Clarke, Damian (2017): Estimating Difference-in-Differences in the Presence of Spillovers.
Preview |
PDF
MPRA_paper_81604.pdf Download (4MB) | Preview |
Abstract
I propose a method for difference-in-differences (DD) estimation in situations where the stable unit treatment value assumption is violated locally. This is relevant for a wide variety of cases where spillovers may occur between quasi-treatment and quasi-control areas in a (natural) experiment. A flexible methodology is described to test for such spillovers, and to consistently estimate treatment effects in their presence. This spillover-robust DD method results in two classes of estimands: treatment effects, and “close” to treatment effects. The methodology outlined describes a versatile and non-arbitrary procedure to determine the distance over which treatments propagate, where distance can be defined in many ways, including as a multi-dimensional measure. This methodology is illustrated by simulation, and by its application to estimates of the impact of state-level text-messaging bans on fatal vehicle accidents. Extending existing DD estimates, I document that reforms travel over roads, and have spillover effects in neighbouring non-affected counties. Text messaging laws appear to continue to alter driving behaviour as much as 30 km outside of affected jurisdictions.
Item Type: | MPRA Paper |
---|---|
Original Title: | Estimating Difference-in-Differences in the Presence of Spillovers |
Language: | English |
Keywords: | Policy evaluation, difference-in-differences, spillovers, natural experiments, SUTVA |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C21 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions D - Microeconomics > D0 - General > D04 - Microeconomic Policy: Formulation, Implementation, and Evaluation K - Law and Economics > K4 - Legal Procedure, the Legal System, and Illegal Behavior > K42 - Illegal Behavior and the Enforcement of Law R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R2 - Household Analysis > R23 - Regional Migration ; Regional Labor Markets ; Population ; Neighborhood Characteristics R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R4 - Transportation Economics > R41 - Transportation: Demand, Supply, and Congestion ; Travel Time ; Safety and Accidents ; Transportation Noise |
Item ID: | 81604 |
Depositing User: | Mr Damian Clarke |
Date Deposited: | 27 Sep 2017 05:06 |
Last Modified: | 26 Sep 2019 12:55 |
References: | A. Abadie. Semiparametric Difference-in-Differences Estimators. Review of Economic Studies, 72(1): 1–19, 2005. A. Abadie, A. Diamond, and J. Hainmueller. Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program. Journal of the American Statistical Association, 105(490):493–505, 2010. R. Abouk and S. Adams. Texting Bans and Fatal Accidents on Roadways: Do They Work? Or Do Drivers Just React to Announcements of Bans? American Economic Journal: Applied Economics, 5(2):179–199, 2013. H. Allcott and D. Keniston. Dutch Disease or Agglomeration? The Local Economic Effects of Natural Resource Booms in Modern America. The Review of Economic Studies, –(–):– – –, forthcoming. D. Almond, L. Edlund, and M. Palme. Chernobyl’s Subclinical Legacy: Prenatal Exposure to Radioactive Fallout and School Outcomes in Sweden. The Quarterly Journal of Economics, 124(4):1729–1772, 2009. M. Angelucci and G. D. Giorgi. Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles’ Consumption? American Economic Review, 99(1):486–508, March 2009. M. Angelucci and V. D. Maro. Program Evaluation and Spillover Effects. SPD Working Papers 1003, Inter-American Development Bank, Office of Strategic Planning and Development Effectiveness (SPD), May 2010. P. M. Aronow and C. Samii. Estimating Average Causal effects under General Interference, with Application to a Social Network Experiment. Annals of Applied Statistics, –(–):— – —, forthcoming. O. Ashenfelter and D. Card. Using the Longitudinal Structure of Earnings to Estimate the Effects of Training Programs. Review of Economics and Statistics, 67(4):648–660, November 1985. S. Baird, A. Bohren, C. McIntosh, and B. Ozler. Optimal Design of Experiments in the Presence of Interference. The Review of Economics and Statistics, -(-):– – –, forthcoming. R. Bénabou. Workings