RI 3UHWRULD...societies rich in social capital were also the most likely to turn on each other when...

University of Pretoria

Department of Economics Working Paper Series Social Capital and Protests in the United States Carolyn Chisadza University of Pretoria Matthew Clance University of Pretoria Rangan Gupta University of Pretoria Working Paper: 2021-39 June 2021 __________________________________________________________ Department of Economics University of Pretoria 0002, Pretoria South Africa Tel: +27 12 420 2413

Social Capital and Protests in the United States

Carolyn Chisadza* Matthew Clance� Rangan Gupta�

June 1, 2021

Abstract

In the last decade we have witnessed rising protests in the United States associated with issuesthat form part of society's social fabric that can either facilitate or break down collective be-haviour. Rising social inequalities can cause people to no longer share the same values and forceindividuals to work against each other. This breakdown in social capital can be a key driverfor protests as the marginalised attempt to voice their grievances. Using social capital datafrom the Social Capital Project and protest data from the GDELT Project for U.S counties, we�nd that higher social capital decreases di�erent types of protests, moreso demonstrations andviolent protests. At a disaggregated level, we �nd that community engagement and collectivee�cacy (i.e. level of social organisation) are better predictors of protests in relation to qualityof household health and level of con�dence in institutions. These results remain consistent whencontrolling for economic and social inequalities, such as income, unemployment and race. The�ndings highlight the importance of social capital in the development process, particularly inmitigating the incentives to engage in violence.

Keywords: social capital, protests, USA

JEL Classi�cation: Z13, D74, O51

*Department of Economics, University of Pretoria, Private Bag X20, Hat�eld 0028, South Africa, E-mail: [email protected].

�Department of Economics, University of Pretoria, Private Bag X20, Hat�eld 0028, South Africa, E-mail:[email protected].

�Department of Economics, University of Pretoria, Private Bag X20, Hat�eld 0028, South Africa, E-mail: [email protected].

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1 Introduction

Social capital is seen as an important contributor to economic development, particularly the suc-

cesses and challenges that countries face. In the last decade we have witnessed rising protests in

the United States (U.S.) associated with race (#blacklivesmatter), gender, Lesbian, Gay, Bisexual,

Transsexual and Queer (LGBTQ+) rights, gun control, immigrants' rights, anti-abortion, anti-

corruption, environment to name a few. All these issues, and more, form part of society's social

fabric that can either facilitate or break down collective behaviour captured by social capital.

Given the increase in episodes associated with protests, sometimes violent protests resulting in

deaths, in the United States, we pose the question: do adverse changes in social capital contribute

to protests in the United States? While there is no de�nitive de�nition of social capital, there

appears to be a consensus in the literature that social capital is closely related to a set of shared

values that allows individuals to work together in a group to e�ectively achieve a common purpose

(Sanginga et al., 2007). Social capital is commonly re�ected as the degree of citizen involvement in

their communities, by levels of trust among community members and trust in institutions (Alcorta

et al., 2020; Galea et al., 2002; Knack, 2002). We propose that social capital is associated with

protests. Rising social inequalities can cause people to no longer share the same values and force

individuals to work against each other. This breakdown in social capital can be a key driver for

protests as the marginalised attempt to voice their grievances. While there is extensive literature

on the e�ect of economic inequality on protests (Baten and Mumme, 2013; Boix, 2008), there is

limited evidence on the links between social capital and protests, or how economic inequalities, such

as wealth or social immobility, can exacerbate social inequalities.

Using a social capital index from the Social Capital Project that captures family, community, and

institutional aspects at U.S. county level and protest data from the Global Database of Events,

Language, and Tone (GDELT) Project, we �nd a negative association between social capital and

di�erent types of protests. The negative e�ects are larger for demonstrations annd violent protests

compared to boycotts, hunger strikes, blockades and non-categorised protests. The implications of

the �ndings suggest that addressing the mechanisms that can delay social capital may assist in less

grievances that lead to protests.

1.1 Related Literature

A main component of social capital is trust which establishes a social network of mutual dependence

and exchange in communities. It is therefore theorised that communities with low social capital

are not e�ective in exercising informal social control and establishing norms that reduce violence

(Sampson and Wilson, 1995). According to Galea et al. (2002), measures of social trust perceptions

and membership in voluntary associations were found to be negatively correlated with homicide rates

in the United States between 1974 and 1993. Moreover, Alcorta et al. (2020) �nd that cognitive

social capital (i.e. shared norms, values, attitudes and beliefs) is negatively associated with political

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violence in Africa. Further evidence is provided by Avdeenko and Gilligan (2015); Hansen-Nord

et al. (2016) on the positive attributes of social capital in minimising risk of crimes and violence by

changing patterns of behaviour or increasing security.

