Research
Please click here to view my research statement.
Publications
- Work From HomeJob Market Paper 1The Future of Work: Remote Opportunities and Female Labor Force ParticipationConditionally Accepted at the Journal of Public Economics
This paper examines the effects of the expansion of work-from-home (WFH) opportunities accelerated by the COVID-19 pandemic on female labor market outcomes. Using a difference-in-differences and event study design, this paper compares individuals in Metropolitan Statistical Areas with higher WFH potential to those in areas with lower WFH potential before and after the pandemic. The results indicate that relative to men, access to WFH opportunities significantly increases the likelihood of female labor force participation, employment, and full-time work. These effects are particularly strong among college-educated mothers and mothers with young children. More broadly, WFH opportunities account for approximately 13 percent of the observed convergence in the male–female labor force participation gap, corresponding to roughly 136,000 additional mothers in the U.S. labor force during the stable post-pandemic period. Furthermore, the mechanism analysis demonstrates that mothers’ own WFH opportunity is the primary driver of the effect, while the spillover from a husband’s WFH remains a secondary but meaningful force. The effect operates by relaxing a caregiving constraint: it is largest where childcare is most costly and works mainly by drawing nonemployed mothers into the labor force. College mothers benefit most because they have greater access to high-teleworkability occupations.
This paper has been or will be presented at the following:
- 10/28/26: Remote Work Conference, Hoover Institution, Stanford University
- 11/24/25: Southern Economic Association (SEA) 95th Annual Meeting
- 6/20/25: Western Economic Association International (WEAI) 100th Annual Conference
- 3/22/25: Rising Star Session, Chinese Economists Society 2025 North America Annual Conference
- 1/27/25: HeLD Seminar, Department of Economics, University of Connecticut
Song, M. (2026). The future of work: Remote opportunities and female labor force participation. Journal of Public Economics (conditionally accepted).
Song, Meng. 2026. "The Future of Work: Remote Opportunities and Female Labor Force Participation." Journal of Public Economics, conditionally accepted.
Song, Meng. "The Future of Work: Remote Opportunities and Female Labor Force Participation." Journal of Public Economics, 2026, conditionally accepted.
@article{song2025a, year = {2026}, title = {The Future of Work: Remote Opportunities and Female Labor Force Participation}, author = {Song, Meng}, journal = {Journal of Public Economics}, note = {Conditionally accepted}, }
Working Papers
- 4-day School WeekJob Market Paper 2When Less is More: School Schedule Reform and Student OutcomesMeng Song and Ruinan Zhao
We examine how school schedule reforms shape students’ outcomes by studying the four-day school week, in which students attend school four rather than five days per week. We use comprehensive administrative data from Colorado, where this schedule is most prevalent in the United States. The dataset spans nearly two decades of entry cohorts and more than one million unique student records, providing us with a unique opportunity to study the policy’s long-run effects. Leveraging quasi-random variation in adoption across districts in a difference-in-differences framework, we estimate how a shortened school week affects students’ educational trajectories. In contrast to much of the existing literature, we find modest improvements in standardized test scores, lower dropout rates, and higher on-time and overall graduation rates. The duration of exposure plays a key role: students with longer and more consistent exposure experience the largest gains in persistence and attainment. Improvements in teacher retention and classroom expenditures also contribute to these gains. At the same time, heterogeneous effects indicate that disadvantaged students benefit less, raising concerns about equity. Overall, the findings suggest that a shorter school week need not necessarily harm student outcomes and can improve both performance and completion when designed and implemented carefully.
This paper has been or will be presented at the following:
- 1/3/27: American Economic Association (AEA) Annual Meeting, ASSA 2027
Song, M., & Zhao, R. (2026). When less is more: School schedule reform and student outcomes [Working paper].
Song, Meng, and Ruinan Zhao. 2026. "When Less is More: School Schedule Reform and Student Outcomes." Working paper.
