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A Brief Moment in the Sun: The Racialized(Re)Construction of Punishment in the AmericanSouth*
Soumyajit Mazumder†Harvard [email protected]
Date last updated: September 30, 2019
Why did policing and incarceration emerge as an institution in the South in light of its oth-erwise “hollow state?” I explore this question in context of the 19th and early 20th century U.S.South–a region which many point to as the crucible of punishment in America. I argue that out-side intervention by the federal government during Reconstruction helped lay the groundworkfor the carceral state. By empowering African Americans without fundamentally changing thesocial structure of Southern society, Reconstruction generated incentives for Southern elites toinvest in repressive state institutions like incarceration and the police to maintain the existingsocial order. I test the argument by assembling a new panel dataset from individual-level ad-ministrative records from Georgia in addition to data on black wealth, black office holding, andthe police force. A difference-in-differences identification strategy demonstrates support for theargument: counties with greater exposure to Reconstruction had higher rates of incarceration es-pecially against blacks after the end of Reconstruction. Additional results on black wealth, blackoffice-holding, and the police force provide suggestive evidence in line with the theory. This arti-cle verifies key arguments going back to W.E.B. Du Bois in addition to emphasizing the centralityof race and coercion in the evolution of American politics.
*I thank Matt Blackwell, Jacob Brown, Dan Carpenter, Chris Chaky, Stephen Chaudoin, Laurel Eckhouse,Ryan Enos, Alex Gourevitch, Allison Harris, Elizabeth Hinton, Jennifer Hochschild, Kaneesha Johnson, MarkoKlasnja, Jason Lyall, Chris Muller, Michael Olson, Jon Rogowski, Ariel White, and Steven White for incisivefeedback and discussions on the manuscript. I would like to thank the Institute for Quantitative Social Scienceat Harvard University for funding early stage data collection for the project. I would also like to thank seminaraudiences atHarvard and EmoryUniversity as well as theAPSA 2019Meeting andAPSA 2019Class and Inequal-ity Poster Session for excellent comments and discussions of the project. The author acknowledges support bythe James M. and Cathleen D. Stone Ph.D. Scholar Fellowship from the Multidisciplinary Program in Inequality& Social Policy at Harvard University. All errors are the author’s own.
†Ph.D. Candidate, Department of Government, Harvard University, web: http://smazumder.me
1
The slave went free; stood a brief moment in the sun;then moved back again toward slavery.
W.E.B. Du Bois
introduction
Maintaining social order is one of the most fundamental activities that states engage in. In
nearly all societies, states attempt to maintain social order coercively through the use of pun-
ishment (Foucault 1995). In the U.S. context and especially in the South, direct state involve-
ment in punishment remained limited up until the Civil War.1 For instance, data from Geor-
gia in Figure 1 shows that incarceration via state imprisonment rapidly grew following the
end of the Civil War and Reconstruction–a trend well-documented throughout the South by
historians of the time period (Ayers 1984; Mancini 1996; Lichtenstein 1996; Blackmon 2008).
When viewed in tandemwith theU.S.’s lack of substantive state institutions in the 19th century
(Skowronek 1982), this sudden growth in incarceration generates the following puzzle: why
did policing and incarceration emerge as an institution in the South in light of its otherwise
“hollow state?”
Starting from the fundamental premise of the centrality of punishment for understanding
the state, this paper goes back in time to the antebellum and post-bellum U.S. South–a region
that many point to as exemplifying many of the qualities of the modern carceral state such
as private corporate involvement, high racial disparities, and periods of rapid expansion–as a
window into understanding the origins of policing in the United States (Blackmon 2008). In
this respect, I refer to this early penal system as the proto-carceral state. A prominent set of
extant research on the topic emphasizes the importance of incarceration–particularly, convict
leasing–as a way to help modernize the South through the development of mining and rail-1Institutions such as chattel slavery in the ante-bellum South relied on vigorous private provision of pun-
ishment and coercion on the plantation. The state’s involvement in this process was largely around recapturingescaped slaves. This piece focuses on the transition of the provision of punishment on the plantation to thestate-led provision of punishment.
2
1820 1840 1860 1880 19000
30
60
90
1810 1820 1830 1840 1850 1860
0.0
2.5
5.0
7.5
10.0
Year
Tota
l Con
vict
Pop
ulat
ion
(Per
100
,000
)
Source: Georgia Convict Ledger and Biennial Report of the Penitentiary
Figure 1: Growth of Incarceration over Time in Georgia, 1817-1911
road infrastructure (Lichtenstein 1996). This set of explanations is insufficient on its own to
explain why African Americans were disproportionately incarcerated and why some counties
had high levels of incarceration while others did not since there were relatively few convict
camps. Moreover, the rise of the carceral state cannot alone be explained as a simple repro-
duction of slavery (Blackmon 2008): incarceration itself was nowhere near the same scale as
chattel slavery. Even more, the institutionalization of policing is even more puzzling within
the context of the South given its long-reliance on an “honor culture” to enforce social order
(Nisbett and Cohen 1996). These features then suggest existing explanations for the rise of
the proto-carceral system are incomplete.
I build on work across history and the social sciences to develop a political framework for
3
understanding variation in the rise of the proto-carceral state (Du Bois 1901b, 2014; Weaver
2007; Acemoglu and Robinson 2008). This framework emphasizes how sudden resistance
by and empowerment of marginalized groups motivate the development of a repressive state
apparatus by elites. The historical turn in this piece necessitates a re-characterization of incar-
ceration and policing during this time period. Drawing on work within the political violence
literature in comparative politics, I conceptualize incarceration during this time period as a
form of state repression meant to suppress the political, economic, and social freedoms of
a targeted population (Davenport 2007).2 By reconceptualizing incarceration as a form of
political violence, it becomes clear why elites would make costly investments in this system:
repression via incarceration is meant to inspire fear among marginalized groups and demo-
bilize them from challenging the existing status quo that puts them into coercive economic
and social relationships with elites (Lerman and Weaver 2010, 2014; Young 2019; White 2019).
I argue that the temporary and incomplete empowerment of a marginalized group gener-
ates incentives for the elite to engage in repression so as tomaintain the existing socio-political
hierarchy. By incomplete empowerment, I mean the material empowerment of a group with-
out the fundamental alteration of the fundamental social structures and hierarchies in a so-
ciety. Applying the framework to the U.S. South during and after the Reconstruction period,
I argue that the incomplete empowerment of African Americans by the federal government
created incentives for white elites to use incarceration as a means to exert social control over
this threatening population. This frameworkmakes two key predictions: (1) areaswith greater
exposure to Reconstruction should, after its end, be more likely to incarcerate their popula-
tions, and (2) that the impact of Reconstruction should be increasing in the proportion of
the population that benefited from it (African Americans). By not fundamentally leveling2Davenport (2007) uses a definition of state repression from Goldstein (2001), who focused on the case of
the United States. Wright (1973) also makes this argument in discussing the role of the prison in Americansociety. The concepts and study of political violence and state repression no longer appear the “mainstream”American politics canon. I return to this point in the conclusion.
4
racialized land-labor power structures in the South, Reconstruction generated incentives for
white elites to set up a highly repressive state apparatus to maintain their dominant status in
the social order.3 Though not a reproduction of slavery itself, I argue that the proto-carceral
state served to maintain highly racialized and coercive relationships between white planter
elites and African Americans.4
To test these predictions, I assemble a new panel dataset from individual-level hand-
written records from the Georgia Convict Ledger from 1817-1868, individual-level data from
administrative records in the Georgia Biennial Report from 1880-1882, and county aggregates
from 1897-1911. In total, this data spans 137 counties, 94 years, and over 15,000 prisoners.
Using a difference-in-differences (DID) design leveraging the sharp removal of troops from
Georgia once the state was re-admitted to the Union in 1870, I find evidence of a strong link
between backlash against Reconstruction and the rise of the proto-carceral state. Moreover,
the impact of Reconstruction is concentrated in areas with high shares of enslaved popula-
tions in 1860. Using data on prisoner race, I show that these results are concentrated among
individuals of color rather than whites, ruling out an alternative explanation rooted in general
rising crime trends across both groups. In addition, the results remain robust to relaxing the
identification assumptions through inclusion of county-specific linear and quadratic trends
in addition to being insensitive to any one outlier county.
To test the hypothesizedmechanism–thatReconstruction incompletely empoweredAfrican
Americans thereby engendering incentives for elites to invest in carceral capacity, I collect
data originally assembled by Du Bois (1901a) on black property holding in Georgia in addi-
tion to data on black office-holding recorded by Foner (1996).5 Further results suggest that3Thus, an important scope condition of the argument is the nature of empowerment. Where the empow-
erment does not fundamentally alter social structure, the theory predicts an investment in repressive state in-stitutions by elites. In situations where social structures do become level, the theory does not have explanatorypower.
4It is in this respect, how incarceration became “slavery by another name” (Blackmon 2008).5By carceral capacity, I mean inputs into policing such as the number of police, establishment of prisons or
labor camps, and equipment (Gilmore 2007).
5
Reconstruction was effective in increasing African Americans’ economic empowerment as
proxied by property wealth. This increase in black wealth is inconsistent with the alternative
explanation that blacks were engaging in more crime given the vast research showing that
improved material status reduces crime (Becker 1968). In line with the argument, I also show
that Reconstruction did not fundamentally alter the land-labor social structure in Georgia:
areas with greater exposure did not see a substantial increase in black landholding. This lack
of a fundamental change in social structure and hierarchy translated into modest gains at
the office as proxied by black office-holding (Foner 1996, 2011). I also show using data from
the U.S. Federal Census that greater exposure to Reconstruction also led to a rise in the size
of the police force. These additional results are consistent with the argument of this paper
that Reconstruction helped to empower African Americans, which generated incentives for
white elites to invest in coercive state infrastructure. Overall, the results paint a clear picture:
state-led punishment emerged as a mechanism to control a population that could threaten
the power of white elites (Du Bois 2014).
