Available via license: CC BY 4.0
Content may be subject to copyright.
Places of Persistence: Slavery and the Geography
of Intergenerational Mobility in the United States
Thor Berger
1
Published online: 3 July 2018
#The Author(s) 2018
Abstract Intergenerational mobility has remained stable over recent decades in the
United States but varies sharply across the country. In this article, I document that areas
with more prevalent slavery by the outbreak of the Civil War exhibit substantially less
upward mobility today. I find a negative link between prior slavery and contemporary
mobility within states, when controlling for a wide range of historical and contempo-
rary factors including income and inequality, focusing on the historical slave states,
using a variety of mobility measures, and when exploiting geographical differences in
the suitability for cultivating cotton as an instrument for the prevalence of slavery. As a
first step to disentangle the underlying channels of persistence, I examine whether any
of the five broad factors highlighted by Chetty et al. (2014a) as the most important
correlates of upward mobility—family structure, income inequality, school quality,
segregation, and social capital—can account for the link between earlier slavery and
current mobility. More fragile family structures in areas where slavery was more
prevalent, as reflected in lower marriage rates and a larger share of children living in
single-parent households, is seemingly the most relevant to understand why it still
shapes the geography of opportunity in the United States.
Keywords Intergenerational mobility.Slavery.Persistence
Introduction
Intergenerational economic mobility is lower in the United States than in most
other developed countries and has remained fairly constant over recent decades
Demography (2018) 55:1547–1565
https://doi.org/10.1007/s13524-018-0693-4
*Thor Berger
thor.berger@ekh.lu.se
1
Department of Economic History & Centre for Economic Demography, Lund University,
Scheelevägen 15B, 223 63 Lund, Sweden
(e.g., Chetty et al. 2014b;Corak2013; Lee and Solon 2009; Mazumder 2005;
Solon 1992).
1
However, large differences in mobility exist within the country. For
example, some areas in the Midwest exhibit rates similar to those observed in high-
mobility Scandinavia, but places in the Southeast are among the least mobile in the
developed world (Chetty et al. 2014a). A sharp regional divide in the extent to which
economic status is transmitted between generations and the relatively stable spatial
variation in upward mobility over time point to local historical factors as potentially
important for understanding the geography of opportunity in the United States.
1
See Aaronson and Mazumder (2008), Long and Ferrie (2013), Clark (2014), and Chetty et al. (2017)fora
longer-term view on mobility in the United States; see Solon (1999) and Black and Devereux (2011)foran
overview of the extensive body of work on intergenerational mobility.
35
40
45
Absolute Upward Mobility
020 40 60 80
% Slaves of 1860 Population
a Absolute upward mobility ( = 25)
2
4
6
8
10
12
P(Child in Q5 | Parent in Q1)
020 40 60 80
% Slaves of 1860 Population
b P(child in Q5 | parent in Q1)
.30
.35
.40
.45
Relative Mobility (rank-rank slope)
020 40 60 80
% Slaves of 1860 Population
c Relative mobility (rank-rank slope)
–1
–.5
0
.5
CZ per-Year Exposure Effect
020 40 60 80
% Slaves of 1860 Population
d CZ per-year exposure effect ( = 25)
Fig. 1 Slavery and mobility in the United States. These figures show binned scatterplots of the CZ–level
relationship between the percentage of the population enumerated as slaves in the 1860 census and four
alternative mobility measures for children born in the early 1980s: (1) the expected income percentile at age 30
for children born to parents at the 25th percentile of the national income distribution (panel a); (2) the
probability that a child born to parents in the bottom income quintile ends up in the top quintile in adulthood
(panel b); (3) the rank-rank slope of child and parent income ranks (panel c); and (4) the causal place effect of
each CZ on adult income for children born to parents at the 25th income percentile (panel d). To construct each
figure, all 499 CZs in the main sample are collapsed into 20 bins based on the share of the population
enumerated as slaves, and for each bin the mean of each respective mobility measure is depicted. The first bin
contains all CZs with no slaves recorded in the 1860 census, and each subsequent bin contains approximately
15 CZs. Also shown are fitted OLS regressions based on the underlying (ungrouped) CZ–level data
1548 T. Be rge r
In this article, I examine whether differences in intergenerational mobility across the
United States reflect the historical distribution of slavery. Against the backdrop of a
growing empirical literature documenting how past events shape present-day outcomes
surveyed in Nunn (2009), I ask whether the legacy of slavery has detrimental long-run
effects on upward mobility. By pairing county-level data from the 1860 census
containing information on the prevalence of slavery with recent mobility estimates
for commuting zones (CZs) from Chetty et al. (2014a), I show that CZs where slavery
was more prevalent by the outbreak of the Civil War exhibit considerably less upward
mobility today.
