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The legacy of slavery on hate crime in the United States
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Research Paper
The legacy of slavery on hate crime in the United States Christian Gunadi Department of Economics, UC, Riverside, CA 92521, USA
a r t i c l e
i n f o
Article history: Received 18 October 2019 Accepted 18 October 2019 Available online xxx JEL classification: N31 N91 J15 Z13
a b s t r a c t Does slavery play a role in explaining why some areas are more prone to hate crimes? Using county-level data on slavery in 1860, I find evidence that U.S. counties with a higher share of slaves in the population more than 150 years ago are more likely to observe hate crime incidents today. One percentage point increase in the share of slaves in the population in 1860 is associated with 0.018 more hate crime incidents per 100,000 population directed at blacks today. Additionally, there is evidence that slavery is associated with more hate crime incidents directed towards Jews and LGBT population. This result supports previous studies which find persistence in cultural norms and racial attitudes. © 2019 University of Venice. Published by Elsevier Ltd. All rights reserved.
Keywords: Slavery Hate crime Norms Persistence
1. Introduction Hate crimes incidents have been rising in recent years. Although overall crime has been declining across major U.S. cities, hate crime incidents rose to a decade high of 2009 in 2018, a 50% increase from the decade low of 1332 in 2013 (CSHE, 2019). The rising trend is not only observed in the United States. In 2018, anti-semitic incidents rose considerably in Germany, Canada, and France (CSHE, 2019). Since crime imposes economic costs, it is important to examine factors that may contribute to the rise of hate crimes.1 In this paper, I examine the role of slavery in explaining the present-day hate crime incidents in the United States. Using county-level data on slavery in 1860, I find evidence that prevalence of slavery is a significant predictor of hate crime incidents today. One percentage point increase in the share of slaves in the population in 1860 is associated with 0.018 more hate crime incidents per 100,000 population directed at blacks. The magnitude of this estimate is economically meaningful. Evaluated at the mean, it corresponds to a 5.8% increase in hate crime incidents directed at blacks. Furthermore, there is evidence that the prevalence of slavery in 1860 is statistically significantly associated with more hate crime incidents directed toward Jews and LGBT population. The findings of this paper contribute to studies that examine the persistence of cultural norms and racial attitudes. One such work is Voigtländer and Voth (2012), which found that localities with a history of pogroms against Jews were more likely to exhibit violence towards Jews during the Nazi regime in Germany. Another work by Adena et al. (2015) discovered that the exposure to anti-Semitic propaganda had its most effective effect in areas where anti-Semitism was historically E-mail address: [email protected] For example, Miller et al. (1993) estimated that violent crime costs 23 billion USD (in 1989 dollars) from lost productivity and approximately 145 billion USD from reduced quality of life in the United States. 1
https://doi.org/10.1016/j.rie.2019.10.004 1090-9443/© 2019 University of Venice. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: C. Gunadi, The legacy of slavery on hate crime in the United States, Research in Economics, https://doi.org/10.1016/j.rie.2019.10.004
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C. Gunadi / Research in Economics xxx (xxxx) xxx Table 1 Hate crime incidents 2017. All incidents Violent incidents Murder-related violent incidents Assault-related violent incidents
7130 2397 12 2196
Non-violent Incidents Intimidation-related non-violent incidents Vandalism-related non-violent incidents
4733 1745 2270
Source: FBI UCR Hate Crime Data obtained from ICPSR (Kaplan, 2019). A hate crime incident is classified as violent if it contains any of the following: murder, assault, rape, kidnapping, and robbery. A violent incident is classified as murder-related if any of the offenses contain murder. Assault-related violent incident and intimidation/vandalism-related non-violent incident is defined in the similar way.