This study makes several contributions to literatures involving incarceration, race, and
American political development. First, this piece speaks directly to scholars interested in
the development of the carceral state. While much of this literature focuses on explaining
the growth of incarceration (Gottschalk 2006; Weaver 2007; Miller 2008; Murakawa 2014;
Gottschalk 2016; Hinton 2017), this piece departs from this literature by studyingwhy policing
and incarceration emerged as institutions to begin with. Building on recent work by Muller
(2018) on Georgia’s convict lease system, I emphasize the way in which economic structures
are ensconced in broader political transformations that may generate incentives for elites to
use the state’s coercive power to hold onto racial hierarchies. This analysis shows striking
similarities with the frontlash thesis offered by Weaver (2007) on the modern carceral state:
the Southern proto-carceral state operated in a way that was designed to subjugate a status-
threatening population. This study builds on and contributes to existing studies of incarcer-
6
ation in the United States by highlighting the tragedy of external empowerment–one of many
factors that drive changes in punishment throughout American history (Smith 2004; Lerman
and Weaver 2010; Jacobs and Jackson 2010; Walker 2014; Alexander 2012; Fortner 2013; Eck-
house 2016; Enns 2016; Pfaff 2017; Forman, Jr. 2017; Beim, Clark, and Lauderdale 2019; Harris
2019; Harvey and Yntiso 2019).
Additionally, this study pushes forward the literature on race and ethnic politics in the
United States. While much of the literature on racial politics emphasizes the role of public
opinion (Gilens 1996; Kinder and Sanders 1996; Valentino and Sears 2005; Tesler and Sears
2010), geography (Key 1949; Enos 2017; Cook, Logan, and Parman, Forthcoming; Reny and
Newman 2018), or electoral politics in shaping black-white relations (Valentino, Hutchings,
and White 2002; Frymer 2010), this study joins and extends the literatures looking at the
independent effect of the state in crystallizing black-white political, social, and economic in-
equality (Frymer 2005; Katznelson 2005; King and Smith 2005; Francis 2014; White, Nathan,
and Faller 2015; Soss and Weaver 2017; Michener 2018; White 2019). By taking the analytical
lens back in time, this study shows and reinforces the idea that the state was used and con-
tinues to be used to stratify societies by race and class (Soss and Weaver 2017). Moreover,
the framework and results presented in this article have serious implications for contempo-
rary debates around racial conflict. Outside efforts to empower marginalized populations
can backfire when these marginalized groups are not able to institutionalize their gains. As
a whole, the upshot of this piece is that studies of racial politics are incomplete insomuch as
the state (whether federal, state, or local) is absent in these accounts.
A renewed stream of American political development work seeks to understand the puz-
zles of democratization in the South and its role in the larger national polity (Mickey 2015;
Acharya, Blackwell, and Sen 2018a; Caughey 2018; Bateman, Katznelson, and Lapinski 2018).
For instance, work by Gibson (2013) and Mickey (2015) shows that the South can generally
be characterized as an “authoritarian enclave” where subnational politics essentially operate
7
as a single-party autocracy. This study enriches this observation by focusing on a particu-
lar dimension of state power–punishment–in subjugating subaltern populations. Viewed in
tandem with studies on Southern paternalism (Alston and Ferrie 1993), white supremacist
institutions were either implicitly or explicitly private-public partnerships. Additionally, this
helps push forward the recent historiographical and empirical literature on the successes and
failures of Reconstruction (Foner 2011). While this study and others show that Reconstruction
did indeed empower African Americans economically (Logan 2018; Rogowski 2018; Chacon
and Jensen 2018; Suryanarayan and White 2019), it also shows that this empowerment came
at the cost of black lives (Stewart and Kitchens 2018). Moreover this study lies in contrast
to Bensel (1990) who argues that Reconstruction represents a failed opportunity at state-
building; instead, the argument and results presented here suggest that Reconstruction ac-
tually helped pave the way for coercive state-building precisely because it was an “unfinished
revolution (Foner 2011).
Finally, this study also contributes to deepening our understanding of violence and re-
pression in America. While many existing studies place primary focus on the use of lynching
as an informal method of whites’ subordination of the African American population (Beck
andTolnay 1990; Francis 2014; Smangs 2016;Weaver 2019), the scale of the proto-carceral state
dwarfed the scale of lynching in the period right after Reconstruction. Data from Georgia,
for instance, puts the number of black lynching victims at approximately 30 while there were
more than 800 black prisoners on hand during this same time period. A contribution of this
study, then, is to highlight and better understand the logic of the first substantive investment
in coercive capacity through incarceration. More broadly, the sheer magnitude of the state-
driven coercion relative to informal systems of subordination demands much further study
by the discipline (Soss and Weaver 2017).6
6Obviously, both incarceration and lynching during this time period are two forms of violence productionthat deserve further study given their enormous importance to the process of American political development(MingFrancis2014).
8
framework: racialized coercion and state power
A large branch of existingwork generally relates the use of formalized punishment as a tool for
white elites to control the subaltern black population (Du Bois 1901b; Myers 1990; Mancini
1996; Du Bois 2014).7 In this section, I distill this literature into several analytical building
blocks to develop a framework for understanding race, punishment, and state power. To
summarize, I argue that the subaltern population’s outside options and salience threaten the
in-group’s positions of economic, political, and cultural status. In response to these features,
elites in societies with sufficient capacity to engage in formalized violence through punish-
ment will do so. This theory essentially places the state as a technology of racial hierarchy
production.
Before I outline the variables emphasized in this article, it is important to address the fol-
lowing question: what does punishment do? Ostensibly, punishment is a means to address
some sort of transgression against a written set of laws that all citizens of a society must abide
by. Yet, two factors transform punishment beyond a technocratic policy designed to correct
or deter certain proscribed behaviors: its uneven application and the way in which punish-
ment is ensconced in social systems. As Holland (2016) shows, the uneven application and
enforcement of laws generates distributive consequences, which can go on to empower some
groups and take away power from others. This feature of punishment is one way in which
formal punishment becomes politicized. Additionally, punishment itself is embedded in so-
cial rules that proscribe some behaviors as “deviant” and others as not. This feature allows
punishment to become not only a way to create order, but also to exert social control (Black
1983). Thus, punishment is a way in which the state exercises coercive power in ways that can
be and often are politicized along group boundaries.
The state’s ability to use repression, often times through incarcerating members of “de-7Throughout the rest of the text, I refer to African Americans as the subaltern population.
9
viant” populations, is a key tactic towardmaintaining power. A large literature in comparative
politics highlights how the police are used in authoritarian societies as a means to exert con-
trol over their populations (Davenport 2007; Greitens 2016). Of course, authoritarian elites
do not jail the entire status threatening subaltern populations. Instead, they opt to some small
fraction of the group as a means to generate a sense of fear among the entire group (Young
2019).
What are the incentives for elites to engage in this sort of repression? I argue that one
such incentive that would drive elites to use formal punishment to control subaltern popula-
tions is the role of outside options–a feature noted in political economy theories of coercion
and exploitation (Chwe 1990; Wright 2000; Acemoglu and Wolitzky 2011). Minority power
is most threatening in situations where that group has the highest outside options. The logic
is that elites can hold power and extract more from groups when they are able to reduce the
value of all the alternative relationships that a minority group might enter into. For instance,
when an individual can credibly exit a relationship with a potentially coercive elite by moving
away or entering into some other occupation or social relationship, then the elite is able to
extract less from that individual. This suggests that in places where the subaltern group be-
comesmaterially empowered (i.e, his/her outside options become higher in value), then elites
should endogenously use the tools they have available in order to reduce the subaltern group’s
outside options. In contexts where there is an incomplete transformation in the fundamental
power structure between elites and the subaltern, the empowerment of the subaltern, even in
amaterial and “real” sense, can lead the elite to dis-empower that group via state coercion and
violence (Acemoglu and Robinson 2008). Formalized punishment and incarceration, then,
provide tools through which white elites can use the state to “lock-up” individuals who pose
a threat to their status and power. This logic, which I formalize in the Online Appendix, can
help to explain variation in the intensity of incarceration.
In this article, I focus on how Reconstruction helped to empower African Americans
10
thereby engendering incentives for elites to use repression. A primary goal of Reconstruction
was to fundamentally alter the race-class relations of the South by empoweringAfricanAmer-
icans to gain land and property (Foner 2007, 2011; Du Bois 2014). With this goal in mind, the
militaries used a whole host of coercive methods to push for this transition in the Southern
social order: “Military oversight of the law changed the dynamics of Southern life; magis-
trates who once held vast powers to regulate their neighborhoods (and whose decisions were
rarely tested by appeal to higher courts) suddenly could be called to answer to nearbymilitary
authorities who could overrule and even depose them” (Downs 2015, p. 30). In addition to
taking control of local institutions, the military played a large role in overseeing elections in
the face of electoral violence. Where the Freedmen’s Bureau could not perform its functions
as a result of staffing issues, the military stepped in to fill these gaps (Downs 2015). While it is
well established that Reconstruction itself did not fundamentally transform Southern society
as many of its more radical proponents wished (Vallely 2004; Foner 2011; Du Bois 2014), re-
cent research on the short to medium-term impact of Reconstruction provides evidence that
it did at least increase black literacy (Stewart and Kitchens 2018), voter turnout (Rogowski
2018), and fiscal capacity (Chacon and Jensen 2018; Suryanarayan and White 2019). Blacks’
material empowerment as a result of the enforcement of Reconstruction suggests that white
elites should have an incentive to use the state to re-exert social control over this population
after the end of its enforcement.
Without a doubt, the end of slavery and the newfound sense of freedom by former slaves
as a result of Reconstruction brought a real sense of empowerment to them. Foner (2011, p.
79) writes,
Blacks relished opportunities to flaunt their liberation from the innumerable reg-
ulations, significant and trivial, associated with slavery. Freedmen held mass
meetings and religious services unrestrained bywhite surveillance, acquired dogs,
11
guns, and liquor (all barred to them under slavery), and refused to yield the side-
walks to whites. They dressed as they pleased, black women sometimes wearing
gaudy finery, carrying parasols, and replacing the slave kerchief with colorful hats
and veils. In the summer of 1865, Charleston saw freedmen occupying ‘some of
the best residences,’ and promenading on King Street ‘arrayed in silks and satins
of all the colors of the rainbow,’ while black schoolchildren sang “John Brown’s
Body’ within ear-shot of Calhoun’s tomb.’ Rural whites complained of ‘insolence’
and ‘insubordination’ among the freedmen, by which they meant any departure
from the deference and obedience expected under slavery. On the Bradford plan-
tation in Florida, one untoward incident followed another. First, the family cook
told Mrs. Bradford ‘if she want any dinner she kin cook it herself.’ Then the for-
mer slaves went off to a meeting with Northern soldiers to discuss ‘our freedom.’
