Highlighted Publications




 

Wheldon, Tallapragada, and Thompson (2025) examine whether political ideology is associated with trust in scientists as sources of cancer information in the United States. They analyze cross-sectional data from the 2024 Health Information National Trends Survey, a nationally representative survey of U.S. adults. They find that overall trust in scientists is high (86%), but it declines as respondents become more politically conservative. Each one-point shift toward conservatis

Blazar et al. (2026) examine whether access to Maryland’s Teacher Academy (TAM) increases entry into teaching. They ask if a high school “grow-your-own” pathway affects students’ later education, careers, and earnings. They use statewide administrative data linking K–12 records, college enrollment and degrees, teacher employment, and unemployment insurance wages. They find that TAM exposure increased the probability of becoming a teacher by 0.6 percentage points, a 45% rise o

Wright, Arora, and Wright (2025) examine whether a non-binding commitment pledge combined with goal setting affects student achievement  . They ask if having students state a target grade, identify study actions, and sign a pledge improves academic performance. The study uses a randomized field experiment in four Principles of Macroeconomics sections at a public university, analyzing survey responses and official course records. Although treated students pledged higher grades

Gagliarducci, Paserman, and Patacchini (2025) examine whether hurricanes influence legislators’ support for climate-related environmental policy. They analyze data on federal disaster declarations (1989–2020) matched to U.S. House districts, combined with records of sponsorship and cosponsorship of “green” bills. They find that representatives from hurricane-hit districts are more likely to support environmental legislation, especially in the year after a storm. The effect is

Ebinger et al. (2025) investigated how the January 2025 Los Angeles wildfires affected local residents’ health. They asked whether emergency department encounters for specific illnesses increased during and after the fires. The study analyzed Cedars-Sinai emergency encounter data from 39 affected or adjacent zip codes, comparing the 90 days post-fire with the same calendar periods in 2018–2024 using interrupted time series models. They found significant excesses in acute pulm

Orr (2026) asks whether evidence-based policy clearinghouses give reliable advice to local policymakers when program impacts vary across places. He examines data from six large multisite randomized controlled trials in education and youth programs, drawing on site-level impact estimates and cross-site variation reported in prior studies. Using a Bayesian model, he evaluates how often a clearinghouse rule based on statistically significant average effects leads to correct loca

Wilk, Deza, Hodge, and Danagoulian (2026) ask whether recreational marijuana laws change adults’ eating habits and physical activity. They examine grocery purchase data from the NielsenIQ Consumer Panel, self-reported exercise from the Behavioral Risk Factor Surveillance System, and time-use data from the American Time Use Survey. They find that recreational marijuana laws increase spending on junk food by about 1.8 percent and raise the likelihood that a grocery trip include

Jacobs et al. (2026) ask whether collaborative robots (cobots) can be used to raise productivity while preserving jobs, rather than displacing workers. They examine existing research, industry case examples, and prior empirical studies on automation, worker safety, ergonomics, and labor markets, rather than analyzing a new dataset. They find that cobots tend to automate specific tasks instead of whole jobs, reducing physical strain and workplace injuries while increasing outp

Agan et al. (2025) ask whether clearing or sealing criminal records improves employment outcomes. They examine linked administrative court records from Maryland, New Jersey, Pennsylvania, and Texas merged with IRS tax data on earnings, employment, and gig work. They find that criminal charges—both convictions and non-convictions—are followed by large and persistent employment declines. However, removing records through Fair Credit Reporting Act rules or Clean Slate laws has l

Shekhar, Leder-Luis, and Akoglu (2026) ask whether unsupervised, explainable machine learning can effectively identify hospitals engaging in potentially fraudulent Medicare billing. They analyze millions of Medicare inpatient claims from 2017, combined with patients’ prior medical histories and hospital characteristics, covering over 2,200 hospitals. Using anomaly-detection algorithms, they rank hospitals based on suspicious coding and spending patterns. The authors find that

Darolia and Turner (2026) ask whether the rapid expansion of apprenticeships in the United States is being matched by credible evidence on program quality, outcomes, and skill development. They examine administrative data from the U.S. Department of Labor’s Registered Apprenticeship system, state-level apprenticeship records, and postsecondary enrollment and completion data. They find that apprenticeships have grown substantially since 2011, expanded into new industries, and

Cowell et al. (2025) ask whether there are specific weeks during pregnancy when exposure to fine particulate air pollution (PM2.5) has the strongest association with birth weight. They analyze data from 16,868 full-term singleton births in the U.S. Environmental Influences on Child Health Outcomes (ECHO) cohort, using weekly, address-level PM2.5 exposure estimates derived from machine-learning models. They find that higher prenatal PM2.5 exposure is associated with lower birt