2
Figure 1illustrates the CZ–level link between the share of the population enumerated
as slaves in the 1860 census and several complementary mobility measures (described in
more detail in the next section). Consistently, these binned scatterplots suggest that
intergenerational transmission of economic status is higher in areas with more prevalent
slavery and that children growing up in these CZs have substantially lower expected
earnings in adulthood relative to children born to parents at similar income levels
elsewhere in the country. For example, the average probability that a child born to
parents in the bottom income quintile will reach the top quintile in adulthood is roughly
4 % in the CZs with the highest prevalence of slavery, whereas it exceeds 10 % for
children in CZs with no recorded slaves in 1860 (see Fig. 1, panel b).
Although the correlations in Fig. 1are highly suggestive, the negative link between
slavery and upward mobility may simply reflect a confounding factor, such as lower
historical levels of industrial development or a more unequal distribution of income and
wealth in the American South rather than slavery per se. However, conditioning on a
wide range of historical and modern development outcomes leaves the negative link
between slavery and upward mobility largely unaffected, suggesting that it does not
mainly reflect the fact that places exposed to slavery were (or are), on average, less
economically developed. A related concern is that the observed relationships may arise
from a sorting of families with worse potential mobility outcomes into areas with more
prevalent slavery, rather than reflect a causal effect of place. Using data from Chetty
and Hendren (2016a,2016b) to estimate the causal effect of each CZ on upward
mobility net of sorting, however, I show that places with more prevalent slavery indeed
exhibit negative place-level effects on the opportunity for economic advancement
among children from low-income families, as depicted in Fig. 1, panel d. Last, to
further support a causal interpretation of this relationship, I document that this link is
also evident when using within-state variation in slavery and mobility, limiting the
sample to the 15 historical slave states at the eve of the Civil War, and exploiting
geographical differences in the suitability for growing cotton as the basis for an
instrumental variables (IV) strategy.
As a starting point to understand why places with more prevalent slavery produce
worse mobility outcomes, I conclude by examining the extent to which the five factors
highlighted by Chetty et al. (2014a) as the most important correlates of upward
mobility within the United States—family structure, income inequality, school quality,
segregation, and social capital—weaken this link. More fragile family structures, as
reflected in a lower share of married adults and a higher share of children living in
2
CZs correspond to local labor markets, which are identified as clusters of counties that exhibit strong
commuting ties within and weak ties between clusters (see Tolbert and Sizer 1996).
Slavery and the Geography of Intergenerational Mobility 1549
households headed by single mothers, are seemingly the most important of the five
candidates in accounting for the fact that upward mobility is lower in areas with a
higher prevalence of slavery. Although this result should be interpreted carefully
because of the challenges involved in identifying the precise underlying causal mech-
anisms and because multiple channels of transmission are likely at work, it is seemingly
consistent with prior work emphasizing the adverse impact of slavery on the evolution
of family structures in the American South after the Civil War and the historical
continuity of family patterns (e.g., Gordon and McLanahan 1991; Miller
forthcoming;Morganetal.1993;Ruggles1994).
