1,721,648 research outputs found
The perils of peer effects
No full textIndividual outcomes are highly correlated with group average outcomes, a fact often interpreted as a causal peer effect. Without covariates, however, outcome-on-outcome peer effects are vacuous, either unity or, if the average is defined as a leave-out mean, determined by a generic intraclass correlation coefficient. When pre-determined peer characteristics are introduced as covariates in a model linking individual outcomes with group averages, the question of whether peer effects or social spillovers exist is econometrically identical to that of whether a 2SLS estimator using group dummies to instrument individual characteristics differs from OLS estimates of the effect of these characteristics. The interpretation of results from models that rely solely on chance variation in peer groups is therefore complicated by bias from weak instruments. With systematic variation in group composition, the weak IV issue falls away, but the resulting 2SLS estimates can be expected to exceed the corresponding OLS estimates as a result of measurement error and for other reasons unrelated to social effects. Research designs that manipulate peer characteristics in a manner unrelated to individual characteristics provide the most compelling evidence on the nature of social spillovers. As an empirical matter, designs of this sort have mostly uncovered little in the way of socially significant causal effects. Keywords: causality; social returns; instrumental variable
Incentives and Services for College Achievement: Evidence from a Randomized Trial
Full text linkMany North American college students have trouble satisfying degree requirements in a timely
manner. This paper reports on a randomized field experiment involving two strategies designed
to improve academic performance among entering full-time undergraduates at a large Canadian
university. One treatment group (“services”) was offered peer advising and organized study
groups. Another (“incentives”) was offered substantial merit-scholarships for solid, but not
necessarily top, first year grades. A third treatment group combined both interventions, while a
control group received neither services nor incentives. Service take-up rates were much higher
for women than for men and for students offered both services and incentives than for those
offered services alone. No program had an effect on men’s grades or other measures of
academic performance. However, the Fall and first-year grades of women in the combined
group were higher than those of women in the control group, and women in this group earned
more course credits and were less likely than controls to be on academic probation. These
differentials persisted through the end of the second year, in spite of the fact that incentives were
given in the first year only. The results suggest that the study skills acquired in response to a
combination of academic support services and incentives can have a lasting effect, at least on
women, and that the combination of services and incentives is more promising than either alone.Canada Millennium Scholarship Foundatio
When Opportunity Knocks, Who Answers? New Evidence on College Achievement Awards
Full text linkhttp://muse.jhu.edu/journals/journal_of_human_resources/v049/49.3.angrist.htmlWe evaluate the effects of academic achievement awards for first- and second-year college students studying at a Canadian commuter college. The award scheme offered linear cash incentives for course grades above 70. Awards were paid every term. Program participants also had access to peer advising by upperclassmen. Program engagement appears to have been high but overall treatment effects were small. The intervention increased the number of courses graded above 70 and points earned above 70 for second-year students but generated no significant effect on overall GPA. Results are somewhat stronger for a subsample of applicants who correctly described the program rules.Spencer FoundationHigher Education Quality Council of Ontari
The Effects of High Stakes High School Achievement Awards: Evidence from a Randomized Trial
No full textThe Israeli matriculation certificate is a prerequisite for most postsecondary schooling. In a randomized trial, we attempted to increase certification rates among low-achievers with cash incentives. The experiment used a school-based randomization design offering awards to all who passed their exams in treated schools. This led to a substantial increase in certification rates for girls but had no effect on boys. Affected girls had a relatively high ex ante chance of certification. The increase in girls' matriculation rates translated into an increased likelihood of college attendance. Female matriculation rates increased partly because treated girls devoted extra time to exam preparation.Falk Institute for Economic Research in IsraelNational Institutes of Healt
Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff
No full textIn regression discontinuity (RD) studies exploiting an award or admissions cutoff, causal effects are nonparametrically identified for those near the cutoff. The effect of treatment on inframarginal applicants is also of interest, but identification of such effects requires stronger assumptions than those required for identification at the cutoff. This article discusses RD identification and estimation away from the cutoff. Our identification strategy exploits the availability of dependent variable predictors other than the running variable. Conditional on these predictors, the running variable is assumed to be ignorable. This identification strategy is used to study effects of Boston exam schools for inframarginal applicants. Identification based on the conditional independence assumptions imposed in our framework yields reasonably precise and surprisingly robust estimates of the effects of exam school attendance on inframarginal applicants. These estimates suggest that the causal effects of exam school attendance for 9th grade applicants with running variable values well away from admissions cutoffs differ little from those for applicants with values that put them on the margin of acceptance. An extension to fuzzy designs is shown to identify causal effects for compliers away from the cutoff. Supplementary materials for this article are available online. Keywords: causal inference; conditional independence assumption; instrumental variables; treatment effectsNational Science Foundation (U.S.) (Award SES-1426541