Such displays of power from the ballotbox to day-to-day life threatened the status that
whites had at the time. Most of the former slaves tended to stay near the plantations where
they were previously enslaved: “In fact, a majority of freedmen did not abandon their home
plantations in 1865, and those who did generally traveled only a few miles” (Foner 2011, p.
81). The presence of such a newly empowered group, then, generated a sense of resentment
by whites. This suggests that the impact of Reconstruction should be increasing the salience
of the newly freed black population. Additionally, the white elite cannot costlessly engage
in coercion against all groups. At a psychological level, engaging in coercion against other
individuals who share the same ethnic/racial identity group as the elite might be cognitively
costly. This becomes less costly as the social distance between the elite’s ethnic identity group
and the subaltern’s ethnic identity group increases (Tajfel and Turner 1986; Enos 2017). From
a strategic perspective, white elites also needed to maintain cross-class cohesion in its project
of white supremacy (Du Bois 2014; Merritt 2017). By punishing and violently coercing one’s
12
own group, this could threaten intra-group cohesion–a point that Du Bois notes throughout
his writings (Du Bois 1901b, 2014). These features of class-race stratified societies can help to
explain why punishment would become racialized in this way. As a result, we should expect
the impact of external empowerment (Reconstruction) on incarceration to be increasing in
the share of the subaltern (formerly enslaved) population.
The theory, then, makes two key testable predictions:
1. H1: Punishment should be increasing in the intensity of Reconstruction.
2. H2: The impact of Reconstruction should be increasing in the relative size of the en-
slaved population.
The following section provides a brief overview of the case used in this study. Then, I
describe the data used to test the argument and present empirical tests of themain hypotheses,
assumptions, and causal mechanisms.
background: formal punishment in a violent society
Many refer to the 19th century U.S. South as a violent society (Ayers 1984; Nisbett and Cohen
1996; Roth 2009). Slavery, for instance, was an institution predicated on the threat and use of
violence against African Americans in order to extract labor from them. Beyond this type of
violence, the South also has a particular relationship with informal and formal punishment.
During the antebellum period, the Southern penal institutions essentially had two different
tracks–one for slaves and one for whites. For slaves, transgressions were nearly exclusively
dealt with on the plantation since slaves were viewed as expensive property. For whites, there
was a system of police, courts, and juries to deal with white criminality–a phenomenon driven
by the way in which slavery generated a sense of destitution for poor, landless whites (Merritt
2017).
13
Following the Civil War and emancipation, punishment on the plantation turned into a
vast formalized system known as convict lease. Noting this shift, Du Bois (2014, p. 415) writes,
“This penitentiary system began to characterize the whole South. In Georgia, at the outbreak
of the Civil War, there were about 200 white felons confined at Milledgeville. There were no
Negro convicts, since under the discipline of slavery, Negroes were punished on the planta-
tion. The white convicts were released to fight in the Confederate armies. The whole criminal
system came to be used as amethod of keeping Negroes at work and intimidating them. Con-
sequently there began to be a demand for jails and penitentiaries beyond the natural demand
due to the rise of crime.” This new system, which allowed private companies and individuals
to hire out incarcerated individuals, represented a reproduction of slavery in nature (forced
labor), but also a departure from it in its scale and industrial nature. With the imposition of
this new system following the end of Reconstruction, the incarcerated population rose over
ten fold to over 3,000 prisoners–the vast majority of whom were African American.8 The
first recorded lessee was leased on May 11th, 1869, but by April 1st, 1879, all of the prisoners
in Georgia were leased out to private corporations (Taylor 1942b). In this sense, one might
think of the convict lease system as one of America’s earliest examples of a private-public
partnership.
Much of this historiography around the rise of the proto-carceral state in the South likens
it to a form of neo-slavery (Oshinsky 1996; Blackmon 2008). Though obviously incarceration
was not on the same scale as slavery, its nature resembled many of the features of slavery. For
instance, the convict lease system forced incarcerated individuals–who were mainly African
American–to work long hours, often under brutal conditions. Taylor (1942b) recounts a se-
vere, but not irregular instance of punishment:
According to the testimony, “Directly the Captain called the negro out and8Statistics based on author’s tabulations. I provide more details and descriptive statistics on the data later in
the article.
14
whipped him. He whipped him a while and put him back on the barrel andmade
him work for a few minutes and then took him off of the barrel and called two
negroes and made them turn the negro across a barrel and hold him down while
he whipped him again ; and after he turned the negro loose, the negro staggered
off to one side and fell across a lumber pile there, and laid there for a while.” He
died shortly afterwards. An inquest was held, with the coroner’s jury consisting
of the guards of the camp. Witnesses testified that the Negro man had become
overheated and had drunk a great deal of water while in that condition. The camp
physician stated that in his opinion the death was caused by Negro’s drinking too
much water; and that was the verdict reached by the jury.
This form of coercion shared a marked continuity with the forms of violence on the ante-
bellum plantation.
As many historians note, much of the convict labor pool was used for projects aimed
at industrializing the South such as building railways and road infrastructure (Zimmerman
1951; Lichtenstein 1996; Worger 2004). The lease system presented a perfect constellation of
political interests where Southern industrialists and racists were able to find common ground
over this policy. Despite many known reports of the brutality of the system, politicians held
onto this system as a journalist documents, “The state treasury has just lost a quarter of a
million dollars through prohibition, and the complete abolition of the lease system would
take as much or more annually from the state treasury. Some of the legislators are coming
to the conclusion that while morality is a very good thing, it don’t go very well with a low
tax rate” (Taylor 1942a). Both the ways in which incarceration allowed for the re-subjugation
of a newly freed population and to augment Southern state budgets helped maintain the the
survival of this system even in the face of harsh criticism by a variety of groups. On top of all
of this violence done against African Americans, the cash coming in through the lease system
15
helped to finance the white public education system (Taylor 1942b). Violence done against
black bodies helped to nurture white ones. At its core, the consolidation of the convict lease
system eventually turned Georgia into a state of prisoners without any prisons.9
That the rise of this system had little to do with overall crime trends seems to be an agreed
upon point by the historical record. Governor James M. Smith of Georgia in 1872 discusses
this issue directly in referring to the rise of incarceration in Georgia: “...this marked increase
in the number of convicts is not due to any augmentation of crime in the South, but is believed
to be the result entirely of a more rigid and proper enforcement of the laws” (quoted in Ayers
(1984)). Thus, crime alone seems to be insufficient to understanding the rise of the proto-
carceral state.10
As the system expanded, it began to face opposition from an alliance of religious groups
and white labor within the context of reports of corruption in the system. In Georgia, the
suffragist Rebecca Latimer Felton lobbied politicians quite fiercely and coordinated religious
groups to demonstrate their opposition to the system largely because this system seemed too
harsh and dehumanizing (Zimmerman 1951). Ironically, Rebecca Latimer Felton also owned
slaves before the war and was noted for being an ardent supporter of lynching. In tandem
with this moral crusade, labor unions opposed the system since they viewed the largely black
convict labor system as competing with white labor. Eventually, Georgia formally abolished
the practice of convict lease in 1908 with the last lease expiring on April 1st, 1909.
The theory developed in this paper argues that at least part of the rise of this system can be
traced back to the way inwhich Reconstruction incompletely empoweredAfricanAmericans.
The following sections outline the data and empirical strategy used to test this argument.9LeFlouria (2015) documents how the proto-carceral state even broke deep norms around gender roles in
the way in which the system forced women to take on traditionally “masculine” economic roles.10It could be the case that such a statement is insincere. Further results on themechanisms, however, provide
evidence inconsistent with this crime explanation.
16
data and descriptive statistics: evidence from georgia
To test the argument, I build a new dataset combining individual-level and county-level ad-
ministrative records on over 15,000 prisoners over the time period 1817-1911 from Georgia. I
then go onto describe the data and show basic descriptive trends and statistics. Figures A1-5
and A1-6 in the Online Appendix show that Georgia represents a typical case in the South
thus allaying worries about external validity.11
Data
The core research design leverages over-time change within counties to assess the empirical
validity of the aforementioned hypotheses. I assemble pre-period data (1817-1865) from the
Georgia Register of Convicts, which records fairly granular information on the identity of the
incarcerated individual. These include the prisoner’s age, name, county of sentencing, crime,
skin color, eye color, and height. From the skin color, I deduce the race of the prisoner de-
pending onwhether theywere described as being “fair” or “sallow” versus some other descrip-
tion. While skin color does not itself equate with ethnicity, it is the best available information
to ascertain the prisoner’s race. For the post-period, I use the first available Biennial Report
of the Principal Keeper of the Georgia Penitentiary, which covers prisoners entering through
the Georgia State Penitentiary System between October, 1880 to October, 1882.12 The Bien-
nial Report records and classifies prisoners based on their race such as “white”, “negro”, and
“mulatto.” In addition, I collect data on individuals also discharged and pardoned during this
period from the same report so as to capture all individuals who had some contact with the
Georgia Penitentiary System between 1880-1882. 13 Finally, I collect additional post-period
data from the Annual Report of the Principal Keeper of the Georgia Penitentiary for the years11See also Aronow and Samii (2016) on the ways in which regression with larger samples do not recovermore
“representative” causal effects than quasi-experimental methods.12Muller (2018) uses data from this report in a recently published article.13I use the term “colored” when presenting and analyzing the data since that is the term used at the time.
17
1897, 1902, 1905, 1907, and 1911. This data only contains aggregate counts of total convicts for
each county and does not contain data on counts by race.
A key concern regarding bias in these records is the potential for systematic undercount-
ing by race and county. In the pre-period, the key concern is that this data does not pick
up individuals incarcerated for petty crimes. Given police and administrative discretion over
this issue, one would expect potential systematic undercounting of African Americans if they
were disproportionately targeted by the whims of the police bureaucracy. Thus, the results
would under-estimate the “true” effect. In the post-period, a key concern is that officials
might not record everyone. This does not seem to be an issue given the convict lease incen-
tive system. Officials needed to record and keep track of all prisoners in order to properly
account and lease out prisoners. Additionally, the research design uses within-county varia-
tion thereby addressing some of these differential measurement error issues that are constant
within county. Therefore, these potential biases do not seem to be a prodigious issue for the
empirical analysis.