Together, these findings constitute new evidence that slavery has a causal
negative impact on upward mobility, thus contributing to a theoretical and empir-
ical literature arguing that slavery negatively affected subsequent local economic
development and exacerbated inequality (Bertocchi and Dimico 2014;Lagerlöf
2005; Mitchener and McLean 2003; Nunn 2008b; Sokoloff and Engerman 2000),
as well as persistently altered political attitudes among Southern whites (Acharya
et al. 2016). Despite recent literature examining historical patterns of mobility
(e.g., Collins and Wanamaker 2015; Long and Ferrie 2013; Olivetti and Paserman
2015; Sacerdote 2005), this is the first study to document a persistent impact of
slavery on spatial differences in upward mobility, thus speaking directly to recent
literature analyzing the geography of opportunity in the contemporary United
States (e.g., Chetty et al. 2014a; Chetty and Hendren 2016a,2016b)aswellas
to a broader literature documenting the long-term impacts of slavery and other
exploitative historical labor-market institutions (e.g., Dell 2010;Nunn2008a;
Nunn and Wantchekon 2011).
Measuring Mobility
A straightforward way to characterize the spatial variation in intergenerational mobility
is to estimate income differences in adulthood for children born to parents in different
locations but at a similar rank in the national income distribution. To obtain such
measures, Chetty et al. (2014a) drew on federal income tax records for more than 10
million children born in the early 1980s, which they linked to their parents. Merging
information on parental household income with children’s family income in adulthood
(when they are approximately age 30) allows for a characterization of mobility patterns
in nearly all CZs in the United States.
3
In the main analysis, I use three complementary measures of mobility derived in
Chetty et al. (2014a) that have been made publicly available at the CZ level through the
Equality of Opportunity Project.
4
First, I examine differences in absolute upward
mobility across CZs captured by the mean rank at age 30 in the national (child) income
distribution for children born to parents at the 25th percentile (p= 25) of the national
(parent) income distribution.
5
Second, a complementary measure of upward mobility is
3
Parental incomes are measured in 1996–2000, and child incomes are measured in 2011–2012.
4
See http://www.equality-of-opportunity.org.
5
Because the rank-rank relationship is approximately linear, the outcomes for children born to parents at the
25th percentile correspond to the average income rank in adulthood for children born in the bottom half of the
income distribution.
1550 T. Be rge r
the probability that a child born to parents in the bottom quintile of the income
distribution reaches the top quintile in adulthood, which is commonly interpreted as
the chance to achieve the so-called American Dream. As shown in Table 1, panel A, a
child born to parents at the 25th percentile, on average, reaches the 42nd percentile at
age 30; a child born in the bottom quintile has, on average, 8.34 % chance of reaching
the top quintile across the CZs in the main sample. Third, I examine spatial differences
in relative mobility, which is captured by the slope in a regression of children’sincome
rank on parents’income rank in each CZ, similar in spirit to conventional intergener-
ational elasticity (IGE) of income estimates for which higher values correspond to less
mobility (Dahl and DeLeire 2008).
An additional source of mobility data is Chetty and Hendren (2016a,2016b),
who exploited the movement of families across CZs and the fact that the outcomes
for children who move converge with the outcomes for children of permanent
residents in the target destination in proportiontothefractionoftheirchildhood
spent there. Thus, if a child moves to a CZ where children of permanent residents at
similar income levels earn more (less) in adulthood, their expected adult earnings
increase (decrease) linearly with the number of years spent there as a child. By
exploiting the variation in the age at which children born to parents at the 25th
income percentile (p= 25) move between CZs, Chetty and Hendren (2016a,2016b)
identified a per-year exposure effect for each CZ that they interpreted as the causal
place-level effect on children’s income in adulthood.
6
As shown in Table 1, panel
A, the average percentage change in adult income (at age 26) from spending an
additional year as a child in a particular CZ in the sample ranges from an annual loss
of 0.91 % to a gain of 1.99 % relative to the national mean, which constitutes a
considerable variation as these per-year effects accumulate over a childhood. As
showninpanelBofTable1, on average, the CZs located in the 15 slave states at the
eve of the Civil War exhibit substantially worse place-level effects, which is also
Tab l e 1 Summary statistics: Mobility and slavery
A. Full Sample B. Slave States
Mean Min. Max. SD Mean Min. Max. SD
% Slaves, 1860 15.19 0 88.99 20.92 27.92 0 88.99 21.35
Absolute Upward Mobility (p= 25) 42.47 33.10 59.50 4.79 40.24 33.10 51.90 3.64
P(Child in Q5 | Parent in Q1) 8.34 2.20 23.30 3.47 6.89 2.20 18.00 2.83
Relative Mobility (rank-rank slope) 0.34 0.16 0.51 0.06 0.37 0.16 0.51 0.05
CZ per-Year Exposure Effect (p= 25) 0.12 -0.91 1.99 0.52 -0.11 -0.91 1.20 0.41
Notes: This table reports descriptive statistics for the share of the population enumerated as slaves in the 1860
census and mobility outcomes for the 499 CZs used in the main analysis (panel A) and the 269 CZs located in
one of the 15 slave states that existed at the eve of the Civil War (panel B). See the main text for a description
of each individual mobility measure.