Several studies also �nd evidence that link social capital with economic growth through better health

outcomes and improved quality of government. For example, Mellor and Milyo (2006) observes that

civic participation and trust are positively associated with individual health status in the United

States, while Anderson et al. (2004) �nds that generalised trust is signi�cantly associated with

contributions in public goods experiments. Empirical work by Knack (2002) shows that aspects

of social capital, such as social trust, volunteering and census response, are linked with better

governmental performance in the United States. He �nds that social capital makes governments

more accountable to broader public interest rather than to narrow interests. Similary, Guiso et al.

(2004) �nds that social capital plays an important role in �nancial development in Italy and the

e�ects are stronger where legal enforcement is weaker. According to Knack and Keefer (1997), trust

and civic norms are stronger in nations with higher and more equal incomes, with institutions that

restrain rent-seeking behaviour of public o�cials, and with educated and ethnically homogeneous

populations. This evidence is corroborated by Zak and Knack (2001) who show that investments and

economic growth are higher in high trust societies. Trust is higher in more ethnically, socially and

economically homogeneous societies and where constarints on the executives are better developed.

Alesina and Ferrara (2000) also �nds that participation in social activities is lower in more unequal

and in more racially or ethnically fragmented localities in the United States.

While most evidence in the literature points to a positive association between social capital and

economic performance, there is however a negative side to social capital. Social capital can, under

certain circumstances, contribute to violence as shown by Alcorta et al. (2020). They �nd that

structural social capital (i.e. civic engagement in religious groups, trade unions, political organisa-

tions) is positively associated with political violence in Africa. Moreover, groups segregated by race,

income class or ethnicity can build trust among group members to the exclusion of the out-group,

thereby reinforcing narrow particularistic interests (Knack, 2002). Kalyvas (2006) also �nds that

societies rich in social capital were also the most likely to turn on each other when con�ict arises

in their community. The racially and ethnically diverse society in the United States, coupled with

rising inequality (e.g. the Census Bureau estimates show that the gini index has increased from

39.7% in 1967 to 48.5% in 2018 (Telford, 2019)) and protests, makes this country an interesting

testing ground for our hypothesis on social capital and protests.

2 Data and Methodology

The dependent variable comprises of the number of protest events at county level for the United

States from the Global Database of Events, Language, and Tone (GDELT) Project (https://

www.gdeltproject.org/). We use the six di�erent categories that are de�ned by the GDELT

Project as falling under the umbrella term "Protest" (parent category 14). These include engaging

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https://www.gdeltproject.org/

https://www.gdeltproject.org/

in political dissent (non-categorised protests), demonstrations, hunger strikes, boycotts, blockades

and violent protests/riots to demand rights, leadership change, policy change, and/or change in

institutions/regime. Figure 1 shows that, on average, protests are on the rise in the United States,

with demonstrations contributing the highest number of events, followed by non-categorised protests

and violent protests.

The main explanatory variable is a county level social capital index that is obtained from the

Social Capital Project (Social Capital Project, 2018). The index is the standardised weighted

sum of four dimensions of social capital: family unity, community health, institutional health and

collective e�cacy. Collectively these dimensions capture quality of family health, level of community

engagement in organisations (non-pro�t, religious, public meetings, or assisting neighbours), level of

trust in institutions (voting, con�dence in corporations, media and public schools) and level of social

organisation (Social Capital Project, 2018). The social capital index ranges from -4.3 indicating

low social capital to 2.9 indicating high social capital. These indicators are from data collected by

various sources between 2006 and 2016, primarily from 2013 forward (Social Capital Project, 2018).

The control variables that are obtained from the American Community Survey at county level

include the log of median household income, percent of adults that graduated from high school,

unemployment rate, gini coe�cient and a black-white segregation index. We also include relative

immobility from Chetty et al. (2014). These variables are commonly associated with protest and

con�ict outcomes in the literature (Alcorta et al., 2020; Galea et al., 2002; Houle, 2019). We expect

a negative correlation between income and protests, as well as education and protests. Wealthier

citizens have more to lose which can increase their opportunity costs of engaging in protests. Better

educated citizens may be more e�ective in demanding for their rights without resorting to violence.

Both the wealth and education mechanisms build on the greed and grievance framework on con�icts

by Collier and Hoe�er (2004). We expect a positive correlation between protests and unemployment

rate as unemployed people can facilitate crowd recruitment for protests (Collier, 2000). Protests are

also more likely where income inequality is high as it gives rise to grievances from the marginalised

groups (Houle, 2019).