Descriptive Statistics
Having assembled this data, one can instantly see the explosion in incarceration in Georgia.
Figure 2 plots the total incarcerated population over time separately by race. Prior to emanci-
pation, two features are clear. First, the overall scale of the proto-carceral state is quite small
in magnitude. Second, whites seem to be the main incarcerated population–a fact consistent
with qualitative accounts (Merritt 2017). Following the Civil War and emancipation, though,
it is clear that incarceration rises dramatically in Georgia. This is a pattern consistent with the
rest of the South as well (Ayers 1984). What is also clear from the post-period is that African
Americans become an increasingly large fraction of the incarcerated population–a fact that
would be prima facie evidence consistent with the historical record and theory.
Was the rise in African American incarceration following the war racially “biased?” Fig-
18
1820 1840 1860 18800
1000
2000
3000
1820 1840 1860
0
50
100
150
200
250
Year
Tota
l Con
vict
Pop
ulat
ion
Race All Colored
Source: Georgia Convict Ledger
Figure 2: Total Incarcerated Population by Race, 1817-1882
Note: Bottom panel zooms in on period right before Emancipation.
ure 3 plots the number of whites and African Americans incarcerated scaled by each group’s
respective populations in 1880. Relative to their populations, people of color were more than
10 times likely to be incarcerated relative to whites. This, again, is consistent with historian’s
characterization of the Southern penal system across the region (Ayers 1984).
research design: continuous difference-in-differences
Unit of Analysis: County
For the analysis, I use the county as the main unit of analysis. The county is the appropriate
unit during this time for several reasons. First, law enforcement and courts in Georgia were
19
0
50
100
150
200
colored whiteRace
Coun
t of P
rison
ers
per 1
00,0
00 P
opul
atio
n (w
ithin
race
)
Blacks more than 10 times likely to be incarcerated relative to whites.
Massive Racial Bias in Post-Bellum Incarceration Rates in Georgia...
Source: Georgia Biennial Prison Reports (1880-1882) and 1880 Decennial Census.
Figure 3:
organized along county boundaries. Essentially, decisions about who to incarcerate were
made from county-to-county. Second, counties are generally the most comparable unit in
Georgia because it is the lowest level of aggregation for which the ledgers report the sentenc-
ing location. Thus, I proceed to use the county as the main unit of analysis.
Dependent Variables
The main dependent variable that I use for the empirical analysis is the total number of con-
victs in a given county in a given year scaled by the total population in 1860. I scale by the total
population in order to make sure that the dependent variable is comparable across counties,
but use 1860 so as to avoid any issues of post-treatment conditioning (Acharya, Blackwell, and
20
Sen 2016a; Montgomery, Nyhan, and Torres 2018). I use the total convicts for the main analy-
sis instead of the natural logarithm since the parallel trends seems to best hold visually in the
total number (see Figure 4). To ensure that my results are robust to alternative transforma-
tions, I also show that the results hold when using the natural logarithm transformation. To
establish the racialization of punishment in addition to discounting alternative explanations
about crime waves, I also compute alternative dependent variables of the scaled white and
black convict populations.
Independent Variables
Data for the main independent variable in this study–Reconstruction intensity–comes from
Downs and Nesbit (2015)’s Mapping Occupation Project to compute the average number of
troops stationed in a given county in Georgia during Reconstruction. I then scale this by the
1860 population and take the natural logarithm. This variable captures the degree to which
the federal government exerted effort at empowering the newly freed population as per Hy-
pothesis 2.
In addition, I consider a number of other alternative explanations. First, I consider the
potential for the sudden enfranchisement of African Americans in the South to confound the
impact of Reconstruction. To measure this, I compute the county-level proportion enslaved
from the 1860 Federal Census county-level aggregates (Haines). Second, I also consider the
role of the destruction caused by the CivilWar. It could be the case that areas were targeted for
Reconstruction in order to rebuild areas that suffered significant infrastructural damage. To
measure this, I create an indicator for whether a county in Georgia intersected with Sherman’s
March to the Sea where General William Tecumseh Sherman razed counties that his troops
crossed on their way from Atlanta to Savannah, Georgia in 1865.14
14I thank James Feigenbaum for sharing data on the location of Sherman’sMarch. For more on the economicdestruction and recovery in response to Sherman’s March, see Feigenbaum, Lee, and Mezzanotti.
21
Measuring the Mechanism: Black Wealth
Thekeymechanismhighlighted in this paper is the role of blacks’ outside options. Tomeasure
this, I rely on data compiled byW.E.B. Du Bois on blackwealth inGeorgia from 1875-1900 and
presented in the Bureau of Labor’s Bulletin (Du Bois 1901a). This data records the total value
of all property held by African Americans in Georgia consistently in 5-year periods from 1875
to 1900. Despite its incredible richness, this data has yet to be used by political scientists (to
the best of the author’s knowledge). For the analysis, I compute the natural logarithm of the
total wealth of African Americans in a given county in each period.
Measuring the Mechanism: Black Officeholding
To measure the degree to which African Americans were politically empowered, I collect
data on black officeholders during and after the Reconstruction period from Foner (1996). I
addition to measuring the descriptive representation of African Americans during this time
period, black officeholders alsomattered substantively in terms of financing public goods pro-
vision (Logan 2018). This data records the county associated with the officeholder. For the
analysis, I create a binary variable for whether the county had any black officeholders.15
Measuring the Mechanism: Police Force Size
Finally, an implication of the theory is that there should be more investments in carceral ca-
pacity in response to African American’s empowerment during Reconstruction. To measure
this, I collect data from the 1850 and 1880 full count U.S. Decennial Census and digitized
by the Minnesota Population Center (Ruggles et al. 2019). From this, I create a count of the
number of individuals employed as police per 10,000 population in 1850 as a measure of state
carceral capacity.16
15The vast majority of counties only had one black elected official if they had any.16I also show that the results are robust to using the log and absolute level measures.
22
Identification Strategy
A simple cross-sectional comparison between counties with high exposure to Reconstruction
versus low exposure to Reconstruction could be driven any number of pre-existing differences
either observable or unobservable to the researcher. It could be the case, for instance, that
certain counties just have a punitive culture and that this is correlatedwith both the treatments
as well as the amount of incarceration. To eliminate these threats to inference, I rely on a
difference-in-differences (DID) framework to recover the causal effect of the key variables of
interest. This relies on comparing changes in the treatment intensity within unit over time.
While many have pointed out deficiencies of this approach when the treatment time period
is staggered (Goodman-Bacon 2018) or when units can select into timing of treatment (Imai
and Kim 2019), the setup in this paper links most closely to the classical DID framework in
which the units are all forced into treatment at the same time following the end of the Civil
War/abolition (1865) and the end of federal occupation in Georgia (1871). Thus, all units are
forced into treatment with the intensity of the treatment based on intensity of the enforcement
of Reconstruction. These types of continuous DID strategies are common in the economics
literature (Duflo 2001; Bailey 2006; Bleakley 2007).
Within this framework, I estimate equations of the following form:
Yc,t = β1Post-1871t ∗ Log(Avg. Troops)c,1870 + πXc,t + ΩCc + ΓTt + ϵc,t (1)
The main outcome of interest is represented by Yc,t in a given county c in time t. To mea-
sure this, I use the number of incarcerated individuals from a county c in time t divided by the
pre-treatment population in 1860 per 100,000. The results are also robust to a log specification
to limit the influence of outliers. The main causal parameter of interest is β, which represents
the impact of each treatment variable on Yc,t. The terms ΩCc and ΓTt represent county and
23
time fixed effects to eliminate unit-level time invariant confounders and to flexibly control for
global time trends. Additionally, the term πXc,t represents time-varying confounders such
as the impact of slavery post-emancipation and Sherman’s March post-Civil War. The term ϵ
is a stochastic error term. Finally, I estimate Equation 1 using Ordinary Least Squares (OLS)
and cluster standard errors at the county.
0
5
10
15
20
1820 1830 1840 1850 1860 1870
Year
Mea
n Co
nvic
ts p
er 1
00,0
00
Reconstruction Any Reconstruction No Reconstruction
Source: Prison—Inmate Administration—Central Register of Convicts, 1817–1976. Series 21/3/27. Georgia State Archives, Morrow, Georgia. Gregory P. Downs, Mapping Occupation Troop Locations Dataset, 2015
Figure 4: Pre-Trends: Reconstruction
A key implication of the identification assumption is that the treated and untreated coun-
ties should be evolving on parallel trends absent the actual treatment. To visually interrogate
this assumption, I create a dichotomous treatment variable for whether a county had any fed-
eral troops. I then plot the average of the incarceration rate (y-axis) over time (x-axis) sepa-
rately by each treatment group in Figure 4 by Reconstruction-status. This plot shows that both
24
the treated and untreated groups seemed to have parallel trends prior to Reconstruction and
actually very little level differences. Thus, these graphical results provide suggestive evidence
in support of the DID identifying assumption.17
Results
Table 1: Main Difference-in-Differences Estimates: Georgia 1817-1882
Num. Convicts per 100,000(1) (2) (3)
Log(Troops per Thousand) 40.127∗∗ 40.040∗ 44.461∗(20.467) (20.634) (22.733)
Main Effect Reconstruction Yes No NoCounty FE No Yes YesYear FE Yes Yes YesTime-Varying Covariates No No YesNumber of Clusters 137 137 137N 7,989 7,989 7,699∗p < .1; ∗∗p < .05; ∗∗∗p < .01Standard errors clustered by county in parentheses.
First, I begin by presenting the main DID results in Table 1. Column 1 begins by pre-
senting the interaction between Reconstruction and post-1871 controlling for the main effect
of Reconstruction and year fixed effects. Next, Column 2 presents the results of a county
fixed effects model and coefficient remains essentially unchanged. These results suggest that
Reconstruction did indeed increase the size of the incarcerated population.