6
As Chetty and Hendren (2016a,2016b) extensively documented, these estimates remain similar also when,
for example, controlling for a range of factors that may have triggered parental moves that may be correlated
with potential outcomes for children, exploiting sibling variation in exposure, or using exogenous shocks to
migration decisions.
Slavery and the Geography of Intergenerational Mobility 1551
evident when comparing differences in observed mobility outcomes using the three
complementary measures in the rows above.
A striking feature of the data is the substantial variation in mobility rates across CZs
and the close correspondence between the level of upward mobility and the prevalence
of slavery. This is evident from Fig. 2, which maps differences in absolute upward
mobility across CZs and the county-level share of the population enumerated as slaves
in the 1860 census: darker shades correspond to higher levels of mobility and slave
shares, respectively.
Another indirect link between slavery and upward mobility is depicted in Fig. 3,
showing how present-day mobility patterns in the South closely mirror the Cotton Belt
that today contains the least upwardly mobile areas in the country. To more systemat-
ically examine the potential link between slavery and mobility, I match county-level
information on the number of individuals enumerated as slaves in the 1860 census
a Upward mobility
b Slavery
Fig. 2 Geography of mobility and slavery in the United States. These maps show differences in
absolute upward mobility across CZs measured as the mean income rank at age 30 for children
born in the early 1980s (1980–1982) to parents at the 25th percentile of the national income
distribution (panel a) and the county-level share of the population that were enumerated as slaves in
the 1860 census (panel b). Each map divides the corresponding variable into ventiles, with darker
shades denoting higher levels of upward mobility (mobility estimates are unavailable for hatched
CZs) and a higher share of slaves, respectively. County (CZ) boundaries are based on maps from
IPUMS NHGIS (Manson et al. 2017)
1552 T. Be rge r
obtained from Haines (2005) to the corresponding CZ using crosswalks available from
the U.S. Department of Agriculture’s Economic Research Service, which I then use to
calculate the share of the population that were slaves in 1860 in each CZ. Each
individual county is matched to a present-day CZ, but because the mobility data are
unavailable for a small number of CZs, the main analysis focuses on the 499 CZs for
which I observe all four main mobility measures and at least one constituent county
exists in the 1860 census.
Empirical Results
OLS Estimates
As a starting point to examine the link between slavery and mobility, I estimate
ordinary least squares (OLS) regressions on the following form:
Mi
c¼αþδS1860
cþλsþXcθþεc;ð1Þ
where Mi
cis a mobility measure ifor CZ c,S1860
cis the percentage of the population
enumerated as slaves in 1860, λ
s
is a full set of state fixed effects, X
c
is a vector of CZ–
level covariates, and ε
c
is an error term. For the statistical inference, I cluster standard
errors at the state level to account for spatial correlation across CZs.
Fig. 3 Cotton cultivation and mobility in the American South. This map shows differences in absolute
upward mobility across CZs measured as the mean income rank at age 30 for children born in the early 1980s
(1980–1982) to parents at the 25th percentile of the national income distribution and cotton output in 1860.
Each dot corresponds to approximately 5,000 (400-pound) bales of ginned cotton produced and the underlying
mobility data is divided into ventiles, with darker shades corresponding to higher rates of upward mobility.
Data on cotton production is drawn from the 1860 Census of Agriculture obtained through IPUMS NHGIS
(Manson et al. 2017)
Slavery and the Geography of Intergenerational Mobility 1553
Tab le 2presents OLS estimates of Eq. (1), showing that CZs with a higher
prevalence of slavery on average exhibit lower upward mobility today.
