Here are some publicly available research papers, briefs and datasets relevant to the science of scaling produced by members of our research networks.
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Finan, F., and Mazzocco, M., 2021. Electoral Incentives and the Allocation of Public Funds. Journal of the European Economic Association, 19(5): 2467-2512. Politicians facing reelection allocate public funds more efficiently; term-limited mayors misallocate resources at the expense of welfare, highlighting how electoral accountability shapes policy implementation.
Finan, F., and Mazzocco, M., 2025. Combating Political Corruption with Policy Bundles. Journal of Political Economy, Volume 133, Number 8.
Bundling anti-corruption interventions (audits, transparency, and sanctions) generates complementarities that make combined policies more effective than any single intervention alone.
Vivalt, E., Coville, A., and KC, S., 2024. Local Knowledge, Formal Evidence, and Policy Decisions. Journal of Development Economics, 174.
Policymakers weight local knowledge heavily relative to formal evidence; understanding this process is essential for promoting evidence-based policy adoption.
Vivalt, E., and Coville, A., 2023. How Do Policymakers Update Their Beliefs? Journal of Development Economics, 165.
Policymakers update beliefs asymmetrically in response to evidence, with prior beliefs and political considerations affecting how new information is incorporated.
De Janvry, A., Finan, F., and Sadoulet, E., 2012. Local Electoral Incentives and Decentralized Program Performance. , The Review of Economics and Statistics. 94(3): 672-685.
The impacts of a conditional cash transfer program in Brazil was 36% larger in reducing school drop-out rates in municipalities governed by mayors who faced reelection possibilities, compared to those with lame-duck mayors.
Guiteras, R., Mobarak, A., 2016. “Does Development Aid Undermine Political Accountability? Leader and Constituent Responses to a Large-Scale Intervention”, Working Paper.
When a village leader’s role in providing an externally-funded, randomly assigned, sanitation program is not clear to constituents, treated constituents attribute credit for the program to their leader. Leaders attempt to both claim credit, and signal their ability by exerting more effort.
Padro i Miquel, G., Qian,N., Yao,Y. 2012. Social Fragmentation, Public Goods and Elections: Evidence from China. Working Paper.
Religious fractionalization in villages leads to less public good expenditures when elections are introduced — indicating that voter heterogeneity can affect the implementation of programs.
Breza, E., Chandrasekhar, A.G. and Viviano, D., 2025. Generalizability with ignorance in mind: learning what we do (not) know for archetypes discovery. arXiv preprint arXiv:2501.13355.
Epanomeritakis, A. and Viviano, D., 2025. Learning What to Learn: Experimental Design when Combining Experimental with Observational Evidence. arXiv preprint arXiv:2510.23434.
Heckman, J., and Vytlacil, E., 2005. Structural Equations, Treatment Effects and Econometric Policy Evaluation. Econometrica, 73(3): 669-738.
The marginal treatment effect (MTE) framework unifies different treatment parameters and enables extrapolation from experimental estimates to policy-relevant populations.
Kitagawa, T., and Ishihara, T., 2021 (revised 2024). Evidence Aggregation for Treatment Choice. Working Paper. Optimal treatment assignment rules can aggregate evidence from multiple studies while accounting for cross-study heterogeneity and statistical uncertainty.
Kline, P., Armstrong, T., and Sun, L., 2023. Adapting to Misspecification. Working Paper.
Methods that remain valid under model misspecification are essential for generalizing findings to new contexts where structural assumptions may not hold.
Meager, R., 2022. Aggregating Distributional Treatment Effects: A Bayesian Hierarchical Analysis of the Microcredit Literature. American Economic Review, 112(6): 1818-1847.
Extending meta-analysis to distributional effects shows that microcredit has limited transformative impacts; heterogeneity within studies is larger than heterogeneity across contexts.
Meager, R., 2019. Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments. American Economic Journal: Applied Economics, 11(1): 57-91.
Aggregating seven microcredit RCTs reveals modest average effects on business profits and household income, with heterogeneity across studies smaller than within-study variation.
Meager, R., and Gechter, M., 2021. Combining experimental and observational studies in meta-analysis: A mutual debiasing approach. Working Paper.
RCTs and observational studies can be combined to debias each other, improving precision in meta-analyses where either source alone would be insufficient.
Mogstad, M., Torgovitsky, A., and Walters, C., 2024. Policy Evaluation with Multiple Instrumental Variables. Journal of Econometrics, 243(1): 105-122.
Multiple instruments can be combined to learn about treatment effects for broader populations than any single instrument identifies.
Mogstad, M., and Torgovitsky, A., 2024. Instrumental Variables with Unobserved Heterogeneity in Treatment Effects. NBER Working Paper, forthcoming Handbook of Labor Economics.
Provides methods to identify treatment effect distributions and extrapolate beyond compliers when effects are heterogeneous.
Mogstad, M., Torgovitsky, A., and Walters, C., 2024. Policy Evaluation with Multiple Instrumental Variables. Journal of Econometrics, 243(1): 105-122.