What do these results mean in substantive terms? Going from a county with no fed-
eral presence to one with the median amount (∆ ≈ Log(3 troops per 1,000)) increased the
number of convicts per 100,000 by about 40. This is approximately a 25% effect above the17I also present additional evidence in FigureA1-4 that Reconstruction exposure is balanced on an exhaustive
set of observable characteristics prior to the Civil War.
25
control-group mean in the post-period. In short, these effects are quite large especially given
the overall explosive growth in the scale of the Georgia incarceration rates.
I also show that these results are robust to controlling for twoprimary alternative explanations–
slavery and Civil War destruction–in Column 3. Encouragingly, the coefficient remains pos-
itive and the magnitude actually increases. This results, then, indicates that other events that
also occurred during this time period are insufficient to explain the patterns found here.
Next, I assess the degree to which these effects persisted using an event study framework.
Here, I estimate lags and leads of the Reconstruction treatment with a regression of the fol-
lowing form:
Yc,t =1905∑
t=1818
(βt1 [Yeart] · Log(Avg. Troops)c,1870
)+ ΩCc + ΓTt + ϵc,t (2)
This not only allows me to estimate the degree to which the effects persist over time, but
also allows me to more formally analyze whether there were pre-existing trends in the data
prior to the end of Reconstruction. I plot the results of this event study analysis in Figure 5
below where each point represents an estimated coefficient of the Reconstruction exposure
measure interacted with a year fixed effect.18
18The baseline year is 1817.
26
Post-Reconstruction
0
25
50
75
1820 1840 1860 1880 1900Year
Estim
ated
Eff
ect
Effect of Reconstruction on incarceration persists over 30 years...
Light shaded region represents 95% confidence intervals. Darker regions represent 90% confidence intervals.
Figure 5: Event Study Estimates
This set of results shows that the impact of Reconstruction seems to have been persistent
over time. Thus, not only did exposure to Reconstruction shape incarceration trends in the
short-run, but also over 30 years after the end of Reconstruction.
27
Num Non-White Convicts per 100,000
Num White Convicts per 100,000
0 10 20 30 40Estimated Effect
Inca
rcer
atio
n Ra
te b
y Ra
ceEffect concentrated among Non-Whites...
Note: Models estimated using county and year fixed effects.
Figure 6: Effect on White versus “Colored” Incarceration Rate
Thick shaded bars represent 90% confidence intervals while light bars represent 95% confi-dence intervals.
Though there is strong evidence showing a causal relationship between slavery or Recon-
struction and incarceration, it might just reflect a general increase in crime. This alternative
explanation potentially has little to do with social control, but instead could reflect crime
waves that took hold in these areas due to changing opportunity-cost incentives. While there
is unfortunately no systematic crime data from this time period at the county-level, the dataset
I assembled does have information on race of the incarcerated individual. If these results are
driven by general crime trends, then one should expect these effects to be symmetric between
whites and non-whites. Using this test, I re-estimate the results from Column 2 of Table 1
and estimate it separately for the white and non-white incarceration rates in Figure 6 shows
28
that the effects are concentrated among African American/colored individuals rather than
whites–a pattern consistent with the argument advanced in this piece and inconsistent with
a general crime channel. Together, the results presented are consistent with the argument
advanced in this piece.19
In addition to the results presented in the paper, I also run a number of robustness checks
(details can be found in the Online Appendix). First, one might be worried about the poten-
tial for military presence during Reconstruction to be driven by factors such as the presence
of racial conflict over the course of Reconstruction. To address this, I demonstrate that the re-
sults are robust to using the location of troops right at the end of theCivilWar–variation that is
plausibly exogenous with respect to later on racial conflict since these locations are primarily
determined by on-the-ground battlefield conditions. Second, a major concern with panel de-
signs is the presence of secular trends by unit (Stephens, Jr. and Yang 2014). I directly address
this issue by estimating models that include county specific linear and quadratic trends. The
point estimates remain unchanged and statistically significant even when estimating these
more demanding specifications. Next, I show that the results remain robust to an alternative
identification strategy that conditions on both county and year fixed effects in addition to the
lagged dependent variable. Then, I show that the results are not driven any one county. I
also show that the results hold when just subsetting to crimes with more discretionary poten-
tial such as property crimes (Muller 2018). Overall, these results provide robust evidence in
support of the argument.19Given the lack of racial breakdowns after 1880, this analysis only uses data from 1817-1880.
29
-40
0
40
80
0.00 0.25 0.50 0.75Moderator: Prop. Enslaved (1860)M
argi
nal E
ffec
t of L
og(T
roop
s pe
r Tho
usan
d) o
n N
umbe
r of N
on-W
hite
Con
vict
s pe
r 100
,000 Effect of Reconstruction on incarceration increasing in intensity of slavery...
Figure 7: Effect Modification of Reconstruction by Intensity of Slavery
Error bars represent 95% confidence intervals of the binned regression estimate. Shaded re-gions represent the 95% confidence intervals of the marginal effect across the support of pro-portion population enslaved in 1860.
Another implication of the argument is that the impact of Reconstruction should only
matter in places with large shares of newly empowered populations. This implies that the ef-
fect of Reconstruction should be increasing in the share of the subaltern population. To test
this, I estimate the impact of exposure to Reconstruction as it varies with the proportion pop-
ulation enslaved in 1860. Figure 7 provides evidence consistent with this hypothesis. While
Reconstruction seems to have had little or even a negative impact on incarceration in areas
with little exposure to slavery, the effect seems to be particularly concentrated in areas with
greater than half the population enslaved.
30
Economic Land
-0.04 0.00 0.04 0.08 0.12 -0.04 0.00 0.04 0.08 0.12
Log(Acres Owned by Blacks per Capita)
Log(Acres Owned by Blacks)
Log(Black Wealth per Capita)
Log(Black Wealth)
Estimated Effect
Out
com
e
but Not Control over Land
Reconstruction Increased Black Wealth
Note: Covariates include 1860 total population, percentage enslaved, rail access, canal access, and wealth.
Figure 8:
Dark shaded bars represent 90% confidence intervals while light bars represent 95% confi-dence intervals.
One major concern with interpreting this interaction is that slavery is not randomly as-
signed. To address this, I also compute the county-level suitability for growing cotton as
a more exogenous measure of a county’s reliance on slavery (Acharya, Blackwell, and Sen
2016b). Results presented in the Online Appendix show that the interaction effect is robust
to using cotton suitability instead of the proportion enslaved thereby supporting a causal in-
terpretation of the effect modification results.
31
Mechanism: The Role of (Incomplete) Black Economic Empowerment
The theoretical framework posits that white elites will bemost likely to use the state to exercise
control over African Americans in areas where African Americans have increased power to
exit coercive relationships with white elites. To test this, I estimate the impact of Reconstruc-
tion on black empowerment as proxied by their wealth as well as landholding. Unfortunately,
data on black wealth does not exist in a readily attainable format prior to Reconstruction so I
am limited to showing simple cross-sectional evidence over time. In this respect, the analysis
here should only be viewed as suggestive. I run analyses regressing the total black wealth in a
county against the measure of Reconstruction exposure used throughout the study control-
ling linearly for the log of the total population, percentage slave population, rail access, canal
access, and overall wealth in 1860. Figure 8 plots the estimated coefficient on Reconstruction
intensity on the total and per capita measure of wealth and land holding. The results provide
suggestive evidence consistent with the theoretical mechanism. Areas with more exposure to
Reconstruction havewealthier black populations, but they did not fundamentally gain control
of land–the root of political power during the period. Additionally, these results are incon-
sistent with an alternative explanation around the role of black criminality since increases in
material status are known to decrease criminal activity (Becker 1968).20 Thus, these results
provide additional empirical support for the theoretical framework advanced in this project
in addition to existing work on historical incarceration (Muller 2018).
Mechanism: The Role of Black Political Empowerment
In addition to looking at the role of black economic empowerment, I also investigate the
impact of Reconstruction on black political empowerment. To do so, I collect data on the20In the Appendix, I also show that there is little support for the notion that Reconstruction made African
Americans at the bottom of the economic distribution worse off. Thus, it is unlikely that these results are drivenby economic incentives to engage in crime.
32
0.00
0.04
0.08
0.12
Bivariate CovariatesModel
Est
imat
ed E
ffect
Reconstruction Increased Black Officeholding...
Note: Covariates include 1860 total population, percentage enslaved, rail access, canal access, and wealth.
Figure 9:
Thick shaded bars represent 90% confidence intervals while thin bands represent 95% confi-dence intervals.
number of black political office holders in a given county following Reconstruction from
Foner (1996)’s directory of black officeholders. The original directory contains detailed bi-
ographical data on all known black/mixed-race elected officials in the post-Reconstruction
South. I collect and aggregate this data to the county-level for Georgia and re-estimate simple
cross-sectional models looking at the impact of Reconstruction exposure on whether a given
county had any black office holders. These results, shown in Figure 9 demonstrate supportive
evidence for the idea that backlash may have occurred because African Americans were not
able to substantially increase their hold over Southern political institutions. On average, areas
withmore exposure to Reconstruction were only slightlymore likely to elect a black politician
33
0
1
2
1850 1860 1870 1880Year
Avg.
Num
ber o
f Pol
ice
per 1
0,00
0
Reconstruction Any Reconstruction Any Reconstruction (Counterfactual) No Reconstruction
Figure 10:
Thick shaded bars represent 90% confidence intervals while thin bands represent 95% confi-dence intervals.
relative to those with less exposure. Thus, these results are again consistent with the results
on land ownership: African Americans were not able to gain control over fundamental eco-
nomic and political institutions thereby creating the space for political backlash by southern
elites.
Mechanism: The Rise of the Southern Police Force
Finally, I assess the role of Reconstruction in increasing the state’s carceral capacity using
data on the county-level growth of the police force from 1850 to 1880.21 To do so, I visually21Data comes from the full population decennial census.
34
demonstrate within a DID framework the growth of the police force comparing trends in the
police force size per 10,000 population between areas with any military presence and those
with none. Figure 10 demonstrates that Reconstruction nearly tripled the size of the police
force in Georgia. These results hold when estimated within a regression framework as well.