7
Each cell
corresponds to an individual regression, with standardized coefficients reported in
brackets to ease interpretation. As shownincolumn1,a1standarddeviation(SD)
increase in the share of the CZ population that were slaves in 1860 is associated
with a reduction in absolute upward mobility of 0.589 SDs today—a sizable effect
that is also highly statistically significant. Put differently, low-income children
growing up in a CZ with a 30 percentage point higher share of the population
enumerated as slaves in 1860, which roughly corresponds to the distance between
7
Although I present unweighted regressions throughout the article, results remain broadly similar in magni-
tude and statistical precision when weighting the reported regressions by CZ populations in either 1860 or
2000, or by the number of children in the 1980–1982 birth cohorts (not reported).
Tab l e 2 Slavery and intergenerational mobility: OLS estimates
(1) (2) (3) (4) (5)
Absolute Upward Mobility (p=25) –0.135** –0.069** –0.053** –0.055** –0.066**
(0.020) (0.009) (0.012) (0.010) (0.011)
[–0.589] [–0.301] [–0.233] [–0.239] [–0.385]
P(Child in Q5 | Parent in Q1) –0.093** –0.053** –0.041** –0.042** –0.053**
(0.014) (0.008) (0.011) (0.011) (0.010)
[–0.563] [–0.322] [–0.245] [–0.252] [–0.398]
Relative Mobility (rank-rank slope) 0.002** 0.001** 0.001** 0.001** 0.001**
(0.0002) (0.0002) (0.0002) (0.0002) (0.0002)
[0.592] [0.451] [0.411] [0.397] [0.438]
CZ per-Year Exposure Effect (p=25) –0.015** –0.009** –0.007** –0.007** –0.009**
(0.002) (0.002) (0.002) (0.001) (0.002)
[–0.601] [–0.346] [–0.294] [–0.299] [–0.452]
State Fixed Effects? No Yes Yes Yes Yes
Historical Controls? No No Yes Yes Yes
ModernControls? NoNoNoYesYes
Sample States All All All All Slave states
Number of Observations (CZs) 499 499 499 499 269
Notes: This table reports OLS estimates of δfrom Eq. (1) where the main right-hand-side variable
is the percentage of the population in each CZ that were enumerated as slaves in 1860, and the
outcome is one of four measures of absolute or relative mobility listed in the leftmost column of
the table and described in more detail in the main text. Column 3 includes CZ–level controls for
access to rail/water transportation; the cash value of farms; the share of improved farmland;
manufacturing output per capita; population; and the share of the population that are free blacks,
employed in manufacturing, and living in urban areas, respectively, based on the 1860 census.
Column 4 adds CZ–level controls for average household income and labor force participation rates
in 2000, income growth between 2000 and 2010, and whether a CZ intersects a metropolitan area.
In column 5, the sample is restricted to CZs that are located in one of the 15 slave states that
existed at the eve of the Civil War. Each cell corresponds to an individual regression with
standardized coefficients reported in brackets, and robust standard errors clustered at the state level
reported in parentheses.
**p<.01
1554 T. Be rge r
the 50th and 75th percentile of the cross–CZ distribution of slave shares, are
predicted to see an average 4.05 (–0.135 × 30) percentile reduction in adult income.
As shown in the subsequent two rows of Table 2, the magnitudes remain similar
also when examining the chance that a child born in the bottom income quintile
ends up in the top quintile, as well as when using the more conventional rank-rank
slope measure of relative mobility. While estimated magnitudes are reduced when
including state fixed effects in column 2 of Table 2, a large and statistically
significant relationship exists between the historical prevalence of slavery and
mobility also within states, suggesting that these correlations do not exclusively
reflect cross-state variation in mobility outcomes.
Although these estimates show that CZs with more prevalent slavery exhibit
substantially lower levels of upward mobility today, they do not reveal whether this
relationship arises due to a causal effect of place or whether it reflects a sorting of
families with worse potential mobility outcomes into these areas. Importantly, OLS
estimates of the association between slavery and the causal place effect of each CZ
reported in the bottom row of Table 2suggest that a 1 SD increase in the share of slaves
in 1860 is associated with a 0.601 SD decrease in the place effect on adult income for
low-income children. Put differently, the estimate reported in column 1 suggests that for
a child growing up in a CZ with a 30 percentage point higher slave share in 1860, each
additional year of childhood spent there leads to an average decrease of 0.45 % (–0.015
× 30) in adult income relative to the national mean; over a childhood of 20 years, this
corresponds to a relative decrease of 9.00 % (–0.45 × 20). Overall, this finding suggests
that the observed differences in upward mobility largely accrue from causal place
effects rather than sorting.