The black-white segregation index measures the degree to which the black minority group is dis-

tributed di�erently than whites aross metropolitan areas. The index ranges from 0 (complete

integration) to 100 (complete segregation). We include this index because black-white di�erences

are still the most important racial division across the United States and such polarised preferences

can make governing more di�cult (Knack, 2002). In addition, we have witnessed increased protests

in the United States related to racial divisons (e.g. black lives matter movement in response to

shootings of Trayvon Martin, Breonna Taylor, Armaud Arbery, and recently the killing of George

Floyd, to name but a few). We therefore expect a positive association between racial inequality and

protests. Relative immobility measures the di�erence in the expected economic outcomes between

children from high-income and low-income families (Chetty et al., 2014). We expect low social

mobility levels to be associated with increased protests (Houle, 2019). Table A1 in the Appendix

4

provides a more detailed explanation of the social capital index and its components, along with the

protest data and control variables.

To examine the e�ects of social capital on protests, we estimate the following speci�cation for

multiple levels of �xed e�ects including heterogeneous slopes and robust standard errors.

ln(Vis) = γ + αzis + βxis + ϕs + εis

where Vis is the mean of number of protests between 2013 and 2016 for county i in state s, zis is

the social capital variable, xis is a vector of control variables, ϕis is unobserved state heterogeneity

and εis is the error term. The data is a cross due to the collection method of the social capital

data. The county data is collected between 2006 and 2016, with the majority of the data collected

in 2013. We have one measure for each county during this period.1

For additional analysis, we also use quantile regressions. The classical linear regression estimates the

mean response of the outcome variable on the explanatory variables. However, there are sometimes

cases when behaviour at the conditional mean may fail to fully capture the patterns in the data,

such as skewed data or data with outliers. In such cases, quantile regressions can provide a useful

alternative methodology to linear regressions. The main advantage for quantile regressions is that

the method allows for understanding distributional relationships between variables outside the mean

of the data, making it bene�cial in understanding outcomes that are non-normally distributed and

that have non-linear relationships with predictor variables. The quantile regressions show the entire

distribution of the dependent variable, is invariant to monotonic transformation of the dependent,

and no distributional assumptions of the error are needed, making the method more robust to

outliers. Given the distributional nature of protests the quantile regressions therefore provide a

robust alternative to check the validity of the linear outcomes from the �xed e�ects model.

3 Results

Table 1 presents the preliminary results for social capital and protests, while Table 2 includes the

control variables. Both sets of results show that protests are negatively associated with social

capital. A unit change in the social capital index decreases protests by between 20 and 40%, with

demonstrations indicating the larger reduction at 44% and violent protests at 40%. The inclusion of

the control variables does not attenuate the negative e�ects on protests from social capital, with the

coe�cients increasing across all protest types. Again, demonstrations (64%) and violent protests

(51%) indicate larger reductions after controlling for wealth, income and race inequality, education,

unemployment and relative immobility. The �ndings suggest that areas with high social capital are

better positioned to promote and enforce pro-social norms that encourage community integration

and discourage social dissolution.

1The results remain robust to several speci�cations where we use di�erent years to calculate the mean of thenumber of protests (2014 to 2016, and 2015 to 2016). The results are available on request.

5

The results for income inequality are statistically signi�cant and positively correlated with most

types of protests except hunger strikes, while race inequality signi�cantly increases demonstrations,

blockades and non-categorised protests. The horizontal inequality framework proposes that the

poor and politically marginalised are the most likely to revolt (Raleigh, 2014). According to Raleigh

(2014); Stewart (2008), uprisings and con�icts are more likely to occur where groups experience eco-

nomic and social exclusions along racial, ethnic or religion lines. These horizontal inequalities can be

compounded by income inequality which increases incentives to use violence as a means to improve

one's relative position. We also �nd a non-monotonic relationship between household income and

protests, speci�cally demonstrations, blockades, violent protests and non-categorised protests. As

wealth increases, the opportunity costs of rebellion also get higher (Collier et al., 2009). We do not

�nd signi�cant results for relative immobility across the protest types. However, contrary to the

expected results, we �nd that the e�ects of education on demonstrations are statistically signi�cant

and positive. Krueger and Maleckova (2003) �nd that violent and radical attacks do not decrease

with higher education. Unemployment rates are signi�cantly associated with increased demonstra-

tions, blockades and non-categorised protests. Unemployment increases alternative opportunities

to earn income, such as joining rebellions (Collier, 2000; Urdal, 2006).

Table 3 reports the results for the social capital sub-indices. We observe that community health

(level of community engagement) reduces demonstrations and boycotts, while collective e�cacy

(level of social organisation) decreases protests across all types. The results for family unity (quality

of health in households) and institutional health (level of con�dence in institutions) are negative but

not signi�cant. Collective e�cacy refers to the ability of members of a community to control the

behaviour of individuals and groups in the community which allows residents to create a safe and

orderly environment. According to the �ndings from the sub-indices, this particular aspect of social

capital, coupled with the level of community engagement, is relatively more e�ective in reducing

protests in U.S. counties than level of con�dence in institutions and quality of health in households.

