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Publications of Robert J Garlick    :chronological  alphabetical  combined listing:

%% Journal Articles   
@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}
}

@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}
}


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