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Don’t Shoot! The Impact of Historical African American Protest on Police Killings of Civilians

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Abstract

Objectives

There is a long-standing history of protests in response to police killings of African American citizens. However, it remains a largely unanswered question as to whether these protest events have had any impact on subsequent police killings of African American civilians. To answer this question, we turn to the over 700 racial uprisings that occurred in the 1960s and early 1970s that were largely triggered by negative and often violent interactions between the African American community and police.

Methods

To determine the impact of racial uprisings on police killings of civilians, we conduct an event-study analysis with a robust set of controls. We employ data on civilian deaths by legal intervention by race, county-level uprising occurrence, and county demographic characteristics. We take advantage of variation in the location and timing of a county’s first uprising to determine the impact of uprisings on police killings of civilians. Our identification strategy relies on pre-existing trends in deaths by legal intervention being uncorrelated with the date of the first uprising in a county.

Results

The results show that counties saw a marked increase in both non-white and white deaths due to legal intervention in the years immediately following an uprising. This initial increase is substantially larger for non-whites relative to white civilians. Deaths due to legal intervention for non-white and white civilians diverge over the medium-to-long run. Non-white deaths resulting from legal intervention remain elevated after nearly a decade while deaths of whites revert to their pre-existing trend after a handful of years. Additional analysis regarding the impact of uprisings on policing shows that total crime and police employment do not change in a significant manner over the long run, however, officers are more likely to be killed or injured on duty.

Conclusions

The results clearly show that historical protest resulted in an increase in civilian deaths by legal intervention regardless of race in the short-run and a seemingly permanent increase in killings of non-white over the medium-to-long run. These results paint a depressing picture in which uprisings represent a structural change in police-civilian relations, adversely affecting white civilians in the short-run and non-white civilians in the short and long-run.

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Fig. 1

Source: The riots data are provided courtesy of Collins and Margo (2007). Carter (1986) is the original source of riots dataset, spanning 1964–1971

Fig. 2

Source: The riots data are provided courtesy of Collins and Margo (2007). Carter (1986) is the original source of riots dataset, spanning 1964–1971

Fig. 3

Source: Vital statistics multiple-cause of death files (US DHHS and ICPSR 2007). The vertical axis corresponds to the total number of deaths due to legal intervention by race

Fig. 4
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Notes

  1. It has been documented by Updegrove et al. (2018) that #BlackLivesMatter received substantially more public support in states with larger numbers of police shootings.

  2. These data are currently being digitized by a team at the University of Victoria’s Racial Uprisings Lab under the direction of the two authors, covering the entire 1992–2017 period.

  3. The NVSS is known to substantially undercount the number of civilians killed by law enforcement, but this is the only dataset available covering the time period of study. This is discussed in greater depth later in the paper along with the empirical implications.

  4. For example, Washington DC experienced over a dozen riots over the 7-year period. Even a much smaller community like Benton Harbor, MI witnessed three riots.

  5. Sociologists have advanced a number of theories as to the underlying causes of the 1960s riots, although there is little consensus the causes. Lieberson and Silverman (1965) believed that a lack of access to political representation was a driving source of riots as there few other mechanisms to convey grievances. Berkowitz (1968) argued that the progress of the Civil Rights era may have heightened expectations for progress, which when not met resulted in violence. Downes (1968) believed that a lack of integration into society made rioting more acceptable as a path to airing grievances. Myers (1997), building off of earlier work by Spilerman (1970, 1971, 1976) sought to empirically evaluate many of these theories, finding little evidence in their favor and, instead, pointing to ethnic competition and diffusion as causes. Carter (1987) finds that having too few or too many police officers can also increase the likelihood of triggering a riot. Economists such as Gunning (1972) and DiPasquale and Glaeser (1998) have similarly sought to explain the rioting decision in a rational choice framework.

  6. There is also evidence that police treat African American protest very differently than those held by white Americans (Davenport et al 2011). Police are much more likely to be present at Black protests and to take direct action, which may in turn make an eruption of violence more likely.

  7. For example, if uprisings were viewed as a form of costly retaliation to police use of force on the civilian population.

  8. Political power gain by African American in large cities is primarily a result of black migration into urban areas and white flight to the suburbs. Both black migration and white flight began well before the 1960s, but racial uprisings may have accelerated white flight.

  9. Relatedly, racial uprisings have been linked to government initiatives providing resources, aid, and funding to riot prone communities as well as the implementation of policies and initiatives to improve police-civilian interactions (Gillezeau 2015; Cunningham 2018; Rahtz 2016).