Communities with social cohesion that is not exacerbated by economic and social inequalities, such

as poverty, unemployment and racial tensions, have less incentives to get involved in protests and

violence.

We �nd similar outcomes for the mitigating e�ect of social capital on protests in the quantile

regressions reported in Tables 4 to 9. Overall, the coe�cients for social capital are negative across

all quantiles. We also observe that coe�cient sizes gradually increase from the 20th to about the 60th

percentiles, indicating that the magnitude of the negative e�ect that social capital has on protests

increases as the number of protests move from low to high in the distribution. For example, in Table

5, the negative e�ect of social capital on demonstrations increases from 36% to 76% as the number

of demonstrations increase from the 10th percentile upwards, with the largest e�ect recorded in the

80th percentile. However, these e�ects are not so signi�cant for hunger strikes. The inclusion of

control variables do not diminish their e�ects on protests nor change the main conclusions of our

�ndings.

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4 Conclusion

This paper argues that social capital decreases protests in the United States. We test this hypothesis

using social capital in U.S. counties and protest data from the GDELT project. We �nd that social

capital is negatively associated with di�erent types of protests, moreso demonstrations and violent

protests. The negative e�ects remain robust when controlling for confounding factors, such as

household income, income and race inequalities, unemployment and education. We also observe

that the negative impact of social capital on protests increases as the number of protests move from

low to high in the quantile results. Furthermore, at a disaggregated level, we �nd that collective

e�cacy is a relatively better predictor for protests.

The implications of the �ndings are twofold. First, understanding the economic outcomes of eco-

nomic inequalities, such as income or labour, are important for economic development, but neglect-

ing the evolving nature and quality of associational life captured by social capital can undermine the

development process. Second, the salience of collective e�cacy as a mitigating factor for protests

should be highlighted as a possible policy lever. Investing in social capital as a means to strengthen

civil society such that communities are safer and more cohesive can result in positive spill-overs on

economic well-being.

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Compliance with Ethical Standards: The authors would like to disclose no potential con�icts of

interest. Also, this research involves secondary macroeconomic data, which does not involve human

participants and/or animals.

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5 Figures and Tables

Figure 1: Number of Protests by types

Figure 1 shows the number of protests by type in the United States between 2006 and 2016.

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Table 1: Protest types and social capital indexProtests-no category Demonstrations Hunger Strikes Strikes/Boycotts Blockades Violent Protests

Social capital -0.339∗∗∗ -0.441∗∗∗ -0.125 -0.269∗∗∗ -0.261∗∗∗ -0.401∗∗∗

(0.085) (0.055) (0.140) (0.075) (0.068) (0.087)

State FE Yes Yes Yes Yes Yes YesF-stat -1721.368 -4666.563 -459.462 -1869.914 -1740.193 -1479.867R-squaredObs 950 2554 258 1095 1067 829

Coe�cients reported. Robust standard errors in parentheses.∗ p < .10, ∗∗ p < .05, ∗∗∗ p < .01

Table 2: Protest types with controls includedProtests-no category Demonstrations Hunger Strikes Strikes/Boycotts Blockades Violent Protests

Social capital -0.420∗∗∗ -0.641∗∗∗ -0.518∗∗ -0.434∗∗∗ -0.318∗∗∗ -0.510∗∗∗

(0.100) (0.059) (0.245) (0.090) (0.078) (0.107)

% adults graduated high school -0.011 0.045∗∗∗ -0.023 -0.009 -0.013 -0.016(0.013) (0.007) (0.029) (0.012) (0.011) (0.016)

Gini 10.705∗∗∗ 14.351∗∗∗ -0.608 10.756∗∗∗ 9.489∗∗∗ 10.595∗∗∗

(1.936) (1.165) (3.990) (1.573) (1.543) (1.843)

Relative immobility -0.330 -0.304 0.137 -0.010 -0.243 -0.402(0.989) (0.527) (2.585) (0.779) (0.813) (0.981)

Unemployment 0.092∗ 0.068∗∗ -0.003 0.020 0.066∗ 0.043(0.052) (0.027) (0.125) (0.040) (0.037) (0.056)

Black-white segregation 1.184∗∗ 1.749∗∗∗ -2.228 0.628 1.015∗∗ 0.383(0.524) (0.307) (1.374) (0.479) (0.467) (0.624)

Household income 38.506∗∗∗ 22.574∗∗∗ 27.446 11.958 20.271∗∗ 36.549∗∗∗

(10.703) (6.408) (26.643) (8.863) (9.293) (10.872)

Household income sq. -1.649∗∗∗ -0.905∗∗∗ -1.173 -0.441 -0.831∗ -1.579∗∗∗

(0.489) (0.295) (1.216) (0.405) (0.425) (0.497)



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Table 3: Protest types and social capital sub-indicesProtests-no category Demonstrations Hunger Strikes Strikes/Boycotts Blockades Violent Protests