high. On female labor force participation, Alesina et al. (2013) found that the descendants of societies that traditionally practiced plough agriculture, which gives a comparative advantage to males for agricultural tasks, have a lower rate of female participation in the workforce. This paper is also related to studies that examine the long-term effect of slavery on contemporary socio-economic outcomes. For example, the work by Engerman and Sokoloff (2002) argued that the reliance on slave labor resulted in extreme economic inequality, which in turn hamper economic growth. Testing the hypothesis proposed by Engerman and Sokoloff (2002), Nunn (2008) found that slavery is negatively correlated with economic development. However, the author did not find evidence that this relationship works through slavery’s effect on economic inequality as argued by Engerman and Sokoloff (2002). Investigating the effect of slavery on inequality across U.S. counties, Bertocchi and Dimico (2014) found that the proportion of slaves in the population in 1860 is associated with higher inequality across races today. The author showed that the observed effect comes from unequal educational attainment between blacks and whites due to slavery. On political attitudes, Acharya et al. (2016) found that whites who currently live in counties that had a high prevalence of slavery in 1860 are more likely to express racial resentment toward blacks. A closely related study to this paper is the work by Gouda and Rigterink (2017), which examines the effect of slavery on contemporary violent crime. The authors found that although slavery is a strong predictor of violent crime rate today, the evidence on the relationship between slavery and violent hate crime directed towards blacks is inconclusive. I complement the work of Gouda and Rigterink (2017) in the following ways. First, I do not limit my analysis only to violent hate crime. This is important since the majority of hate crime incidents are non-violent in the form of vandalism or intimidation (Table 1).2 Additionally, since slavery may promote a culture that is hostile against minorities in general, I also examine whether slavery is associated with hate crime directed towards minorities other than blacks. Second, to address the potential bias from measurement error and other unobserved factors, I use soil suitability for cotton as an instrument. The instrument used by Gouda and Rigterink (2017), temperature suitability for malaria, seems unlikely to meet exclusion restriction for a valid instrument since crime rates have been found to be sensitive to changes in temperature (Field, 1992; Harp and Karnauskas, 2018; Heilmann and Kahn, 2019). It is possible that an area with suitable temperature for malaria is also an area with a climate that is conducive to commit a crime. Finally, I examine the effect of slavery in the period characterizes by the rise in hate crimes (2015–2017). The rest of the paper is constructed as follows. Section 2 describes the data and empirical methodology used in the analysis. Section 3 documents the results of the analysis. Section 4 concludes. 2. Empirical methodology and data To examine the effect of slavery on contemporary hate crime, I focus the analysis to counties in the slave states.3 Since counties located in slave states in the south are likely to be different than those located in the free state, focusing the analysis only on counties located in the slave states will allow a cleaner identification of the effect of slavery. In other words, the variation for the identification of the effect will come from counties within the slave states, which have similar characteristics and institutions. Formally, I consider the following empirical specifications:
ycs = δs + γ Slaverycs,1860 + Xcs α + εcs
(1)
2 A hate crime incident in UCR data can contain multiple offenses. I classify a hate crime incident as violent if it contains any of the following: murder, assault, rape, kidnapping, and robbery. 3 Following Bertocchi and Dimico (2014), the following states are classified as slave states: Alabama, Arkansas, Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland, Missouri, Mississippi, North Carolina, South Carolina, Tennessee, Texas, and Virginia. Only counties that can be matched to historical 1860 data are used in the analysis.
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Table 2 Summary statistics. Mean
SD
Min
Max
0.28 23.12 37.09 15.52
0.22 35.83 16.93 2.92
0.00 0.02 0.00 8.67
0.93 933.11 90.91 23.66
All incidents: Anti-black Anti-white Anti-hispanics Anti-jewish Anti-arab/muslim Anti-lgbt
0.31 0.19 0.04 0.02 0.03 0.06
0.84 0.85 0.22 0.12 0.18 0.36
0.00 0.00 0.00 0.00 0.00 0.00
11.65 13.01 2.71 1.48 2.72 7.40
Violent incidents: Anti-black Anti-white Anti-hispanics Anti-jewish Anti-arab/muslim Anti-lgbt
0.11 0.07 0.01 0.00 0.00 0.02
0.42 0.33 0.10 0.05 0.04 0.21
0.00 0.00 0.00 0.00 0.00 0.00
5.88 3.79 1.94 1.23 0.78 4.16
Non-violent incidents: Anti-black Anti-white Anti-hispanics Anti-jewish Anti-arab/muslim Anti-lgbt
0.20 0.13 0.02 0.02 0.02 0.04
0.59 0.63 0.18 0.11 0.18 0.30
0.00 0.00 0.00 0.00 0.00 0.00
7.20 9.88 2.71 1.48 2.72 7.40
County characteristics in 1860 Slaves/population Population density Cotton suitability index Mean annual temperature Hate crime incidents per 100,000 (averaged over 2015–2017)
Sources: Information on county’s characteristics in 1860 is obtained from Historical, Demographic, Economic, and Social Data: The United States, 1790–2002 available on ICPSR (Haines and ICPSR, 2010). FBI UCR hate crime incidents data is obtained from ICPSR (Kaplan, 2019). Cotton Suitability Index and Mean Annual Temperature are based on the raster data obtained from FAO Global Agro-ecological Zones.
