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| Publications of Robert J Garlick :chronological combined listing:%% Journal Articles @article{fds320586, Author = {Garlick, RJ}, Title = {Academic Peer Effects with Different Group Assignment Policies: Residential Tracking versus Random Assignment}, Journal = {Economic Research Initiatives at Duke (ERID)}, Number = {220}, Pages = {53 pages}, Year = {2016}, Month = {March}, Abstract = {I study the relative academic performance of students tracked or randomly assigned to South African university dormitories. Tracking reduces low-scoring students' GPAs but has little effect on high-scoring students. This lowers mean GPA and raises GPA dispersion. I also directly estimate peer effects using random variation in peer groups across dormitories. Living with higher-scoring peers raises students' GPAs and this effect is larger for low-scoring students. Peer effects operate largely within race groups but operate both within and across programs of study. This suggests that spatial proximity alone does not generate peer effects. Interaction of some sort is required, but direct academic collaboration is not the relevant form of interaction. I integrate the results from variation in group assignment policies and variation in group composition by drawing on the matching and sorting literatures. Both sets of results imply that own and peer academic performance are substitutes in GPA production and that GPA may be a concave function of peer group performance. The cross-dormitory results correctly predict a negative effect of tracking on low-scoring students but understate the magnitude of the observed effect. I show that this understatement reflects both policy-sensitive parameter estimates and problems with extrapolation outside the support of the data observed under random assignment. This underlines the value of using both cross-policy and cross-group variation to study peer effects.}, Key = {fds320586} } @article{fds320584, Author = {Garlick, RJ and Orkin, K and Quinn, S}, Title = {Call Me Maybe: Experimental Evidence on Using Mobile Phones to Survey Microenterprises}, Journal = {Economic Research Initiatives at Duke (ERID)}, Number = {224}, Pages = {49 pages}, Year = {2016}, Month = {July}, Abstract = {High-frequency data is useful to measure volatility, reduce recall bias, and measure dynamic treatment effects. We conduct the first experimental evaluation of high-frequency phone surveys in a developing country or with microenterprises. We randomly assign microenterprise owners to monthly in-person, weekly in-person, or weekly phone interviews. We find high-frequency phone surveys are useful and accurate. Phone and in-person surveys yield similar measurements, with few large or significant differences in reported outcome means or distributions. Neither interview frequency nor medium affects reported outcomes in a common in-person endline. Phone surveys reduce costs without increasing permanent attrition from the panel.}, Key = {fds320584} } @article{fds320585, Author = {Garlick, RJ and Hyman, J}, Title = {Data vs. Methods: Quasi-Experimental Evaluation of Alternative Sample Selection Corrections for Missing College Entrance Exam Score Data}, Journal = {Economic Research Initiatives at Duke (ERID)}, Number = {221}, Pages = {96 pages}, Year = {2016}, Month = {June}, Abstract = {In 2007, Michigan began requiring all high school students to take the ACT college entrance exam. This natural experiment allows us to evaluate the performance of several parametric and semiparametric sample selection correction models. We apply each model to the censored, prepolicy test score data and compare the predicted values to the uncensored, post-policy distribution. We vary the set of model predictors to imitate the varying levels of data detail to which a researcher may have access. We find that predictive performance is sensitive to predictor choice but not correction model choice. All models perform poorly using student demographics and school- and district-level characteristics as predictors. However, all models perform well when including students’ prior and contemporaneous scores on other tests. Similarly, correction models using group-level data perform better with more finely disaggregated groups, but produce similar predictions under different functional form assumptions. Our findings are not explained by an absence of selection, the assumptions of the parametric models holding, or the data lacking sufficient variation to permit useful semiparametric estimation. We conclude that “data beat methods” in this setting: gains from using less restrictive econometric methods are small relative to gains from seeking richer or more disaggregated data.}, Key = {fds320585} } | |
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