Family Unity -0.040 -0.028 -0.035 0.062 -0.010 0.016(0.084) (0.045) (0.256) (0.070) (0.066) (0.103)

Community Health -0.079 -0.584∗∗∗ -0.222 -0.272∗∗ -0.129 -0.031(0.127) (0.080) (0.289) (0.112) (0.110) (0.158)

Institutional Health -0.104 -0.091 -0.612∗ -0.064 -0.067 -0.335∗∗

(0.139) (0.073) (0.319) (0.108) (0.110) (0.150)

Collective E�cacy -0.242∗∗∗ -0.317∗∗∗ -0.262∗ -0.282∗∗∗ -0.190∗∗∗ -0.281∗∗∗

(0.068) (0.046) (0.151) (0.063) (0.055) (0.075)

% adults graduated high school -0.011 0.051∗∗∗ -0.012 -0.003 -0.010 -0.011(0.014) (0.008) (0.029) (0.013) (0.012) (0.017)

Gini 10.012∗∗∗ 14.803∗∗∗ 0.044 10.256∗∗∗ 8.981∗∗∗ 9.691∗∗∗

(1.969) (1.152) (4.377) (1.549) (1.560) (1.930)

Relative immobility -0.042 -0.044 0.735 0.074 -0.167 0.336(1.003) (0.526) (2.737) (0.787) (0.838) (1.014)

Unemployment 0.102∗ 0.068∗∗ -0.021 0.032 0.068∗ 0.080(0.054) (0.028) (0.127) (0.041) (0.038) (0.057)

Black-white segregation 1.447∗∗∗ 1.754∗∗∗ -1.803 1.017∗∗ 1.215∗∗∗ 0.862(0.514) (0.303) (1.339) (0.444) (0.466) (0.631)

Household income 39.215∗∗∗ 21.046∗∗∗ 14.934 11.748 18.868∗∗ 37.509∗∗∗

(11.045) (6.678) (26.863) (9.235) (9.324) (11.642)

Household income sq. -1.690∗∗∗ -0.876∗∗∗ -0.593 -0.457 -0.778∗ -1.625∗∗∗

(0.503) (0.308) (1.225) (0.421) (0.426) (0.531)



Table 4: Quantile Results - Protests (not categorised)10 20 30 40 50 60 70 80 90

Social capital -0.000 -0.174∗ -0.344∗ -0.497∗∗ -0.531∗∗ -0.509∗∗ -0.468∗∗ -0.360∗ -0.203(0.125) (0.094) (0.135) (0.098) (0.100) (0.116) (0.166) (0.168) (0.309)

% adults graduated high school -0.000 -0.004 0.007 0.013 0.010 0.004 0.007 0.021 -0.041(0.015) (0.010) (0.012) (0.010) (0.013) (0.019) (0.024) (0.020) (0.038)

Gini 0.000 6.515∗∗ 7.224∗∗ 9.200∗∗ 10.640∗∗ 12.658∗∗ 14.461∗∗ 14.996∗∗ 16.720∗∗

(2.472) (2.036) (2.722) (2.022) (2.149) (2.709) (3.203) (2.702) (4.947)

Relative immobility 0.000 -0.440 -1.102 -0.893 -0.464 0.049 0.081 0.120 1.262(1.291) (0.975) (1.058) (1.003) (1.126) (1.236) (1.669) (1.576) (3.042)

Unemployment 0.000 0.059 0.051 0.067 0.081∗ 0.081 0.090 0.132 0.206(0.054) (0.049) (0.051) (0.049) (0.049) (0.074) (0.099) (0.116) (0.131)

Black-white segregation -0.000 1.253∗ 1.719∗∗ 1.183∗ 1.016∗ 1.335∗ 1.596∗ 2.372∗ 1.706(0.665) (0.512) (0.556) (0.471) (0.585) (0.616) (0.717) (1.021) (1.827)

Household income 0.000 18.983∗ 23.641∗ 24.016∗ 28.726∗ 40.014∗ 36.922∗ 25.999 44.902∗

(13.695) (11.051) (13.370) (10.676) (13.629) (15.674) (20.843) (22.871) (18.276)

Household income sq. -0.000 -0.799 -1.004 -1.006∗ -1.216∗ -1.722∗ -1.570 -1.076 -1.885∗

(0.627) (0.507) (0.615) (0.491) (0.624) (0.721) (0.956) (1.052) (0.819)

State FE Yes Yes Yes Yes Yes Yes Yes Yes YesPseudo-R2 0.041 0.198 0.214 0.211 0.219 0.231 0.230 0.201 0.170Obs 926 926 926 926 926 926 926 926 926

Coe�cients reported. Standard errors in parentheses.∗ p < .10, ∗ p < .05, ∗∗ p < .01

