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Visual Inference and Graphical Representation in Regression Discontinuity Designs

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Listed:
  • Korting, Christina

    (University of Delaware)

  • Lieberman, Carl

    (U.S. Census Bureau)

  • Matsudaira, Jordan

    (Columbia University)

  • Pei, Zhuan

    (Cornell University)

  • Shen, Yi

    (University of Waterloo)

Abstract

Despite the widespread use of graphs in empirical research, little is known about readers' ability to process the statistical information they are meant to convey ("visual inference"). We study visual inference within the context of regression discontinuity (RD) designs by measuring how accurately readers identify discontinuities in graphs produced from data generating processes calibrated on 11 published papers from leading economics journals. First, we assess the effects of different graphical representation methods on visual inference using randomized experiments. We find that bin widths and fit lines have the largest impacts on whether participants correctly perceive the presence or absence of a discontinuity. Incorporating the experimental results into two decision theoretical criteria adapted from the recent economics literature, we find that using small bins with no fit lines to construct RD graphs performs well and recommend it as a starting point to practitioners. Second, we compare visual inference with widely used econometric inference procedures. We find that visual inference achieves similar or lower type I error rates and complements econometric inference.

Suggested Citation

  • Korting, Christina & Lieberman, Carl & Matsudaira, Jordan & Pei, Zhuan & Shen, Yi, 2021. "Visual Inference and Graphical Representation in Regression Discontinuity Designs," IZA Discussion Papers 14923, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp14923
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    1. Stefano DellaVigna & Devin Pope, 2018. "Predicting Experimental Results: Who Knows What?," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2410-2456.
    2. Timothy B. Armstrong & Michal Kolesár, 2018. "Optimal Inference in a Class of Regression Models," Econometrica, Econometric Society, vol. 86(2), pages 655-683, March.
    3. Raj Chetty & John N. Friedman & Nathaniel Hilger & Emmanuel Saez & Diane Whitmore Schanzenbach & Danny Yagan, 2011. "How Does Your Kindergarten Classroom Affect Your Earnings? Evidence from Project Star," The Quarterly Journal of Economics, Oxford University Press, vol. 126(4), pages 1593-1660.
    4. Sebastian Calonico & Matias D. Cattaneo & Rocio Titiunik, 2014. "Robust Nonparametric Confidence Intervals for Regression‐Discontinuity Designs," Econometrica, Econometric Society, vol. 82, pages 2295-2326, November.
    5. David Card & Andrew Johnston & Pauline Leung & Alexandre Mas & Zhuan Pei, 2015. "The Effect of Unemployment Benefits on the Duration of Unemployment Insurance Receipt: New Evidence from a Regression Kink Design in Missouri, 2003-2013," American Economic Review, American Economic Association, vol. 105(5), pages 126-130, May.
    6. Benabou, Roland & Falk, Armin & Tirole, Jean, 2018. "Narratives, Imperatives, and Moral Reasoning," IZA Discussion Papers 11665, Institute of Labor Economics (IZA).
    7. Joshua Schwartzstein & Adi Sunderam, 2021. "Using Models to Persuade," American Economic Review, American Economic Association, vol. 111(1), pages 276-323, January.
    8. David Card & David S. Lee & Zhuan Pei, 2009. "Quasi-Experimental Identification and Estimation in the Regression Kink Design," Working Papers 1206, Princeton University, Department of Economics, Industrial Relations Section..
    9. Christina Korting & Carl Lieberman & Jordan Matsudaira & Zhuan Pei & Yi Shen, 2020. "Visual Inference and Graphical Representation in Regression Discontinuity Designs," Working Papers 638, Princeton University, Department of Economics, Industrial Relations Section..
    10. David Card & David S. Lee & Zhuan Pei, 2009. "Quasi-Experimental Identification and Estimation in the Regression Kink Design," Working Papers 1206, Princeton University, Department of Economics, Industrial Relations Section..
    11. Cameron, A. Colin & Gelbach, Jonah B. & Miller, Douglas L., 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 238-249.
    12. Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell & Rocío Titiunik, 2019. "Regression Discontinuity Designs Using Covariates," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 442-451, July.
    13. Mahbubul Majumder & Heike Hofmann & Dianne Cook, 2013. "Validation of Visual Statistical Inference, Applied to Linear Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 942-956, September.
    14. 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.
    15. repec:hrv:faseco:30750027 is not listed on IDEAS
    16. Toru Kitagawa & Shosei Sakaguchi & Aleksey Tetenov, 2021. "Constrained Classification and Policy Learning," Papers 2106.12886, arXiv.org.
    17. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
    18. Zhuan Pei & Jörn-Steffen Pischke & Hannes Schwandt, 2019. "Poorly Measured Confounders are More Useful on the Left than on the Right," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 205-216, April.
