IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v206y2018i2p395-413.html
   My bibliography  Save this article

Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods

Author

Listed:
  • Callaway, Brantly
  • Li, Tong
  • Oka, Tatsushi

Abstract

This paper shows that the Conditional Quantile Treatment Effect on the Treated is identified under (i) a Conditional Distributional Difference in Differences assumption and (ii) a new assumption that the dependence (the copula) between the change in untreated potential outcomes and the initial level of untreated potential outcomes is the same for the treated group and untreated group. We consider estimation and inference with discrete covariates and propose a uniform inference procedure based on the exchangeable bootstrap. Finally, we estimate the effect of increasing the minimum wage on the distribution of earnings for subgroups defined by race, gender, and education.

Suggested Citation

  • Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
  • Handle: RePEc:eee:econom:v:206:y:2018:i:2:p:395-413
    DOI: 10.1016/j.jeconom.2018.06.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407618301027
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jeconom.2018.06.008?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Arindrajit Dube, 2019. "Minimum Wages and the Distribution of Family Incomes," American Economic Journal: Applied Economics, American Economic Association, vol. 11(4), pages 268-304, October.
    2. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    3. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
    4. Christoph Rothe, 2012. "Partial Distributional Policy Effects," Econometrica, Econometric Society, vol. 80(5), pages 2269-2301, September.
    5. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
    6. Chernozhukov, Victor & Fernández-Val, Iván & Hoderlein, Stefan & Holzmann, Hajo & Newey, Whitney, 2015. "Nonparametric identification in panels using quantiles," Journal of Econometrics, Elsevier, vol. 188(2), pages 378-392.
    7. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data estimation via quantile regressions," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 61-94, October.
    8. Rivera Drew, Julia A. & Flood, Sarah & Warren, John Robert, 2014. "Making full use of the longitudinal design of the Current Population Survey: Methods for linking records across 16 months\m{1}," Journal of Economic and Social Measurement, IOS Press, issue 3, pages 121-144.
    9. Markus Frölich & Blaise Melly, 2013. "Unconditional Quantile Treatment Effects Under Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 346-357, July.
    10. Henry S. Farber, 1997. "The Changing Face of Job Loss in the United States, 1981-1995," Working Papers 761, Princeton University, Department of Economics, Industrial Relations Section..
    11. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
    12. Ozkan Eren & Serkan Ozbeklik, 2014. "Who Benefits From Job Corps? A Distributional Analysis Of An Active Labor Market Program," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(4), pages 586-611, June.
    13. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    14. Abrevaya, Jason & Dahl, Christian M, 2008. "The Effects of Birth Inputs on Birthweight," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 379-397.
    15. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2016. "Identification of panel data models with endogenous censoring," Journal of Econometrics, Elsevier, vol. 194(1), pages 57-75.
    16. Djebbari, Habiba & Smith, Jeffrey, 2008. "Heterogeneous impacts in PROGRESA," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 64-80, July.
    17. Susan Athey & Guido W. Imbens, 2006. "Identification and Inference in Nonlinear Difference-in-Differences Models," Econometrica, Econometric Society, vol. 74(2), pages 431-497, March.
    18. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, January.
    19. Riddell, W. Craig & Song, Xueda, 2011. "The impact of education on unemployment incidence and re-employment success: Evidence from the U.S. labour market," Labour Economics, Elsevier, vol. 18(4), pages 453-463, August.
    20. Antonio F. Galvao & Kengo Kato, 2014. "Estimation and Inference for Linear Panel Data Models Under Misspecification When Both n and T are Large," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 285-309, April.
    21. Gronau, Reuben, 1974. "Wage Comparisons-A Selectivity Bias," Journal of Political Economy, University of Chicago Press, vol. 82(6), pages 1119-1143, Nov.-Dec..
    22. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    23. Brantly Callaway & Tong Li, 2019. "Quantile treatment effects in difference in differences models with panel data," Quantitative Economics, Econometric Society, vol. 10(4), pages 1579-1618, November.
    24. Tue Gørgens & Chirok Han & Sen Xue, 2019. "Moment Restrictions and Identification in Linear Dynamic Panel Data Models," Annals of Economics and Statistics, GENES, issue 134, pages 149-176.
    25. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    26. Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2006. "Welfare Reform and Children's Living Arrangements," Journal of Human Resources, University of Wisconsin Press, vol. 41(1).
    27. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    28. Bryan S. Graham & Jinyong Hahn & Alexandre Poirier & James L. Powell, 2015. "Quantile Regression with Panel Data," NBER Working Papers 21034, National Bureau of Economic Research, Inc.
    29. Bitler, Marianne P. & Gelbach, Jonah B. & Hoynes, Hilary W., 2008. "Distributional impacts of the Self-Sufficiency Project," Journal of Public Economics, Elsevier, vol. 92(3-4), pages 748-765, April.
    30. Fan, Yanqin & Yu, Zhengfei, 2012. "Partial identification of distributional and quantile treatment effects in difference-in-differences models," Economics Letters, Elsevier, vol. 115(3), pages 511-515.
    31. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    32. repec:fth:prinin:382 is not listed on IDEAS
    33. Marianne P. Bitler & Hilary W. Hoynes & Thurston Domina, 2014. "Experimental Evidence on Distributional Effects of Head Start," NBER Working Papers 20434, National Bureau of Economic Research, Inc.