where ycs is the crime rate in county c that is located in state s averaged over the 2015–2017 period to minimize measurement error. Xcs is a set of control variables, and δ s is the state fixed effects. To take into account differences in initial economic prosperity across counties, I follow the literature (e.g., Acemoglu et al., 2008; Nunn, 2008) by adding control for population density in 1860. Since changes in temperature have been found to be an important determinant of crime rates (Field, 1992; Harp and Karnauskas, 2018; Heilmann and Kahn, 2019), I include temperature as an additional control. The main variable of interest is Slaveryc, 1860 , which is defined as the share of slaves in the population in 1860. The data on hate crime is obtained from FBI Hate Crime Uniform Crime Report available on ICPSR (Kaplan, 2019).4 Contemporary county population estimates are obtained from the Census Bureau.5 The information on slavery prevalence is obtained from Historical, Demographic, and Social Data: the United States 1790–2002 available on ICPSR (Haines and ICPSR, 2010).6 The temperature in each county is obtained from Food and Agriculture Organization Global Agro-ecological Zones (FAO GAEZ) raster data by averaging the value of grids contained within the county.7 Soil suitability for cotton is also obtained in a similar way from FAO GAEZ.8 The summary statistics are reported in Table 2. A potential concern with estimating equation (1) with a simple OLS is that the effect of slavery on hate crime may be biased caused by measurement error and other unobserved factors. Following Bertocchi and Dimico (2014), I address this concern by instrumenting slavery prevalence in 1860 with soil suitability for cotton. As noted by the authors, early cotton production has been associated with the use of slaves. To be valid as an instrument, soil suitability for cotton must fulfill two conditions. First, it must affect contemporary hate crime only through the change in slavery prevalence. Although it seems unlikely that soil suitability for cotton to have a direct effect on contemporary hate crime, this condition is essentially untestable. Another requirement is that soil suitability for cotton must be strongly correlated with slavery in 1860. At first glance, areas with more slaves per capita are also the places with higher soil suitability for cotton (Fig. 1). I test this condition formally in a regression model, and the results are reported in Table 3. To ease the interpretation, I standardize
4 FBI defines a hate crime as “a committed criminal offense which is motivated, in whole or in part, by the offender’s bias(es) against a race, religion, disability, sexual orientation, ethnicity, gender, or gender identity.” 5 Available from https://www.census.gov/data/datasets/time-series/demo/popest/2010s-counties-total.html. 6 The information on the size of the population in 1860 is also obtained from this dataset. Since county-level area size information is not available in 1860, I use the area size data in 1880 to calculate initial population density. 7 The temperature is measured in centigrade scale. 8 Cotton suitability index has a value ranging from 0 to 100, in which increasing value implies higher soil suitability for cotton.
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Fig. 1. Cotton suitability index, mean annual temperature, and slavery. Sources: Information on county’s characteristics in 1860 is obtained from Historical, Demographic, Economic, and Social Data: The United States, 1790–2002 available on ICPSR (Haines and ICPSR, 2010). Cotton Suitability Index and Mean Annual Temperature are based on the raster data obtained from FAO Global Agro-ecological Zones. Table 3 Cotton suitability index and slavery rate. (1)
(2)
Robust first-stage F-stats
0.094∗ ∗ ∗ (0.027) 12.46
0.081∗ ∗ ∗ (0.014) 34.82
Controls: State fixed effects Population density in 1860 and temperature Observations
Yes No 1082
Yes Yes 1082
Cotton suitability index
Notes: The estimates show the effect of an increase in cotton suitability index on slavery rate. The regressions include control for population density in 1860, mean annual temperature, and state fixed effects. Information on county’s characteristics in 1860 is obtained from Historical, Demographic, Economic, and Social Data: The United States, 1790–2002 available on ICPSR (Haines and ICPSR, 2010). Cotton Suitability Index and Mean Annual Temperature are based on the data obtained from FAO Global Agro-ecological Zones. Clustered robust standard error at the state level in parentheses. ∗ p < .1, ∗∗ p < .05, ∗∗∗ p < .01.
the cotton suitability index to have the mean of zero and a standard deviation of one. The estimate suggests that a one standard deviation increase in the cotton suitability index is associated with 9.4 percentage points increase in the share of slaves in the population. The robust first stage F-statistics indicate that the instrument is sufficiently strong with a value of 12.46, larger than Staiger and Stock (1994) rule of thumb of 10. When initial population density and temperature are added as controls, the estimate becomes more precise, and the first-stage F-statistics jump to 34.82, suggesting that the instrument fulfilled the condition that it is strongly correlated with slavery. 3. Main results The main results of the analysis are reported in Table 4. A one percentage point increase in the share of slaves in the population in 1860 is associated with 0.005 percentage points rise in contemporary anti-black hate crime incidents per 100,000 (Column 1 of Panel A). Using soil suitability for cotton as an instrument leads to a higher estimate of 0.018. Evaluated at Please cite this article as: C. Gunadi, The legacy of slavery on hate crime in the United States, Research in Economics, https://doi.org/10.1016/j.rie.2019.10.004
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Table 4 Slavery and hate crime incidents per 100,000.
