I am an Assistant Professor of Economics and Education at Teachers College,  Columbia University and Co-Chair of the Ed-Tech Initiative at J-PAL. My research studies information problems in human capital development. I use behavioral economics, big data and randomized controlled trials to identify low-cost, scalable interventions that improve education outcomes. 

                                                    Peter Bergman


Parent-Child Information Frictions and Human Capital Investment: Evidence from a Field Experiment Investment

(Conditionally Accepted, Journal of Political Economy )
Abstract: This paper studies information frictions between parents and their children, and how these affect human capital investments. I provide detailed, biweekly information to a random sample of parents about their child's missed assignments and grades and find parents have upwardly-biased beliefs about their child's effort. Providing additional information attenuates this bias and improves student achievement. Using data from the experiment, I then estimate a persuasion game between parents and their children that shows the treatment effect is due to a combination of more accurate beliefs and reduced monitoring costs. The experimental results and policy simulations from the model demonstrate that improving the quality of school reporting or providing frequent information to parents about their child's effort in school can produce gains in achievement at a low cost.

(Updated) The Risks and Benefits of School Integration for Participating Students: Evidence from a Randomized Desegregation Program

Abstract: This paper studies the impact of a lottery-based desegregation program that allows minority students to transfer to seven school districts serving higher-income, predominantly-white families. While prior research has studied the impacts of such a program receiving students, this paper studies the effects on participating students. In the short run, students who receive an offer to transfer are more likely to be classified as requiring special education and their test scores increase in several subjects. In the medium run, college enrollment increases by 8 percentage points for these students. This is due to greater attendance at two-year colleges. There is no overall effect on the likelihood of voting. However, the offer to transfer significantly increases the likelihood of arrest. This is driven primarily by increases in arrests for non-violent offenses. Almost all of these effects---both the risks and the benefits---stem from impacts on male students. Male students have higher test scores, college enrollment rates, and are significantly more likely to vote, but they also experience nearly all of the effects on arrests.

Better Together? Social Networks in Truancy and the Targeting of Treatment

with Magdalena Bennett (Submitted)
Abstract: Truancy predicts many risky behaviors and adverse outcomes. We use detailed administrative data to construct social networks based on students who miss class together. We simulate these networks to show that certain students systematically coordinate their absences in the observed data. Leveraging a parent-information intervention on student absences, we find spillover effects from treated students onto peers in their network; excluding these effects understates the intervention's cost effectiveness by 19%. We show there is potential to use these networks to implement costly interventions more efficiently. We develop an algorithm that incorporates spillovers and treatment-effect heterogeneity identified by machine-learning techniques to target interventions more efficiently given a budget constraint.

Using Predictive Analytics to Track Students: Evidence from a Multi-College Experiment

with Elisabeth Barnett, Beth Kopko and Vikash Reddy
Abstract: Tracking is widespread in U.S. education. In higher education alone, at least 71% of post-secondary institutions use a test to track students, and more than 80% of these institutions use a test as the sole criterion to determine placement. While recent research has shown that tracking can have positive effects on student learning, inaccurate placement has consequences: students face misaligned curricula and must pay tuition for remedial courses that do not bear credits toward graduation. We develop an algorithm to place students that combines multiple measures with predictive analytics. We then conduct an experiment across multiple colleges to evaluate its impact. Compared to colleges’ most commonly-used placement test, the algorithm is more predictive of future performance and substantially increases placements into college-level courses. This is particularly true for English courses and for female, Black and Hispanic students. The algorithm tends to predict pass rates more accurately in math than English.

The Impact of Defaults on Technology Adoption, and its Underappreciation by Policymakers

with Todd Rogers (Revise and Resubmit, Organizational Behavior and Human Decision Processes)
Abstract: We conduct a field experiment to understand how enrollment defaults affect the take up and impact of an education technology designed to help parents improve student achievement. The standard strategy schools use to introduce this system to parents—online signup—induces negligible adoption. Simplifying the enrollment process modestly increases adoption, primarily among parents of higher-performing students. Automatically enrolling parents dramatically increases adoption. Automatic enrollment significantly and meaningfully improves student achievement. Survey results suggest that automatic enrollment is uncommon, and that it may be uncommon because its impact is unanticipated by policymakers. Surveyed superintendents, principals, and family engagement coordinators overestimate the take-up rate of the standard condition by 38 percentage points and underestimate the take-up rate of automatic enrollment by 31 percentage points. After learning the actual take-up rates under each enrollment condition, there is a corresponding 140% increase in the willingness to pay for the technology when shifting implementation from opt-in enrollment to opt-out enrollment.