Thus, these results demonstrate that Southern elites seemed to have significantly invested in
Georgia’s coercive capacity as proxied by the size of the police.
conclusion
What explains the rise of the proto-carceral state? I explore this question within the context
of Georgia during the 19th century–a state that many historians view as being a paragon of
Southern punishment during the time period. Building off of the work of many scholars, I
synthesize existing arguments into a parsimonious theoretical framework to understand the
ways in which newly empowered subaltern populationsmight only experience such power for
a brief moment in time when a white, wealthy elite maintains control over the state’s coercive
capacity. Using newly assembled panel data on over 130 counties across the time period 1817-
1911, I show that the external (yet incomplete) empowerment of African Americans led to an
increase in incarceration. This effect is concentrated solely among African Americans and not
whites–a pattern consistent with a political account of the rise of this system and inconsistent
with theories stress the role of general crime waves. In addition, data assembled from Bureau
of Labor reports on black wealth in Georgia and data on black elected politicians provide
suggestive evidence consistent with the causalmechanisms. I also show that areas with greater
exposure to Reconstruction also saw a larger increase the size of the police force consistent
with the argument. In short, this project lends systematic empirical credence to arguments
about punishment and state power that go as far back as Du Bois (1901b) yet remain highly
relevant today.
35
While this study uses Georgia as a window into studying the origins of the proto-carceral
state, it remains to be determined the degree to which the lessons learned form this case can
be applied to understand the rest of the United States. Particularly, the South’s history of racial
repression by whites might make the incentives outlined in this article particularly acute rela-
tive to other regions (Acharya, Blackwell, and Sen 2018a). In this respect, the theory may not
apply to societies that lack such racialized conflict. Future research would benefit from focus-
ing on the degree to which racial conflict per se animates the creation of repressive political
institutions like prisons and policing.
The theory and data collected here open up many new avenues of inquiry into the role
of incarceration in the political development of the United States. A natural question that
comes out of this study is whether these early, local incarceration patterns persist until today.
Furthermore, we might ask ourselves under to what degree the policing of African American
communities in the past continues the shape the lives of members of this community today.
Interpreted against the backdrop of other recent work in American politics, which urges
us to more carefully consider the role of repression, violence, and coercion (Soss and Weaver
2017), this study suggests that students of American politics ought to more thoroughly inter-
rogate the way in which state-led repression has so fundamentally shaped themarginalization
of groups based on their identities. For instance, we might ask ourselves to what degree did
the rise of other class-based subaltern movements such as labor suffer from the same sorts of
repressive carceral strategies in the North?22 Not only was repression a strategy the state pur-
sued against the labor movement, but also civil rights organizations following World War II.
Lynchings of African Americans and immigrants, often done under the tacit or explicit con-
sent of the state, served as a way to reinforce the social bonds needed to maintain a repressive
Jim Crow system (Smangs 2016). Even today, the revelation of systematic extraction from
African Americans in Ferguson via the police suggests the continued relevance of state-led22I thank Alex Gourevitch for raising this inquiry.
36
violence for the lives of those on the periphery.
Incarceration, when politically motivated, should be viewed analytically as an act of po-
litical violence. If we take this idea seriously, then existing analytical and theoretical tools
we use to study American politics, could be deficient to understanding cause, process, and
consequence of incarceration as political violence.23 Fortunately, students of comparative
politics have much to contribute to our understanding given their long emphasis on the mi-
cro, meso, and macro-structures that shape political violence (Tilly 2003; Wilkinson 2004;
Kalyvas 2006; Weinstein 2006; Wood 2012; Fujii 2013; Cohen 2016). Thus, this study joins
recent work such as Davenport (2015) and Weaver (2019) in using concepts from the compar-
ative literature on political violence to better understand how repression works in the United
States. Clearly, the historical and contemporary politics of the United States are rife with ex-
amples of political violence; as such, our research ought to take more seriously its occurrence
(and non-occurrence) and how the politics of American repression intersect with the politics
of identity.
23There are, however, some recent studies that attempt to understand the causes and consequences of violenceproduction in American history (Kalmoe 2018; Obert 2018; Hall, Huff, and Kuriwaki, Forthcoming)
37
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Online Appendix
A0
Contents
Contents A0
Formalizing the Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A1
Data Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A9
Convict Records . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A12
Black Land Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A14
Balance Check of Pre-Treatment Baseline Observables . . . . . . . . . . . . . . . A15
External Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A16
Additional Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . A19
Ruling Out Crime as an Alternative Explanation . . . . . . . . . . . . . . . . . . A25
Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A28
A0
A1
formalizing the framework
Baseline Model Setup and Predictions
I build a static game theoretic model of a potentially repressive society inspired by features
identified by the historical work on Southern society following the Civil War as described
above. This model abstracts away from many features of the specific coalitions and bureau-
cratic features of Southern society so as to elucidate the underlying political economy and
social identity incentives that would influence why Southern elites decided to invest in in-
carceration as a strategy of social control. The model delivers two key predictions, which I
test using newly collected data: (1) that raising the value of the subaltern group’s “outside op-
tions” induces more repression and (2) greater social distance between the elite group and the
subaltern generates more repression.
The society is composed of representative actors from two groups–an elite E and labor
L.24 While I assume that E is unified in terms of types, I allow L to be differentiated by
its social distance from E represented by the term ∆. Larger values of ∆ indicate less social
distance between E and L. This variable captures, in the context of the U.S. South, the social
distance between whites and blacks as guided by race. In doing so, this model parsimoniously
captures situations of class-conflict conditioned by ethnic and racial identity concerns.
Initially, E offers a wage w ∈ R+ to L. Next, L chooses to accept or reject E’s offer.
If L accepts the offer, the game ends and players receive their utilities UE = Π − w and
UL = u(w). In this case, E receives some utility from output under a “free” labor pool Π
minus the amount of wagesw thatE must pay toL. L receives utility u(w) from the contract
with E.25
24I assume a representative actor from each group, which abstracts away from issues like coordination or col-lective action. Since these are not features that are central to the phenomenon I wish to explain, this assumptionis reasonable in this situation.
25These contracts are not “free” in the sense that they are made in the shadow of repression as described inthe next stage of the game.
A2
When L rejects the offer, E can decide to use repression r ∈ R+ against L to forcibly
extract from L. Using repression, however, comes at a cost c(r) = ∆2r2 that is convex and
increasing in the closeness ofE andL in social terms.26 These costs might come from the fact
that repression requires concentrating government resources in “unproductive” ways in ad-
dition to the way in which repression reduces the supply of labor available for other activities.
I allow the cost to vary by ∆ to capture both the psychological costs of engaging in violence
against members of one’s own group and the potential costs that E might play in terms of
maintaining a cross-class coalition among members of their own racial/ethnic group. I al-
low repression to forcibly generate output at the rate β ∈ R+ thus generating benefits for E
equal toΠβr.27 This captures both a general feature of coercion as being “productive”/output-
enhancing as per Acemoglu and Wolitzky (2011) in addition to capturing the feature of the
Southern prison system (and many other carceral systems around the world) that force pris-
oners to work. After deciding how much repression to engage in, actors receive their utilities
UE = Πβr − ∆2r2 and UL = u(g)− r. In this situation where L decides not to accept, they
receive an outside wage equal to g. This outside-wage/option can be thought of as the value
of other careers outside of picking or growing cotton for the planter elite.
To summarize, the sequence of play is as follows:
1. E offers a wage w ∈ R+ to L.
2. L can accept or reject this offer.
• If L accepts, the game ends and each group receives utilities.
3. IfL rejects, thenE chooses the amount of repression r ∈ R+ to engage in. Utilities are
realized.26While the model in this paper parameterizes the cost function c(r) using quadratic loss, the general com-
parative statics hold so long as the cost function is convex and differentiable.27Values ofβ ∈ [0, 1] imply a relatively lower rate of return on repression relative to the ”free-market” output.
A3
Solving the game for Subgame Perfect Nash Equilibria (SPNE) via backwards induction
yields the following results:
Proposition 1: Positive shocks to outside options will induce more repression for sufficiently
high values of β.
This proposition captures the tradeoff that L makes when weighing the benefits of “exit-
ing” and pursuing their outside options against the costs of repression that they might face
when doing so. From the perspective of E, repression is only credible when the benefits are
sufficiently high. So long as the value of g is sufficiently high, L still stands to benefit from
pursuing the outside option. As described above, African American ex-slaves pursued many
ways to “exit” from their previously coercive relationships with the planter elite despite the
risks that this would entail. In this sense, embracing a “brief moment in the sun” entailed the
risk of going back into slave-like relationships through state repression.
While the above model makes the assumption of deterministic costs, I relax this assump-
tion in the Online Appendix and show that the basic results hold when considering the po-
tential to be repressed as a costly lottery with risk-neutral preferences. I also consider an ex-
tension where the value of the outside option g is probabilistically determined and the basic
results hold.
Proposition 2: Greater social distance between groups induces more repression.
The intuition behind this result is straightforward. Repression, as described above, re-
quires the elite to engage in actions that might raise the risk of revolt, breaking one’s ethnic
coalition, or simple psychological costs of doing harm to member’s of one’s social identity
group. As these groups get farther apart in social distance (ie, the identity group of the labor
pool is farther apart from the identity group of the elite), the cost of engaging in repression
by E against L becomes lower. Thus, increases in social distance increases repression.28
28Repression itself can also endogenously increase social distance. See Acharya, Blackwell, and Sen (2018b)for a formal model of this process.
A4
Moreover, the impact of social distance should be increasing in the value of the outside
option. This is because for low values of g, repression is less likely to happen in equilibrium.
As g increases however, repression increases at a rate inversely proportional to social distance.
The theory, then, makes several testable predictions:
1. H1: Punishment should be increasing in the size of the subaltern group where the size
proxies for the salience of ∆.
2. H2: Punishment should be increasing in the value of the subaltern group’s outside op-
tions g.
3. H3: The impact of group size should be especially salient where the group has a higher
outside option.
Model Proofs and Extensions
Proof. Proof of Proposition 1: The Effect of Outside Options
Given the Subgame Perfect Equilibrium Concept used in this paper, I solve the game via
backwards induction. Starting at the terminal node whereE must choose a level of repression
r ∈ R+. At this node, E’s utility function is UE = Πβr − ∆2r2.
