Peter Bergman, PhD
Associate Professor of Economics - UT-Austin
Co-Chair, Improving Education Outcomes Initiative - J-PAL (MIT)
Innovation Fellow - Schmidt Futures
Research Associate - National Bureau of Economic Research
Founder & Director - Learning Collider
Co-Founder & Chief Science Officer - Diffusion Venture Studio
With generous support from Schmidt Futures, Citadel, and the Bill & Melinda Gates Foundation, I founded and now direct Learning Collider. Our lab partners with some of the largest technology platforms and social-impact organizations to connect high-dimensional datasets with skilled researchers to innovate, test, and scale interventions that promote economic mobility and equitable impact. Our projects and partnerships focus on opportunities in Education, Housing, and Workforce, incorporating experimentation, machine learning, algorithm fairness, discrimination measurements, and related methodologies.
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Working Papers
Building Resilient Education Systems: Evidence from Large-Scale Randomized Trials in Five Countries
NBER Working Paper released May 2023 | with Noam Angrist, Micheal Ainomugisha, Sai Pramod Bathena, Colin Crossley, Claire Cullen, Thato Letsomo, Moitshepi Matsheng, Rene Marlon Panti, Shwetlena Sabarwal, and Tim Sullivan
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Education systems need to withstand frequent shocks, including conflict, disease, natural disasters, and climate events, all of which routinely close schools. During these emergencies, alternative models are needed to deliver education. However, rigorous evaluation of effective educational approaches in these settings is challenging and rare, especially across multiple countries. We present results from large-scale randomized trials evaluating the provision of education in emergency settings across five countries: India, Kenya, Nepal, Philippines, and Uganda. We test multiple scalable models of remote instruction for primary school children during COVID-19, which disrupted education for over 1 billion schoolchildren worldwide. Despite heterogeneous contexts, results show that the effectiveness of phone call tutorials can scale across contexts. We find consistently large and robust effect sizes on learning, with average effects of 0.30-0.35 standard deviations. These effects are highly cost-effective, delivering up to four years of high-quality instruction per $100 spent, ranking in the top percentile of education programs and policies. In a subset of trials, we randomized whether the intervention was provided by NGO instructors or government teachers. Results show similar effects, indicating scalability within government systems. These results reveal it is possible to strengthen the resilience of education systems, enabling education provision amidst disruptions, and to deliver cost-effective learning gains across contexts and with governments.
A Seven-College Experiment Using Algorithms to Track Students: Impacts and Implications for Equity and Fairness
NBER Working Paper updated February 2023 | with Elizabeth Kopko and Julio Rodriguez
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Tracking is widespread in education. In U.S. post-secondary education alone, at least 71% of colleges use a test to track students into various courses. However, there are concerns that placement tests lack validity and unnecessarily reduce education opportunities for students from under-represented groups. While research has shown that tracking can improve 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 that uses algorithms to predict college readiness and track students into courses. Compared to the most widely-used placement tests in the country, the algorithms are more predictive of future performance. We conduct an experiment across seven colleges to evaluate the effects of algorithmic placement. Placement rates into college-level courses increase substantially without reducing pass rates. Algorithmic placement generally, though not always, narrows differences in college placement rates and remedial course taking across demographic groups. We use the experimental design and variation in placement rates to assess the disparate impact of each system. Test scores exhibit substantially more discrimination than algorithms; a significant share of test-score disparities between Hispanic or Black students and white students is explained by discrimination. We also show that the selective labels problem nearly doubles the prediction error for college English performance but has almost no impact on the prediction error for college math performance. A detailed cost analysis shows that algorithmic placement is socially efficient: it increases college credits earned while saving costs for students and the government.
Education for All? A Nationwide Audit Study of School Choice
Revise and Resubmit, Quarterly Journal of Economics | with Isaac McFarlin, Jr.
