Search EdWorkingPapers
Search EdWorkingPapers by author, title, or keywords.
Search
Methodology, measurement and data
Given the spike of homicides in conflict zones of Colombia after the 2016 peace agreement, I study the causal effect of violence on college test scores. Using a difference-in-difference design with heterogeneous effects, I show how this increase in violence had a negative effect on college learning, and how this negative effect is mediated by factors such as poverty, college major, degree type, and study mode. A 10% increase in the homicide rate per 100,000 people in conflict zones of Colombia, had a negative impact on college test scores equivalent to 0.07 standard deviations in the English section of the test. This negative effect is larger in the case of poor and female students who saw a negative effect of approximately 0.16 standard deviations, equivalent to 3.4 percentage points out of the final score. Online and short-cycle students suffer a larger negative effect of 0.14 and 0.19 standard deviations respectively. This study provides among the first evidence of the negative effect of armed conflict on college learning and offers policy recommendations based on the heterogeneous effects of violence.
At schools with low grading standards, students receive higher school-awarded grades across multiple courses than students with the same skills receive at schools with high grading standards. A new methodology shows grading standards vary substantially, certainly enough to affect post-secondary opportunities, across high schools in Alberta. Schools with low grading standards are more likely to be private, rural, offer courses for students returning to high school, have smaller course cohorts, have a smaller percentage of lone parent households and a larger percentage of well-educated parents. Variation in grading standards changes post-secondary opportunities in systematic ways.
Our study examines roughly 2,000 novice teachers’ responses about how they account for students’ cultural, ethnic/racial, and linguistic diversity. We qualitatively analyze robust open-ended survey responses to explore teachers’ reported strategies for how they integrate asset-based pedagogy (ABP). We identify codes related to these strategies and then investigate them by participant demographics. This illuminates both the predictive validity of our qualitative analyses as well as provides initial evidence as to whether certain characteristics are associated with critical techniques. Our findings inform practitioners of a suite of ABP strategies as well as districts and policymakers about how novice teachers are processing asset-based instruction and who to target support in this vital pedagogical area.
Teachers’ sense-making of student behavior determines whether students get in trouble and are formally disciplined. Status categories, such as race, can influence perceptions of student culpability, but the degree to which teachers’ initial identification of student misbehavior exacerbates racial disproportionality in discipline receipt is unknown.This study provides the first systematic documentation of teachers’ use of office discipline Referrals (ODRs) in a large, diverse urban school district in California that specifies the identity of both the referred and referring individuals in all ODRs. We identify teachers exhibiting extensive referring behavior, or the top 5 percent referrers based on the number of ODRs they make in a given year and evaluate their contributions to disciplinary disparities. We find that “top referrers” effectively double the racial gaps in ODRs for both Black-White and Hispanic-White comparisons. These gaps are mainly driven by higher numbers of ODRs issued for Black and Hispanic students due to interpersonal offences and defiance, and also partially convert to racial gaps in suspensions. Both the level and racial compositions of the school sites where “top referrers” serve and their personal traits seem to explain some of their frequent referring behavior. Targeting supports and interventions to “top referrers” might afford an important opportunity to reduce racial disciplinary gaps
Lottery-based identification strategies offer potential for generating the next generation of evidence on U.S. early education programs. Our collaborative network of five research teams applying this design in early education and methods experts has identified six challenges that need to be carefully considered in this next context: 1) available baseline covariates may not be very rich; 2) limited data on the counterfactual; 3) limited and inconsistent outcome data; 4) weakened internal validity due to attrition; 5) constrained external validity due to who competes for oversubscribed programs; and 6) difficulties answering site-level questions with child-level randomization. We offer potential solutions to these six challenges and concrete recommendations for the design of future lottery-based early education studies.
We provide evidence that graduated driver licensing (GDL) laws, originally intended to improve public safety, impact human capital accumulation. Many teens use automobiles to access both school and employment. Because school and work decisions are interrelated, the effects of automobile-specific mobility restrictions are ambiguous. Using a novel triple-difference research design, we find that restricting mobility significantly reduces high school dropout rates and teen employment. We develop a multiple discrete choice model that rationalizes unintended consequences and reveals that school and work are weak complements. Thus, improved educational outcomes reflect decreased access to leisure activities rather than reduced labor market access.
We study the returns to experience in teaching, estimated using supervisor ratings from classroom observations. We describe the assumptions required to interpret changes in observation ratings over time as the causal effect of experience on performance. We compare two difference-in-differences strategies: the two-way fixed effects estimator common in the literature, and an alternative which avoids potential bias arising from effect heterogeneity. Using data from Tennessee and Washington, DC, we show empirical tests relevant to assessing the identifying assumptions and substantive threats—e.g., leniency bias, manipulation, changes in incentives or job assignments—and find our estimates are robust to several threats.
Short-cycle higher education programs (SCPs) can play a central role in skill development and higher education expansion, yet their quality varies greatly within and among countries. In this paper we explore the relationship between programs’ practices and inputs (quality determinants) and student academic and labor market outcomes. We design and conduct a novel survey to collect program-level information on quality determinants and average outcomes for Brazil, Colombia, Dominican Republic, Ecuador, and Peru. Categories of quality determinants include training and curriculum, infrastructure, faculty, link with productive sector, costs and funding, and practices on student admission and institutional governance. We also collect administrative, student-level data on higher education and formal employment for SCP students in Brazil and Ecuador and match it to survey data. Using machine learning methods, we select the quality determinants that predict outcomes at the program and student levels. Estimates indicate that some quality determinants may favor academic and labor market outcomes while others may hinder them. Two practices predict improvements in all labor market outcomes in Brazil and Ecuador—teaching numerical competencies and providing job market information—and one practice—teaching numerical competencies—additionally predicts improvements in labor market outcomes for all survey countries. Since quality determinants account for 20-40 percent of the explained variation in student-level outcomes, quality determinants might have a role shrinking program quality gaps. Findings have implications for the design and replication of high-quality SCPs, their regulation, and the development of information systems.
This paper estimates a dynamic model of college enrollment, progression, and graduation. A central feature of the model is student effort, which has a direct effect on class completion and an indirect effect mitigating risks on class completion and college persistence. The estimated model matches rich administrative data for a representative cohort of college students in Colombia. Estimates indicate that effort has a much greater impact than ability on class completion. Failing to consider effort as an input to class completion leads to overestimating ability’s role by a factor of two or three. It also promotes tuition discounts based on a pre-determined student trait—ability—rather than effort, which can be affected through policy, thus limiting higher education’s potential for social mobility.
Short-cycle higher education programs (SCPs), lasting two or three years, capture about a quarter of higher education enrollment in the world and can play a key role enhancing workforce skills. In this paper, we estimate the program-level contribution of SCPs to student academic and labor market outcomes, and study how and why these contributions vary across programs. We exploit unique administrative data from Colombia on the universe of students, institutions, and programs to control for a rich set of student, peer, and local choice set characteristics. We find that program-level contributions account for about 60-70 percent of the variation in student-level graduation and labor market outcomes. Our estimates show that programs vary greatly in their contributions, across and especially within fields of study. Moreover, the estimated contributions are strongly correlated with program outcomes but not with other commonly used quality measures. Programs contribute more to formal employment and wages when they are longer, have been provided for a longer time, are taught by more specialized institutions, and are offered in larger cities.