12

Table 5: Quantile Results - Demonstrations10 20 30 40 50 60 70 80 90

Social capital -0.359∗∗ -0.531∗∗ -0.622∗∗ -0.668∗∗ -0.718∗∗ -0.755∗∗ -0.752∗∗ -0.760∗∗ -0.693∗∗

(0.113) (0.078) (0.065) (0.067) (0.071) (0.072) (0.072) (0.069) (0.114)

% adults graduated high school 0.048∗∗ 0.046∗∗ 0.045∗∗ 0.049∗∗ 0.047∗∗ 0.051∗∗ 0.051∗∗ 0.044∗∗ 0.033∗

(0.017) (0.009) (0.009) (0.008) (0.008) (0.007) (0.008) (0.011) (0.013)

Gini 10.446∗∗ 12.186∗∗ 12.263∗∗ 13.154∗∗ 14.773∗∗ 15.720∗∗ 16.316∗∗ 16.624∗∗ 18.271∗∗

(2.280) (1.536) (1.378) (1.228) (1.369) (1.294) (1.401) (1.687) (1.898)

Relative immobility -0.135 -0.963 -0.605 -0.497 -0.649 -0.686 -0.230 0.179 -0.502(0.974) (0.679) (0.670) (0.544) (0.600) (0.660) (0.787) (0.869) (1.108)

Unemployment 0.085∗ 0.049 0.022 0.022 0.040 0.038 0.038 0.053 0.094∗

(0.039) (0.031) (0.028) (0.031) (0.035) (0.035) (0.036) (0.035) (0.053)

Black-white segregation 1.199∗ 1.488∗∗ 1.534∗∗ 1.789∗∗ 1.654∗∗ 1.838∗∗ 2.153∗∗ 2.481∗∗ 2.602∗∗

(0.466) (0.450) (0.396) (0.351) (0.313) (0.332) (0.383) (0.402) (0.605)

Household income 26.800∗ 22.288∗ 17.282∗ 10.523 15.900∗ 16.944∗∗ 16.548∗ 17.413∗ 23.156∗∗

(11.881) (9.572) (10.502) (7.227) (6.919) (6.513) (7.438) (7.262) (8.907)

Household income sq. -1.165∗ -0.917∗ -0.674 -0.357 -0.597∗ -0.642∗ -0.617∗ -0.649∗ -0.901∗

(0.548) (0.438) (0.483) (0.332) (0.318) (0.301) (0.346) (0.335) (0.409)



Table 6: Quantile Results - Hunger Strikes10 20 30 40 50 60 70 80 90

Social capital -0.187 -0.115 -0.242 -0.263 -0.364 -0.277 -0.707 -0.957 -0.320(0.178) (0.205) (0.274) (0.243) (0.296) (0.327) (0.546) (0.698) (0.490)

% adults graduated high school -0.017 -0.015 0.001 -0.002 -0.028 -0.008 -0.005 -0.039 -0.160∗∗

(0.026) (0.031) (0.028) (0.024) (0.051) (0.057) (0.057) (0.127) (0.048)

Gini 2.578 2.989 -1.112 -2.460 -0.459 -1.486 1.287 -1.835 -3.046(4.790) (4.503) (4.877) (5.003) (6.417) (7.768) (5.836) (6.977) (4.688)

Relative immobility -0.700 -0.395 0.171 0.140 -0.241 -0.437 -0.941 0.207 -5.494(2.792) (3.664) (3.207) (2.509) (3.285) (4.270) (3.237) (3.417) (3.898)

Unemployment -0.033 -0.004 0.097 0.116 0.028 0.117 -0.029 0.037 0.320(0.124) (0.102) (0.139) (0.120) (0.176) (0.199) (0.225) (0.439) (0.282)

Black-white segregation -1.057 -0.638 -0.299 -0.178 -0.720 -0.357 -4.531 -8.379∗ -6.791∗∗

(1.110) (1.170) (1.626) (1.484) (2.542) (2.670) (2.995) (3.621) (2.486)

Household income 28.682 15.945 -3.309 2.671 23.496 30.349 40.186 20.969 68.398(29.170) (28.598) (24.568) (24.008) (37.543) (37.667) (44.752) (126.322) (42.273)

Household income sq. -1.292 -0.711 0.207 -0.063 -0.985 -1.305 -1.745 -0.823 -2.999(1.344) (1.327) (1.114) (1.100) (1.688) (1.689) (2.018) (5.782) (1.935)



13

Table 7: Quantile Results - Strikes/Boycotts10 20 30 40 50 60 70 80 90

Social capital 0.000 0.000 -0.250∗ -0.398∗∗ -0.533∗∗ -0.563∗∗ -0.473∗∗ -0.507∗∗ -0.463∗

(0.057) (0.058) (0.114) (0.108) (0.095) (0.100) (0.111) (0.133) (0.213)

