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Field experiments and public policy: festina lente

Published online by Cambridge University Press:  14 July 2020

GLENN W. HARRISON
GLENN W. HARRISON*
Affiliation:
Department of Risk Management & Insurance and Center for the Economic Analysis of Risk, Robinson College of Business, Georgia State University, Atlanta, GA, USA School of Economics, University of Cape Town, Cape Town, South Africa
*
*Correspondence to: Department of Risk Management & Insurance and Center for the Economic Analysis of Risk, Robinson College of Business, Georgia State University, Atlanta, GA, USA. E-mail: [email protected].
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Abstract

The current state of the art in field experiments does not give me any confidence that we should be assuming that we have anything worth scaling, assuming we really care about the expected welfare of those about to receive the instant intervention. At the very least, we should be honest and explicit about the need for strong priors about the welfare effects of changes in averages of observables to warrant scaling. What we need is a healthy dose of theory and the implied econometrics.

Type
Articles
Information
Behavioural Public Policy , Volume 5 , Special Issue 1: Field Experiments and Public Policy , January 2021 , pp. 117 - 124
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press

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References

Angrist, J. D. and Pischke, J.-S. (2009), Mostly Harmless Econometrics: An Empiricist's Companion, Princeton: Princeton University Press.CrossRefGoogle Scholar
Balakrishnan, U., Haushofer, J., and Jakiela, P. (2020), ‘How Soon Is Now? Evidence of Present Bias from Convex Time Budget Experiments’, Experimental Economics, 23: 294–321.CrossRefGoogle Scholar
Banerjee, A., Banerji, R., Berry, J., Duflo, E., Kannan, H., Mukerji, S., Shotland, M., and Walton, M. (2017), ‘From Proof of Concept to Scalable Policies: Challenges and Solutions, with an Application’, Journal of Economic Perspectives, 31(4): 73–102.CrossRefGoogle Scholar
Blackwell, M., Iacus, S. King, G., and Porro, G. (2009), ‘cem: Coarsened Exact Matching in Stata’, Stata Journal, 9(4): 524–546.CrossRefGoogle Scholar
Bottai, M. and Orsini, N., (2019), ‘qmodel: A Command for Fitting Parametric Quantile Models’, Stata Journal, 19(2): 261–293.CrossRefGoogle Scholar
Camerer, C. F. and Hogarth, R. (1999), ‘The Effects of Financial Incentives in Experiments: A Review and Capital-Labor Framework’, Journal of Risk and Uncertainty, 19: 7–42.CrossRefGoogle Scholar
Carter, M. R., Tjernström, E., and Toledo, P. (2019), ‘Heterogeneous Impact Dynamics of a Rural Business Development Program in Nicaragua’, Journal of Development Economics, 138: 77–98.CrossRefGoogle Scholar
Coller, M., Harrison, G. W., and McInnes, M. M. (2002), ‘Evaluating the Tobacco Settlement: Are the Damages Awards Too Much or Not Enough?” American Journal of Public Health, 92(6): 984–989.CrossRefGoogle ScholarPubMed
de Haan, T. and Lind, J. (2018), “Good Nudge Lullaby”: Choice Architecture and Default Bias Reinforcement’, Economic Journal, 128(610): 1180–1206.Google Scholar
Ferber, R. and Hirsch, W. Z. (1978), ‘Social Experimentation and Economic Policy: A Survey’, Journal of Economic Literature, 16(4): 1379–1414.Google Scholar
Ferber, R. and Hirsch, W. Z. (1982), Social Experimentation and Economic Policy, New York: Cambridge University Press.Google Scholar
Fisman, R., Jakiela, P., and Kariv, S. (2017), ‘Distributional Preferences and Political Behavior’, Journal of Public Economics, 155: 1–10.CrossRefGoogle Scholar
Friedlander, D., and Burtless, G. (1995), Five Years After: The Long-Term Effects of Welfare-to-Work Programs, New York: Russell Sage Foundation.Google Scholar
