ECO372-Assignment 3
1. Political Parties and Labor Market Outcomes: Evidence from US States [15 pts]
Questions
(a) Present the summary statistics (mean, standard deviation) for all of the variables in states and time periods in which a Democrat won the gubernatorial election relative to those in which the Republican gubernatorial candidate won the
election, for black and white individuals separately. Briefly characterize this population and interpret the results [2 pt].
[Hint: use regress command using these predetermined and outcome characteristics as dependent variables, using option “if black2==1”; cluster standard errors at the state level using “cluster(state2)” option]
(b) Generate a set of Regression Discontinuity plots – for Black and White
individuals separately – to show (a) the mean employment rate by win margin cell (using the margin of victory grouping cells (marginggg) as the running variable, as well as (b) the fit of the relationship between group–specific employment rates and the running variable and potential discontinuity in states and time periods in which a Democract gubernatorial candidate won the election, using polynomial models. Interpret the results. [3 pts] [Hints: use collapse command, with option “by(marginggg black2) cw”. After running regressions on collapsed data and using the predict command to generate fits of the regression models, use the following graph command to generate each figure: twoway (scatter y x if z==1, xline(c_o)) (line y_hat x if z==1)]
(c) Using the larger dataset made available for the assignment, generate estimates of the discontinuity in employment rates of Black individuals using linear, quadratic, and cubic polynomial models on the running variable (margin of victory, or marginvvv), including state and year fixed effects. Assess the robustness of the estimates with and without predetermined individual controls (e.g., gender, age, education, marital status). Replicate the empirical exercise for White individuals. Interpret the results. [5 pts] [Hint: Estimate OLS regression models with i.state2 and i.year2 fixed effects, cluster standard errors at the state2 level]
(d) Generate analogous RD estimates using the local polynomial RD estimation using the Calonico, Cattaneo and Titiunik (2016) optimal bandwidth and robust (bias–corrected) confidence intervals procedure, for Black and White individuals separately. Interpret the results. [2 pts] [Hint: use rdrobust command using vce(cluster state2) option]
(e) Estimate the balance of predetermined covariates around the discontinuity as tests of the RDD continuity assumption using the available data, for Black and White individuals separately. For simplicity, you can use the Calonico, Cattaneo, and Titiunik (2016) rdrobust procedure. Interpret the results. What other tests could be conducted if you had access to the raw individual–level data? [3 pts]
(f) Bonus question: using the regression models in part (1c), test whether the election outcome at the discontinuity has statistically significantly different effects for Black and White individuals in these states. [No hints] [Bonus: 3 pts]
2. Vulnerability in the Brazil Semi–Arid and Political Clientelism
Questions
(a) Estimate the OLS relationship between cistern ownership and clientelist private goods requests of citizens to politicians, with and without the inclusion of municipality fixed effects (ImunID2–ImunID40). Interpret the results. [1 pt]. [Hint: use regress command; cluster standard errors at the neighborhood cluster level using “cluster(clusters)” option]
(b) Estimate the effect of random assignment to the treatment group on (a) the probability of receiving a cistern (first–stage) , as well as on (b) the probability of making private requests to political representatives during the electoral period
(reduced form), including municipality fixed effects (ImunID2–ImunID40) Interpret the results. [2 pts]
[Hint: use regress command; cluster standard errors at the neighborhood cluster level using “cluster(clusters)” option]
(c) Estimate the effect of receiving a cistern on private requests exploiting the random assignment to the treatment group as an instrumental variable for cistern ownership, , including municipality fixed effects (ImunID2–ImunID40) Interpret the results. How does this IV estimate relate to the estimates from question b above? [4 pts] [Hint: use ivregress command; cluster standard errors at the neighborhood cluster level using “vce(cluster clusters)” option]