An empirical concern is that the spatial distribution of slavery in 1860 may be
correlated with a variety of omitted factors, which in turn may influence differences in
mobility.
8
Therefore, column 3 of Table 2controls for a wide range of historical
development outcomes capturing differences in agricultural development, manufactur-
ing activity, population, transportation, and urbanization.
9
A similar argument can be
made regarding contemporary confounding factors that may reflect a differential
development in areas with higher slave shares that is unrelated to slavery itself. To
that end, column 4 also controls for a range of modern outcomes, including household
income, income growth, labor force participation, and whether a CZ is part of an urban
area, although several of these regressors are likely to constitute “bad controls”(Angrist
8
Although race is not observed for individuals in the data, the link between slavery and mobility exists also
when focusing on cross–CZ differences in mobility rates restricted to individuals living in ZIP codes where at
least 80 % of the population are non-Hispanic whites. Among the 449 CZs for which these data are available,
the OLS estimate (standard error) from regressing absolute upward mobility in “white”ZIP codes on the
percentage of slaves in 1860 is –0.084 (0.026), which is smaller in absolute terms than the estimate in column
1ofTable2but still sizable and statistically significant. Furthermore, Chetty et al. (2014a) reported that the
unweighted correlation between mobility for predominantly whites and the full sample is 0.91, which suggests
that the observed CZ–level differences in upward mobility do not mainly reflect individual-level race
differences in mobility.
9
From the 1860 census, I calculate the (ln) population, the share of the population that are free blacks, and the
share living in cities with at least 2,500 inhabitants. As proxies for agriculturaldevelopment, I calculate the (ln)
cash value of farms and the share of improved farmland of total (improved and unimproved) farmland. Last, I
create a dummy variable indicating whether any county in a CZ had access to water or rail transportation, and
calculate the share employed in manufacturing and the (ln) manufacturing output per capita, respectively.
Slavery and the Geography of Intergenerational Mobility 1555
and Pischke 2008).
10
Last, to shed light on whether the relationship is fully driven by
differences between the South relative to other parts of the country, column 5 restricts
the analysis to CZs located in one of the 15 slave states, thus identifying the relationship
from the intensive rather than extensive margin of slavery. Although these additional
controls and sample restrictions lead to moderate declines in estimated magnitudes, a
substantial and statistically significant link between a legacy of slavery and lower
upward mobility persists, which suggests that this relationship is not mainly
reflecting any of these observable factors.
11
A remaining threat to the validity of these OLS estimates, however, is that the
prevalence of slavery may be correlated with unobservable historical or contemporary
factors that in turn shape differences in mobility. As a first step to assess this empirical
threat, I use the method developed by Altonji et al. (2005) and applied by Nunn and
Wantchekon (2011) to assess how large the selection on unobservable characteristics
has to be relative to the selection on observable factors in order to explain away the
estimated link between slavery and mobility. In practice, it entails estimating two
different models: one with a restricted set of regressors, and one with an extensive
set of controls. By denoting the estimates in the restricted and full model δ
R
and δ
F
,
respectively, the ratio δ
F
/(δ
R
−δ
F
) yields how large the selection on unobservable
characteristics has to be relative to the selection on the observable factors included in
the full model to render the estimates economically insignificant.
Tab le 2presents OLS estimates of the link between absolute upward mobility and
the prevalence of slavery that correspond to a bare-bones specification with state fixed
effects but without any controls (column 2) and when adding the full set of historical
and modern controls, respectively (column 4). Calculating the Altonji et al. (2005)ratio
based on these estimates suggests that the selection on unobservable factors needs to be
almost four times as large as the selection on observable factors to explain away the
results.
