Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/dls/wpaper/0337.html
   My bibliography  Save this paper

Design of Partial Population Experiments with an Application to Spillovers in Tax Compliance

Author

Listed:
  • Guillermo Cruces

    (CEDLAS-IIE-FCE-UNLP & CONICET & U. of Nottingham)

  • Dario Tortarolo

    (DECRG World Bank)

  • Gonzalo Vazquez-Bare

    (UC Santa Barbara)

Abstract
We develop a framework to analyze partial population experiments, a generalization of the cluster experimental design where clusters are assigned to different treatment intensities. Our framework allows for heterogeneity in cluster sizes and outcome distributions. We study the large-sample behavior of OLS estimators and cluster-robust variance estimators and show that (i) ignoring cluster heterogeneity may result in severely underpowered experiments and (ii) the cluster-robust variance estimator may be upward-biased when clusters are heterogeneous. We derive formulas for power, minimum detectable effects, and optimal cluster assignment probabilities. All our results apply to cluster experiments, a particular case of our framework. We set up a potential outcomes framework to interpret the OLS estimands as causal effects. We implement our methods in a large-scale experiment to estimate the direct and spillover effects of a communication campaign on property tax compliance. We find an increase in tax compliance among individuals directly targeted with our mailing, as well as compliance spillovers on untreated individuals in clusters with a high proportion of treated taxpayers.

Suggested Citation

  • Guillermo Cruces & Dario Tortarolo & Gonzalo Vazquez-Bare, 2024. "Design of Partial Population Experiments with an Application to Spillovers in Tax Compliance," CEDLAS, Working Papers 0337, CEDLAS, Universidad Nacional de La Plata.
    • Handle: RePEc:dls:wpaper:0337
    as

