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.
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.
Email: bergman [at] tc [dot] columbia [dot] edu Phone: (212) 678-3932 Office: 417 Thorndike Curriculum Vitae (pdf)
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.
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.
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.
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.
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
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)
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.
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.
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
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
J-PAL Ed Tech
Columbia Committee on the Economics of Education
Email: bergman [at] tc [dot] columbia [dot] edu
Phone: (212) 678-3932
Office: 417 Thorndike
Curriculum Vitae (pdf)