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Jumpy or Kinky? Regression Discontinuity without the Discontinuity

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  • Yingying Dong

    (Department of Economics, University of California-Irvine)

Abstract

Regression Discontinuity (RD) models identify local treatment effects by associating a discrete change in the mean outcome with a corresponding discrete change in the probability of treatment at a known threshold of a running variable. This paper shows that it is possible to identify the RD model treatment effect without a discontinuity. In particular, identification can come from a slope change (a kink) instead of a discrete level change (a jump) in the treatment probability. The intuition is based on L'hopital's rule. The identification results can also be interpreted using instrumental variables models. Estimators are proposed that can be applied in the presence or absence of a discontinuity, by exploiting either a jump, or a kink, or both. The proposed estimators are applied to investigate the "retirement-consumption puzzle." In particular, I estimate the impact of retirement on household food consumption by exploiting changes in the retirement probability at 62, the early retirement age in the US.

Suggested Citation

  • Yingying Dong, 2011. "Jumpy or Kinky? Regression Discontinuity without the Discontinuity," Working Papers 111207, University of California-Irvine, Department of Economics.
  • Handle: RePEc:irv:wpaper:111207
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    References listed on IDEAS

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    1. Erich Battistin & Agar Brugiavini & Enrico Rettore & Guglielmo Weber, 2009. "The Retirement Consumption Puzzle: Evidence from a Regression Discontinuity Approach," American Economic Review, American Economic Association, vol. 99(5), pages 2209-2226, December.
    2. Pedro Carneiro & James J. Heckman & Edward Vytlacil, 2010. "Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin," Econometrica, Econometric Society, vol. 78(1), pages 377-394, January.
    3. Brian A. Jacob & Lars Lefgren, 2004. "Remedial Education and Student Achievement: A Regression-Discontinuity Analysis," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 226-244, February.
    4. repec:oup:restud:v:79:y::i:3:p:933-959 is not listed on IDEAS
    5. Michael D. Hurd & Susann Rohwedder, 2003. "The Retirement-Consumption Puzzle Anticipated and Actual Declines in Spending at Retirement," Working Papers DRU-3009, RAND Corporation.
    6. Yingying Dong & Arthur Lewbel, 2015. "Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models," The Review of Economics and Statistics, MIT Press, vol. 97(5), pages 1081-1092, December.
    7. John Ameriks & Andrew Caplin & John Leahy, 2007. "Retirement Consumption: Insights from a Survey," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 265-274, May.
    8. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
    9. Lemieux, Thomas & Milligan, Kevin, 2008. "Incentive effects of social assistance: A regression discontinuity approach," Journal of Econometrics, Elsevier, vol. 142(2), pages 807-828, February.
    10. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    11. Emma Aguila & Orazio Attanasio & Costas Meghir, 2011. "Changes in Consumption at Retirement: Evidence from Panel Data," The Review of Economics and Statistics, MIT Press, vol. 93(3), pages 1094-1099, August.
    12. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-209, January.
    13. 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.
    14. 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.
    15. 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.
    16. Jonathan Guryan, 2001. "Does Money Matter? Regression-Discontinuity Estimates from Education Finance Reform in Massachusetts," NBER Working Papers 8269, National Bureau of Economic Research, Inc.
    17. Emma Aguila & Orazio P. Attanasio & Costas Meghir, 2008. "Changes in Consumption at Retirement," Working Papers 621, RAND Corporation.
    18. Helena Skyt Nielsen & Torben Sørensen & Christopher Taber, 2010. "Estimating the Effect of Student Aid on College Enrollment: Evidence from a Government Grant Policy Reform," NBER Chapters, in: Income Taxation, Trans-Atlantic Public Economics Seminar (TAPES), pages 185-215, National Bureau of Economic Research, Inc.
    19. 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.
    20. David Card & Carlos Dobkin & Nicole Maestas, 2008. "The Impact of Nearly Universal Insurance Coverage on Health Care Utilization: Evidence from Medicare," American Economic Review, American Economic Association, vol. 98(5), pages 2242-2258, December.
    21. Yingying Dong & Arthur Lewbel, 2011. "Regression Discontinuity Marginal Threshold Treatment Effects," Working Papers 111205, University of California-Irvine, Department of Economics.
    22. 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.
    23. B. Douglas Bernheim & Jonathan Skinner & Steven Weinberg, 2001. "What Accounts for the Variation in Retirement Wealth among U.S. Households?," American Economic Review, American Economic Association, vol. 91(4), pages 832-857, September.
    24. 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.
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    Cited by:

    1. Murphy, Richard & Wyness, Gill, 2015. "Testing means-tested aid," LSE Research Online Documents on Economics 66060, London School of Economics and Political Science, LSE Library.
    2. Engels, Barbara & Geyer, Johannes & Haan, Peter, 2017. "Pension incentives and early retirement," Labour Economics, Elsevier, vol. 47(C), pages 216-231.
    3. Camille Landais, 2015. "Assessing the Welfare Effects of Unemployment Benefits Using the Regression Kink Design," American Economic Journal: Economic Policy, American Economic Association, vol. 7(4), pages 243-278, November.
    4. Yingying Dong, 2012. "Regression Discontinuity Applications with Rounding Errors in the Running Variable," Working Papers 111206, University of California-Irvine, Department of Economics.
    5. Jan Fidrmuc & J. D. Tena, 2013. "National Minimum Wage and Employment of Young Workers in the UK," CESifo Working Paper Series 4286, CESifo.
    6. Martin González-Rozada & Hernan Ruffo, 2022. "The welfare effects of unemployment insurance in Argentina. New estimates using changes in the schedule of transfers," Department of Economics Working Papers 2022_01, Universidad Torcuato Di Tella.
    7. Fe, Eduardo & Hollingsworth, Bruce, 2012. "Estimating the eect of retirement on mental health via panel discontinuity designs," MPRA Paper 38162, University Library of Munich, Germany.
    8. Ganong, Peter & Jäger, Simon, 2014. "A Permutation Test and Estimation Alternatives for the Regression Kink Design," IZA Discussion Papers 8282, Institute of Labor Economics (IZA).
    9. Hansen, Benjamin & Nguyen, Tuan & Waddell, Glen R., 2017. "Benefit Generosity and Injury Duration: Quasi-Experimental Evidence from Regression Kinks," IZA Discussion Papers 10621, Institute of Labor Economics (IZA).
    10. repec:hrv:faseco:34222894 is not listed on IDEAS
    11. Sebastian Garmann, 2014. "The causal effect of coalition governments on fiscal policies: evidence from a Regression Kink Design," Applied Economics, Taylor & Francis Journals, vol. 46(36), pages 4490-4507, December.
    12. Zhongjun Qu & Jungmo Yoon, 2019. "Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(4), pages 625-647, October.
    13. Yingying Dong & Arthur Lewbel, 2011. "Regression Discontinuity Marginal Threshold Treatment Effects," Working Papers 111205, University of California-Irvine, Department of Economics.

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    More about this item

    Keywords

    Regression discontinuity; Fuzzy design; Local average treatment effect; Identification; Jump; Kink; Threshold; Retirement; Consumption;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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