About

Ofer Malamud is a Professor of Human Development and Social Policy and a Faculty Fellow at the Institute for Policy Research at Northwestern University. He is an economist with a focus on education policy from an international perspective. His research concentrates on three main areas: educational investments over the life course, the role of technology in the formation of human capital, and the impact of general and specific education on labor market outcomes. Malamud has studied these topics across various institutional settings in countries including Chile, England, Israel, Mexico, Peru, Romania, Scotland, and the United States. He is also a research associate at the National Bureau of Economic Research and a member of the CESifo Research Network.

Research topics

• Artificial Intelligence
• Sociology
• Computer Science
• Political Science
• Advertising
• Library science
• Internet privacy
• Psychology
• Public administration
• Law
• Developmental psychology
• World Wide Web
• Business

Selected publications

• Streaks to Success? The Effects of Highlighting Streaks on Student Effort and Learning

  National Bureau of Economic Research · 2025-08-01

  reportOpen accessSenior author
  PublisherDOI

• Research Insights: Do Children Benefit from Personal Laptops in the Long Run?

  2025-02-18

  reportOpen access

  The OLPC program, designed by a team at the MIT Media Lab, aimed to improve educational outcomes among low-income primary students around the world by providing affordable personal laptops. The government of Peru launched its national OLPC program in 2009 targeting the nations most impoverished regions. Improving learning outcomes was a critical goal considering that only 14% of second grade primary students met the national mathematics standard and 23% did so for reading in 2009. While similar initiatives were implemented worldwide, their long-term impacts remain underexplored. This study focuses on 531 public, multigrade, rural primary schools, assessing the program's impact on academic achievement and grade progression through 2019.

  PublisherOA PDFDOI

• Laptops in the long run: Evidence from the one laptop per child program in rural Peru

  Journal of Public Economics · 2025-11-25 · 1 citations

  articleCorresponding
  PublisherDOI

• Laptops in the Long Run: Evidence from the One Laptop per Child Program in Rural Peru

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• The Effects of AI Feedback: Evidence from a Large-Scale Math Experiment

  AEA Randomized Controlled Trials · 2025-12-07

  dataset
  PublisherDOI

• Laptops in the Long Run: Evidence from the One Laptop per Child Program in Rural Peru

  National Bureau of Economic Research · 2025-11-01

  reportOpen access

  This paper examines a large-scale randomized evaluation of the One Laptop Per Child (OLPC) program in 531 Peruvian rural primary schools.We use administrative data on academic performance and grade progression over 10 years to estimate the long-run effects of increased computer access on (i) school performance over time and (ii) students' educational trajectories.Following schools over time, we find no significant effects on academic performance but some evidence of negative effects on grade progression.Following students over time, we find no significant effects on primary and secondary completion, academic performance in secondary school, or university enrollment.Survey data indicate that computer access significantly improved students' computer skills but not their cognitive skills; treated teachers received some training but did not improve their digital skills and showed limited use of technology in classrooms, suggesting the need for additional pedagogical support.

  PublisherDOI

• Streaking to Success: The Effects of Highlighting Streaks on Student Effort and Learning

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Self-, Peer-, and Teacher Perceptions Under School Tracking

  The Review of Economics and Statistics · 2025-01-28 · 1 citations

  article1st authorCorresponding

  Abstract We examine student, teacher, and peer perceptions of effort, ability, performance, and self-confidence in Romania's highly tracked schools. We find that: (1) students just above a cutoff—tracked into high-achieving classes—have less favorable self-perceptions than those just below (“big-fish-little-pond” effects); (2) students perceive peers in their classes more favorably (“in-group bias”); (3) this bias is stronger in lower-achieving classes; (4) students perceive themselves more positively than others perceive them (“illusory superiority”); (5) this bias is stronger among lower-achieving students (“Krueger-Dunning effects”). In short, being tracked into lower-achieving classes does not appear to negatively affect self-perceptions.

  PublisherDOI

• Expert vs. AI Feedback: Evidence from a Large-Scale Math Experiment

  AEA Randomized Controlled Trials · 2025-11-28

  datasetSenior author
  PublisherDOI

• The Effects of AI Feedback: Evidence from a Large-Scale Math Experiment

  AEA Randomized Controlled Trials · 2025-12-07

  dataset
  PublisherDOI

Frequent coauthors

• Abigail Wozniak

  Federal Reserve Bank of Minneapolis

  134 shared

• Kasey Buckles

  University of Notre Dame

  64 shared

• Melinda Sandler Morrill

  North Carolina State University

  58 shared

• Andreas Hagemann

  Ross School

  53 shared

• Robert Kaestner

  41 shared

• Cristian Pop-Eleches

  36 shared

• Diether Beuermann

  Inter-American Development Bank

  27 shared

• Julián Cristia

  23 shared

Education

• Ph.D., Education

  University of California, Los Angeles

  2000

• M.A., Education

  University of California, Los Angeles

  1996

• B.A., Education

  University of California, Los Angeles

  1994