Frumento, P., and Bottai, M. (2016), ‘Parametric Modeling of Quantile Regression Coefficient Functions’, Biometrics, 72: 74–84.CrossRefGoogle ScholarPubMed
Frumento, P., and Bottai, M. (2017), ‘Parametric Modeling of Quantile Regression Coefficient Functions with Censored and Truncated Data’, Biometrics, 73: 1179–1188.CrossRefGoogle ScholarPubMed
Goldman, M. and Kaplan, D. M. (2018), ‘Comparing Distributions by Multiple Testing Across Quantiles or CDF Values’, Journal of Econometrics, 206(1): 143–166.CrossRefGoogle Scholar
Harrison, G. W. (2011), ‘Randomisation and Its Discontents’, Journal of African Economies, 20(4): 626–652.CrossRefGoogle Scholar
Harrison, G. W. (2013), ‘Field Experiments and Methodological Intolerance’, Journal of Economic Methodology, 20(2): 103–117.CrossRefGoogle Scholar
Harrison, G. W. (2019), ‘The Behavioral Welfare Economics of Insurance’, Geneva Risk & Insurance Review, 44(2): 137–175.CrossRefGoogle Scholar
Harrison, G. W. and Ng, J. M. (2016), ‘Evaluating the Expected Welfare Gain from Insurance’, Journal of Risk and Insurance, 83(1): 91–120.CrossRefGoogle Scholar
Harrison, G. W., and Ng, J. M. (2018), ‘Welfare Effects of Insurance Contract Non-Performance’, Geneva Risk and Insurance Review, 43(1): 39–76.CrossRefGoogle Scholar
Harrison, G. W., and Ng, J. M. (2019), ‘Behavioral Insurance and Economic Theory: A Literature Review’, Risk Management & Insurance Review, 22: 133–182.CrossRefGoogle Scholar
Harrison, G. W., Lau, M. I., and Rutström, E. (2009), ‘Risk Attitudes, Randomization to Treatment, and Self-Selection into Experiments’, Journal of Economic Behavior and Organization, 70(3): 498–507.CrossRefGoogle Scholar
Harrison, G. W., Lau, M. I., and Yoo, H. Il (2020), ‘Risk Attitudes, Sample Selection and Attrition in a Longitudinal Field Experiment’, Review of Economics & Statistics, 102(3): 552–568.CrossRefGoogle Scholar
Hausman, J. A. and Wise, D. A. (1985), Social Experimentation, Chicago: University of Chicago Press.CrossRefGoogle Scholar
Heckman, J. J. (2010), ‘Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy’, Journal of Economic Literature, 48(2): 356–398.CrossRefGoogle ScholarPubMed
Iacus, S., King, G., and Porro, G. (2011), ‘Multivariate Matching Methods That Are Monotonic Imbalance Bounding’, Journal of the American Statistical Association, 106(493): 345–361.CrossRefGoogle Scholar
Imai, K., King, G., and Nall, C. (2009), ‘The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation’, Statistical Science, 24(1): 29–53.CrossRefGoogle Scholar
Imbens, G. W. and Rubin, D. B. (2015), Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction, New York: Cambridge University Press.CrossRefGoogle Scholar
Jakiela, P., and Ozier, O. (2019), ‘The Impact of Violence on Individual Risk Preferences: Evidence from a Natural Experiment’, Review of Economics & Statistics, 101(3): 547–559.CrossRefGoogle Scholar
Kaplan, D. M. (2019), ‘distcomp: Comparing Distributions’, Stata Journal, 19(4): 832–848.CrossRefGoogle Scholar
Moffitt, R. (1986), ‘Review of Social Experimentation’, Journal of Political Economy, 94(5), : 1121–1126.CrossRefGoogle Scholar
Moffitt, R. (1998), ‘Review of Five-Years After: The Long-Term Effects of Welfare-to-Work Programs’, Industrial Labor Relations Review, 51(2): 327–329.Google Scholar
Rubin, D. B. (2000), “Statistical Issues in the Estimation of the Causal Effects of Smoking Due to the Conduct of the Tobacco Industry,” in Gastwirth, J.L. (ed.), Statistical Science in the Courtroom, New York: Springer-Verlag.Google Scholar
Rubin, D. B. (2001a), ‘Estimating the Causal Effects of Smoking’, Statistics in Medicine, 20: 1395–1414.CrossRefGoogle Scholar
Rubin, D. B. (2001b), ‘Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation’, Health Services & Outcome Research Methodology, 2: 169–188.CrossRefGoogle Scholar