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Identifying the effect of persuasion

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  • Sung Jae Jun

    (Institute for Fiscal Studies and Pennsylvania State University)

  • Sokbae (Simon) Lee

    (Institute for Fiscal Studies and Columbia University)

Abstract

We set up an econometric model of persuasion and study identification of key parameters under various scenarios of data availability. We find that a commonly used measure of persuasion does not estimate the persuasion rate of any population in general. We provide formal identification results, recommend several new parameters to estimate, and discuss their interpretation. We revisit two strands of the empirical literature on persuasion to show that the persuasive effect is highly heterogeneous and studies based on binary instruments provide limited information about the average persuasion rate in a heterogeneous population.

Suggested Citation

  • Sung Jae Jun & Sokbae (Simon) Lee, 2018. "Identifying the effect of persuasion," CeMMAP working papers CWP19/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:19/18
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    1. Ruben Enikolopov & Maria Petrova & Ekaterina Zhuravskaya, 2011. "Media and Political Persuasion: Evidence from Russia," American Economic Review, American Economic Association, vol. 101(7), pages 3253-3285, December.
    2. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-455, June.
    3. Dirk Bergemann & Stephen Morris, 2019. "Information Design: A Unified Perspective," Journal of Economic Literature, American Economic Association, vol. 57(1), pages 44-95, March.
    4. Stefano Della Vigna & Ruben Enikolopov & Vera Mironova & Maria Petrova & Ekaterina Zhuravskaya, 2014. "Cross-Border Media and Nationalism: Evidence from Serbian Radio in Croatia," American Economic Journal: Applied Economics, American Economic Association, vol. 6(3), pages 103-132, July.
    5. Stefano DellaVigna & John A. List & Ulrike Malmendier, 2012. "Testing for Altruism and Social Pressure in Charitable Giving," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(1), pages 1-56.
    6. Charles F. Manski, 1997. "Monotone Treatment Response," Econometrica, Econometric Society, vol. 65(6), pages 1311-1334, November.
    7. 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.
    8. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    9. Sokbae Lee & Bernard Salanié, 2018. "Identifying Effects of Multivalued Treatments," Econometrica, Econometric Society, vol. 86(6), pages 1939-1963, November.
    10. Matthew Gentzkow & Jesse M. Shapiro & Michael Sinkinson, 2011. "The Effect of Newspaper Entry and Exit on Electoral Politics," American Economic Review, American Economic Association, vol. 101(7), pages 2980-3018, December.
    11. Stefano DellaVigna & Matthew Gentzkow, 2010. "Persuasion: Empirical Evidence," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 643-669, September.
    12. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    13. Emir Kamenica & Matthew Gentzkow, 2011. "Bayesian Persuasion," American Economic Review, American Economic Association, vol. 101(6), pages 2590-2615, October.
    14. Craig E. Landry & Andreas Lange & John A. List & Michael K. Price & Nicholas G. Rupp, 2006. "Toward an Understanding of the Economics of Charity: Evidence from a Field Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(2), pages 747-782.
    15. Tamer, Elie, 2010. "Partial Identification in Econometrics," Scholarly Articles 34728615, Harvard University Department of Economics.
    16. Vittorio Bassi & Imran Rasul, 2017. "Persuasion: A Case Study of Papal Influences on Fertility-Related Beliefs and Behavior," American Economic Journal: Applied Economics, American Economic Association, vol. 9(4), pages 250-302, October.
    17. Yeon-Koo Che & Wouter Dessein & Navin Kartik, 2013. "Pandering to Persuade," American Economic Review, American Economic Association, vol. 103(1), pages 47-79, February.
    18. Stefano DellaVigna & Ethan Kaplan, 2007. "The Fox News Effect: Media Bias and Voting," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(3), pages 1187-1234.
    19. Guido W. Imbens & Charles F. Manski, 2004. "Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 72(6), pages 1845-1857, November.
    20. Xiaohong Chen & Oliver Linton & Ingrid Van Keilegom, 2003. "Estimation of Semiparametric Models when the Criterion Function Is Not Smooth," Econometrica, Econometric Society, vol. 71(5), pages 1591-1608, September.
    21. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 487-535.
    22. Pedro Carneiro & James J. Heckman & Edward J. Vytlacil, 2011. "Estimating Marginal Returns to Education," American Economic Review, American Economic Association, vol. 101(6), pages 2754-2781, October.