  10. Of note, Olzak (1990, 1992) has previously found evidence of an economic competition threat effect underlying the riots.

  11. For robustness, we also consider specifications using the date of a county’s most severe uprisings, although we believe these results to be less precisely identified than those surrounding the occurrence of a first riot. More often than not, a county’s first riot is also its most severe riot.

  12. Focusing on the timing is important as results from Updegrove et al. (2018) suggest that protests are more likely to occur and have greater participation in areas with greater levels of police violence against civilians.

  13. See Jacobson et al. (1993).

  14. Urban-by-year fixed effects are constructed by interacting year indicator variables with five categories of a county’s population share in urban areas, u: 0, 0 < u < 25, 25 ≤ u < 50, 50 ≤ u < 75, 75 ≤ u ≤ 100. This captures the differential utilization of police resources and changes in use of force with varying degrees of urbanization.

  15. These data were kindly provided to us by Collins and Margo (2007) who used this data in their groundbreaking on the impact of the 1960s uprisings on African American property values.

  16. These data exclude deaths due to legal execution.

  17. It is important to note that the Vital Statistics recording of deaths by law enforcement contain many shortcomings related to completeness and accuracy due to political pressure and heterogeneity in data collection methods as a result of the voluntary nature of ICD coding (Sherman and Langworthy 1979; Fyfe 2002; Loftin et al. 2003). Despite these shortcomings, the Vital Statistics remains the most consistent and complete collection of deaths by law enforcement intervention for the time period of interest and a reliable source of police homicides for regression analysis (Sherman and Langworthy 1979). Moreover, heterogeneity in the recording of civilian deaths due to law enforcement is captured by county fixed effects assuming data collection efforts vary across counties but are time-invariant.

  18. It is important to note that, although the number of police killings of civilians is quite similar in the 1960s, non-white deaths per non-white population is considerably higher than white deaths per white population.

  19. For some prominent examples demonstrating the extent of this undercount refer to Sherman and Langworthy (1979), Barber et al. (2016), and Quinn (2014)

  20. Propensity scores are calculated using covariates in Table 1. Weights are rescaled to sum to one for non-rioting counties and rioting counties all receive the same weight (1/N, where N is the number of rioting counties).

  21. Our inability to link riots to growth rates in police killings of non-white civilians is contrary to Williamson et al (2018) finding that BLM protest activity is correlated with previous killings of African Americans. This could be a matter of methodology or the fact that BLM protests are starkly different from the 1960s uprisings.

  22. For non-white deaths, the population weights refer to the 1960 non-white population. Similarly, for the analysis of white deaths, the population weights refer to the 1960 white population. Weighted least squares is used to make error term homoscedastic. Results without population weights are available upon request.

  23. Collins and Margo (2007) highlight the idiosyncrasies of the 1960 uprisings, detailing how routine event escalate into wide-spread riots due to unforeseen exchanges or interpretations of exchanges between the police and the black community.

  24. This limits us to the 272 counties across the United States that experienced at least one riot over the period.

  25. The pre-treatment effects are also statistically significant in this case and we are worried that the common trend assumption fails when imposing a non-linear model. We use the Poisson model to highlight the robustness of our results, but we use ordinary least squares throughout our analysis. We do this because (1) the common trends assumption, (2) the independence of occurrence condition of the Poisson model likely does not hold, and (3) over-dispersion will reduce the size of the standard errors, potentially leading to statistically significant joint pre-treatment and post-treatment effects. The main take away from the Poisson regressions is that the estimated joint pre-treatment and post-treatment effects from the Poisson regression have the same sign as the ordinary least squares regressions. Relatedly, see Athey and Imbens (2006) for a discussion of non-linear difference-in-difference modeling using a non-parametric approach. They propose a non-parametric model that provides a counterfactual distribution to identify the impact of a policy variable (treatment).

  26. In particular, the results in Boustan (2010) suggest that while white-flight post-1960 was almost entirely concentrated amongst cities that experienced an uprising in contrast to the phenomenon pre-1960.

  27. Population is constructed by interpolating 1960 census population to 1968 population provided by SEER. Many of the cities experienced their first riot prior to 1968, therefore these results must be viewed with caution.

  28. Table 4 in “Appendix” report joint treatment effects accounting for riot diffusion. Including a dummy variable that captures if a contiguous county experience a riot in the previous year produces qualitatively similar results as in Fig. 6.

