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Methodology, measurement and data
How do college non-completers list schooling on their resumes? The negative signal of not completing might outweigh the positive signal of attending but not persisting. If so, job-seekers might hide non-completed schooling on their resumes. To test this we match resumes from an online jobs board to administrative educational records. We find that fully one in three job-seekers who attended college but did not earn a degree omit their only post-secondary schooling from their resumes. We further show that these are not casual omissions but are strategic decisions systematically related to schooling characteristics, such as selectivity and years of enrollment. We also find evidence of lying, and show which degrees listed on resumes are most likely untrue. Lastly, we discuss implications. We show not only that this implies a commonly held assumption, that employers perfectly observe schooling, does not hold, but also that we can learn about which college experiences students believe are most valued by employers.
In multisite experiments, we can quantify treatment effect variation with the cross-site treatment effect variance. However, there is no standard method for estimating cross-site treatment effect variance in multisite regression discontinuity designs (RDD). This research rectifies this gap in the literature by systematically exploring and evaluating methods for estimating the cross-site treatment effect variance in multisite RDDs. Specifically, we formalize a fixed intercepts/random coefficients (FIRC) RDD model and develop a random effects meta-analysis (Meta) RDD model for estimating cross-site treatment effect variance. We find that a restricted FIRC model works best when the running variables' relationship to the outcome is stable across sites but can be biased otherwise. In those instances, we recommend using either the unrestricted FIRC model or the meta-analysis model; with the unrestricted FIRC model generally performing better when the average number of in-bandwidth observations is less than 120 and the meta-analysis model performing better when the average number of in-bandwidth observations is above 120. We apply our models to a high school exit exam policy in Massachusetts that required students who passed the high school exit exam but were still determined to be nonproficient to complete an ``Education Proficiency Plan" (EPP). We find the EPP policy had a positive local average treatment effect on whether students completed a math course their senior year on average across sites, but that the impact varied enough such that a third of schools could have had a negative impact.
Growing evidence shows that a student's growth mindset (the belief that intelligence is malleable) can benefit their academic achievement. However, due to limited information, little is known about how a teachers’ growth mindset affects their students’ academic achievement. In this paper, we study the impact of teacher growth mindset on academic achievement for a nationwide sample of 8th and 10th grade students in Chile in 2017. Using a student fixed effect model that exploits data from two subject teachers for each student, we find that being assigned to a teacher with a growth mindset increases standardized test scores by approximately 0.02 standard deviations, with larger effects on students with high GPAs and particularly on students in low socioeconomic schools.
Standardized assessments are widely used to determine access to educational resources with important consequences for later economic outcomes in life. However, many design features of the tests themselves may lead to psychological reactions influencing performance. In particular, the level of difficulty of the earlier questions in a test may affect performance in later questions. How should we order test questions according to their level of difficulty such that test performance offers an accurate assessment of the test taker's aptitudes and knowledge? We conduct a field experiment with about 19,000 participants in collaboration with an online teaching platform where we randomly assign participants to different orders of difficulty and we find that ordering the questions from easiest to most difficult yields the lowest probability to abandon the test, as well as the highest number of correct answers. Consistent results are found exploiting the random variation of difficulty across test booklets in the Programme for International Student Assessment (PISA), a triannual international test, for the years of 2009, 2012, and 2015, providing additional external validity. We conclude that the order of the difficulty of the questions in tests should be considered carefully, in particular when comparing performance between test-takers who have faced different order of questions.
After increasing in the 1970s and 1980s, time to bachelor’s degree has declined since the 1990s. We document this fact using data from three nationally representative surveys. We show that this pattern is occurring across school types and for all student types. Using administrative student records from 11 large universities, we confirm the finding and show that it is robust to alternative sample definitions. We discuss what might explain the decline in time to bachelor’s degree by considering trends in student preparation, state funding, student enrollment, study time, and student employment during college.
