in household’s direct schooling expenditures for neither children in households with relatively low levels of assets nor those in households with relatively high levels of assets.

Overall the TOT estimates are not significant, although the ITT estimates are sometimes significant. In this setting, the ITT estimates give the average impact of being assigned the option of cashing out the Bolivida while the TOT estimates give the average impact of actually receiving the Bolivida. To the extent that participation in any social program is at the end voluntary, selective take-up is a well-known source of bias in any impact evaluation of this kind. The gold standard for identifying causal impacts in a setting such as the one studied in this paper is to use the program assignment rule as an instrumental variable for treatment status. However, households almost certainly base their choices on things that are unobservable to the researcher. To the extent that Bolivida receipt might respond to unobservable factors that might be correlated with factors that simultaneous affect children’s human capital investments, I believe that the reported ITT estimates are the most appropriate measure of causal impacts in this setting. Yet, just which one of the two estimates might be better to look at would eventually depend on what the researcher wants to learn from the impact evaluation.