Panel A: All incidents Slaves/Population in 1860 (OLS) Slaves/population in 1860 (2SLS) Panel B: Violent incidents Slaves/population in 1860 (OLS) Slaves/population in 1860 (2SLS) Panel C: Non-violent incidents Slaves/Population in 1860 (OLS) Slaves/population in 1860 (2SLS) Observations
Anti-black
Anti-white
Anti-hispanics
Anti-jewish
Anti-arab/muslim
Anti-lgbt
0.493∗ ∗ (0.204) 1.777∗ ∗ (0.735)
−0.102 (0.300) −0.074 (0.558)
0.034 (0.070) 0.257 (0.222)
0.012 (0.016) 0.215∗ (0.115)
0.019 (0.015) 0.209 (0.147)
0.072 (0.051) 0.595∗ ∗ (0.275)
0.179∗ (0.088) 0.588∗ ∗ ∗ (0.195)
−0.012 (0.073) 0.299 (0.260)
0.013 (0.021) 0.098 (0.106)
−0.007 (0.010) 0.056 (0.043)
0.002 (0.005) −0.011 (0.029)
0.003 (0.007) 0.329∗ (0.174)
0.314∗ (0.149) 1.189∗ ∗ (0.596) 1082
−0.091 (0.230) −0.373 (0.335) 1082
0.021 (0.055) 0.158 (0.143) 1082
0.018 (0.012) 0.159∗ (0.082) 1082
0.017 (0.013) 0.221 (0.139) 1082
0.069 (0.048) 0.266∗ ∗ (0.135) 1082
Notes: The estimates show the effect of an increase in slavery rate on the hate crime incidents per 100,000 population. The regressions include control for population density in 1860, mean annual temperature, and state fixed effects. The 2SLS estimates are obtained using cotton suitability index as instrument for slavery rate in 1860. Information on county’s characteristics in 1860 is obtained from Historical, Demographic, Economic, and Social Data: The United States, 1790–2002 available on ICPSR (Haines and ICPSR, 2010). FBI UCR hate crime incidents data is obtained from ICPSR (Kaplan, 2019). Cotton Suitability Index and Mean Annual Temperature are constructed based on the raster data obtained from FAO Global Agro-ecological Zones. Clustered robust standard error at the state level in parentheses. ∗ p < .1, ∗∗ p < .05, ∗∗∗ p < .01.
the mean, this estimate corresponds to a 5.8% rise in hate crime incidents rate against blacks for every one percentage point increase in the share of slaves in the population. Further analysis suggests that this increase is driven by the rise in non-violent hate crime incidents against blacks (Panels B and C). One percentage point increase in the share of slaves in the population in 1860 is associated with a rise of 0.012 non-violent hate crime incidents per 100,000 directed towards blacks. The estimate for violent hate crime is lower at 0.006. The other columns in Table 4 report the results of the analysis for other groups. Focusing on the instrumental variable estimates, there is evidence that slavery is associated with a rise in hate crime incidents per 100,000 directed toward Jews and LGBT population. A one percentage point increase in the share of slaves in the population in 1860 is associated with 0.002 and 0.006 rises in the hate crime incidents per 100,000 against Jews and LGBT population, respectively. These estimates are economically meaningful. Evaluated at the sample mean, it corresponds to approximately a 10% increase in hate crime incidents per 100,000 directed towards Jews and LGBT population. In sum, the results of the analysis suggest that slavery has a role in explaining the present-day hate crime incidents in the United States. An increase in the share of slaves in the population in 1860 is not only associated with a rise in hate crime incidents directed toward blacks but also an increase in hate crime incidents against Jews and LGBT population. This finding supports the hypothesis that slavery promotes a culture that is hostile against minorities in general. 4. Conclusion In recent years, there is a rise in hate crime incidents around the world. Since crime imposes economic costs, there is a need to examine factors that may contribute to this rise in hate crime incidents. Using county-level data on slavery prevalence in 1860, I found evidence that slavery plays a role in explaining the present-day hate crime incidents in the United States. One percentage point increase in the share of slaves in the population in 1860 is associated with 0.018 more hate crime incidents per 100,000 population directed at blacks today. Furthermore, there is evidence that slavery is associated with more hate crime incidents directed towards Jews and LGBT population, suggesting that slavery promotes a culture that is hostile against minorities in general. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Acemoglu, D., Johnson, S., Robinson, J.A., Yared, P., 2008. Income and democracy. Am. Econ. Rev. 98 (3), 808–842. Acharya, A., Blackwell, M., Sen, M., 2016. The political legacy of American slavery. J. Polit. 78 (3), 621–641. Adena, M., Enikolopov, R., Petrova, M., Santarosa, V., Zhuravskaya, E., 2015. Radio and the rise of the Nazis in prewar Germany. Q. J. Econ. 130 (4), 1885–1939. Alesina, A., Giuliano, P., Nunn, N., 2013. On the origins of gender roles: women and the plough. Q. J. Econ. 128 (2), 469–530.
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Bertocchi, G., Dimico, A., 2014. Slavery, education, and inequality. Eur. Econ. Rev. 70, 197–209. CSHE, 2019. Report to the nation: 2019 factbook on hate and extremism in the U.S. and internationally. https://csbs.csusb.edu/sites/csusb_csbs/files/CSHE% 202019%20Report%20to%20the%20Nation%20FINAL%207.29.19%2011%20PM.pdf. Engerman, S.L., Sokoloff, K.L., 2002. Factor endowments, inequality, and paths of development among new world economics. Technical Report. National Bureau of Economic Research. Field, S., 1992. The effect of temperature on crime. Br. J. Criminol. 32 (3), 340–351. Gouda, M., Rigterink, A., 2017. The long-term effect of slavery on violent crime: evidence from US counties. Available at SSRN 2358389. Haines, M. R., ICPSR, 2010. Historical, Demographic, Economic, and Social Data: The United States, 1790–2002. https://doi.org/10.3886/ICPSR02896.v3. Harp, R.D., Karnauskas, K.B., 2018. The influence of interannual climate variability on regional violent crime rates in the United States. GeoHealth 2 (11), 356–369. Heilmann, K., Kahn, M.E., 2019. The Urban Crime and Heat Gradient in High and Low Poverty Areas. Technical Report. National Bureau of Economic Research. Kaplan, J., 2019. Jacob kaplan’s concatenated files: uniform crime reporting (UCR) program data: hate crime data 1991–2017. http://doi.org/10.3886/ E103500V5. Miller, T.R., Cohen, M.A., Rossman, S.B., 1993. Victim costs of violent crime and resulting injuries. Health Aff. 12 (4), 186–197. Nunn, N., 2008. Slavery, inequality, and economic development in the Americas. Inst. Econ. Perform. 15, 148–180. Staiger, D. O., Stock, J. H., 1994. Instrumental variables regression with weak instruments. Voigtländer, N., Voth, H.-J., 2012. Persecution perpetuated: the medieval origins of anti-semitic violence in Nazi Germany. Q. J. Econ. 127 (3), 1339–1392.
