Daniel Ortega CAF and IESA

1. Comments on:“The Effects of Income Shocks on Child Labor and Conditional Cash Transfer Programs as an Insurance Mechanism for Schooling” by Monica Ospina Daniel Ortega CAF and IESA

2. The paper • Asks whether CCTs may serve as insurance when HHs face income shocks: Does “Familias en Accion” reduce the HH’s use of child labor as a coping strategy in the event of a shock? • Answer: Yes, and this is specially so along the intensive margin (work and study hours) • Data: FA evaluation survey between 2002 and 2005 (6,519 HHs; 30,985 individuals; 13,737 under 18)

3. Comments: General framing • The paper’s question is interesting, but requires better framing • In essence, it estimates heterogeneous impacts of a CCT across households under different types of shocks • This straightforward description would help the exposition greatly, and would better highlight the relevant estimation issues

4. Comments: motivation • Why is the intensive margin important? • How extensive is study-work? • Can we sum the utility effects of these activities to get an aggregate welfare effect? • If taking the intensive margin into account is quantitatively as important as the extensive margin, then this would be really good motivation

5. Comments: theory • Strictly speaking, we cannot really talk of a CCT program as being an insurance scheme, unless its design was such that you entered the program in the event of adverse shock (maybe people who qualify have had a string of bad shocks that has left them in poverty) • A CCT increases the opportunity cost of child labor (price effect) but it also increases HH’s disposable income, so it has income and wealth effects. The direction of the incentives to shift the allocation of time do not depend on the presence of shocks • This highlights the need for a more detailed description of the model and the role of a CCT in it, which is currently absent

6. Comments: estimation • Paper presents matching Dif in Dif estimator to control for potential omitted variables bias arising from “quasi-randomization” • In general, the identification assumptions of matching estimators are the same as OLS. The difference lies in the weights given to each observation (Angrist (1998), Angrist and Pischke (2009), which is specially relevant when heterogeneous effects are suspected (such as this paper) • It would therefore be important to present both the matching and the standard Diff in Diff estimates (for the common support sample)

7. Comments: results • One general concern is the low incidence of shocks: less than 2% for all shocks except crop loss and illness, which are around 12% • 2% of the sample is approximately 280 children, 140 in treatment and 140 in control communities, which implies a very large “Minimum Detectable Effect” given that randomization is at the level of community (no power) • Even in the case of crop loss and illness it would be important to calculate the power of the test

8. Comments: results • It is important to show the number of observations in all tables • The interpretation of the results should be based on comparing the size of the coefficients (differential impacts for groups subject to different types of shocks) • Overall, the results suggest that both in the intensive and extensive margins, FA has had a smaller impact on schooling and child labor for more disadvantaged children • It is necessary to be convincing about the importance of the impact on the intensive margin