I am an Associate Professor of Economics and Education at Teachers College, Columbia University and Co-Chair of the Education Technology Initiative at Abdul Latif Jameel Poverty Action Lab (J-PAL) at MIT.

With generous support from Schmidt Futures, Citadel, and the Gates Foundation, I have started a research lab, The Learning Collider. Our lab partners with large-scale technology companies and organizations collecting high-dimensional data to open up their data to researchers and to innovate, test, and scale interventions that promote social mobility. We currently focus on education, employment, and housing, with a particular attention to applications of machine learning, algorithmic fairness, and related tools. We are fortunate to work with some of the largest edtech, hiring, and housing platforms in the world.

Please feel free to ask me about our work and partnerships.

 
                                               Peter Bergman


Working Papers

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

(Revise and Resubmit, Journal of Political Economy)
Abstract: Over the last 40 years, efforts to desegregate schools have largely been undone and intra-district programs have limited scope to stem the resulting rise in segregation. This is the first paper to study the short-run and long-run impacts of an inter-district desegregation program on the minority students given an opportunity to transfer to majority-white school districts. Students who are given the opportunity to transfer districts attend schools that are 73 percentage points more white than schools they would have attended. Transferring students have higher test scores, and, over the longer run, an increase in college enrollment by 8 percentage points. At the same time, there is an increase in special education classification and arrests, which are largely for non-violent offenses. Both the benefits and the risks of the desegregation program accrue to male students.

Creating Moves to Opportunity: Experimental Evidence on Barriers to Neighborhood Choice

with Raj Chetty, Stefanie Deluca, Nathan Hendren, Lawrence Katz and Christopher Palmer
(Revise and Resubmit, American Economic Review)
Abstract: Low-income families in the United States tend to live in neighborhoods that offer limited opportunities for upward income mobility. One potential explanation for this pattern is that lowincome families prefer such neighborhoods for other reasons, such as affordability or proximity to family and jobs. An alternative explanation is that families do not move to high-opportunity areas because of barriers that prevent them from making such moves. We test between these two explanations using a randomized controlled trial with housing voucher recipients in Seattle and King County. We provided services to reduce barriers to moving to high-upward-mobility neighborhoods: customized search assistance, landlord engagement, and short-term financial assistance. The intervention increased the fraction of families who moved to high-upward-mobility areas from 14% in the control group to 54% in the treatment group. Families induced to move to higher opportunity areas by the treatment do not make sacrifices on other dimensions of neighborhood quality and report much higher levels of neighborhood satisfaction. These findings imply that most low-income families do not have a strong preference to stay in low-opportunity areas; instead, barriers in the housing search process are a central driver of residential segregation by income. Interviews with families reveal that the capacity to address each family's needs in a specific manner, from emotional support to brokering with landlords to financial assistance, was critical to the program's success. Using quasi-experimental analyses and comparisons to other studies, we show that more standardized policies--increasing voucher payment standards in high-opportunity areas or informational interventions--have much smaller impacts. We conclude that redesigning affordable housing policies to provide customized assistance in housing search could reduce residential segregation and increase upward mobility substantially.

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

with Isaac McFarlin Jr.
(Revise and Resubmit, Quarterly Journal of Economics)
Abstract: School choice may allow schools to "cream skim" students perceived as easier to educate. To test this, we sent emails from fictitious parents to 6,452 schools in 29 states and Washington, D.C. The fictitious parent asked whether any student is eligible to apply to the school and how to apply. Each email signaled a randomly assigned attribute of the child. We find that schools are less likely to respond to inquiries from students with poor behavior, low achievement, or a special need. Lower response rates to students with a potentially significant special need are driven by charter schools. Otherwise, these results hold for traditional public schools in areas of school choice and high-value added schools.

Hiring as Exploration

with Danielle Li and Lindsey Raymond (submitted)
Abstract: In looking for the best workers over time, firms must balance exploitation (selecting from groups with proven track records) with exploration (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on "supervised learning" approaches, are designed solely for exploitation. In this paper, we view hiring as a contextual bandit problem and build a resume screening algorithm that values exploration by evaluating candidates according to their upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves both the quality (as measured by eventual offer and acceptance rates) and the diversity of candidates selected for an interview relative to the firm's existing practices. The same is not true for traditional supervised learning based algorithms, which improve quality but select far fewer minority applicants. In an extension, we show that exploration-based algorithms are also able to learn more effectively about simulated changes in applicant quality over time. Together, our results highlight the importance of incorporating exploration in developing hiring algorithms that are potentially both more efficient and equitable.

