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Addressing Seasonality in Veil of Darkness Tests for Discrimination: An Instrumental Variables Approach

Author

Listed:
  • Jesse Kalinowski

    (Quinnipiac University)

  • Matthew B. Ross

    (New York University)

  • Stephen L. Ross

    (University of Connecticut)

Abstract
Veil of Darkness tests identify discrimination by exploiting seasonal variation in the timing of sunset to compare the rate that minorities are stopped by police at the same hour of the day in daylight versus darkness. Such tests operate under the presumption that race is more easily observed by police prior to traffic stops during daylight relative to darkness. This paper addresses concerns that seasonal variation in traffic patterns could bias Veil of Darkness tests. The conventional approach to addressing seasonality is to restrict the sample to a window around Daylight Savings Time (DST) changes when the time of sunset is abruptly changed by one hour twice a year. However, this restriction reduces the variation in the timing of sunset potentially exacerbating measurement error in daylight and may still fail to address seasonality. The latter point is due to the fact that a substantial fraction of the seasonal change in daylight hours occur in the fall and spring (near DST) because of the elliptical nature of earth’s orbit. Therefore, we consider an alternative to simply restricting the sample to fall and spring where we instead apply an instrumental variables and fuzzy regression discontinuity approach. Our approach allows us to isolate the treatment effect associated with one hour of additional daylight on the share of police stops that are of African-American motorists. We find larger racial differences in Texas highway patrol stops using the regression discontinuity approach as compared to the annual sample, even though traditional approaches using the DST sample yield smaller estimates than the annual sample. The larger estimates are robust to the fall DST change sample, addressing concerns that motorists are tired and more accident prone immediately after the spring DST change.

Suggested Citation

  • Jesse Kalinowski & Matthew B. Ross & Stephen L. Ross, 2019. "Addressing Seasonality in Veil of Darkness Tests for Discrimination: An Instrumental Variables Approach," Working papers 2019-07, University of Connecticut, Department of Economics.
    • Handle: RePEc:uct:uconnp:2019-07
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    References listed on IDEAS

    as
    1. Ritter, Joseph A., 2017. "How do police use race in traffic stops and searches? Tests based on observability of race," Miscellaneous Publications 253354, University of Minnesota, Department of Applied Economics.
    2. Ritter, Joseph A., 2017. "How do police use race in traffic stops and searches? Tests based on observability of race," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 82-98.
    3. Austin C. Smith, 2016. "Spring Forward at Your Own Risk: Daylight Saving Time and Fatal Vehicle Crashes," American Economic Journal: Applied Economics, American Economic Association, vol. 8(2), pages 65-91, April.
    4. Jesse Kalinowski & Matthew B. Ross & Stephen L. Ross, 2017. "Endogenous Driving Behavior in Tests of Racial Profiling in Police Traffic Stops," Working papers 2017-03, University of Connecticut, Department of Economics, revised Mar 2020.
    5. Grogger, Jeffrey & Ridgeway, Greg, 2006. "Testing for Racial Profiling in Traffic Stops From Behind a Veil of Darkness," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 878-887, September.
    6. William C. Horrace & Shawn M. Rohlin, 2016. "How Dark Is Dark? Bright Lights, Big City, Racial Profiling," The Review of Economics and Statistics, MIT Press, vol. 98(2), pages 226-232, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Police; Traffic Stops; Seasonality; Measurement Error; Veil of Darkness; Racial Profiling; Racial Discrimination; Regression Discontinuity; Instrumental Variables;
    All these keywords.

    JEL classification:

    • K14 - Law and Economics - - Basic Areas of Law - - - Criminal Law
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
    • H11 - Public Economics - - Structure and Scope of Government - - - Structure and Scope of Government

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