    Download full text from publisher

    File URL: http://www.cedlas.econo.unlp.edu.ar/wp/wp-content/uploads/doc_cedlas337.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bruno Crépon & Esther Duflo & Marc Gurgand & Roland Rathelot & Philippe Zamora, 2013. "Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(2), pages 531-580.
    2. Miriam Bruhn & David McKenzie, 2009. "In Pursuit of Balance: Randomization in Practice in Development Field Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 1(4), pages 200-232, October.
    3. Hirano, Keisuke & Hahn, Jinyong, 2010. "Design of randomized experiments to measure social interaction effects," Economics Letters, Elsevier, vol. 106(1), pages 51-53, January.
    4. Jan-Emmanuel De Neve & Clément Imbert & Johannes Spinnewijn & Teodora Tsankova & Maarten Luts, 2021. "How to Improve Tax Compliance? Evidence from Population-Wide Experiments in Belgium," Journal of Political Economy, University of Chicago Press, vol. 129(5), pages 1425-1463.
    5. Susan Athey & Dean Eckles & Guido W. Imbens, 2018. "Exact p-Values for Network Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 230-240, January.
    6. Weigel, Jonathan, 2020. "The participation dividend of taxation: how citizens in Congo engage more with the state when it tries to tax them," LSE Research Online Documents on Economics 104561, London School of Economics and Political Science, LSE Library.
    7. Djogbenou, Antoine A. & MacKinnon, James G. & Nielsen, Morten Ørregaard, 2019. "Asymptotic theory and wild bootstrap inference with clustered errors," Journal of Econometrics, Elsevier, vol. 212(2), pages 393-412.
    8. Francesco Drago & Friederike Mengel & Christian Traxler, 2020. "Compliance Behavior in Networks: Evidence from a Field Experiment," American Economic Journal: Applied Economics, American Economic Association, vol. 12(2), pages 96-133, April.
    9. repec:hal:pseose:halshs-00840901 is not listed on IDEAS
    10. Dina Pomeranz & José Vila-Belda, 2019. "Taking State-Capacity Research to the Field: Insights from Collaborations with Tax Authorities," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 755-781, August.
    11. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2019. "Inference under covariate‐adaptive randomization with multiple treatments," Quantitative Economics, Econometric Society, vol. 10(4), pages 1747-1785, November.
    12. Michael P. Leung, 2021. "Rate-Optimal Cluster-Randomized Designs for Spatial Interference," Papers 2111.04219, arXiv.org, revised Sep 2022.
    13. Hansen, Bruce E. & Lee, Seojeong, 2019. "Asymptotic theory for clustered samples," Journal of Econometrics, Elsevier, vol. 210(2), pages 268-290.
    14. Edward Miguel & Michael Kremer, 2004. "Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities," Econometrica, Econometric Society, vol. 72(1), pages 159-217, January.
    15. Guillaume Basse & Avi Feller, 2018. "Analyzing Two-Stage Experiments in the Presence of Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 41-55, January.
    16. Dina Pomeranz, 2015. "No Taxation without Information: Deterrence and Self-Enforcement in the Value Added Tax," American Economic Review, American Economic Association, vol. 105(8), pages 2539-2569, August.
    17. Boning, William C. & Guyton, John & Hodge, Ronald & Slemrod, Joel, 2020. "Heard it through the grapevine: The direct and network effects of a tax enforcement field experiment on firms," Journal of Public Economics, Elsevier, vol. 190(C).
    18. Vazquez-Bare, Gonzalo, 2023. "Identification and estimation of spillover effects in randomized experiments," Journal of Econometrics, Elsevier, vol. 237(1).
    19. Andrew V. Carter & Kevin T. Schnepel & Douglas G. Steigerwald, 2017. "Asymptotic Behavior of a t -Test Robust to Cluster Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 99(4), pages 698-709, July.
    20. Esther Duflo & Emmanuel Saez, 2003. "The Role of Information and Social Interactions in Retirement Plan Decisions: Evidence from a Randomized Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 815-842.
    21. Manuela Angelucci & Giacomo De Giorgi, 2009. "Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles' Consumption?," American Economic Review, American Economic Association, vol. 99(1), pages 486-508, March.
    22. G W Basse & A Feller & P Toulis, 2019. "Randomization tests of causal effects under interference," Biometrika, Biometrika Trust, vol. 106(2), pages 487-494.
    23. David Puelz & Guillaume Basse & Avi Feller & Panos Toulis, 2022. "A graph‐theoretic approach to randomization tests of causal effects under general interference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 174-204, February.
    24. Kosuke Imai & Zhichao Jiang & Anup Malani, 2021. "Causal Inference With Interference and Noncompliance in Two-Stage Randomized Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 632-644, April.
    25. Carrillo, Paul E. & Castro, Edgar & Scartascini, Carlos, 2021. "Public good provision and property tax compliance: Evidence from a natural experiment," Journal of Public Economics, Elsevier, vol. 198(C).
    26. Johannes Haushofer & Jeremy Shapiro, 2016. "The Short-term Impact of Unconditional Cash Transfers to the Poor: ExperimentalEvidence from Kenya," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1973-2042.
    27. Xavier Giné & Ghazala Mansuri, 2018. "Together We Will: Experimental Evidence on Female Voting Behavior in Pakistan," American Economic Journal: Applied Economics, American Economic Association, vol. 10(1), pages 207-235, January.
    28. Felipe Barrera-Osorio & Marianne Bertrand & Leigh L. Linden & Francisco Perez-Calle, 2011. "Improving the Design of Conditional Transfer Programs: Evidence from a Randomized Education Experiment in Colombia," American Economic Journal: Applied Economics, American Economic Association, vol. 3(2), pages 167-195, April.
    29. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    30. Diether W. Beuermann & Julian Cristia & Santiago Cueto & Ofer Malamud & Yyannu Cruz-Aguayo, 2015. "One Laptop per Child at Home: Short-Term Impacts from a Randomized Experiment in Peru," American Economic Journal: Applied Economics, American Economic Association, vol. 7(2), pages 53-80, April.
    31. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
    32. Jonathan L Weigel, 2020. "The Participation Dividend of Taxation: How Citizens in Congo Engage More with the State When it Tries to Tax Them," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(4), pages 1849-1903.
    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. Antinyan, Armenak & Asatryan, Zareh, 2019. "Nudging for tax compliance: A meta-analysis," ZEW Discussion Papers 19-055, ZEW - Leibniz Centre for European Economic Research.
    2. Hernández-Agramonte, Juan Manuel & Namen, Olga & Näslund-Hadley, Emma & Biehl, Maria Loreto, 2024. "Supporting early childhood development remotely: Experimental evidence from SMS messages," Journal of Development Economics, Elsevier, vol. 166(C).