Song, Meng, and Ruinan Zhao. "When Less is More: School Schedule Reform and Student Outcomes." Working paper, 2026.
@article{song2025b, year = {2026}, title = {When Less is More: School Schedule Reform and Student Outcomes}, author = {Song, Meng and Zhao, Ruinan}, note = {Working paper}, } - Climate & BehaviorClimate Change is Decreasing Risk Aversion GloballyRemy Levin and Meng SongUnder Review at the Review of Financial Studies
We estimate the effects of climate change on individual risk preferences, using global cross-sectional preference data linked to personal climate histories. We find that a 1◦C increase in mean lifetime temperature is associated with a 0.318-unit decrease in relative risk aversion globally. We replicate the results in panel datasets from four countries containing longitudinal measures of risk preferences. Historical projections indicate that climate change between 1981 and 2026 decreased median global risk aversion by 16%. Our results suggest that climate change may be shaping economic preferences in ways that could erode support for its own mitigation.
Levin, R., & Song, M. (2026). Climate change is decreasing risk aversion globally [Working paper].
Levin, Remy, and Meng Song. 2026. "Climate Change is Decreasing Risk Aversion Globally." Working paper.
Levin, Remy, and Meng Song. "Climate Change is Decreasing Risk Aversion Globally." Working paper, 2026.
@article{song2025d, year = {2026}, title = {Climate Change is Decreasing Risk Aversion Globally}, author = {Levin, Remy and Song, Meng}, note = {Working paper}, } - Gen AIWho Gains and Who Adapts? The Effects of Generative AI on U.S. Local Labor MarketsManuscript Available upon Request
This paper examines how the diffusion of generative artificial intelligence following the release of ChatGPT has affected U.S. local labor markets. I construct a measure of generative-AI exposure for every U.S. local labor market, reconciling occupation-level exposure scores developed by OpenAI and Anthropic with pre-ChatGPT occupational employment shares and with large-language-model classification of occupational task descriptions and real-time job postings. Exploiting geographic variation in exposure in a difference-in-differences and event study design, I estimate effects on employment across local labor markets and individuals, and allow those effects to vary across industries and demographic groups. Causal forest estimation, clustering, and sentiment analysis of job-posting text are used to characterize which places and which workers adjust, and through which margins. Preliminary results suggest that generative-AI exposure and remote-work potential overlap across occupations and co-vary across local labor markets. As a result, estimating the effects of either technology in isolation may confound its impact with that of the other, motivating a joint analysis of the two.
Song, M. (2025). Who gains and who adapts? The effects of generative AI on U.S. local labor markets [Working paper].
Song, Meng. 2025. "Who Gains and Who Adapts? The Effects of Generative AI on U.S. Local Labor Markets." Working paper.
Song, Meng. "Who Gains and Who Adapts? The Effects of Generative AI on U.S. Local Labor Markets." Working paper, 2025.
@article{song2025c, year = {2025}, title = {Who Gains and Who Adapts? The Effects of Generative AI on U.S. Local Labor Markets}, author = {Song, Meng}, note = {Working paper}, } - Firearm MeasurementThe Tricky Truth About Establishing Firearm Ownership in Survey DataIn Preparation for Submission to the Journal of Policy Analysis and Management (Policy Insights)
Survey-based estimates of firearm ownership underpin firearm policy evaluation, yet the major surveys ask the question differently. We randomly assign 4,691 Gallup Panel respondents to one of four established measures of personal and household firearm ownership, drawn from the General Social Survey, Gallup, the National Firearms Survey, and a new instrument developed from cognitive interviews with gun owners. Personal ownership estimates do not differ across measures, but household ownership estimates do, by more than five percentage points, and question wording also changes item nonresponse. Household ownership figures should be compared across surveys only with question wording in view, and standardized, cognitively tested instruments would improve the evidence base for policy.
Dineen, J. N., Raissian, K., & Song, M. (2026). The tricky truth about establishing firearm ownership in survey data [Working paper].