Taking the derivative with respect to r, we get the following first-order condition:
∂UE
∂r= Πβ −∆r (3)
Setting this equal to zero, we can solve for the optimal level of repression r∗:
r∗ =Πβ
∆(4)
We can now substitute E’s expression for the optimal level of repression to characterize
L’s “exit” constraint:
A5
u(g) ≥ u(w) +Πβ
∆(5)
Here, L chooses to “exit” and pursue their outside options if and only if their utility from
the outside option is greater than the utility they would receive from entering into “wage”
labor plus the cost he would face from repression.
From this constraint, we can see that positive shocks to g will induce more repression.
A6
Proof. Proof of Proposition 2: The Effect of Social Distance
Consider E’s expression for the optimal level of repression r∗. From this equation, it is
clear to see that an increase in ∆ (closer social distance between the E and L) reduces the
optimal level of repression. This is because the derivative of r∗ with respect to ∆ is strictly
negative:
∂r∗
∂∆=
−Πβ
∆2< 0 (6)
A7
Proof. Risky Lottery of Labor’s Outside Option
Instead of considering a deterministic cost of repression for labor, we can reinterpret that
term as an “expected” utility as follows:
UL = (1− p)U(g)− pc(r) (7)
Where p ∈ (0, 1] represents the probability of getting caught. So long as U(g) is still
sufficiently high, L will take the risky lottery.
Now we can re-write L’s ”exit” constraint as:
(1− p)U(g)− pr∗ ≥ u(w) (8)
=⇒ U(g) ≥ u(w) + pr∗
1− p(9)
A8
Proof. Probabilistic Outside Option
Assume we have a mass ofLi which with outside option gi ∼ F (.)with mean µ and F (.)
symmetric around µ.. This allows us to rewrite Li’s constraint as
U(gi) ≥ u(w) + pr∗
1− p(10)
This generates a cutpoint where Li with sufficiently high draws of gi will decide to exit.
All else equal, increasing the average µ increases the proportion of individuals that E will
attempt to repress. Those under the cut-point are offered “free”-market wages.
A9
data appendix
• Incarceration (pre-Reconstruction):
– Source: Georgia Register of Convicts. Accessed through Ancestry.com.
– Level of analysis: Individual
• Incarceration (1880-1882):
– Source: Biennial Report of the Principal Keeper of the Georgia Penitentiary, 1880-
1882
–
– Level of analysis: Individual
• Incarceration (1897-1911):
– Source: Annual Report of the Principal Keeper of the Georgia Penitentiary
– Level of analysis: County
• Population (1810-1910):
– Source: Haines, Michael R., and Inter-university Consortium for Political and So-
cial Research. Historical, Demographic, Economic, and Social Data: The United
States, 1790-2002. Ann Arbor, MI: Inter-university Consortium for Political and
Social Research [distributor], 2010-05-21.
– Level of analysis: County
• Average number of troops during Reconstruction
– Source: Gregory P. Downs and Scott Nesbit, Mapping Occupation: Force, Freedom,
and the Army in Reconstruction
A10
– Level of analysis: County
• Proportion population enslaved (1860):
– Source: Haines, Michael R., and Inter-university Consortium for Political and So-
cial Research. Historical, Demographic, Economic, and Social Data: The United
States, 1790-2002. Ann Arbor, MI: Inter-university Consortium for Political and
Social Research [distributor], 2010-05-21.
– Level of analysis: County
• Sherman’s March Locations
– Source: US War Department (1901): The War of the Rebellion: a Compilation of
the Official Records of the Union and Confederate Armies. US Government Printing
Office, Thousand Oaks, CA.
– Level of analysis: County
• Freedmen’s Bureau locations:
– Source: https://www.google.com/maps/d/u/0/viewer?ie=UTF8&oe=UTF8&
msa=0&mid=11kGBhLi6gJ9jvXqF9I2X47uHAgk&ll=32.47155077708247%2C-88.
26405249999999&z=5
– Level of analysis: County
• Total Black Property Wealth (1875-1900):
– Source: Du Bois, W.E.B. 1901. The Negro Landholder of Georgia. Department of
Labor.
– Level of analysis: County
• Black Officeholding:
A11
– Source: Foner, Eric. 1996. Freedom’s Lawmakers: A Directory of Black Officehold-
ers during Reconstruction. Louisiana State Press.
– Level of analysis: Individual
• Police Force Size:
– Source: Steven Ruggles, Sarah Flood, Ronald Goeken, Josiah Grover, Erin Meyer,
Jose Pacas, and Matthew Sobek. IPUMS USA: Version 9.0 [dataset]. Minneapolis,
MN: IPUMS, 2019.
– Level of analysis: Individual
A12
convict records
Figure A1-1: Sample from the Georgia Convict Register, 1817-1868
A13
Figure A1-2: Sample from the Biennial Report of the Georgia Penitentiary, 1880-1868
A14
black land values
Figure A1-3: Sample from the Bureau of Labor Bulletin on Black Wealth
A15
balance check of pre-treatment baseline observables
The lack of level differences in the pre-treatment period suggests two things: (1) that the
difference-in-differences design essentially leans on cross-sectional variation for estimating
causal effects and (2) places that experienced Reconstruction did not seem to differ on the
baseline outcome variable. To assuage concerns over cross-sectional imbalances, I perform
an omnibus balance check regressing the Reconstruction exposure variable on an exhaustive
set of observable, pre-treatment covariates. These include demographics such as population,
urbanization, and slave intensity; economic variables such as manufacturing intensity and
wealth; social variables such as church counts; and climactic variables such as suitability for
growing different crops. The test here suggest that Reconstruction exposure is balanced on
these pre-treatment characteristics thereby supporting the causal interpretation of the results.
A16
Cotton Suitability
Log (Acres Improved)
Log (Mfg. Estab.)
Log (Mfg. Output)
Log (Personal Estate)
Log (Real Estate)
Log (Total Pop.)
Log(Churches)
Maize Suitability
Pct. Free Blacks
Pct. Slave
Pct. Urban
Rail Access
Rice Suitability
Sugarcane Suitability
Tobacco Suitability
Water Access
Wheat Suitability
-2 -1 0 1
Estimated Coefficient
Var
iabl
e (1
860)
Balance on Pre-Treatment Observables
Omnibus F-test p-value = 0.175
Figure A1-4: Balance Check
external validity
Though the study leans on the case of Georgia for both analytical and empirical reasons, Geor-
gia is emblematic of the Former Confederacy. The following figures demonstrate that this is
the case using census data from 1860. Thus, there are reasons to believe that this case is in-
structive of broader patterns in the South.
A17
Pct. Enslaved Pct. Free (of Blacks)
0 40 80 0 40 80Alabama
Arkansas
Florida
Georgia
Louisiana
Mississippi
North Carolina
South Carolina
Tennessee
Texas
Virginia
value
stat
e_na
me
Figure A1-5: Distribution of Demographic Variables Across the Former Confederacy
A18
Log(Real Estate Wealth) NA
Log(Mfg. Establishments) Log(Personal Estate Wealth) Log(Population)
0 5 10 15 0 5 10 15
0 5 10 15Alabama
Arkansas
Florida
Georgia
Louisiana
Mississippi
North Carolina
South Carolina
Tennessee
Texas
Virginia
Alabama
Arkansas
Florida
Georgia
Louisiana
Mississippi
North Carolina
South Carolina
Tennessee
Texas
Virginia
value
stat
e_na
me
Figure A1-6: Distribution of Economic Variables Across the Former Confederacy
A19
additional descriptive statistics
0.00
0.02
0.04
0.06
0 20 40 60 80
Age (in years)
dens
ity
Race white colored
Age Distribution by Race
Figure A1-7: Age Distributions of Convicts in 1880-1882 by Race
A20
Other Property Violent
Distribution of Charges by Type: Georgia, 1880-1882
One box = one prisoner
Figure A1-8: Distribution of Crimes by Type in 1880-1882
A21
31°N
32°N
33°N
34°N
35°N
86°W 85°W 84°W 83°W 82°W 81°W
0 250 500 750Change in Num. Convicts per 100,000
Change in Num. Convicts per 100,000 between 1860-1880
Figure A1-9: Map of Convict Population Growth in Georgia
A22
Before Reconstruction Post-Reconstruction
0 1 2 3 0 1 2 3
0
100
200
300
400
Log(Troops per Thousand)
Num
. Con
vict
s pe
r 100
,000
Binned scatter plot created using the binsreg package.
Figure A1-10: Binned Scatter plot of raw relationship.
Bins computing using optimal binning procedure developed in Cattaneo, Crump, Farrell andFeng (2019).
A23
Before Reconstruction Post-Reconstruction
0 2 4 0 2 4
0
500
1000
1500
2000
2500
Log(Troops per Thousand)
Num
. Con
vict
s pe
r 100
,000
Figure A1-11: Raw scatter plot of relationship.
A24
31°N
32°N
33°N
34°N
35°N
86°W 85°W 84°W 83°W 82°W 81°W
0 1 2 3 4 5Log(Total Troops per 1000)
Geographic Distribution of Reconstruction Exposure
Figure A1-12: Map of Reconstruction Exposure Variable.
A25
ruling out crime as an alternative explanation
A key alternative mechanism for the findings is the potential for Reconstruction to lead to
more incarceration by disrupting economic conditions and leading to more incentives to en-
gage in crime. While the results on black wealth presented in the main text of the manuscript
run counter to this on average, it may be the case that some African Americans are actually
made worse off as a result of Reconstruction and therefore have more incentives to engage
in crime. To address this issue, I rely on data from the 1880 U.S. Census full count file for
the state of Georgia. From this dataset, I can compute the Log(Occupation Score) for each
individual i in county c in Georgia. This variable, commonly used as a proxy for economic
status in the economics and sociology literatures, represents the log of the income of a person
in a given occupation in thousands of 1950 dollars.
Using this data, I address the alternative explanation that the results may driven by incen-
tives to engage in crime in two ways. First, in Figure A1-13 I show that African Americans
and whites have remarkably similar occupation scores in 1880. Thus, any racial differences in
the economic incentives to engage in crime are minimal just descriptively.
A26
Female Male
0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5
Black
White
Race
Log(
Occ
upat
ion
Scor
e)Racial differences in incomes small relative to differences in incarceration
Source: 1880 U.S. Census Note: Occupation scores are defined as the median income of an individual in a given occupation in hundreds of 1950 dollars.