of a City: Location, Education, and Production. The Quarterly Journal of Economics, 108(3):619–652, 1993. M. Bertrand, E. Duflo, and S. Mullainathan. How Much Should We Trust Differences-in-Differences Estimates? Quarterly Journal of Economics, 119(1):249–275, February 2004. L. E. Blume, W. A. Brock, S. N. Durlauf, and Y. M. Ioannides. Identification of Social Interactions. In J. Benhabib, A. Bisin, and M. Jackson, editors, Handbook of Social Economics, volume 1, chapter 18, pages 853–964. Elsevier, June 2011. K. Borusyak and X. Jaravel. Revisiting event study designs. Working paper, 2016. URL http://papers.ssrn.com/sol3/papers.cfm?abstract id=2826228. W. A. Brock and S. N. Durlauf. Discrete Choice with Social Interactions. The Review of Economic Studies, 68(2):235–260, 2001. A. C. Cameron and D. L. Miller. A practitioner’s guide to cluster-robust inference. The Journal of Human Resources, 50(2):317–72, 2015. A. C. Cameron, J. B. Gelbach, and D. L. Miller. Bootstrap-based improvements for inference with clustered errors. The Review of Economics and Statistics, 90(3):414–427, August 2008. D. R. Cox. Planning of Experiments. John Wiley & Sons Inc., New York, New York, 1958. B. Crépon, E. Duflo, M. Gurgand, R. Rathelot, and P. Zamora. Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment. The Quarterly Journal of Economics, 128(2):531–580, 2013. C. de Chaisemartin and X. D’Haultfoeuille. Fuzzy Differences-in-Differences. The Review of Economic Studies, –(-):– – –, forthcoming. N. Doudchenko and G. W. Imbens. Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis. Working Paper 22791, National Bureau of Economic Research, October 2016. J. J. Heckman and E. Vytlacil. Structural Equations, Treatment Effects, and Econometric Policy Evaluation. Econometrica, 73(3):669–738, 05 2005. M. G. Hudgens and M. E. Halloran. Toward Causal Inference with Interference. Journal of the American Statistical Association, 103(482):832–842, 2008. G. Imbens and T. Lemieux. Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2):615–635, 2008. L. Liu and M. G. Hudgens. Large Sample Randomization Inference of Causal Effects in the Presence of Interference. Journal of the American Statistical Association, 109(505):288–301, 2014. T.-T. Lu and S.-H. Shiou. Inverses of 2 × 2 Block Matrices. Computers & Mathematics with Applications, 43(1–2):119–129, 2002. J. Ludwig and D. Miller. Does head start improve children’s life chances? Evidence from a regression discontinuity design. Quarterly Journal of Economics, 122(1):159–208, 2007. C. F. Manski. Identification of treatment response with social interactions. The Econometrics Journal, 16(1):S1–S23, February 2013. C. McIntosh. Estimating Treatment Effects from Spatial Policy Experiments: An Application to Ugandan Microfinance. The Review of Economics and Statistics, 90(1):15–28, February 2008. E. Miguel and M. Kremer. Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities. Econometrica, 72(1):159–217, 01 2004. H. S. Najafi, S. A. Edalatpanah, and G. A. Gravvanis. An efficient method for computing the inverse of arrowhead matrices. Applied Mathematics Letters, 33(1):1–5, 2014. P. R. Rosenbaum. Interference Between Units in Randomized Experiments. Journal of the American Statistical Association, 102(477):191–200, 2007. D. B. Rubin. Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology, 66(5):688–701, 1974. D. B. Rubin. Bayesian Inference for Causal Effects: The Role of Randomization. The Annals of Statistics, 6(1):34–58, January 1978. D. B. Rubin. Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment. Journal of the American Statistical Association, 75(371):591–593, September 1980. M. E. Sobel. What Do Randomized Studies of Housing Mobility Demonstrate?: Causal Inference in the Face of Interference. Journal of the American Statistical Association, 101(476):1398–1407, 2006. E. J. Tchetgen Tchetgen and T. J. VanderWeele. On causal inference in the presence of interference. Statistical Methods in Medical Research, 21(1):55–75, 2010. T. Zajonc. Boundary Regression Discontinuity Design. Dissertation, Harvard University, May 2012. A. Zellner. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57(298):348–368, June 1962. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/81604 |