IDEAS home Printed from https://ideas.repec.org/a/aea/aecrev/v113y2023i11p2937-73.html
   My bibliography  Save this article

Selection on Welfare Gains: Experimental Evidence from Electricity Plan Choice

Author

Listed:
  • Koichiro Ito
  • Takanori Ida
  • Makoto Tanaka

Abstract

We study a problem in which policymakers need to screen self-selected individuals by unobserved heterogeneity in social welfare gains from a policy intervention. In our framework, the marginal treatment effects and marginal treatment responses arise as key statistics to characterize social welfare. We apply this framework to a randomized field experiment on electricity plan choice. Consumers were offered welfare-improving dynamic pricing with randomly assigned take-up incentives. We find that price-elastic consumers—who generate larger welfare gains—are more likely to self-select. Our counterfactual simulations quantify the optimal take-up incentives that exploit observed and unobserved heterogeneity in selection and welfare gains.

Suggested Citation

  • Koichiro Ito & Takanori Ida & Makoto Tanaka, 2023. "Selection on Welfare Gains: Experimental Evidence from Electricity Plan Choice," American Economic Review, American Economic Association, vol. 113(11), pages 2937-2973, November.
  • Handle: RePEc:aea:aecrev:v:113:y:2023:i:11:p:2937-73
    DOI: 10.1257/aer.20210150
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/aer.20210150
    Download Restriction: no

    File URL: https://doi.org/10.3886/E190381V1
    Download Restriction: no

    File URL: https://www.aeaweb.org/doi/10.1257/aer.20210150.appx
    Download Restriction: no

    File URL: https://www.aeaweb.org/doi/10.1257/aer.20210150.ds
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.