Broken Tax Breaks? Evidence from a Tax Credit Information Experiment with 1,000,000 Students

with Jeff Denning and Day Manoli (Conditionally Accepted, Journal of Policy Analysis and Management)
Abstract: There is increasing evidence that tax credits for college do not affect college enrollment. This may be because prospective students do not know about tax benefits for credits or because the design of tax credits is not conducive to affecting educational outcomes. We focus on changing the salience of tax benefits by providing information about tax benefits for college using a sample of over 1 million students or prospective students in Texas. We sent emails and letters to students that described tax benefits for college and tracked college outcomes. For all three of our samples---rising high school seniors, already enrolled students, and students who had previously applied to college but were not currently enrolled---information about tax benefits for college did not affect enrollment or reenrollment. We test whether effects vary according to information frames and found that no treatment arms changed student outcomes. We conclude that salience is not the primary reason that tax credits for college do not affect enrollment.

Leveraging Parents through Technology: The Impact of High-Frequency Information on Student Achievement

with Eric Chan (Revise and Resubmit, Journal of Human Resources)
Abstract: We partner text-messaging technology with school information systems to automate the gathering and provision of high-frequency information on students' academic progress to parents. In an experiment across 22 schools, we use this technology to send weekly automated messages to parents about their child's missed assignments, grades, and class absences. We pre-specified five primary outcomes. The intervention reduces course failures by 38%, increases class attendance by 17% and increases retention. The positive effects are particularly large for students with below-average GPA and students in high school, which persisted into a second year. There are no effects on state test scores, though the exams, which were new and zero stakes for students, were subsequently discontinued; students used substantially less than the expected amount of time to complete them. In contrast, we find significant improvements on in-class exam scores. Our results show this technology can improve student performance relatively cheaply and at scale.

The Effects of Making Performance Information Public: Regression Discontinuity Evidence from Los Angeles Teachers

with Matt Hill, (Forthcoming, Economics of Education Review)
Abstract: This paper uses school-district data and a regression discontinuity design to study the effects of making teachers' value-added ratings available to the public and searchable by name. We find that classroom compositions change as a result of this new information. In particular, high-scoring students sort into the classrooms of published, high-value added teachers. This sorting occurs when there is within school-grade variation in teachers' value added.

Successful Schools and Risky Behaviors Among Low-Income Adolescents

Pediatrics. with Mitchell Wong, Karen Coller, Rebecca Dudovitz, David Kennedy, Richard Buddin, Martin Shapiro, Sheryl Kataoka, Arleen Brown, Chi Hong Tseng, and Paul Chung

Nudging Technology Use: Descriptive and Experimental Evidence from School Information Systems

(Conditionally Accepted, Education Finance and Policy)
Abstract: Given significant expenditures on education technologies, important questions are who adopts these technologies and why, and whether promoting usage impacts student outcomes. This paper studies the adoption and ability to promote usage of one type of technology that is increasingly ubiquitous: school-to-parent communication technologies. Analyzing usage data from a Learning Management System across several hundred schools and then conducting a two-stage experiment across 59 schools to nudge the use of this technology by families, I find that 57% of families ever use it and adoption correlates strongly with measures of income and student achievement. Using a survey experiment I find that informing families about research on the value of school-to-parent communication technologies can promote adoption and there is evidence that social norms influence adoption as well. While a simple nudge increases usage and modestly improves student achievement, without more significant intervention these technologies may exacerbate gaps in information access and student performance across income and performance levels.

Parent Skills and Information Asymmetries: Experimental Evidence from Home Visits and Text Messages in Middle and High Schools

with Chana Edmond-Verley and Nicole Notario-Risk (Forthcoming, Economics of Education Review )
Abstract: This paper studies the ability to foster parent skills and resolve information problems as a means to improving student achievement. We conducted a three-arm randomized control trial in which community-based organizations provided regular information to families about their child's academic progress in one arm and supplemented this with home visits on skills-based information in a separate arm. Math and English test scores improved for the treatment arm with home visits. There are large effects on retention for both groups during the year, though learning gains tend to accrue for students with average-and-above baseline performance and students at the lower-end of the distribution appear marginally retained.

Education for All? A Nationwide Audit Study of Schools of Choice

with Isaac McFarlin, Jr.

Housing Search Frictions: Evidence from Detailed Search Data and a Field Experiment

with Eric Chan and Adam Kapor (slides)

Linking Families Together (LIFT) Study: A randomized trial to prevent adolescent substance use by reducing information asymmetries between parent and child

with Kulwant Dosanjh, Rebecca Dudovitz and Mitchell Wong (Submitted)

Research in Progress

Creating Moves to Opportunity Project

with Raj Chetty, Stefanie Deluca, Nathan Hendren, Lawrence Katz and Christopher Palmer