Assignment 3: PROGRESA conditional cash transfers and
\nsecondary school enrollment
\nOctober 31, 2022
\nThe data come from Bobonis and Finan (2009, Review of Economics and Statistics) who looked
\nat the effect of PROGRESA conditional cash transfers on school enrollment and the role of peer
\neffects. Data was collected in 1997 (baseline) and by 1998 (endline\/post=1) only some villages
\n(treat=1) had received the transfers. Treatment across villages was randomized. Within each vil lages, only households with a low enough welfare index were considered poor and therefore eligible
\n(eligible=1) to receive transfers; other households were deemed ineligible (even in the treatment
\nvillages). The data include the following variables for students and heads of households (collected
\nat baseline unless otherwise specified):
\nfolio \u2013 household ID
\nnumero \u2013 individual ID
\nyear \u2013 year
\nvillage \u2013 village ID
\nsc \u2013 school enrollment
\neligible \u2013 PROGRESA eligible
\nwelf are \u2013 eligibility welfare index (lower for the poor)
\npost \u2013 =1 for 1998, =0 for 1997
\ntreat \u2013 PROGRESA treatment village (=1), control (=0)
\ngender \u2013 (=1 Male, =0 if Female)
\nindig \u2013 (=1 indigenous, =0 otherwise)
\nhohedu \u2013 Head of household schooling attainment
\nhohsex \u2013 Head of household gender (=1 Male, =0 Female)
\nhohage \u2013 Head of household age
\nf amn \u2013 household size
\ndistsec \u2013 minimum distance to secondary school
\nmindist \u2013 minimum distance between the village and large urban center
\nmeanvillsc \u2013 average school enrollment of eligible children in the village\/year
\n1
\n(a) What can we say about the determinants of secondary school enrollment based on the
\n(cross-sectional) relationships in the data (for 1997)? [2pts]
\n(b) At what level should you cluster the standard errors in (a)? [1pt]
\n(c) Which characteristics are systematically (statistically) different between eligible (poor) and
\nineligible (\u201crich\u201d) households? [2pts]
\n(d) Provide evidence that the across-village randomized treatment was really randomized [2pts]
\n(e) Provide an estimate of the average treatment effect (ATE) of PROGRESA transfers (on
\nschool enrollment) for the eligible children and the ATE for the ineligible children using the pro gram randomization and interpret the results [2pts]
\n(f) At what level should we cluster the standard errors in parts (d) and (e)? [1pt]
\n(g) We can also estimate the ATE of village-level PROGRESA treatment using a difference in-difference estimator \u2013 do this for the eligible children and discuss how these estimates relate to
\nyour estimates in (e) as well as what we learn about overall trends in schooling [2pts]
\n(h) Suppose we only had data from treatment villages. Use the eligible and ineligible children
\nto estimate an average treatment effect using a difference-in-difference specification. Discuss the
\npotential bias from this estimate. [2pts]
\n(i) Suppose we wanted to estimate the causal effect of the village-level school enrollment rate
\nof eligible children on school enrollment of ineligible children. Provide an OLS and a IV (2SLS)
\nestimate of this relationship [hint: you can use the randomized treatment as the instrument, and
\ncan calculate the IV coefficient in excel using the reduced form and first-stage coefficients], discuss
\nwhether the IV conditions are satisfied and interpret the results. [2pts]
\n(j) Suppose we only had data from treatment villages in the post-period (1998). Noting that
\neligibility for the program is related to the welfare (poverty) index, provide as close to a causal
\nestimate as one can estimate given the data limitations and discuss the intuition for why your
\napproach is superior to simply regressing school enrollment on program eligibility and available
\ncontrol variables [hint: think in terms of the logic of a regression discontinuity design] [4pts]
\n2
\n3<\/p>\n","protected":false},"excerpt":{"rendered":"
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