12
Although this exercise thus suggests that unobservable factors must be very
influential to explain away the link between slavery and present-day mobility, I next
proceed to develop an IV strategy to further reduce concerns that unobservable
confounding factors are driving the results.
10
To account for differences in economic structure today, I include the average household income and labor
force participation in 2000, income growth between 2000 and 2010, and a dummy variable for whether a CZ
intersects a metropolitan area from Chetty et al. (2014a). As suggestedby an anonymous referee, the long-term
effects of slavery may also reflect differences in geographical mobility across CZs. Controlling for contem-
porary migration in/outflows in each CZ, however, does little to affect the link between slavery and mobility
(not reported).
11
Sokoloff and Engerman (2000) argued that (land) inequality is intrinsically linked to slavery and to
subsequent economic development. To evaluate whether historical inequality accounts for the effect of slavery
and whether it directly shapes present-day patterns of mobility, I construct a Gini coefficient of the farm size
distribution using a procedure similar to that used by Nunn (2008b) with data from the 1860 census. In a
subsample of CZs (n= 458) for which I observe land inequality, the link between slavery and lower upward
mobilityremains highly statistically significant and similar in magnitude across all four outcomes when adding
the Gini coefficient to similar specifications as in column 4 of Table 2, but land inequality itself is seemingly
unrelated to present-day patterns of mobility (not reported).
12
Based on the estimates in Table 2, columns 2 and 4, the ratio is calculated as −0.055 / ((−0.069) −(−0.055)) =
3.93. Similar exercises can be carried out for the other outcomes reported in Table 2. Consistently, the high
Altonji et al. (2005) ratios (3.50–11.00) suggest that selection on unobservable factors needs to be very
influential relative to the observable factors included to explain away the estimated effect.
1556 T. Be rge r
IV Estimates
To identify whether the link between slavery and mobility is causal, I exploit differ-
ences in the suitability for cultivating cotton as an instrument for the intensity of slavery
in 1860 based on data from the FAO’s Global Agro-Ecological Zone (FAO-GAEZ)
database taken from Hornbeck and Naidu (2014).
13
Cotton was the main cash crop
cultivated in the South that relied heavily on slave labor, and differences in the
suitability for cotton cultivation can therefore be expected to constitute an important
determinant of the prevalence of slavery (Acharya et al. 2016) but presumably do not
directly affect present-day differences in upward mobility.
A tight link between suitability for cultivating cotton and the use of slave labor is
indeed reflected in a strong first-stage relationship between the prevalence of slavery in
1860 and differences in suitability across CZs, as shown in Table 3, panel B.
14
Although this finding highlights the relevance of the instrument, the exclusion restric-
tion may still be violated if cotton suitability affects mobility through other channels
than slavery. To assess the validity of the instrument, I examine whether cotton
suitability is uniquely associated with negative mobility outcomes in areas where
slavery was widespread. Figure 4presents binned scatterplots of within-state differ-
ences in the mean suitability for cultivating cotton and absolute upward mobility across
CZs with no slaves in 1860 according to the census and CZs where at least one slave
was recorded. I find no apparent relationship between cotton suitability and upward
mobility in CZs with no slavery but a sharply negative relationship in areas where
slavery was prevalent. Together, this reduced-form evidence thus suggests that cotton
suitability is uniquely associated with lower mobility in areas where slavery was
widespread, which indirectly supports the exclusion restriction.
Panel A of Table 3presents two-stage least squares (2SLS) estimates of Eq. (1),
showing that the negative and highly statistically significant link between slavery and
mobility exist also when instrumenting for differences in slave shares across CZs. To
further mitigate concerns that the exclusion restriction may be violated, I sequentially
include state fixed effects and the full set of historical and modern controls in columns
1–3 and limit the sample to the historical slave states in column 4. Thus, for the
exclusion restriction to be violated, the instrument must affect mobility through
channels other than, for example, contemporary income or historical differences in
industrial development.
15
Across all four mobility outcomes, the link between slavery
and mobility persists, thus suggesting that the correlation documented earlier presum-
ably reflects a causal relationship. Moreover, 2SLS estimates reported in columns 3 and
4 of Table 3are larger in absolute terms compared with the corresponding OLS
13
Cotton suitability reflects the mean suitability for cultivating cotton based on county-level estimates on
climate, growing conditions, and soil types as defined in the FAO-GAEZ database (version 3.0). Maximum
potential yields are based on climate averages between 1961 and 1990 and assuming rain-fed conditions and
intermediate input levels.