Multiple instruments can be combined to learn about treatment effects for broader populations than any single instrument identifies.
Mogstad, M., Torgovitsky, A., and Walters, C., 2021. The Causal Interpretation of Two-Stage Least Squares with Multiple Instrumental Variables. American Economic Review, 111(11): 3663-3698.
2SLS with multiple instruments estimates a weighted average of LATEs, with weights that can place negative weight on some complier groups.
Mogstad, M., and Torgovitsky, A., 2018. Identification and Extrapolation of Causal Effects with Instrumental Variables. Annual Review of Economics, 10: 577-613.
Reviews methods for moving beyond LATE to identify policy-relevant treatment effects using instrumental variables and marginal treatment effects.
Mogstad, M., Torgovitsky, A., and Walters, C., 2017. Beyond LATE with a Discrete Instrument. Journal of Political Economy, 125(4): 985-1039.
Partial identification methods can bound treatment effects for always-takers and never-takers, extending what can be learned beyond the complier population.
Vivalt, E., 2020. How Much Can We Generalize from Impact Evaluations? Journal of the European Economic Association, 18(6): 3045-3089.
Treatment effects vary substantially across development RCTs; a single study’s results are often a poor predictor of effects in other contexts, limiting external validity.
Chassang, S., i Miquel,G., and Snowberg, E., 2012. Selective Trials: A Principal-Agent Approach to Randomized Controlled Experiments. American Economic Review, 102 (4): 1279-1309.
Selective trials can disentangle the effects of treatment, and unobserved effort decisions taken by experimental subjects — which helps improve external validity.
Heckman, J., Vytlacil, E., 2005. Structural Equations, Treatment Effects, and Econometric Policy Evaluation. Econometrica, 73(3): 669-738
Authors use the marginal treatment effect (MTE) to unify different treatment parameters and constructs and compares alternative policy relevant treatment effects, thus providing a bridge between structural and treatment effect parameters.
Rosenzweig, M., Udry, C., 2020. Evidence Validity in a Stochastic World. The Review of Economic Studies, 87 (1): 343–381.
The returns to investments in agriculture and enterprises have significant inter-temporal variability due to exogenous macro-level shocks. The lower-bound confidence intervals of this variability are substantially wider than those solely on sampling error from single-year samples.
Chandrasekhar, A., et al., 2025. Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization. Econometrica, Volume 93, Issue 4.
Different nudges have heterogeneous effects across populations; network-based interventions and social signaling can amplify immunization uptake.
Bergquist, L., McIntosh, C., and Startz, M., 2024. Search Costs, Intermediation, and Trade: Experimental Evidence from Ugandan Agricultural Markets. Working Paper. Reducing search frictions in agricultural markets changes price dispersion and intermediary margins, with equilibrium effects that differ from direct treatment effects.
Bergquist, L., Faber, B., Fally, T., Hoelzlein, M., Miguel, E., and Rodríguez-Clare, A., 2022. “Scaling Up Agricultural Policy Interventions: Theory and Evidence from Uganda,” Working Paper.
General equilibrium price effects can substantially alter the welfare impacts of agricultural policies at scale; partial equilibrium estimates miss market-level adjustments.
Bergquist, L., and Dinerstein, M., 2020. Competition and Entry in Agricultural Markets: Experimental Evidence from Kenya. American Economic Review, 110(12): 3705-3747.
Market structure affects how price shocks transmit through agricultural value chains; increased competition benefits farmers through improved price transmission.
Kitagawa, T., and Wang, G., 2023. Who should get vaccinated? Individualized allocation of vaccines over SIR network. Journal of Econometrics, 232(1): 109-126.
Optimal vaccine allocation depends on network structure; network-based targeting can substantially outperform uniform or risk-based allocation strategies.
Akram, A., Chowdhury, S., and Mobarak, A., 2017. “Effects of Emigration on Rural Labor Markets”, Working Paper.
Emigration increases rural employment and wages for non-migrants. Unsubsidized households are more likely to migrate if more people in their village receive migration subsidies.
Beaman, BenYishay, A. , Magruder, and Mobarak, A., 2015. “Can Network Theory based Targeting Increase Technology Adoption?”. American Economic Review, 111(6): 1918-43.
Introducing technologies to optimal households based on network theory increases technology diffusion. Both reduced form and structural estimates suggest most farmers need to learn about a technology from multiple people before they adopt.
BenYishay, A., Mobarak, A, 2018, “Social Learning and Incentives for Experimentation and Communication,” Review of Economic Studies, 86(3): 976-1009.
Farmers are most convinced to adopt new technology by other farmers with whom they share a group identity, or who face similar agricultural conditions.
Guiteras, R., Levinsohn, J., and Mobarak, A. 2018, Demand Estimation with Strategic Complementarities: Sanitation in Bangladesh. Working Paper.