Christakis et al. (2026) examine whether adult attitudes—especially parental attitudes—support banning student smartphone access during the school day. They ask whether support for school smartphone bans is widespread across countries and which individual characteristics predict that support. They analyze cross-sectional survey data from 35,018 adults across 35 countries, using logistic regression to adjust for demographics, parental status, life satisfaction, and digital beh

Gupta, Mittal, and Van Nieuwerburgh (2022) examine how the shift to remote work has affected the value of commercial office real estate. They ask whether work-from-home practices have permanently reduced office demand, rents, and building values. They analyze lease-level data from CompStak covering 105 U.S. office markets from 2000–2023, combined with office occupancy data, firm remote-work policies, and REIT returns. They find that remote work caused large declines in lease

Bloom, Dahl, and Rooth (2025) examine whether the post-pandemic rise in working from home causally increased employment for people with physical disabilities. They ask whether expanded access to remote work explains the sharp increase in disability employment after COVID-19. They use U.S. Current Population Survey data from 2018–2019 and 2022–2024, combined with occupation-level measures of work from home. They find that a 1 percentage point increase in work from home raises

Dahl and Knepper (2025) examine why workplace sexual harassment is frequently underreported and whether fear of employer retaliation plays a central role. They use administrative data on sexual harassment charges filed with the Equal Employment Opportunity Commission from 1995 to 2016, combined with county-level unemployment data and major reductions in unemployment insurance benefits, especially in North Carolina. They find that weaker labor market conditions discourage repo

Matter and Hodler (2024) ask whether Google’s web search personalization creates ideological segregation in election-related search results during the 2020 U.S. election. They study this using a large-scale field experiment with 150 synthetic users (“bots”) assigned different partisan browsing habits and locations across 25 U.S. cities, issuing identical election-related Google searches over several months. They find substantial personalization in search results, driven mainl

Kahleova et al. (2025) asked whether adopting a low-fat vegan diet reduces greenhouse gas emissions and cumulative energy demand. They analyzed 16-week dietary records from overweight adults who were randomly assigned to either a vegan group or a control group. They linked each participant’s food intake to environmental impact databases to estimate emissions and energy use. They found that the vegan group reduced emissions by about 1313 g CO2-eq per person per day, while the

McBain et al. (2025) ask how often U.S. adolescents and young adults use generative AI for mental health advice and how helpful they find it. They analyze nationally representative survey data from 1,058 youths ages 12–21. They find that 13.1 percent reported using generative AI for advice when sad, angry, or nervous, and usage rose to 22.2 percent among those ages 18–21. Among users, 65.5 percent sought advice at least monthly and 92.7 percent rated it somewhat or very helpf

Federle et al. (2025) ask how wars affect economies in the country where fighting occurs and in nearby or connected countries. They assemble a new 150-year, 60-country dataset containing macroeconomic indicators and war casualty data. They find that an average-intensity war causes output in the war-site economy to fall by about 10 percent, consumer prices to rise roughly 20 percent, and capital stock and productivity to decline. They also show that countries trading with the

Baker, Halberstam, Kroft, Mas, and Messacar (2025) ask whether faculty unionization changes the distribution of wages in Canadian higher education. They analyze longitudinal administrative salary data from 1970–2022 and link it to the staggered rollout of faculty unions. They find that unionization raises wages at the bottom of the distribution by roughly 10 percent while leaving top salaries unchanged, compressing inequality. On average, salaries rise about 2 percent in the

Pathania and Netessine (2026) ask whether opening Amazon distribution facilities increases local economic well-being. They examine county-level data from 2011–2019, including employment-to-population ratios, poverty rates, and median household income, and they use midsized counties as treatment cases. They combine matching with Callaway–Sant’Anna difference-in-differences to address selection and staggered treatment. They find that after Amazon enters a county, the employment

Allcott et al. (2024) ask whether deactivating Facebook or Instagram improves users’ emotional well-being during the 2020 U.S. election period. They analyze survey data from more than 30,000 adult users who were randomly assigned to deactivate for six weeks (treatment) or one week (control). They find that Facebook deactivation increased an emotional state index by about 0.060 standard deviations, while Instagram increased it by about 0.041 standard deviations. They report th

Fadlon, Fugleholm, and Nielsen (2025) ask whether income losses after a spouse’s death worsen survivors’ mental health. They use Danish administrative data from 1995–2018, including death records, prescription drug purchases, and household income information for the full population. They find that survivors sharply increase their use of mental-health medications after the death, with take-up doubling in the first month (about +10.5 percentage points) and remaining about 10 pe