    23. Nguimkeu, Pierre & Denteh, Augustine & Tchernis, Rusty, 2019. "On the estimation of treatment effects with endogenous misreporting," Journal of Econometrics, Elsevier, vol. 208(2), pages 487-506.
    24. Gregory J. Martin & Ali Yurukoglu, 2017. "Bias in Cable News: Persuasion and Polarization," American Economic Review, American Economic Association, vol. 107(9), pages 2565-2599, September.
    25. Gerber, Alan S. & Green, Donald P., 2000. "The Effects of Canvassing, Telephone Calls, and Direct Mail on Voter Turnout: A Field Experiment," American Political Science Review, Cambridge University Press, vol. 94(3), pages 653-663, September.
    26. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    27. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    28. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 70, Elsevier.
    29. Matthew Gentzkow & Emir Kamenica, 2017. "Competition in Persuasion," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(1), pages 300-322.
    30. Rossella Calvi & Arthur Lewbel & Denni Tommasi, 2022. "LATE With Missing or Mismeasured Treatment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1701-1717, October.
    31. Alan S. Gerber & Dean Karlan & Daniel Bergan, 2009. "Does the Media Matter? A Field Experiment Measuring the Effect of Newspapers on Voting Behavior and Political Opinions," American Economic Journal: Applied Economics, American Economic Association, vol. 1(2), pages 35-52, April.
    32. Chunrong Ai & Xiaohong Chen, 2003. "Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions," Econometrica, Econometric Society, vol. 71(6), pages 1795-1843, November.
    33. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    34. Newey, Whitney K, 1994. "The Asymptotic Variance of Semiparametric Estimators," Econometrica, Econometric Society, vol. 62(6), pages 1349-1382, November.
    35. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    36. Guido W. Imbens, 2010. "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 399-423, June.
    37. Daniel Ackerberg & Xiaohong Chen & Jinyong Hahn & Zhipeng Liao, 2014. "Asymptotic Efficiency of Semiparametric Two-step GMM," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(3), pages 919-943.
    38. Yuyu Chen & David Y. Yang, 2019. "The Impact of Media Censorship: 1984 or Brave New World?," American Economic Review, American Economic Association, vol. 109(6), pages 2294-2332, June.
    39. Jorg Stoye, 2009. "More on Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 77(4), pages 1299-1315, July.
    40. Joel L. Horowitz & Sokbae Lee, 2023. "Inference in a Class of Optimization Problems: Confidence Regions and Finite Sample Bounds on Errors in Coverage Probabilities," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(3), pages 927-938, July.
    41. Matthew Gentzkow, 2006. "Television and Voter Turnout," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(3), pages 931-972.
    42. Philip J. Cross & Charles F. Manski, 2002. "Regressions, Short and Long," Econometrica, Econometric Society, vol. 70(1), pages 357-368, January.
    43. Guido W. Imbens & Donald B. Rubin, 1997. "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 555-574.
    44. Elie Tamer, 2010. "Partial Identification in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 167-195, September.
    45. Alan Gerber & Donald Green, 2000. "The effects of canvassing, direct mail, and telephone contact on voter turnout: A field experiment," Natural Field Experiments 00248, The Field Experiments Website.
    46. repec:oup:restud:v:84:y::i:1:p:300-322. is not listed on IDEAS
    47. repec:feb:framed:0087 is not listed on IDEAS
    48. repec:hal:pseose:halshs-01053370 is not listed on IDEAS
    49. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
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    Cited by:

    1. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.
    2. Sung Jae Jun & Sokbae (Simon) Lee, 2020. "Causal inference in case-control studies," CeMMAP working papers CWP19/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Galasso, Vincenzo & Morelli, Massimo & Nannicini, Tommaso & Stanig, Piero, 2024. "The Populist Dynamic: Experimental Evidence on the Effects of Countering Populism," IZA Discussion Papers 16796, Institute of Labor Economics (IZA).
    4. Wenlong Ji & Lihua Lei & Asher Spector, 2023. "Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects," Papers 2310.08115, arXiv.org.

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    More about this item

    Keywords

    Communication; Media; Persuasion; Partial Identification; Treatment Effects;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media

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