    34. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2016. "IV Quantile Regression for Group‐Level Treatments, With an Application to the Distributional Effects of Trade," Econometrica, Econometric Society, vol. 84, pages 809-833, March.
    35. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    36. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    37. Xavier d'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2013. "Nonlinear difference-in-differences in repeated cross sections with continuous treatments," CeMMAP working papers CWP40/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    38. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    39. Henry S. Farber, 1997. "The Changing Face of Job Loss in the United States, 1981-1995," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 28(1997 Micr), pages 55-142.
    40. V. Chernozhukov & C. Hansen, 2013. "Quantile Models with Endogeneity," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 57-81, May.
    41. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    42. Victor Chernozhukov & Iván Fernández‐Val & Jinyong Hahn & Whitney Newey, 2013. "Average and Quantile Effects in Nonseparable Panel Models," Econometrica, Econometric Society, vol. 81(2), pages 535-580, March.
    43. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-694, July.
    44. Rosen, Adam M., 2012. "Set identification via quantile restrictions in short panels," Journal of Econometrics, Elsevier, vol. 166(1), pages 127-137.
    45. Henry S. Farber, 1997. "The Changing Face of Job Loss in the United States, 1981-1995," Working Papers 761, Princeton University, Department of Economics, Industrial Relations Section..
    46. Antonio F. Galvao & Carlos Lamarche & Luiz Renato Lima, 2013. "Estimation of Censored Quantile Regression for Panel Data With Fixed Effects," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1075-1089, September.
    47. Li, Tong & Oka, Tatsushi, 2015. "Set identification of the censored quantile regression model for short panels with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 363-377.
    48. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
    49. Arindrajit Dube & T. William Lester & Michael Reich, 2010. "Minimum Wage Effects Across State Borders: Estimates Using Contiguous Counties," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 945-964, November.
    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. Brantly Callaway & Tong Li, 2019. "Quantile treatment effects in difference in differences models with panel data," Quantitative Economics, Econometric Society, vol. 10(4), pages 1579-1618, November.
    2. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
    3. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    4. Jessica Ya Sun, 2020. "Welfare consequences of access to health insurance for rural households: Evidence from the New Cooperative Medical Scheme in China," Health Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 337-352, March.
    5. Damian Clarke & Manuel Llorca Jaña & Daniel Pailañir, 2023. "The use of quantile methods in economic history," Historical Methods: A Journal of Quantitative and Interdisciplinary History, Taylor & Francis Journals, vol. 56(2), pages 115-132, April.
    6. David Bounie & Youssouf Camara, 2020. "Card-Sales Response to Merchant Contactless Payment Acceptance," Post-Print hal-02296302, HAL.
    7. Peng, Ying & Tian, Chuanhao & Wen, Haizhen, 2021. "How does school district adjustment affect housing prices: An empirical investigation from Hangzhou, China," China Economic Review, Elsevier, vol. 69(C).
    8. Afrouz Azadikhah Jahromi & Brantly Callaway, 2022. "Heterogeneous Effects of Job Displacement on Earnings," Empirical Economics, Springer, vol. 62(1), pages 213-245, January.
    9. Bounie, David & Camara, Youssouf, 2020. "Card-sales response to merchant contactless payment acceptance," Journal of Banking & Finance, Elsevier, vol. 119(C).
    10. Wolfgang Frimmel & Bernhard Schmidpeter & Rene Wiesinger & Rudolf Winter-Ebmer, 2022. "Mandatory Wage Posting, Bargaining and the Gender Wage Gap," Economics working papers 2022-02, Department of Economics, Johannes Kepler University Linz, Austria.
    11. Lyu, Chaofeng & Xie, Zhe & Li, Zhi, 2022. "Market supervision, innovation offsets and energy efficiency: Evidence from environmental pollution liability insurance in China," Energy Policy, Elsevier, vol. 171(C).
    12. Carolina Caetano & Brantly Callaway, 2022. "Difference-in-Differences with Time-Varying Covariates in the Parallel Trends Assumption," Papers 2202.02903, arXiv.org, revised May 2023.
    13. Pedro H. C. Sant'Anna, 2016. "Program Evaluation with Right-Censored Data," Papers 1604.02642, arXiv.org.
    14. Jiafeng Gu, 2021. "Effects of Patent Policy on Outputs and Commercialization of Academic Patents in China: A Spatial Difference-in-Differences Analysis," Sustainability, MDPI, vol. 13(23), pages 1-17, December.
    15. William Prieto Bustos & Cristian Darío Castillo Robayo & Jacobo Campo Robledo & Juliana Molina Dominguez, 2024. "Impact of Venezuelan Migration on the Informal Workforce of Native Workers in Colombia," Economies, MDPI, vol. 12(2), pages 1-25, February.
    16. Saibal Ghosh, 2020. "Bank Lending and Monetary Transmission: Does Politics Matter?," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(2), pages 359-381, June.
    17. Soichiro Yamauchi, 2020. "Difference-in-Differences for Ordinal Outcomes: Application to the Effect of Mass Shootings on Attitudes toward Gun Control," Papers 2009.13404, arXiv.org.
    18. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
    19. Masayuki Sawada, 2019. "Noncompliance in randomized control trials without exclusion restrictions," Papers 1910.03204, arXiv.org, revised Jun 2021.
    20. David Bounie & Youssouf Camara, 2019. "Card-Sales Response to Merchant Contactless Payment Acceptance: Causal Evidence," Working Papers hal-02296302, HAL.
    21. Gonçalves, S. & Rodrigues, T.P. & Chagas, A.L.S., 2020. "The impact of wind power on the Brazilian labor market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    22. Brantly Callaway, 2022. "Difference-in-Differences for Policy Evaluation," Papers 2203.15646, arXiv.org.
    23. Eréndira León Bravo, 2022. "Three essays on education, wages, and the labour market in Mexico," Economics PhD Theses 0322, Department of Economics, University of Sussex Business School.