Latrine adoption by households in rural villages are strategic complements, and this effect appears to be driven by changing social norms. Simulations from a structural model predict the the optimal subsidy policies for encouraging adoption.
Kaboski, Joseph P., and Robert M. Townsend. 2012. “The Impact of Credit on Village Economies.” American Economic Journal: Applied Economics, 4 (2): 98-133.
Thailand’s ‘Million Baht Village Fund’ increased consumption, agricultural investment, income growth, and wages, while decreasing asset growth.
Mobarak, A., Rosenzweig, M., 2013. “Informal Risk Sharing, Index Insurance, and Risk-Taking in Developing Countries,” AER: Papers and Proceedings
When formal insurance carries basis risk it is a complement to informal insurance. Weather insurance to cultivators increases high-risk high-yield production decisions, which increases wage volatility for laborers.
Munshi, K., & Rosenzweig, M. 2016. Networks and misallocation: Insurance, migration, and the rural-urban wage gap. American Economic Review, 106(1): 46-98.
Caste-based rural insurance networks can disincentivize migration and contribute to urban-rural productivity differences. Structural estimates show that small improvements in formal insurance decrease the spatial misallocation of labor by substantially increasing migration.
Attanasio, O., Levell, P., Low, H., and Sánchez-Marcos, V., 2018. Aggregating Elasticities: Intensive and Extensive Margins of Women’s Labor Supply. Econometrica, 86(6): 2049-2082. Micro labor supply elasticities aggregate non-linearly to macro responses; ignoring extensive margin heterogeneity leads to biased aggregate predictions.
Attanasio, O., Cortes, D., Maldonado, D., et al., 2024. Parental Investments and Skills Formation During Infancy and Youth: Long Term Evidence From an Early Childhood Intervention. NBER Working Paper. An early childhood program in Colombia improved adult earnings and education, demonstrating that pilot effects can persist at scale over decades.
Kline, P., Gaubert, C., Vergara, D., and Yagan, D., 2025. Place-Based Redistribution. American Economic Review, 115(1). Place-based policies have welfare effects that depend on migration responses and local multipliers; optimal spatial policy differs substantially from person-based transfers.
Kline, P., 2024. Firm wage effects. Handbook of Labor Economics, vol. 5.
Firm-level policies affect wages through rent-sharing; understanding firm effects is essential for predicting wage impacts of labor market interventions at scale.
Kline, P., and Moretti, E., 2014. People, Places, and Public Policy: Some Simple Welfare Economics of Local Economic Development Programs. Annual Review of Economics, 6: 629-662. Local development programs generate welfare effects through wages, rents, and migration; simple cost-benefit calculations can miss general equilibrium welfare impacts.
Buera, F., Kaboski, J., Shin, Y., 2017. The Macroeconomics of Microfinance. The Review of Economic Studies, 88(1): 126–161. A macroeconomic model, validated using the results of randomized evaluations, shows different partial and general equilibrium effects of microcredit. In the long-run, economy-wide microcredit lowers savings which raises interest rates, and increases welfare particularly for the poor and marginal entrepreneurs.
Buera, F. J., Kaboski, J. P., & Shin, Y. (2017). Taking stock of the evidence on micro-financial interventions. In The Economics of Poverty Traps. University of Chicago Press.
Micro-financial interventions can help segments of the population increase their income and consumption, but there is little evidence that these interventions can lead to wide-scale, transformative impacts akin to escaping aggregate poverty traps.
Buera, F., Kaboski, J., and Shin,Y., 2014. “Macro-perspective on Asset Grants Programs: Occupational and Wealth Mobility.” American Economic Review, 104 (5): 159-64.
Provides a simple quantitative general equilibrium model of occupational choice with credit market frictions to analyze the aggregate and distributional effects of asset transfer programs. It finds that the impacts of such programs on the wealth distribution are short-lived.
Kaboski, J., Lipscomb, M, and Midrigan, V., 2014. “The Aggregate Impact of Household Saving and Borrowing Constraints: Designing a Field Experiment in Uganda.” American Economic Review, 104 (5): 171-76.
Access to cash loans leads to short-term, transitory increases in output, consumption, and investment that dissipate in the little long-run. Conversely, access to asset-financed loans leads to lower shortrun benefits, but larger increases in investment, consumption, and entrepreneurship in the long-run.
Kaboski, J. P., & Townsend, R. M. (2011). A structural evaluation of a large-scale quasi-experimental microfinance initiative. Econometrica, 79(5), 1357-1406.
Evaluates consumption and welfare impacts of a large-scale, exogenous, microcredit intervention and finds heterogeneous benefits amongst households and that the costs of the program exceed the sum of its benefits.
Lagakos, D., Mobarak, A., and Waugh, M., 2017. “The Welfare Effects of Encouraging Rural- Urban Migration”. Econometrica, 91(3): 803-837.
A structural model that measures the net welfare effects of seasonal migration subsidies shows that the subsidies benefit mostly the extreme poor, and that the welfare gains are larger than those obtained from unconditional cash transfers.