Please cite this article as: C. Gunadi, The legacy of slavery on hate crime in the United States, Research in Economics, https://doi.org/10.1016/j.rie.2019.10.004
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Research in Economics xxx (xxxx) xxx
Contents lists available at ScienceDirect
Research in Economics journal homepage: www.elsevier.com/locate/rie
Research Paper
The legacy of slavery on hate crime in the United States Christian Gunadi Department of Economics, UC, Riverside, CA 92521, USA
a r t i c l e
i n f o
Article history: Received 18 October 2019 Accepted 18 October 2019 Available online xxx JEL classification: N31 N91 J15 Z13
a b s t r a c t Does slavery play a role in explaining why some areas are more prone to hate crimes? Using county-level data on slavery in 1860, I find evidence that U.S. counties with a higher share of slaves in the population more than 150 years ago are more likely to observe hate crime incidents today. One percentage point increase in the share of slaves in the population in 1860 is associated with 0.018 more hate crime incidents per 100,000 population directed at blacks today. Additionally, there is evidence that slavery is associated with more hate crime incidents directed towards Jews and LGBT population. This result supports previous studies which find persistence in cultural norms and racial attitudes. © 2019 University of Venice. Published by Elsevier Ltd. All rights reserved.
Keywords: Slavery Hate crime Norms Persistence
1. Introduction Hate crimes incidents have been rising in recent years. Although overall crime has been declining across major U.S. cities, hate crime incidents rose to a decade high of 2009 in 2018, a 50% increase from the decade low of 1332 in 2013 (CSHE, 2019). The rising trend is not only observed in the United States. In 2018, anti-semitic incidents rose considerably in Germany, Canada, and France (CSHE, 2019). Since crime imposes economic costs, it is important to examine factors that may contribute to the rise of hate crimes.1 In this paper, I examine the role of slavery in explaining the present-day hate crime incidents in the United States. Using county-level data on slavery in 1860, I find evidence that prevalence of slavery is a significant predictor of hate crime incidents today. One percentage point increase in the share of slaves in the population in 1860 is associated with 0.018 more hate crime incidents per 100,000 population directed at blacks. The magnitude of this estimate is economically meaningful. Evaluated at the mean, it corresponds to a 5.8% increase in hate crime incidents directed at blacks. Furthermore, there is evidence that the prevalence of slavery in 1860 is statistically significantly associated with more hate crime incidents directed toward Jews and LGBT population. The findings of this paper contribute to studies that examine the persistence of cultural norms and racial attitudes. One such work is Voigtländer and Voth (2012), which found that localities with a history of pogroms against Jews were more likely to exhibit violence towards Jews during the Nazi regime in Germany. Another work by Adena et al. (2015) discovered that the exposure to anti-Semitic propaganda had its most effective effect in areas where anti-Semitism was historically E-mail address: [email protected] For example, Miller et al. (1993) estimated that violent crime costs 23 billion USD (in 1989 dollars) from lost productivity and approximately 145 billion USD from reduced quality of life in the United States. 1
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C. Gunadi / Research in Economics xxx (xxxx) xxx Table 1 Hate crime incidents 2017. All incidents Violent incidents Murder-related violent incidents Assault-related violent incidents
7130 2397 12 2196
Non-violent Incidents Intimidation-related non-violent incidents Vandalism-related non-violent incidents
4733 1745 2270
Source: FBI UCR Hate Crime Data obtained from ICPSR (Kaplan, 2019). A hate crime incident is classified as violent if it contains any of the following: murder, assault, rape, kidnapping, and robbery. A violent incident is classified as murder-related if any of the offenses contain murder. Assault-related violent incident and intimidation/vandalism-related non-violent incident is defined in the similar way.