    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. Graham, Bryan S. & Hahn, Jinyong & Poirier, Alexandre & Powell, James L., 2018. "A quantile correlated random coefficients panel data model," Journal of Econometrics, Elsevier, vol. 206(2), pages 305-335.
    2. Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.
    3. Brantly Callaway & Tong Li, 2019. "Quantile treatment effects in difference in differences models with panel data," Quantitative Economics, Econometric Society, vol. 10(4), pages 1579-1618, November.
    4. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data estimation via quantile regressions," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 61-94, October.
    5. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    6. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2016. "IV Quantile Regression for Group‐Level Treatments, With an Application to the Distributional Effects of Trade," Econometrica, Econometric Society, vol. 84, pages 809-833, March.
    7. Kaspar Wüthrich, 2020. "A Comparison of Two Quantile Models With Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 443-456, April.
    8. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    9. Harding, Matthew & Lamarche, Carlos, 2019. "A panel quantile approach to attrition bias in Big Data: Evidence from a randomized experiment," Journal of Econometrics, Elsevier, vol. 211(1), pages 61-82.
    10. 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.
    11. Bryan S. Graham & Jinyong Hahn & Alexandre Poirier & James L. Powell, 2015. "Quantile regression with panel data," CeMMAP working papers CWP12/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Antecol, Heather & Eren, Ozkan & Ozbeklik, Serkan, 2013. "The effect of Teach for America on the distribution of student achievement in primary school: Evidence from a randomized experiment," Economics of Education Review, Elsevier, vol. 37(C), pages 113-125.
    13. Strittmatter, Anthony, 2019. "Heterogeneous earnings effects of the job corps by gender: A translated quantile approach," Labour Economics, Elsevier, vol. 61(C).
    14. Galvao, Antonio F. & Gu, Jiaying & Volgushev, Stanislav, 2020. "On the unbiased asymptotic normality of quantile regression with fixed effects," Journal of Econometrics, Elsevier, vol. 218(1), pages 178-215.
    15. Jungmo Yoon & Antonio F. Galvao, 2020. "Cluster robust covariance matrix estimation in panel quantile regression with individual fixed effects," Quantitative Economics, Econometric Society, vol. 11(2), pages 579-608, May.
    16. Takuya Ishihara, 2020. "Panel Data Quantile Regression for Treatment Effect Models," Papers 2001.04324, arXiv.org, revised Nov 2021.
    17. Volpe Martincus, Christian & Carballo, Jerónimo, 2010. "Beyond the average effects: The distributional impacts of export promotion programs in developing countries," Journal of Development Economics, Elsevier, vol. 92(2), pages 201-214, July.
    18. Pereda-Fernández, Santiago, 2023. "Identification and estimation of triangular models with a binary treatment," Journal of Econometrics, Elsevier, vol. 234(2), pages 585-623.
    19. Victor Chernozhukov & Ivan Fernandez-Val & Martin Weidner, 2018. "Network and panel quantile effects via distribution regression," CeMMAP working papers CWP21/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Hernæs, Øystein M., 2020. "Distributional effects of welfare reform for young adults: An unconditional quantile regression approach," Labour Economics, Elsevier, vol. 65(C).

    More about this item

    Keywords

    Quantile treatment effects; Copula; Panel data;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

    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:eee:econom:v:206:y:2018:i:2:p:395-413. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    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.