    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. Guillermo Cruces & Dario Tortarolo & Gonzalo Vazquez-Bare, 2022. "Design of two-stage experiments with an application to spillovers in tax compliance," IFS Working Papers W22/32, Institute for Fiscal Studies.
    2. John A. List & Fatemeh Momeni & Yves Zenou, 2020. "The Social Side of Early Human Capital Formation: Using a Field Experiment to Estimate the Causal Impact of Neighborhoods," Working Papers 2020-187, Becker Friedman Institute for Research In Economics.
    3. Francis J. DiTraglia & Camilo Garcia-Jimeno & Rossa O'Keeffe-O'Donovan & Alejandro Sanchez-Becerra, 2020. "Identifying Causal Effects in Experiments with Spillovers and Non-compliance," Papers 2011.07051, arXiv.org, revised Jan 2023.
    4. Sarah Baird & Aislinn Bohren & Craig McIntosh & Berk Ozler, 2014. "Designing Experiments to Measure Spillover Effects," PIER Working Paper Archive 14-032, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    5. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org.
    6. Gonzalo Vazquez-Bare, 2017. "Identification and Estimation of Spillover Effects in Randomized Experiments," Papers 1711.02745, arXiv.org, revised Jan 2022.
    7. DiTraglia, Francis J. & García-Jimeno, Camilo & O’Keeffe-O’Donovan, Rossa & Sánchez-Becerra, Alejandro, 2023. "Identifying causal effects in experiments with spillovers and non-compliance," Journal of Econometrics, Elsevier, vol. 235(2), pages 1589-1624.
    8. Vazquez-Bare, Gonzalo, 2023. "Identification and estimation of spillover effects in randomized experiments," Journal of Econometrics, Elsevier, vol. 237(1).
    9. Jizhou Liu, 2023. "Inference for Two-stage Experiments under Covariate-Adaptive Randomization," Papers 2301.09016, arXiv.org, revised Oct 2024.
    10. Sarah Baird & Aislinn Bohren & Craig McIntosh & Berk Ozler, 2015. "Designing Experiments to Measure Spillover Effects, Second Version," PIER Working Paper Archive 15-021, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Jun 2015.
    11. James G. MacKinnon & Matthew D. Webb, 2020. "When and How to Deal with Clustered Errors in Regression Models," Working Paper 1421, Economics Department, Queen's University.
    12. Baylis, Kathy & Ham, Andres, 2015. "How important is spatial correlation in randomized controlled trials?," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205586, Agricultural and Applied Economics Association.
    13. List, John A. & Momeni, Fatemeh & Zenou, Yves, 2019. "Are Estimates of Early Education Programs Too Pessimistic? Evidence from a Large-Scale Field Experiment that Causally Measures Neighbor Effects," Working Paper Series 1293, Research Institute of Industrial Economics.
    14. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    15. Zenou, Yves & List, John & Momeni, Fatemeh, 2019. "Are Estimates of Early Education Programs Too Pessimistic? Evidence from a Large-Scale Field Experiment that Causally Measures," CEPR Discussion Papers 13725, C.E.P.R. Discussion Papers.
    16. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Testing for the appropriate level of clustering in linear regression models," Journal of Econometrics, Elsevier, vol. 235(2), pages 2027-2056.
    17. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2021. "Wild Bootstrap and Asymptotic Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 505-519, March.
    18. Heitzig, Chris & O’Keeffe-O’Donovan, Rossa, 2024. "Spillover Effects and Diffusion of Savings Groups," World Development, Elsevier, vol. 173(C).
    19. Susan Athey & Guido Imbens, 2016. "The Econometrics of Randomized Experiments," Papers 1607.00698, arXiv.org.
    20. Djogbenou, Antoine A. & MacKinnon, James G. & Nielsen, Morten Ørregaard, 2019. "Asymptotic theory and wild bootstrap inference with clustered errors," Journal of Econometrics, Elsevier, vol. 212(2), pages 393-412.

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • H71 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Taxation, Subsidies, and Revenue
    • H26 - Public Economics - - Taxation, Subsidies, and Revenue - - - Tax Evasion and Avoidance
    • H21 - Public Economics - - Taxation, Subsidies, and Revenue - - - Efficiency; Optimal Taxation
    • O23 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - Fiscal and Monetary Policy in Development

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:dls:wpaper:0337. 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: Ana Pacheco (email available below). General contact details of provider: https://edirc.repec.org/data/funlpar.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.