Propensity Score Matching Methods for Non-experimental Causal Studies

This paper considers causal inference and sample selection bias in non-experimental settings in which: (i) few units in the non-experimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pre-treatment characteristics. We propose the use of propensity score matching methods and implement them using data from the NSW experiment. Following Lalonde (1986), we pair the experimental treated units with non-experimental comparison units from the CPS and PSID and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. We show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units.

Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs

This paper uses propensity score methods to address the question: how well can an observational study estimate the treatment impact of a program? Using data from Lalonde's (1986) influential evaluation of non-experimental methods, we demonstrate that propensity score methods succeed in estimating the treatment impact of the National Supported Work Demonstration. Propensity score methods reduce the task of controlling for differences in pre-intervention variables between the treatment and the non-experimental comparison groups to controlling for differences in the estimated propensity score (the probability of assignment to treatment, conditional on covariates). It is difficult to control for differences in pre-intervention variables when they are numerous and when the treatment and comparison groups are dissimilar, whereas controlling for the estimated propensity score, a single variable on the unit interval, is a straightforward task. We apply several methods, such as stratification on the propensity score and matching on the propensity score, and show that they result in accurate estimates of the treatment impact.

Was there a Riverside miracle? An hierarchical framework for evaluating programs with grouped data

This paper uses data from the Greater Avenues for Independence (GAIN) demonstration to discuss the evaluation of programs that are implemented at multiple sites. Two frequently used methods are pooling the data or using fixed effects (an extreme version of which estimates separate models for each site). The former approach, however, ignores site effects. Though the latter incorporates site effects, it lacks a framework for predicting the impact of subsequent implementations of the program (e.g., will a new implementation resemble Riverside or Alameda?). I present an hierarchical model that lies between these two extremes. For the GAIN data, I demonstrate that the model captures much of the site-to-site variation of treatment effects, but has less uncertainty than a model which estimates treatment effects separately for each site. I also show that uncertainty in predicting site effects is important: when the predictive uncertainty is ignored, the treatment impact for the Riverside sites is significant, but when we consider predictive uncertainty, the impact for the Riverside sites is insignificant. Finally, I demonstrate that the model is able to extrapolate site effects with reasonable accuracy, when the site for which the prediction is being made does not differ substantially from the sites already observed. For example, the San Diego treatment effects could have been predicted based on observable site characteristics, but the Riverside effects are consistently underestimated

Child labor, income shocks, and access to credit

Although a growing theoretical literature points to credit constraints asan important source of inefficiently high child labor, little work has been done to assess its empirical relevance. Using panel data from Tanzania, the authors find that households respond to transitory income shocks by increasing child labor, but that the extent to which child labor is used as a buffer is lower when households have access to credit. These findings contribute to the empirical literature on the permanent income hypothesis by showing that credit-constrained households actively use child labor to smooth their income. Moreover, they highlight a potentially important determinant of child labor and, as a result, a mechanism that can be used to tackle it.Environmental Economics&Policies,Labor Policies,Children and Youth,Economic Theory&Research,Health Economics&Finance,Health Economics&Finance,Street Children,Environmental Economics&Policies,Youth and Governance,Children and Youth

Propensity Score Matching Methods for Non-Experimental Casual Studies

This paper considers casual inference and sample selection bias in non-experimental settings in which: (i) few unites in the non-experimental comparison group are comparable to treatment units; and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pre-treatment characteristics. We propose the use of propensity score matching methods, and implement them using data from the NSW experiment. Following Lalonde (1986), we pair the experimental treated units with non-experimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using out methods to the benchmark results from the experiment. We show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units

Child labor: The role of income variability and access to credit across countries

This paper examines the relationship between child labor and access to credit at a cross-country level. Even though this link is theoretically central to child labor, so far there has been little work done to assess its importance empirically. We measure child labor as a country aggregate, and credit constraints are proxied by the extent of financial development. These two variables display a strong negative relationship, which we show is robust to selection on observables (by controlling for a wide range of variables such as GDP per capita, urbanization, initial child labor, schooling, fertility, legal institutions, inequality, and openness, and by allowing for a nonparametric functional form), and to selection on unobservables (by allowing for fixed effects). We find that the magnitude of the association between our proxy of access to credit and child labor is large in the subsample of poor countries. Moreover, in the absence of developed financial markets, households appear to resort substantially to child labor in order to cope with income variability. This evidence suggests that policies aimed at widening households' access to credit could be effective in reducing the extent of child labor

The consequences of child labor : evidence from longitudinal data in rural Tanzania

This paper exploits a unique longitudinal data set from Tanzania to examine the consequences of child labor on education, employment choices, and marital status over a 10-year horizon. Shocks to crop production and rainfall are used as instrumental variables for child labor. For boys, the findings show that a one-standard-deviation (5.7 hour) increase in child labor leads 10 years later to a loss of approximately one year of schooling and to a substantial increase in the likelihood of farming and of marrying at a younger age. Strikingly, there are no significant effects on education for girls, but there is a significant increase in the likelihood of marrying young. The findings also show that crop shocks lead to an increase in agricultural work for boys and instead lead to an increase in chore hours for girls. The results are consistent with education being a lower priority for girls and/or with chores causing less disruption for education than agricultural work. The increased chore hours could also account for the results on marriage for girls.Street Children,Youth and Governance,Labor Policies,Labor Markets,Children and Youth

Do households resort to child labor to cope with income shocks?

Using four rounds of panel household data from the Kagera region of Tanzania, we show that transitory income shocks - measured by the value of crop lost by farming households - lead to significantly increased child labor. A one standard deviation increase in the shock is associated with a 10% increase in mean child working hours. Moreover, we find that households with collateralizable assets - which we interpret as a proxy for access to credit - are better able to offset the effects of income shocks. This evidence supports the view that credit market imperfections are an important determinant of child labor