% adults graduated high school 0.000 -0.000 -0.008 0.007 0.002 0.000 0.003 0.000 -0.050∗

(0.008) (0.008) (0.014) (0.011) (0.011) (0.012) (0.012) (0.014) (0.028)

Gini -0.000 0.000 8.570∗∗ 9.468∗∗ 10.348∗∗ 13.757∗∗ 14.149∗∗ 15.989∗∗ 16.171∗∗

(1.183) (1.250) (1.720) (1.334) (1.696) (1.917) (2.078) (2.571) (3.881)

Relative immobility -0.000 -0.000 0.146 0.283 0.374 0.993 0.125 -0.286 2.050(0.617) (0.617) (0.716) (0.822) (0.750) (0.792) (0.870) (1.152) (1.976)

Unemployment 0.000 0.000 0.001 0.002 0.009 -0.004 0.052 0.008 0.067(0.027) (0.029) (0.046) (0.049) (0.043) (0.044) (0.056) (0.055) (0.103)

Black-white segregation 0.000 0.000 0.718 1.437∗∗ 0.918∗ 0.505 0.846 0.552 0.149(0.345) (0.343) (0.472) (0.469) (0.494) (0.552) (0.566) (0.627) (1.546)

Household income 0.000 0.000 6.481 -0.892 2.656 10.499 13.916∗ 10.939 36.623(7.240) (7.265) (12.776) (10.302) (10.557) (9.813) (7.479) (11.740) (22.552)

Household income sq. -0.000 -0.000 -0.216 0.136 -0.010 -0.357 -0.519 -0.380 -1.519(0.334) (0.335) (0.581) (0.471) (0.482) (0.447) (0.342) (0.543) (1.038)



Table 8: Quantile Results - Blockades10 20 30 40 50 60 70 80 90

Social capital -0.000 -0.000 -0.203∗ -0.256∗∗ -0.335∗∗ -0.396∗∗ -0.535∗∗ -0.640∗∗ -0.428∗∗

(0.062) (0.060) (0.087) (0.078) (0.085) (0.088) (0.152) (0.131) (0.138)

% adults graduated high school -0.000 -0.000 -0.004 0.001 -0.002 -0.007 0.010 0.002 -0.017(0.010) (0.010) (0.011) (0.011) (0.013) (0.015) (0.014) (0.017) (0.021)

Gini 0.000 0.000 4.369∗∗ 6.222∗∗ 8.996∗∗ 9.734∗∗ 9.758∗∗ 12.202∗∗ 13.109∗∗

(1.285) (1.267) (1.421) (1.547) (2.118) (1.939) (1.580) (3.336) (1.912)

Relative immobility -0.000 -0.000 -0.044 -0.138 -0.837 -1.329 -1.864 -1.756∗ 0.832(0.634) (0.618) (0.690) (0.820) (0.989) (1.082) (1.393) (1.036) (1.519)

Unemployment 0.000 0.000 0.016 0.022 0.068 0.057 0.033 0.007 0.171∗

(0.031) (0.029) (0.038) (0.041) (0.045) (0.048) (0.068) (0.073) (0.098)

Black-white segregation -0.000 -0.000 0.982∗ 1.557∗∗ 1.766∗∗ 1.579∗∗ 1.879∗ 1.350 1.080(0.359) (0.374) (0.411) (0.493) (0.551) (0.537) (0.821) (0.870) (1.092)

Household income 0.000 0.000 -6.698 6.777 17.295 21.322 13.934 8.074 -5.300(8.115) (8.208) (12.238) (10.225) (13.984) (14.809) (12.405) (31.634) (35.635)

Household income sq. -0.000 -0.000 0.364 -0.240 -0.698 -0.882 -0.541 -0.249 0.399(0.372) (0.377) (0.563) (0.468) (0.645) (0.681) (0.569) (1.472) (1.645)



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Table 9: Quantile Results - Violent Protests10 20 30 40 50 60 70 80 90

Social capital 0.000 0.000 -0.243∗∗ -0.433∗∗ -0.564∗∗ -0.643∗∗ -0.806∗∗ -0.850∗∗ -0.477∗

(0.077) (0.074) (0.088) (0.095) (0.113) (0.131) (0.170) (0.221) (0.237)

% adults graduated high school 0.000 0.000 0.008 0.005 0.003 -0.013 -0.024 -0.030 -0.040(0.010) (0.009) (0.011) (0.013) (0.012) (0.020) (0.026) (0.028) (0.042)

Gini -0.000 -0.000 7.809∗∗ 9.580∗∗ 9.338∗∗ 10.094∗∗ 12.251∗∗ 13.250∗∗ 10.247∗

(1.553) (1.530) (1.634) (1.568) (2.022) (2.146) (2.902) (2.411) (4.804)