Dineen, Jennifer Necci, Kerri Raissian, and Meng Song. 2026. "The Tricky Truth About Establishing Firearm Ownership in Survey Data." Working paper.
Dineen, Jennifer Necci, Kerri Raissian, and Meng Song. "The Tricky Truth About Establishing Firearm Ownership in Survey Data." Working paper, 2026.
@article{song2026a, year = {2026}, title = {The Tricky Truth About Establishing Firearm Ownership in Survey Data}, author = {Dineen, Jennifer Necci and Raissian, Kerri and Song, Meng}, note = {Working paper}, } - AI & EducationMajoring in the Age of AI: Generative AI Exposure and Students’ Field and Career ChoicesManuscript in Preparation
Song, M. (2026). Majoring in the age of AI: Generative AI exposure and students’ field and career choices [Working paper].
Song, Meng. 2026. "Majoring in the Age of AI: Generative AI Exposure and Students’ Field and Career Choices." Working paper.
Song, Meng. "Majoring in the Age of AI: Generative AI Exposure and Students’ Field and Career Choices." Working paper, 2026.
@article{song2026b, year = {2026}, title = {Majoring in the Age of AI: Generative AI Exposure and Students' Field and Career Choices}, author = {Song, Meng}, note = {Working paper}, } - AI BenchmarkSocialScientistBench: Benchmarking AI Systems on Social Science Research TasksManuscript in Preparation. Multi-author collaboration across ten social science disciplines; responsible for the economics and education tracks
Song, M. (2026). SocialScientistBench: Benchmarking AI systems on social science research tasks [Working paper].
Song, Meng. 2026. "SocialScientistBench: Benchmarking AI Systems on Social Science Research Tasks." Working paper.
Song, Meng. "SocialScientistBench: Benchmarking AI Systems on Social Science Research Tasks." Working paper, 2026.
@article{song2026c, year = {2026}, title = {SocialScientistBench: Benchmarking AI Systems on Social Science Research Tasks}, author = {Song, Meng}, note = {Working paper}, } - Technical High SchoolNot the Institution, but the Field: Evidence from Career and Technical High SchoolManuscript Available upon Request
Brunner, E. J., Dougherty, S. M., Ross, S. L., & Song, M. (2025). Not the institution, but the field: Evidence from career and technical high school [Working paper].
Brunner, Eric J., Shaun M. Dougherty, Stephen L. Ross, and Meng Song. 2025. "Not the Institution, but the Field: Evidence from Career and Technical High School." Working paper.
Brunner, Eric J., Shaun M. Dougherty, Stephen L. Ross, and Meng Song. "Not the Institution, but the Field: Evidence from Career and Technical High School." Working paper, 2025.
@article{song2025e, year = {2025}, title = {Not the Institution, but the Field: Evidence from Career and Technical High School}, author = {Brunner, Eric J. and Dougherty, Shaun M. and Ross, Stephen L. and Song, Meng}, note = {Working paper}, } - Firearm PolicyFraming, Messengers, and Measurement: Lessons from Mixed-Methods Firearm Policy ExperimentsManuscript in Preparation
Dineen, J. N., Raissian, K., & Song, M. (2025). Framing, messengers, and measurement: Lessons from mixed-methods firearm policy experiments [Working paper].
Dineen, Jennifer Necci, Kerri Raissian, and Meng Song. 2025. "Framing, Messengers, and Measurement: Lessons from Mixed-Methods Firearm Policy Experiments." Working paper.
Dineen, Jennifer Necci, Kerri Raissian, and Meng Song. "Framing, Messengers, and Measurement: Lessons from Mixed-Methods Firearm Policy Experiments." Working paper, 2025.
@article{song2025f, year = {2025}, title = {Framing, Messengers, and Measurement: Lessons from Mixed-Methods Firearm Policy Experiments}, author = {Dineen, Jennifer Necci and Raissian, Kerri and Song, Meng}, note = {Working paper}, }