    File URL: https://libkey.io/10.1257/aer.20210150?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Raj Chetty, 2009. "Sufficient Statistics for Welfare Analysis: A Bridge Between Structural and Reduced-Form Methods," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 451-488, May.
    2. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    3. S. Borenstein, 2013. "Effective and Equitable Adoption of Opt-In Residential Dynamic Electricity Pricing," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 42(2), pages 127-160, March.
    4. James J. Heckman, 2010. "Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 356-398, June.
    5. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2018. "Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters," Econometrica, Econometric Society, vol. 86(5), pages 1589-1619, September.
    6. Manasi Deshpande & Yue Li, 2019. "Who Is Screened Out? Application Costs and the Targeting of Disability Programs," American Economic Journal: Economic Policy, American Economic Association, vol. 11(4), pages 213-248, November.
    7. Amy Finkelstein & Matthew J Notowidigdo, 2019. "Take-Up and Targeting: Experimental Evidence from SNAP," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(3), pages 1505-1556.
    8. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
    9. Liran Einav & Amy Finkelstein & Yunan Ji & Neale Mahoney, 2022. "Voluntary Regulation: Evidence from Medicare Payment Reform," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(1), pages 565-618.
    10. Severin Borenstein & Lucas W. Davis, 2016. "The Distributional Effects of US Clean Energy Tax Credits," Tax Policy and the Economy, University of Chicago Press, vol. 30(1), pages 191-234.
    11. Patrick Kline & Christopher R. Walters, 2016. "Evaluating Public Programs with Close Substitutes: The Case of HeadStart," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1795-1848.
    12. Severin Borenstein, 2002. "The Trouble With Electricity Markets: Understanding California's Restructuring Disaster," Journal of Economic Perspectives, American Economic Association, vol. 16(1), pages 191-211, Winter.
    13. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    14. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    15. Philipp Eisenhauer & James J. Heckman & Edward Vytlacil, 2015. "The Generalized Roy Model and the Cost-Benefit Analysis of Social Programs," Journal of Political Economy, University of Chicago Press, vol. 123(2), pages 413-443.
    16. Michael Callen & Mohammad Isaqzadeh & James D. Long & Charles Sprenger, 2014. "Violence and Risk Preference: Experimental Evidence from Afghanistan," American Economic Review, American Economic Association, vol. 104(1), pages 123-148, January.
    17. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    18. Ali Hortaçsu & Seyed Ali Madanizadeh & Steven L. Puller, 2017. "Power to Choose? An Analysis of Consumer Inertia in the Residential Electricity Market," American Economic Journal: Economic Policy, American Economic Association, vol. 9(4), pages 192-226, November.
    19. Ahmad Faruqui & Sanem Sergici, 2011. "Dynamic pricing of electricity in the mid-Atlantic region: econometric results from the Baltimore gas and electric company experiment," Journal of Regulatory Economics, Springer, vol. 40(1), pages 82-109, August.
    20. Liran Einav & Amy Finkelstein & Paul Schrimpf, 2010. "Optimal Mandates and the Welfare Cost of Asymmetric Information: Evidence From the U.K. Annuity Market," Econometrica, Econometric Society, vol. 78(3), pages 1031-1092, May.
    21. Meredith Fowlie & Catherine Wolfram & C. Anna Spurlock & Annika Todd & Patrick Baylis & Peter Cappers, 2017. "Default Effects and Follow-On Behavior: Evidence from an Electricity Pricing Program," NBER Working Papers 23553, National Bureau of Economic Research, Inc.
    22. Hunt Allcott & Michael Greenstone, 2017. "Measuring the Welfare Effects of Residential Energy Efficiency Programs," NBER Working Papers 23386, National Bureau of Economic Research, Inc.
    23. Frank A. Wolak, 2011. "Do Residential Customers Respond to Hourly Prices? Evidence from a Dynamic Pricing Experiment," American Economic Review, American Economic Association, vol. 101(3), pages 83-87, May.
    24. Koichiro Ito & Takanori Ida & Makoto Tanaka, 2018. "Moral Suasion and Economic Incentives: Field Experimental Evidence from Energy Demand," American Economic Journal: Economic Policy, American Economic Association, vol. 10(1), pages 240-267, February.
    25. Callen, Mike & Isaqzadeh, Mohammad & Long, James D. & Sprenger, Charles, 2014. "Violence and risk preference: experimental evidence from Afghanistan," LSE Research Online Documents on Economics 102932, London School of Economics and Political Science, LSE Library.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luis E. GONZALES & ITO Koichiro & Mar REGUANT, 2022. "The Dynamic Impact of Market Integration: Evidence from renewable energy expansion in Chile," Discussion papers 22050, Research Institute of Economy, Trade and Industry (RIETI).
    2. Capitán, Tabaré & Alpízar, Francisco & Madrigal-Ballestero, Róger & Pattanayak, Subhrendu K., 2021. "Time-varying pricing may increase total electricity consumption: Evidence from Costa Rica," Resource and Energy Economics, Elsevier, vol. 66(C).