School's Out: Experimental Evidence on Limiting Learning Loss Using "Low-Tech" in a Pandemic

with Noam Angrist and Moitshepi Matsheng (submitted)
Abstract: Schools closed extensively during the COVID-19 pandemic and occur in other settings, such as teacher strikes and natural disasters. This paper provides some of the first experimental evidence on strategies to minimize learning loss when schools close. We run a randomized trial of low-technology interventions - SMS messages and phone calls – with parents to support their child. The combined treatment cost-effectively improves learning by 0.12 standard deviations. We develop remote assessment innovations, which show robust learning outcomes. Our findings have immediate policy relevance and long-run implications for the role of technology and parents as partial educational substitutes when schooling is disrupted.

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

with Eric Chan and Adam Kapor (submitted)
Abstract: To investigate the role of information frictions in low-income families' housing choices, we experimentally varied the availability of school quality information on a nationwide website of housing listings for families with housing vouchers. We use this variation, together with detailed data on families' search behaviors and location choices, to estimate a model of housing search and neighborhood choice that incorporates imperfect information and potentially biased beliefs. Having data from both the treatment and control groups allows us to estimate families' prior beliefs about school quality and each group's apparent valuation of school quality. We find that imperfect information and biased beliefs about school quality cause low-income families to live in neighborhoods with lower-performing, more segregated schools, and that ignoring this information problem would lead to biased estimates of families' valuation of school quality. Providing school quality information causes families to choose neighborhoods with schools that have 1.5 percentage point higher proficiency rate on state exams. Families not only observe school quality with noise, but systematically underestimate school quality conditional on neighborhood characteristics. If we had assumed full information, we would have estimated that the control group valued school quality relative to their commute downtown by less than half that of the treatment group.

Using Predictive Analytics to Track Students: Evidence from a Seven-College Experiment (Just updated)

with Elizabeth Kopko and Julio Rodriguez
Abstract: Tracking is widespread in U.S. education. In post-secondary education alone, at least 71% of colleges use a test to track students. However, there are concerns that the most frequently used college placement exams lack validity and reliability, and unnecessarily place students from under-represented groups into remedial courses. 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 alternative system to place students that uses predictive analytics to combine multiple measures into a placement instrument. Compared to colleges’ existing placement tests, the algorithm is more predictive of future performance. We then conduct an experiment across seven colleges to evaluate the algorithm’s effects on students. Placement rates into college-level courses increased substantially without reducing pass rates. Adjusting for multiple testing, algorithmic placement generally, though not always, narrowed gaps in college placement rates and remedial course taking across demographic groups. A detailed cost analysis shows that the algorithmic placement system is socially efficient: it saves costs for students while increasing college credits earned, which more than offsets increased costs for colleges. Costs could be reduced with improved data digitization, as opposed to entering data by hand.


Published and Forthcoming Papers

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

(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.

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

with Magdalena Bennett
(Forthcoming, Journal of Labor Economics)
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.

Simplification and Defaults Affect Adoption and Impact of Technology, But Decision Makers Do Not Realize It

with Jessica Lasky-Fink and Todd Rogers
(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.

Is Information Enough? The Effect of Information about Education Tax Benefits on Student Outcomes

with Jeff Denning and Day Manoli
(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 Low-Cost Technology: The Impact of High-Frequency Information on Student Achievement

with Eric Chan
(Journal of Human Resources)
Abstract: We partnered a low-cost communication technology with school information systems to automate the gathering and provision of information on students' academic progress to parents of middle and high school students. We sent weekly, automated alerts to parents about their child's missed assignments, grades, and class absences. The alerts reduced course failures by 28%, increased class attendance by 12\%, and increased student retention, though there was no impact on state test scores. There were larger effects for below-median GPA students and high school students. We sent over 32,000 messages at a variable cost of $63.

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

with Matt Hill
(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.

Low-cost strategies to empower parents through behavioral science

(Behavioral Science & Policy)
Abstract: Parents are one of the most powerful determinants of a child's education outcomes. However, behavioral and informational barriers impede parents' engagement with their children. Parenting is complex and limited cognitive bandwidth steers parents' attention away from education investments with long-run returns and toward tasks with immediate returns. Monitoring children is difficult because school-to-parent communication is poor, and parents have inflated perceptions of their child's performance. Poverty exacerbates these problems. This paper shows how the provision of timely, actionable information to parents can attenuate these barriers and improve parental engagement from kindergarten through high school. In particular, providing this information via text message can improve education outcomes at low cost.

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

(Education Finance and Policy)
Abstract: As schools are making significant investments in education technologies it is important to assess whether various products are adopted by their end users and whether they are effective as used. 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. While a simple nudge increases usage and modestly improves student achievement, without more significant intervention to encourage usage by disadvantaged families, these technologies may exacerbate gaps in information access 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
(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.

Engaging Parents to Prevent Adolescent Substance Use: A Randomized Controlled Trial

with Kulwant Dosanjh, Rebecca Dudovitz and Mitchell Wong
(American Journal of Public Health)

Successful Schools and Risky Behaviors Among Low-Income Adolescents

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