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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
Revise and Resubmit, Review of Economic Studies | with Danielle Li and Lindsey Raymond
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This paper views hiring as a contextual bandit problem: to find 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. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm's existing practices. The same is not true for traditional supervised learning based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. In an extension, we show that exploration-based algorithms are also able to learn more effectively about simulated changes in applicant hiring potential over time. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.
Housing Search Frictions: Evidence from Detailed Search Data and a Field Experiment
Revise and Resubmit, Journal of Political Economy | with Eric Chan and Adam Kapor
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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.
The Risks & Benefits of School Integration for Participating Students: Evidence from a Randomized Desegregation Program
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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.
Published & Forthcoming Papers
Creating Moves to Opportunity: Experimental Evidence on Barriers to Neighborhood Choice
Conditionally Accepted, American Economic Review | with Raj Chetty, Stefanie Deluca, Nathan Hendren, Lawrence Katz, and Christopher Palmer
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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 families prefer such neighborhoods for other reasons, such as affordability or proximity to family and jobs. An alternative explanation is that they do not move to high-opportunity areas because of a lack of information or barriers that prevent them from making such moves. We test between these explanations using a twophase randomized controlled trial with housing voucher recipients in Seattle and King County. We first provided a bundle of resources to facilitate moves to high-upward-mobility neighborhoods: information about high-opportunity areas, short-term financial assistance, customized assistance during the housing search process, and connections to landlords. This bundled intervention increased the fraction of families who moved to high-upward-mobility areas from 15% in the control group to 53% in the treatment group. To understand the mechanisms underlying this effect, we ran a second phase with three arms: (1) information about high-opportunity areas and financial assistance only; (2) reduced support services in addition to information and financial assistance; and (3) full support services, as in the original bundled intervention. The full services had five times as large a treatment effect as the information and financial incentives treatment and three times as large an effect as the reduced support intervention, showing that high-intensity, customized support enables moves to opportunity. Interviews with randomly selected families reveal that the program succeeded by relaxing families’ bandwidth constraints and addressing their specific needs, from identifying suitable units to providing emotional support to brokering with landlords. Families induced to move to higher opportunity areas tend to stay in their new neighborhoods in subsequent years and report higher levels of neighborhood satisfaction after moving. Our findings imply that many low-income families do not have a strong preference to stay in low-opportunity areas and that barriers in the housing search process are a central driver of residential segregation by income.
Learning Curve: Progress in the Replication Crisis
AEA Papers and Proceedings, May 2023 | with Noam Angrist, Claire Cullen, Michael Ainomugisha, Sai Pramod Bathena, Colin Crossley, Thato Letsomo, Moitshepi Matsheng, Rene Marlon Panti, Shwetlena Sabarwal, and Tim Sullivan
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Is it necessarily the case that programs and policies experience diminishing returns as they are adapted across contexts, scaled up, and delivered through government systems?
In this paper, we investigate this question leveraging detailed monitoring data from a five-country randomized replication study of a phone tutoring program for disadvantaged students – one of the largest multi-country replication efforts in education to date. Randomized trials have proliferated, yet an analysis of top journals shows that fewer than 1 percent of recent randomized studies have been conducted across multiple countries, notwithstanding notable examples in education such as Teaching at the Right Level (Banerjee et al. 2017) and a few early grade reading interventions (Lucas et al. 2014).
We report implementation fidelity results over time, across countries, and for both government and NGO implementation. These replication studies took place in India, Kenya, Nepal, Philippines, and Uganda and built on a proof-of-concept of phone-based tutoring in Botswana during covid-19, which improved learning outcomes by 0.12 standard deviations (Angrist, Bergman, and Matsheng, 2022).