    3. Nakai, Miwa & von Loessl, Victor & Wetzel, Heike, 2024. "Preferences for dynamic electricity tariffs: A comparison of households in Germany and Japan," Ecological Economics, Elsevier, vol. 223(C).
    4. Christina Gravert, 2024. "From Intent to Inertia: Experimental Evidence from the Retail Electricity Market," CESifo Working Paper Series 11139, CESifo.
    5. Hirofumi Kurokawa & Shusaku Sasaki, 2023. "How Does Opt-in Work? A Field Experiment on Financial Incentives for Physical Activity," Discussion Papers in Economics and Business 23-01, Osaka University, Graduate School of Economics.
    6. Lang, Corey & Qiu, Yueming (Lucy) & Dong, Luran, 2023. "Increasing voluntary enrollment in time-of-use electricity rates: Findings from a survey experiment," Energy Policy, Elsevier, vol. 173(C).
    7. Pébereau, Charles & Remmy, Kevin, 2023. "Barriers to real-time electricity pricing: Evidence from New Zealand," International Journal of Industrial Organization, Elsevier, vol. 89(C).
    8. Luther Yap, 2022. "Sensitivity of Policy Relevant Treatment Parameters to Violations of Monotonicity," Working Papers 655, Princeton University, Department of Economics, Industrial Relations Section..
    9. Takanori Ida & Takunori Ishihara & Koichiro Ito & Daido Kido & Toru Kitagawa & Shosei Sakaguchi & Shusaku Sasaki, 2021. "Paternalism, Autonomy, or Both? Experimental Evidence from Energy Saving Programs," Papers 2112.09850, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Wenjie & Ida, Takanori & Shimada, Hideki, 2020. "Default effect versus active decision: Evidence from a field experiment in Los Alamos," European Economic Review, Elsevier, vol. 128(C).
    2. Robert W. Hahn & Robert D. Metcalfe, 2021. "Efficiency and Equity Impacts of Energy Subsidies," American Economic Review, American Economic Association, vol. 111(5), pages 1658-1688, May.
    3. Takanori Ida & Takunori Ishihara & Koichiro Ito & Daido Kido & Toru Kitagawa & Shosei Sakaguchi & Shusaku Sasaki, 2022. "Choosing Who Chooses: Selection-Driven Targeting in Energy Rebate Programs," NBER Working Papers 30469, National Bureau of Economic Research, Inc.
    4. Takanori Ida & Takunori Ishihara & Koichiro Ito & Daido Kido & Toru Kitagawa & Shosei Sakaguchi & Shusaku Sasaki, 2021. "Paternalism, Autonomy, or Both? Experimental Evidence from Energy Saving Programs," Papers 2112.09850, arXiv.org.
    5. Laura Castell & Marc Gurgand & Clément Imbert & Todor Tochev, 2024. "Take-up of Social Benefits: Experimental Evidence from France," Institut des Politiques Publiques halshs-04720989, HAL.
    6. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 871-905, August.
    7. Matthias Westphal & Daniel A Kamhöfer & Hendrik Schmitz, 2022. "Marginal College Wage Premiums Under Selection Into Employment," The Economic Journal, Royal Economic Society, vol. 132(646), pages 2231-2272.
    8. Rothstein, Jesse & Von Wachter, Till, 2016. "Social Experiments in the Labor Market," Department of Economics, Working Paper Series qt7957p9g6, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    9. Philipp Eisenhauer & James J. Heckman & Edward Vytlacil, 2015. "The Generalized Roy Model and the Cost-Benefit Analysis of Social Programs," Journal of Political Economy, University of Chicago Press, vol. 123(2), pages 413-443.
    10. Yu-Chang Chen & Haitian Xie, 2022. "Personalized Subsidy Rules," Papers 2202.13545, arXiv.org, revised Mar 2022.
    11. Thomas M. Russell, 2020. "Policy Transforms and Learning Optimal Policies," Papers 2012.11046, arXiv.org.
    12. Han, Sukjin & Yang, Shenshen, 2024. "A computational approach to identification of treatment effects for policy evaluation," Journal of Econometrics, Elsevier, vol. 240(1).
    13. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    14. Thoresen, Thor O. & Vattø, Trine E., 2015. "Validation of the discrete choice labor supply model by methods of the new tax responsiveness literature," Labour Economics, Elsevier, vol. 37(C), pages 38-53.
    15. Kayo Murakami & Hideki Shimada & Yoshiaki Ushifusa & Takanori Ida, 2022. "Heterogeneous Treatment Effects Of Nudge And Rebate: Causal Machine Learning In A Field Experiment On Electricity Conservation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1779-1803, November.
    16. Christensen, Peter & Francisco, Paul & Myers, Erica & Shao, Hansen & Souza, Mateus, 2024. "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, Elsevier, vol. 234(C).
    17. Romuald Meango & Esther Mirjam Girsberger, 2023. "Identification of Ex ante Returns Using Elicited Choice Probabilities: an Application to Preferences for Public-sector Jobs," Papers 2303.03009, arXiv.org, revised Jun 2024.
    18. Christopher Conlon & Julie Holland Mortimer, 2021. "Empirical properties of diversion ratios," RAND Journal of Economics, RAND Corporation, vol. 52(4), pages 693-726, December.
    19. Burlig, Fiona & Preonas, Louis & Woerman, Matt, 2020. "Panel data and experimental design," Journal of Development Economics, Elsevier, vol. 144(C).
    20. Giesecke, Matthias & Schuß, Eric, 2019. "Heterogeneity in marginal returns to language training of immigrants," IAB-Discussion Paper 201919, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

    More about this item

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • L98 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Government Policy

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:aecrev:v:113:y:2023:i:11:p:2937-73. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.