  29. It is important to note that county level crime statistics, police employment levels, and police killed on duty suffers severely from underreporting by local police agencies and inconsistent reporting due to the voluntary nature of the FBI Uniform Crime Reports data collection efforts. Results are to be interpreted with caution.

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Acknowledgements

We would like to thank Chris Auld, Martin Farnham, Andrew Goodman-Bacon, Abraham Wickelgren and participants at the Allied Social Science Association, Canadian Network for Economic History, Population Association of America, and American Law and Economics annual meetings for their comments on earlier versions of this work as well as students at the University of Texas Law and Economics Seminar. We also like to thank William J. Collins and Robert Margo for sharing the riot data for the 1964–1971 period. Any errors or omissions are ours alone.

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  1. University of Memphis, Memphis, USA

    Jamein P. Cunningham

  2. University of Victoria, Victoria, Canada

    Rob Gillezeau

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Appendix

Appendix

See Figs. 11, 12, 13, 14, 15 and Tables 2, 3, 4, 5.

Fig. 11
figure 11

The average pre-trend difference in civilian deaths by legal intervention, rioting versus non-rioting counties—evidence of location. Notes: Figure plots the average difference in pre-period growth rates in police killings of civilians between rioting and non-rioting counties

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Fig. 12
figure 12

Pre-trend relationship between date of first riot and deaths by legal intervention—evidence of timing. Notes: Regression coefficients and predicted values are from univariate regressions of the dependent variable Δ in non-white deaths due to legal intervention on the year the first racial uprising occurred in a county. The slope in Fig. 4 is − 0.0313 (0.0535)

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Fig. 13
figure 13

Estimates of effects of first riot on the population dynamics. Notes: Figures display weighted least-squares estimates obtained from estimating by Eq. 1. All panels display estimates from model 1 which includes county, C, effects, urban-by-year, U, effects, state-by-year, S, effects and covariates, X, from the 1960 census interacted with a time trend and is plotted with a solid line and circle markers. Heteroscedasticity-robust standard errors clustered by county are presented by dashed lines and circle markers

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Fig. 14
figure 14

Estimates of effects of first riot on the log of violent crime per 100,000 residents. Notes: Figures display weighted least-squares estimates obtained from estimating by Eq. 1. The dependent variable is the log of violent crime per 100,000 residents. Model 1 includes county, C, effects, urban-by-year, U, effects, state-by-year, S, effects and covariates, X, from the 1960 census interacted with a time trend and is plotted with a solid line and circle markers. Model 2 includes county, C, effects, year, Y, effects but reweight observations by propensity scores based on the probability of rioting to reweight non-rioting counties. Heteroscedasticity-robust standard errors clustered by county are presented by dashed lines and circle markers for model 1 and square markers for model 2. Both models use 1960 population by race as weights and use non-rioting counties as the comparison group

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Fig. 15
figure 15

Estimates of effects of first riot on the log of murder per 100,000 residents. Notes: Figures display weighted least-squares estimates obtained from estimating by Eq. 1. The dependent variable is the log of murder per 100,000 residents. Model 1 includes county, C, effects, urban-by-year, U, effects, state-by-year, S, effects and covariates, X, from the 1960 census interacted with a time trend and is plotted with a solid line and circle markers. Model 2 includes county, C, effects, year, Y, effects but reweight observations by propensity scores based on the probability of rioting to reweight non-rioting counties. Heteroscedasticity-robust standard errors clustered by county are presented by dashed lines and circle markers for model 1 and square markers for model 2. Both models use 1960 population by race as weights and use non rioting counties as the comparison group. To calculate the murder rate, we use a data imputation process similar to Chalfin and McCrary (2018). We impute the following \(f\left( {murder} \right) = \left\{ {\begin{array}{*{20}l} {{ \ln }\left( 2 \right) - 1 + \frac{murder}{2}} \hfill & { if\; murder < 2} \hfill \\ {{ \ln }\left( {murder} \right) } \hfill & {else} \hfill \\ \end{array} } \right.\)

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Table 2 Determinants of location rioting and timing of first riot
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Table 3 Heterogeneous effects
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Table 4 Robustness check: diffusion
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Table 5 Police killed on duty stratified by white population quartiles
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Cunningham, J.P., Gillezeau, R. Don’t Shoot! The Impact of Historical African American Protest on Police Killings of Civilians. J Quant Criminol 37, 1–34 (2021). https://doi.org/10.1007/s10940-019-09443-8

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