high. On female labor force participation, Alesina et al. (2013) found that the descendants of societies that traditionally practiced plough agriculture, which gives a comparative advantage to males for agricultural tasks, have a lower rate of female participation in the workforce. This paper is also related to studies that examine the long-term effect of slavery on contemporary socio-economic outcomes. For example, the work by Engerman and Sokoloff (2002) argued that the reliance on slave labor resulted in extreme economic inequality, which in turn hamper economic growth. Testing the hypothesis proposed by Engerman and Sokoloff (2002), Nunn (2008) found that slavery is negatively correlated with economic development. However, the author did not find evidence that this relationship works through slavery’s effect on economic inequality as argued by Engerman and Sokoloff (2002). Investigating the effect of slavery on inequality across U.S. counties, Bertocchi and Dimico (2014) found that the proportion of slaves in the population in 1860 is associated with higher inequality across races today. The author showed that the observed effect comes from unequal educational attainment between blacks and whites due to slavery. On political attitudes, Acharya et al. (2016) found that whites who currently live in counties that had a high prevalence of slavery in 1860 are more likely to express racial resentment toward blacks. A closely related study to this paper is the work by Gouda and Rigterink (2017), which examines the effect of slavery on contemporary violent crime. The authors found that although slavery is a strong predictor of violent crime rate today, the evidence on the relationship between slavery and violent hate crime directed towards blacks is inconclusive. I complement the work of Gouda and Rigterink (2017) in the following ways. First, I do not limit my analysis only to violent hate crime. This is important since the majority of hate crime incidents are non-violent in the form of vandalism or intimidation (Table 1).2 Additionally, since slavery may promote a culture that is hostile against minorities in general, I also examine whether slavery is associated with hate crime directed towards minorities other than blacks. Second, to address the potential bias from measurement error and other unobserved factors, I use soil suitability for cotton as an instrument. The instrument used by Gouda and Rigterink (2017), temperature suitability for malaria, seems unlikely to meet exclusion restriction for a valid instrument since crime rates have been found to be sensitive to changes in temperature (Field, 1992; Harp and Karnauskas, 2018; Heilmann and Kahn, 2019). It is possible that an area with suitable temperature for malaria is also an area with a climate that is conducive to commit a crime. Finally, I examine the effect of slavery in the period characterizes by the rise in hate crimes (2015–2017). The rest of the paper is constructed as follows. Section 2 describes the data and empirical methodology used in the analysis. Section 3 documents the results of the analysis. Section 4 concludes. 2. Empirical methodology and data To examine the effect of slavery on contemporary hate crime, I focus the analysis to counties in the slave states.3 Since counties located in slave states in the south are likely to be different than those located in the free state, focusing the analysis only on counties located in the slave states will allow a cleaner identification of the effect of slavery. In other words, the variation for the identification of the effect will come from counties within the slave states, which have similar characteristics and institutions. Formally, I consider the following empirical specifications:
ycs = δs + γ Slaverycs,1860 + Xcs α + εcs
(1)
2 A hate crime incident in UCR data can contain multiple offenses. I classify a hate crime incident as violent if it contains any of the following: murder, assault, rape, kidnapping, and robbery. 3 Following Bertocchi and Dimico (2014), the following states are classified as slave states: Alabama, Arkansas, Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland, Missouri, Mississippi, North Carolina, South Carolina, Tennessee, Texas, and Virginia. Only counties that can be matched to historical 1860 data are used in the analysis.
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Table 2 Summary statistics. Mean
SD
Min
Max
0.28 23.12 37.09 15.52
0.22 35.83 16.93 2.92
0.00 0.02 0.00 8.67
0.93 933.11 90.91 23.66
All incidents: Anti-black Anti-white Anti-hispanics Anti-jewish Anti-arab/muslim Anti-lgbt
0.31 0.19 0.04 0.02 0.03 0.06
0.84 0.85 0.22 0.12 0.18 0.36
0.00 0.00 0.00 0.00 0.00 0.00
11.65 13.01 2.71 1.48 2.72 7.40
Violent incidents: Anti-black Anti-white Anti-hispanics Anti-jewish Anti-arab/muslim Anti-lgbt
0.11 0.07 0.01 0.00 0.00 0.02
0.42 0.33 0.10 0.05 0.04 0.21
0.00 0.00 0.00 0.00 0.00 0.00
5.88 3.79 1.94 1.23 0.78 4.16
Non-violent incidents: Anti-black Anti-white Anti-hispanics Anti-jewish Anti-arab/muslim Anti-lgbt
0.20 0.13 0.02 0.02 0.02 0.04
0.59 0.63 0.18 0.11 0.18 0.30
0.00 0.00 0.00 0.00 0.00 0.00
7.20 9.88 2.71 1.48 2.72 7.40
County characteristics in 1860 Slaves/population Population density Cotton suitability index Mean annual temperature Hate crime incidents per 100,000 (averaged over 2015–2017)
Sources: Information on county’s characteristics in 1860 is obtained from Historical, Demographic, Economic, and Social Data: The United States, 1790–2002 available on ICPSR (Haines and ICPSR, 2010). FBI UCR hate crime incidents data is obtained from ICPSR (Kaplan, 2019). Cotton Suitability Index and Mean Annual Temperature are based on the raster data obtained from FAO Global Agro-ecological Zones.