Figure A1-13: Racial Differences in Occupation Scores, 1880
Next, I estimate more formally the impact of Reconstruction on the distribution of occu-
pation scores for African Americans. Formally, I estimate equations of the following form:
1
(Log(Occupation Score)i,c,b > Y ∗
)= βLog(Avg. Troops)c,1870+λBirth Yearb+ΩXc+ϵi,c,b
The term Y ∗ is some value across the empirical support of Log(Occupation Score)i,c,b for
African Americans in 1880. I vary Y ∗ across the support of occupation scores and re-estimate
the above equation. This in essence represents the continuous version of a quantile regression
thus allowing me to assess distributional effects. I also control for age fixed effects as well pre-
treatment 1860 county-level covariates such as population, wealth, transportation access, and
A27
proportion enslaved. The results, shown in Figure A1-14 demonstrate that Reconstruction
seems to have essentially raised the middle of the distribution without harming the lower
end. In essence, all African Americans seem to have been made better off by Reconstruction
thereby lending even less support to the alternative mechanism that African Americans were
more likely to engage in crime as a result of some having been made worse off.
0.00
0.02
0.04
0.06
0 1 2 3 4Log(Occscore)
Est
imat
ed E
ffect
Note: Models estimated using OLS controlling for 1860 total population, prop. enslaved, manufacturing, and wealth.
Figure A1-14: Distributional Impacts of Reconstruction on Black Occupation Scores
A28
robustness checks
Robustness to Using Measure of Troops at End of Civil War
Over the course of Reconstruction in Georgia, one might be concerned that exposure to the
military might be driven by factors such as the amount of racial conflict or violence thus con-
founding the relationship between Reconstruction and incarceration. To address this con-
cern, I instead use a measure of troop presence right after the end of the Civil War in 1865 as
themain explanatory variables. The location of troops at the end of the CivilWar is a function
of local battlefield considerations, which are arguably exogenous with respect to later on in-
carceration patterns. Using this measure, I re-analyze the main results and demonstrate that
they remain robust.
Table A1-1: Robustness to Using Troops Located Right after End of Civil War
Num. Convicts per 100,000(1) (2) (3)
Log(Troops per Thousand) 49.644∗∗ 48.314∗∗ 51.192∗∗(22.577) (22.382) (23.467)
Main Effect Reconstruction Yes No NoCounty FE No Yes YesYear FE Yes Yes YesTime-Varying Covariates No No YesNumber of Clusters 137 137 137N 7,989 7,989 7,699∗p < .1; ∗∗p < .05; ∗∗∗p < .01Standard errors clustered by county in parentheses.
A29
Num Non-White Convicts per 100,000
Num White Convicts per 100,000
0 10 20 30 40 50Estimated Effect
Inca
rcer
atio
n Ra
te b
y Ra
ceEffect concentrated among Non-Whites...
Note: Models estimated using county and year fixed effects.
A30
Post-Reconstruction
0
30
60
90
1820 1840 1860 1880 1900Year
Estim
ated
Eff
ect
Effect of Reconstruction on incarceration persists over 30 years...
Light shaded region represents 95% confidence intervals. Darker regions represent 90% confidence intervals.
Robustness to Inclusion of Freedmen’s Bureaus
One potential concern with the analysis is that the results may instead be driven simply by
the location of Freedmen’s Bureaus offices instead of the actual military enforcement of Re-
construction. To address this, I geolocate Freedmen’s Bureau locations onto 1880 Georgia
county boundaries. Then, I re-estimate the main difference-in-differences models with only
Freedmen’s Bureau status interacted with the post 1871 variable and simultaneously with the
main Reconstruction intensity variable used in the main analysis. The results indicate that
the impact of military occupation is unaffected by the location of Freedmen’s Bureaus.
A31
Table A1-2: Difference-in-Differences Estimates (Freedmen’s Bureau): Georgia 1817-1882
Num. Convicts per 100,000(1) (2)
Any Freedmen’s Bureau Office 34.163 −3.933(43.541) (32.203)
Log(Troops per Thousand) 40.531∗∗(18.865)
County FE YES YESYear FE YES YESNumber of Clusters 137 137N 7,989 7,989∗p < .1; ∗∗p < .05; ∗∗∗p < .01Standard errors clustered by county in parentheses.
A32
Interaction with Freedmen’s Bureau Location
The argument highlights the role of empowerment in shaping the incentives for state repres-
sion via incarceration. To further bolster this argument, I investigate whether the military
enforcement of Reconstruction had an interactive effect with the supply of state bureaucratic
resources through the Freedmen’s Bureau. The results suggest that there is indeed a posi-
tive interactive effect. The effects of military exposure seem to be greatest in areas with the
presence of a Freedmen’s Bureau.
0
50
100
150
Any Freedmen's Bureau Office No Freedmen's Bureau OfficeFreedmen's Bureau Presence
Est
imat
ed E
ffect
Concentrated in Areas with Freedmen's Bureau Presence
Impact of Military Exposure
A33
County-specific Linear Trends
Table A1-3: Robustness to County Linear Trends
Num. Convicts per 100,000(1) (2)
Log(Troops per Thousand) 36.368∗ 36.966∗(18.739) (19.668)
County FE YES YESYear FE YES YESTime-Varying Covariates NO YESNumber of Clusters 132 132N 7,989 7,699∗p < .1; ∗∗p < .05; ∗∗∗p < .01Standard errors clustered by county in parentheses.
A34
County-specific Quadratic Trends
Table A1-4: Robustness to County Quadratic Trends
Num. Convicts per 100,000(1) (2)
Log(Troops per Thousand) 25.613∗∗∗ 25.549∗∗∗(9.792) (9.790)
County FE YES YESYear FE YES YESTime-Varying Covariates NO YESNumber of Clusters 132 132N 7,989 7,699∗p < .1; ∗∗p < .05; ∗∗∗p < .01Standard errors clustered by county in parentheses.
A35
Lagged Dependent Variable
Table A1-5: Robustness to Lagged Dependent Variable
Num. Convicts per 100,000(1) (2) (3)
Log(Troops per Thousand) 7.420∗∗ 8.119∗∗ 9.214∗∗(3.486) (3.814) (4.429)
Main Effect Reconstruction Yes No NoCounty FE No Yes YesYear FE YES YES YESTime-Varying Covariates No No YesLagged DV YES YES YESNumber of Clusters 132 132 132N 7,852 7,852 7,567∗p < .1; ∗∗p < .05; ∗∗∗p < .01Standard errors clustered by county in parentheses.
A36
Log Specification
Table A1-6: Robustness to Log Transformation of Total Convicts per 100,000
Log(Num. Convicts per 100,000)(1) (2) (3)
Log(Troops per Thousand) 0.087 0.086 0.074(0.059) (0.059) (0.064)
Main Effect Reconstruction Yes No NoCounty FE No Yes YesYear FE YES YES YESTime-Varying Covariates No No YesNumber of Clusters 132 132 132N 7,989 7,989 7,699∗p < .1; ∗∗p < .05; ∗∗∗p < .01Standard errors clustered by county in parentheses.
A37
Interaction with Cotton Suitability
Table A1-7: Effect Moderation by Cotton Suitability
Num. Non-White Convicts per 100,000(1) (2) (3)
Log(Troops per Thousand)*Post −43.767∗∗ −43.740∗∗ −36.830∗∗(17.862) (17.992) (17.848)
Log(Troops per Thousand) 0.124(0.314)
Log(Troops per Thousand)*Post*Cotton Suitability 0.014∗∗∗ 0.014∗∗∗ 0.013∗∗∗(0.003) (0.003) (0.003)
Log(Troops per Thousand)*Cotton Suitability 0.303(0.852)
Main Effect Reconstruction Yes No NoCounty FE No Yes YesYear FE YES YES YESNumber of Clusters 132 132 132N 7,308 7,308 7,043∗p < .1; ∗∗p < .05; ∗∗∗p < .01Standard errors clustered by county in parentheses.
A38
Impact on Black Migration
Table A1-8: Estimated Impact on Migration
Log(Non-White Population)(1) (2) (3)
Log(Troops per Thousand) 0.093∗ −0.051 −0.008(0.053) (0.057) (0.051)
Trends No Linear QuadraticCounty FE Yes Yes YesYear FE Yes Yes YesTime-Varying Covariates Yes Yes YesNumber of Clusters 137 137 137N 7,854 7,854 7,854∗p < .1; ∗∗p < .05; ∗∗∗p < .01Standard errors clustered by county in parentheses.
A39
Impact on Property Crimes
0
10
20
30
Covariates No CovariatesModel
Est
imat
ed E
ffect
Effect of Reconstruction among Property Crimes
Note: Covariates include 1860 total population, percentage enslaved, wealth, rail access, and canal access.
Figure A1-15: Impact Concentrated among Discretionary Crimes
A40
Results Not Driven by Any One County
0
20
40
60
80
1331
313
107
1329
913
155
1320
913
315
1306
913
191
1317
513
291
1318
713
047
1324
313
235
1308
913
091
1328
513
019
1301
513
271
1306
713
211
1313
313
059
1311
113
255
1312
313
227
1305
513
049
1310
313
301
1327
513
295
1324
113
149
1312
913
093
1313
913
001
1330
313
151
1323
913
229
1306
313
137
1319
713
263
1302
913
317
1325
113
065
1316
713
249
1321
713
119
1317
913
077
1319
913
007
1315
913
027
1306
113
233
1310
113
273
1305
313
193
1303
513
033
1317
713
221
1315
713
245
1303
713
231
1325
913
115
1314
113
009
1309
913
297
1315
313
215
1307
913
163
1329
313
269
1330
713
169
1304
513
145
1308
313
087
1303
913
207
1323
713
057
1311
713
319
1310
513
195
1326
513
125
1303
113
147
1304
113
181
1320
513
051
1313
513
201
1318
913
223
1332
113
073
1324
713
143
1308
513
113
1321
913
203
1328
913
011
1328
113
311
1321
313
097
1309
513
267
1330
513
261
1312
713
185
1307
113
021
1312
1
Fips Code of County Dropped
Est
imat
ed E
ffect
Robustness to Sequentially Dropping Counties.
Note: Models estimated using county and year fixed effects.
Figure A1-16: Results Robust to Sequentially Dropping Counties from Dataset