Relative immobility 0.000 0.000 -0.939 -0.707 -0.501 -0.331 0.491 1.766 2.856(0.826) (0.795) (0.849) (0.943) (1.048) (1.264) (1.282) (1.707) (2.702)

Unemployment 0.000 0.000 0.043 0.054 0.060 0.048 0.087 0.025 0.089(0.040) (0.038) (0.045) (0.053) (0.059) (0.067) (0.084) (0.110) (0.116)

Black-white segregation 0.000 0.000 0.769 0.703 0.192 0.423 -0.703 -0.072 1.620(0.502) (0.491) (0.534) (0.590) (0.616) (0.839) (0.967) (1.191) (2.726)

Household income 0.000 -0.000 17.834∗ 24.755∗ 24.227∗ 32.405∗∗ 31.201∗ 41.019∗∗ 24.906(7.972) (7.340) (9.255) (12.085) (10.130) (10.412) (16.972) (15.701) (44.059)

Household income sq. -0.000 0.000 -0.762∗ -1.066∗ -1.028∗ -1.390∗∗ -1.307∗ -1.745∗ -0.964(0.367) (0.337) (0.426) (0.553) (0.464) (0.475) (0.770) (0.711) (2.021)



6 Appendix

In Tables A1 and A2, we report the variable de�nitions and statistics.

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Table A1: List of Variables and De�nitions

Variable Description Source

Protests (not cat-

egorised)

All civilian demonstrations and other collective

actions carried out as protests against the target

actor not otherwise speci�ed in categories below

GDELT Project, http://gdeltproject.org/

Demonstrations Dissent collectively, publicly show negative feel-

ings or opinions; rally, gather to protest a policy,

action, or actor(s)

http://gdeltproject.org/

Hunger Strikes Protest by refusing to eat until certain demands

are met


Strikes/Boycotts Protest by refusing to work or cooperate until

certain demands are met


Blockades Protest by blocking entry and/or exit into build-

ing or area


Violent Protests Protest forcefully, in a potentially destructive

manner


Social Capital standardised weighted sum of sub-indices (fam-

ily unity, community health, institutionl health,

collective e�cacy)

The Social Capital Project

Family Unity weighted index of the share of births that are to

unwed mothers, the percentage of children living

in families headed by a single parent, and the

percentage of women ages 35-44 who are married

(and not separated)


Community

Health

weighted index of non-religious non-pro�ts per

capita, religious congregations per capita, and

the informal civil society subindex (volunteers,

attended public gathering, assisted neighbours,

served on committees etc.)


Institutional

Health

weighted index of average (over 2012 and 2016)

of votes in the presidential election per citizen

age 18+, mail-back response rates for 2010 cen-

sus, con�dence in Institutions Sub-Index (con�-

dence in corporations, in the media, and in pub-

lic schools)


Collective E�-

cacy

Violent crimes per 100,000 The Social Capital Project

% adults gradu-

ated high school

Percent of adults that graduated from high

school

American Community Survey, 2012-2016

Gini Gini coe�cient American Community Survey, 2012-2016

Relative immobil-

ity

rank-rank slope, which gives the expected num-

ber of income percentiles in adulthood separat-

ing the richest and poorest children

(Chetty et al., 2014)

Unemployment Unemployment rate American Community Survey, 2012-2016

Black-white seg-

regation

Black-white segregation index measures the de-

gree to which the minority group is distributed

di�erently than whites aross census tracts

American Community Survey, 2012-2016

Household income Median household income American Community Survey, 2012-2016

16







Table A2: Descriptive Statistics

Obs Mean Std.Dev. Min. Max.

Protests (not categorised) 3142 1.38 17.28 0.00 760.00

Demonstrations 3142 14.41 133.90 0.00 5911.00

Hunger Strikes 3142 0.74 15.90 0.00 688.00

Strikes/Boycotts 3142 1.28 13.75 0.00 606.00

Blockades 3142 0.59 3.71 0.00 114.00

Violent Protests 3142 0.85 11.02 0.00 488.00

Social capital 2992 0.00 1.00 -4.32 2.97

Family Unity 3021 0.00 1.00 -4.93 2.66

Community Health 3139 0.00 1.00 -1.67 7.07

Institutional Health 3112 -0.00 1.00 -4.66 2.99

Collective E�cacy 3023 0.00 1.00 -8.42 1.22

% adults graduated high school 3142 85.81 6.54 48.50 98.70

Gini 3142 0.44 0.04 0.32 0.63

Relative immobility 2765 0.33 0.07 0.07 0.66

Unemployment 3142 4.03 1.69 0.00 18.80

Black-white segregation 3142 0.37 0.12 0.00 0.85

Household income 3140 47975.24 12598.79 18972.00 125672.00

Sources: GDELT Project, Social Capital Project, American Community Survey, (Chetty et al., 2014).

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