where ycs is the crime rate in county c that is located in state s averaged over the 2015–2017 period to minimize measurement error. Xcs is a set of control variables, and δ s is the state fixed effects. To take into account differences in initial economic prosperity across counties, I follow the literature (e.g., Acemoglu et al., 2008; Nunn, 2008) by adding control for population density in 1860. Since changes in temperature have been found to be an important determinant of crime rates (Field, 1992; Harp and Karnauskas, 2018; Heilmann and Kahn, 2019), I include temperature as an additional control. The main variable of interest is Slaveryc, 1860 , which is defined as the share of slaves in the population in 1860. The data on hate crime is obtained from FBI Hate Crime Uniform Crime Report available on ICPSR (Kaplan, 2019).4 Contemporary county population estimates are obtained from the Census Bureau.5 The information on slavery prevalence is obtained from Historical, Demographic, and Social Data: the United States 1790–2002 available on ICPSR (Haines and ICPSR, 2010).6 The temperature in each county is obtained from Food and Agriculture Organization Global Agro-ecological Zones (FAO GAEZ) raster data by averaging the value of grids contained within the county.7 Soil suitability for cotton is also obtained in a similar way from FAO GAEZ.8 The summary statistics are reported in Table 2. A potential concern with estimating equation (1) with a simple OLS is that the effect of slavery on hate crime may be biased caused by measurement error and other unobserved factors. Following Bertocchi and Dimico (2014), I address this concern by instrumenting slavery prevalence in 1860 with soil suitability for cotton. As noted by the authors, early cotton production has been associated with the use of slaves. To be valid as an instrument, soil suitability for cotton must fulfill two conditions. First, it must affect contemporary hate crime only through the change in slavery prevalence. Although it seems unlikely that soil suitability for cotton to have a direct effect on contemporary hate crime, this condition is essentially untestable. Another requirement is that soil suitability for cotton must be strongly correlated with slavery in 1860. At first glance, areas with more slaves per capita are also the places with higher soil suitability for cotton (Fig. 1). I test this condition formally in a regression model, and the results are reported in Table 3. To ease the interpretation, I standardize
4 FBI defines a hate crime as “a committed criminal offense which is motivated, in whole or in part, by the offender’s bias(es) against a race, religion, disability, sexual orientation, ethnicity, gender, or gender identity.” 5 Available from https://www.census.gov/data/datasets/time-series/demo/popest/2010s-counties-total.html. 6 The information on the size of the population in 1860 is also obtained from this dataset. Since county-level area size information is not available in 1860, I use the area size data in 1880 to calculate initial population density. 7 The temperature is measured in centigrade scale. 8 Cotton suitability index has a value ranging from 0 to 100, in which increasing value implies higher soil suitability for cotton.
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Fig. 1. Cotton suitability index, mean annual temperature, and slavery. Sources: Information on county’s characteristics in 1860 is obtained from Historical, Demographic, Economic, and Social Data: The United States, 1790–2002 available on ICPSR (Haines and ICPSR, 2010). Cotton Suitability Index and Mean Annual Temperature are based on the raster data obtained from FAO Global Agro-ecological Zones. Table 3 Cotton suitability index and slavery rate. (1)
(2)
Robust first-stage F-stats
0.094∗ ∗ ∗ (0.027) 12.46
0.081∗ ∗ ∗ (0.014) 34.82
Controls: State fixed effects Population density in 1860 and temperature Observations
Yes No 1082
Yes Yes 1082
Cotton suitability index
Notes: The estimates show the effect of an increase in cotton suitability index on slavery rate. The regressions include control for population density in 1860, mean annual temperature, and state fixed effects. Information on county’s characteristics in 1860 is obtained from Historical, Demographic, Economic, and Social Data: The United States, 1790–2002 available on ICPSR (Haines and ICPSR, 2010). Cotton Suitability Index and Mean Annual Temperature are based on the data obtained from FAO Global Agro-ecological Zones. Clustered robust standard error at the state level in parentheses. ∗ p < .1, ∗∗ p < .05, ∗∗∗ p < .01.
the cotton suitability index to have the mean of zero and a standard deviation of one. The estimate suggests that a one standard deviation increase in the cotton suitability index is associated with 9.4 percentage points increase in the share of slaves in the population. The robust first stage F-statistics indicate that the instrument is sufficiently strong with a value of 12.46, larger than Staiger and Stock (1994) rule of thumb of 10. When initial population density and temperature are added as controls, the estimate becomes more precise, and the first-stage F-statistics jump to 34.82, suggesting that the instrument fulfilled the condition that it is strongly correlated with slavery. 3. Main results The main results of the analysis are reported in Table 4. A one percentage point increase in the share of slaves in the population in 1860 is associated with 0.005 percentage points rise in contemporary anti-black hate crime incidents per 100,000 (Column 1 of Panel A). Using soil suitability for cotton as an instrument leads to a higher estimate of 0.018. Evaluated at Please cite this article as: C. Gunadi, The legacy of slavery on hate crime in the United States, Research in Economics, https://doi.org/10.1016/j.rie.2019.10.004
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Table 4 Slavery and hate crime incidents per 100,000.