    19. Andrew Gelman & Guido Imbens, 2019. "Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 447-456, July.
    20. Kahneman, Daniel & Tversky, Amos, 1979. "Prospect Theory: An Analysis of Decision under Risk," Econometrica, Econometric Society, vol. 47(2), pages 263-291, March.
    21. Sebastian Calonico & Matias D Cattaneo & Max H Farrell, 2020. "Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 192-210.
    22. Brown, Alexander L. & Imai, Taisuke & Vieider, Ferdinand & Camerer, Colin, 2020. "Meta-Analysis of Empirical Estimates of Loss-Aversion," MetaArXiv hnefr, Center for Open Science.
    23. Peter Ganong & Simon Jäger, 2018. "A Permutation Test for the Regression Kink Design," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 494-504, April.
    24. Janet Currie & Henrik Kleven & Esmée Zwiers, 2020. "Technology and Big Data Are Changing Economics: Mining Text to Track Methods," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 42-48, May.
    25. Jinyong Hahn & Petra Todd & Wilbert Van der Klaauw, 1999. "Evaluating the Effect of an Antidiscrimination Law Using a Regression-Discontinuity Design," NBER Working Papers 7131, National Bureau of Economic Research, Inc.
    26. Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2020. "Transparency in Structural Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 711-722, October.
    27. Guido Imbens & Karthik Kalyanaraman, 2012. "Optimal Bandwidth Choice for the Regression Discontinuity Estimator," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 933-959.
    28. Jonathan Chapman & Erik Snowberg & Stephanie Wang & Colin Camerer, 2018. "Loss Attitudes in the U.S. Population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE)," CESifo Working Paper Series 7262, CESifo.
    29. 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.
    30. Zhuan Pei & Yi Shen, 2017. "The Devil is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable," Advances in Econometrics, in: Regression Discontinuity Designs, volume 38, pages 455-502, Emerald Group Publishing Limited.
    31. Matias D. Cattaneo & Juan Carlos Escanciano (ed.), 2017. "Regression Discontinuity Designs," Advances in Econometrics, Emerald Publishing Ltd, volume 38, number aeco.2017.38.
    32. Chen, Daniel L. & Schonger, Martin & Wickens, Chris, 2016. "oTree—An open-source platform for laboratory, online, and field experiments," Journal of Behavioral and Experimental Finance, Elsevier, vol. 9(C), pages 88-97.
    33. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
    34. Raj Chetty & Nathaniel Hendren & Patrick Kline & Emmanuel Saez, 2014. "Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States," The Quarterly Journal of Economics, Oxford University Press, vol. 129(4), pages 1553-1623.
    35. Matthew Rabin & Richard H. Thaler, 2001. "Anomalies: Risk Aversion," Journal of Economic Perspectives, American Economic Association, vol. 15(1), pages 219-232, Winter.
    36. Fan, Jianqing & Yao, Qiwei, 1998. "Efficient estimation of conditional variance functions in stochastic regression," LSE Research Online Documents on Economics 6635, London School of Economics and Political Science, LSE Library.
    37. Lee, David S., 2008. "Randomized experiments from non-random selection in U.S. House elections," Journal of Econometrics, Elsevier, vol. 142(2), pages 675-697, February.
    38. Guido Imbens & Stefan Wager, 2019. "Optimized Regression Discontinuity Designs," The Review of Economics and Statistics, MIT Press, vol. 101(2), pages 264-278, May.
    39. McCrary, Justin, 2008. "Manipulation of the running variable in the regression discontinuity design: A density test," Journal of Econometrics, Elsevier, vol. 142(2), pages 698-714, February.
    40. Ted O'Donoghue & Jason Somerville, 2018. "Modeling Risk Aversion in Economics," Journal of Economic Perspectives, American Economic Association, vol. 32(2), pages 91-114, Spring.
    41. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    42. Isaiah Andrews & Jesse M. Shapiro, 2021. "A Model of Scientific Communication," Econometrica, Econometric Society, vol. 89(5), pages 2117-2142, September.
    43. Joshua D. Angrist & Miikka Rokkanen, 2015. "Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1331-1344, December.
    44. Abhijit V. Banerjee & Sylvain Chassang & Sergio Montero & Erik Snowberg, 2020. "A Theory of Experimenters: Robustness, Randomization, and Balance," American Economic Review, American Economic Association, vol. 110(4), pages 1206-1230, April.
    45. Jeffrey T. Leek & Roger D. Peng, 2015. "Statistics: P values are just the tip of the iceberg," Nature, Nature, vol. 520(7549), pages 612-612, April.
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    Cited by:

    1. Christina Korting & Carl Lieberman & Jordan Matsudaira & Zhuan Pei & Yi Shen, 2020. "Visual Inference and Graphical Representation in Regression Discontinuity Designs," Working Papers 638, Princeton University, Department of Economics, Industrial Relations Section..

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    More about this item

    Keywords

    graphical methods; visual inference; regression discontinuity design; expert prediction; statistical decision theory; scientific communication;
    All these keywords.

    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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