Experimental Evidence on Learning Using Low-tech When School is Out
Nature Human Behavior, June 2022 | with Noam Angrist and Moitshepi Matsheng
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School closures occurred extensively during the COVID-19 pandemic, and occur in other settings, such as teacher strikes and natural disasters. The cost of school closures has proven to be substantial, particularly for households of lower socioeconomic status, but little evidence exists on how to mitigate these learning losses. This paper provides experimental evidence on strategies to support learning when schools close. We conduct a large-scale randomized trial testing two low-technology interventions—SMS messages and phone calls—with parents to support their child in Botswana. The combined treatment improves learning by 0.12 standard deviations, which translates to 0.89 standard deviations of learning per US$100, ranking among the most cost-effective interventions to improve learning. 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 to support education provision during school disruptions.
Better Together? Social Networks in Truancy and the Targeting of Treatment
Journal of Labor Economics, January 2021 | with Magdalena Bennett
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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.
Parent-Child Information Frictions and Human Capital Investment: Evidence from a Field Experiment
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This paper studies information frictions between parents and children and their effect on human capital investments. I provide biweekly information to a random sample of parents about their child’s missed assignments. Parents have upwardly biased beliefs about their child’s effort. Providing information attenuates this bias and improves student achievement. Using data from the experiment, I estimate a persuasion game between parents and their children that shows that the treatment effect is due to more accurate beliefs and reduced monitoring costs. Policy simulations from the model demonstrate that improving school reporting or providing more information to parents can increase learning at low cost.
Leveraging Parents through Low-Cost Technology: The Impact of High-Frequency Information on Student Achievement
Journal of Human Resources | with Eric Chan
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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 27 percent, increased class attendance by 12 percent, 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. More than 32,000 text messages were sent at a variable cost of $63.
Simplification and Defaults Affect Adoption and Impact of Technology, but Decision Makers Do Not Realize It
Organizational Behavior and Human Decision Processes | with Jessica Lasky-Fink and Todd Rogers
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A field experiment (N = 6976) examines how enrollment defaults affect adoption and impact of an education technology that sends weekly automated alerts on students’ academic progress to parents. We show that a standard (high-friction) opt-in process induces extremely low parent take-up (< 1%), while a simplified process yields higher enrollment (11%). Yet, with such low take-up, both fail to improve average student achievement. Meanwhile, automatically enrolling parents increases take-up to 95% and improves student achievement as measured by GPA and course passing. The GPA of students whose parents were automatically enrolled increased by an average of 0.06 points, and one in four students did not fail a class they would have otherwise failed. Surveys show automatic enrollment is uncommon, and its impact is underestimated: District leaders overestimate take-up under standard opt-in processes by about 40 percentage points and underestimate take-up under automatic enrollment by 29 percentage points. After learning the actual take-up rates, district leaders report being willing to pay substantially more for the technology when implemented under automatic enrollment than by standard opt-in processes.
Is Information Enough? The Effect of Information about Education Tax Benefits on Student Outcomes
Journal of Policy Analysis and Management | with Jeff Denning and Day Manoli
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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.
The Effects of Making Performance Information Public: Regression Discontinuity Evidence from Los Angeles Teachers
Economics of Education Review | with Matt Hill
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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.
How Behavioral Science can Empower Parents to Improve Children’s Educational Outcomes
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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
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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 & Information Asymmetries: Experimental Evidence from Home Visits and Text Messages in Middle and High Schools
Economics of Education Review | with Chana Edmond-Verley and Nicole Notario-Risk
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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
American Journal of Public Health | with Kulwant Dosanjh, Rebecca Dudovitz and Mitchell Wong
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
In the News
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Seattle may have figured out how to get more poor people into better housing.
April 2023
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Mobile phones can enable learning during school disruptions. Here’s how.
July 2022
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What the pandemic taught educators.
June 2022
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How to use correlation to make predictions.
April 2022
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UT researcher: Austin rent spike to hit struggling families even harder.
January 2022
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For kids at home, ‘a small intervention makes a big difference.’
October 2020
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AllHere gets a ‘nudge’ to scale low-cost, research-backed way to improve student outcomes.
June 2020