Undergraduate Econometrics Instruction: Through Our Classes, Darkly
No full textThe past half‐century has seen economic research become increasingly empirical, while the nature of empirical economic research has also changed. In the 1960s and 1970s, an empirical economist's typical mission was to "explain" economic variables like wages or GDP growth. Applied econometrics has since evolved to prioritize the estimation of specific causal effects and empirical policy analysis over general models of outcome determination. Yet econometric instruction remains mostly abstract, focusing on the search for "true models" and technical concerns associated with classical regression assumptions. Questions of research design and causality still take a back seat in the classroom, in spite of having risen to the top of the modern empirical agenda. This essay traces the divergent development of econometric teaching and empirical practice, arguing for a pedagogical paradigm shift
Causal Effects of Monetary Shocks: Semiparametric Conditional Independence Tests with a Multinomial Propensity Score
No full textWe develop semiparametric tests for conditional independence in time series models of causal effects. Our
approach is motivated by empirical studies of monetary policy effects. Our approach is semiparametric
in the sense that we model the process determining the distribution of treatment the policy propensity
score but leave the model for outcomes unspecfi ed. A conceptual innovation is that we adapt the
cross-sectional potential outcomes framework to a time series setting. We also develop root-T consistent
distribution-free inference methods for full conditional independence testing, appropriate for dependent
data and allowing for first-step estimation of the (multinomial) propensity score.National Science Foundation (U.S.) (SES-0095132)National Science Foundation (U.S.) (SES-0523186
Multiple Experiments for the Causal Link between the Quantity and Quality of Children
No full textThis paper presents evidence on the child-quantity/child-quality trade-off using quasi-experimental
variation due to twin births and preferences for a mixed sibling-sex composition, as well as ethnic
differences in the effects of these variables. Our sample includes groups with very high fertility. An innovation in our econometric approach is the juxtaposition of results from multiple instrumental variables (IV) strategies, capturing the effects of fertility over different ranges for different sorts of people. To increase precision, we develop an estimator that combines different instrument sets across partially-overlapping parity-specific sub-samples. Our results are remarkably consistent in showing no evidence of a quantity-quality trade-off
Interpreting Tests of School VAM Validity
No full textWe develop over-identification tests that use admissions lotteries to assess the predictive value of regression-based value-added models (VAMs). These tests have degrees of freedom equal to the number of quasi-experiments available to estimate school effects. By contrast, previously implemented VAM validation strategies look at a single restriction only, sometimes said to measure forecast bias. Tests of forecast bias may be misleading when the test statistic is constructed from many lotteries or quasi-experiments, some of which have weak first stage effects on school attendance. The theory developed here is applied to data from the Charlotte-Mecklenberg School district analyzed by Deming (2014).National Science Foundation (U.S.)Laura and John Arnold FoundationSpencer Foundatio
Leveraging Lotteries for School Value-Added: Testing and Estimation
No full textConventional value-added models (VAMs) compare average test scores across schools after regression-adjusting for students' demographic characteristics and previous scores. This article tests for VAM bias using a procedure that asks whether VAM estimates accurately predict the achievement consequences of random assignment to specific schools. Test results from admissions lotteries in Boston suggest conventional VAM estimates are biased, a finding that motivates the development of a hierarchical model describing the joint distribution of school valueadded, bias, and lottery compliance. We use this model to assess the substantive importance of bias in conventional VAM estimates and to construct hybrid valueadded estimates that optimally combine ordinary least squares and lottery-based estimates of VAM parameters. The hybrid estimation strategy provides a general recipe for combining nonexperimental and quasi-experimental estimates. While still biased, hybrid school value-added estimates have lower mean squared error than conventional VAMestimates. Simulations calibrated to the Boston data show that, bias notwithstanding, policy decisions based on conventional VAMs that control for lagged achievement are likely to generate substantial achievement gains. Hybrid estimates that incorporate lotteries yield further gains
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