Local governments spend over 12 billion dollars annually funding the operation of 15,000 public libraries in the United States. This funding supports widespread library use: more than 50% of Americans visit public libraries each year. But despite extensive public investment in libraries, surprisingly little research quantifies the effects of public libraries on communities and children. We use data on the near-universe of U.S. public libraries to study the effects of capital spending shocks on library resources, patron usage, student achievement, and local housing prices. We use a dynamic difference-in-difference approach to show that library capital investment increases children’s attendance at library events by 18%, children’s checkouts of items by 21%, and total library visits by 21%. Increases in library use translate into improved children’s test scores in nearby school districts: a $1,000 or greater per-student capital investment in local public libraries increases reading test scores by 0.02 standard deviations and has no effects on math test scores. Housing prices do not change after a sharp increase in public library capital investment, suggesting that residents internalize the increased cost and improved quality of their public libraries.
Occupational credentials provide an additional—and, at times, alternative—path other than traditional academic degrees for individuals to increase productivity and demonstrate their abilities and qualifications to employers. These credentials take the form of licenses and certifications. Although a critical part of the workforce landscape, the literature on the returns to credentials is inadequate, with prior research having limited causal identification, typically relying on OLS regressions which do not sufficiently control for selection. Using questions that identify credential receipt from the 2015 and 2016 Current Population Surveys, we construct an instrumental variable of local peer influence using the within-labor market credential rate of individuals sharing the same sociodemographic characteristics, while controlling for the same group’s average wages and a suite of demographic and geographic controls. We use this instrument in a marginal treatment effects estimator, which allows for estimation of the average treatment effect and determines the direction of selection, and we estimate the effects of credentials on labor market outcomes. We find large, meaningful returns in the form of increased employment, an effect which is concentrated primarily among women. The effect of having a credential on log wages is higher for those in the sub-baccalaureate labor market, suggesting the potential role of occupational credentials as an alternative path to marketable human capital and a signal of skills in the absence of a bachelor’s degree.
Using individual data from PIAAC and aggregate data on GDP and unemployment for the US, Europe, and Spain, we test how macroeconomic conditions experienced at age eighteen affect the following decisions in post-secondary and tertiary education: i) enrollment ii) dropping-out, iii) type of degree completed, iv) area of specialization, and v) time-to-degree. We also analyze how the effects differ by gender and parental background. Our findings are different for each of these geographies, which shows that the impacts of macroeconomic conditions on higher education decisions depend on context, such as labor markets and education systems. By analyzing various components of higher education together, we are able to obtain a clearer picture of how potential mechanisms linked to lower opportunity costs of education and reduced ability to pay during economic downturns interact to determine student selection.
Employers may favor applicants who played college sports if athletics participation contributes to leadership, conscientiousness, discipline, and other traits that are desirable for labor-market productivity. We conduct a resume audit to estimate the causal effect of listing collegiate athletics on employer callbacks and test for subgroup effects by ethnicity, gender, and sport type. We applied to more than 450 jobs on a large, well-known job board. For each job listing we submitted two fictitious resumes, one of which was randomly assigned to include collegiate varsity athletics. Overall, listing a college sport does not produce a statistically significant change in the likelihood of receiving a callback or interview request. However, among non-white applicants, athletes are 3.2 percentage points less likely to receive an interview request (p = .04) relative to non-athletes. We find no statistically significant differences among males or females.
Recent interest to promote and support replication efforts assume that there is well-established methodological guidance for designing and implementing these studies. However, no such consensus exists in the methodology literature. This article addresses these challenges by describing design-based approaches for planning systematic replication studies. Our general approach is derived from the Causal Replication Framework (CRF), which formalizes the assumptions under which replication success can be expected. The assumptions may be understood broadly as replication design requirements and individual study design requirements. Replication failure occurs when one or more CRF assumptions are violated. In design-based approaches to replication, CRF assumptions are systematically tested to evaluate the replicability of effects, as well as to identify sources of effect variation when replication failure is observed. In direct replication designs, replication failure is evidence of bias or incorrect reporting in individual study estimates, while in conceptual replication designs, replication failure occurs because of effect variation due to differences in treatments, outcomes, settings, and participant characteristics. The paper demonstrates how multiple research designs may be combined in systematic replication studies, as well as how diagnostic measures may be used to assess the extent to which CRF assumptions are met in field settings.