Panel A: All incidents Slaves/Population in 1860 (OLS) Slaves/population in 1860 (2SLS) Panel B: Violent incidents Slaves/population in 1860 (OLS) Slaves/population in 1860 (2SLS) Panel C: Non-violent incidents Slaves/Population in 1860 (OLS) Slaves/population in 1860 (2SLS) Observations
Anti-black
Anti-white
Anti-hispanics
Anti-jewish
Anti-arab/muslim
Anti-lgbt
0.493∗ ∗ (0.204) 1.777∗ ∗ (0.735)
−0.102 (0.300) −0.074 (0.558)
0.034 (0.070) 0.257 (0.222)
0.012 (0.016) 0.215∗ (0.115)
0.019 (0.015) 0.209 (0.147)
0.072 (0.051) 0.595∗ ∗ (0.275)
0.179∗ (0.088) 0.588∗ ∗ ∗ (0.195)
−0.012 (0.073) 0.299 (0.260)
0.013 (0.021) 0.098 (0.106)
−0.007 (0.010) 0.056 (0.043)
0.002 (0.005) −0.011 (0.029)
0.003 (0.007) 0.329∗ (0.174)
0.314∗ (0.149) 1.189∗ ∗ (0.596) 1082
−0.091 (0.230) −0.373 (0.335) 1082
0.021 (0.055) 0.158 (0.143) 1082
0.018 (0.012) 0.159∗ (0.082) 1082
0.017 (0.013) 0.221 (0.139) 1082
0.069 (0.048) 0.266∗ ∗ (0.135) 1082
Notes: The estimates show the effect of an increase in slavery rate on the hate crime incidents per 100,000 population. The regressions include control for population density in 1860, mean annual temperature, and state fixed effects. The 2SLS estimates are obtained using cotton suitability index as instrument for slavery rate in 1860. Information on county’s characteristics in 1860 is obtained from Historical, Demographic, Economic, and Social Data: The United States, 1790–2002 available on ICPSR (Haines and ICPSR, 2010). FBI UCR hate crime incidents data is obtained from ICPSR (Kaplan, 2019). Cotton Suitability Index and Mean Annual Temperature are constructed based on the raster data obtained from FAO Global Agro-ecological Zones. Clustered robust standard error at the state level in parentheses. ∗ p < .1, ∗∗ p < .05, ∗∗∗ p < .01.
the mean, this estimate corresponds to a 5.8% rise in hate crime incidents rate against blacks for every one percentage point increase in the share of slaves in the population. Further analysis suggests that this increase is driven by the rise in non-violent hate crime incidents against blacks (Panels B and C). One percentage point increase in the share of slaves in the population in 1860 is associated with a rise of 0.012 non-violent hate crime incidents per 100,000 directed towards blacks. The estimate for violent hate crime is lower at 0.006. The other columns in Table 4 report the results of the analysis for other groups. Focusing on the instrumental variable estimates, there is evidence that slavery is associated with a rise in hate crime incidents per 100,000 directed toward Jews and LGBT population. A one percentage point increase in the share of slaves in the population in 1860 is associated with 0.002 and 0.006 rises in the hate crime incidents per 100,000 against Jews and LGBT population, respectively. These estimates are economically meaningful. Evaluated at the sample mean, it corresponds to approximately a 10% increase in hate crime incidents per 100,000 directed towards Jews and LGBT population. In sum, the results of the analysis suggest that slavery has a role in explaining the present-day hate crime incidents in the United States. An increase in the share of slaves in the population in 1860 is not only associated with a rise in hate crime incidents directed toward blacks but also an increase in hate crime incidents against Jews and LGBT population. This finding supports the hypothesis that slavery promotes a culture that is hostile against minorities in general. 4. Conclusion In recent years, there is a rise in hate crime incidents around the world. Since crime imposes economic costs, there is a need to examine factors that may contribute to this rise in hate crime incidents. Using county-level data on slavery prevalence in 1860, I found evidence that slavery plays a role in explaining the present-day hate crime incidents in the United States. One percentage point increase in the share of slaves in the population in 1860 is associated with 0.018 more hate crime incidents per 100,000 population directed at blacks today. Furthermore, there is evidence that slavery is associated with more hate crime incidents directed towards Jews and LGBT population, suggesting that slavery promotes a culture that is hostile against minorities in general. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Acemoglu, D., Johnson, S., Robinson, J.A., Yared, P., 2008. Income and democracy. Am. Econ. Rev. 98 (3), 808–842. Acharya, A., Blackwell, M., Sen, M., 2016. The political legacy of American slavery. J. Polit. 78 (3), 621–641. Adena, M., Enikolopov, R., Petrova, M., Santarosa, V., Zhuravskaya, E., 2015. Radio and the rise of the Nazis in prewar Germany. Q. J. Econ. 130 (4), 1885–1939. Alesina, A., Giuliano, P., Nunn, N., 2013. On the origins of gender roles: women and the plough. Q. J. Econ. 128 (2), 469–530.
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Please cite this article as: C. Gunadi, The legacy of slavery on hate crime in the United States, Research in Economics, https://doi.org/10.1016/j.rie.2019.10.004