14
As indicated in the bottom of Table 3, the Kleibergen-Paap Fstatistic from the first stage is sufficiently large
to reduce weak instrument concerns in all four specifications.
15
A particular concern is that differences in the suitability for cultivating cotton may be correlated with other
geographic features that in turn may have affected long-term development patterns. However, although
controlling for other geographic characteristics (such as altitude and ruggedness) from the USGS National
Elevation Dataset in the first stage somewhat weakens the power of the instrument, the second stage estimates
are of a similar magnitude and remain highly statistically significant (not reported).
Slavery and the Geography of Intergenerational Mobility 1557
estimates reported in columns 4 and 5 of Table 2. This finding is consistent with
measurement error in the historical slave data, a downward bias in the OLS estimates
due to omitted factors, or that the 2SLS estimates reflect a local average treatment
effect. These estimates consistently suggest that areas with more prevalent slavery
exhibit substantially lower chances for upward mobility and that these differences arise
because of negative causal place effects, raising a pertinent question: why do these
places produce worse outcomes for low-income children some 150 years after slavery
was formally abolished?
Tab l e 3 Slavery and intergenerational mobility: 2SLS estimates
(1) (2) (3) (4)
A. Outcome: Absolute/Relative Mobility
(second stage)
Absolute upward mobility (p=25) –0.122* –0.121
†
–0.112
†
–0.106**
(0.048) (0.065) (0.060) (0.030)
[–0.534] [–0.528] [–0.489] [–0.622]
P(Child in Q5 | Parent in Q1) –0.097* –0.106* –0.091* –0.066**
(0.041) (0.051) (0.047) (0.022)
[–0.583] [–0.639] [–0.550] [–0.501]
Relative mobility (rank-rank slope) 0.003** 0.004** 0.004** 0.004**
(0.001) (0.001) (0.001) (0.001)
[1.197] [1.431] [1.371] [1.522]
CZ per-year exposure effect (p=25) –0.013* –0.012
†
–0.012
†
–0.012*
(0.006) (0.006) (0.006) (0.006)
[–0.518] [–0.501] [–0.469] [–0.614]
B. Outcome: % Slaves, 1860 (first stage)
Cotton suitability 15.908** 11.703** 11.767** 17.352**
(3.879) (3.617) (3.512) (4.016)
[0.267] [0.197] [0.198] [0.281]
State Fixed Effects? Yes Yes Yes Yes
Historical Controls? No Yes Yes Yes
Modern Controls? No No Yes Yes
Kleibergen-Paap FStatistic 16.82 10.47 11.23 18.67
Sample States All All All Slave states
Number of Observations (CZs) 499 499 499 269
Notes: Panel A reports two-stage least squares (2SLS) estimates of δfrom Eq. (1), where the
percentage of the population in each CZ that were enumerated as slaves in 1860 is instrumented with
cotton suitability based on data from the FAO-GAEZ, and the outcome is one of four measures of
absolute or relative mobility listed in the leftmost column of the table and described in more detail in
the main text. Panel B presents the corresponding first stage estimates (across the CZs in the full and
restricted sample, the mean cotton suitability is 0.31 (SD = 0.35) and 0.40 (SD = 0.35), respectively).
See the notes for Table 2for a description of the additional controls. Each cell corresponds to an
individual regression with standardized coefficients reported in brackets, and robust standard errors
clustered at the state level reported in parentheses.
†
p<.10;*p<.05;**p<.01
1558 T. Be rge r
Assessing Potential Channels of Persistence
A good starting point to disentangle the persistent effects of slavery on mobility
patterns is to ask whether any of the factors that Chetty et al. (2014a)identifiedas
the most important correlates of mobility across CZs (family structure, income inequal-
ity, school quality, segregation, and social capital) can account for the fact that areas
with higher slave shares by the outbreak of the Civil War exhibit substantially lower
upward mobility today. If any of these factors are relevant to understand this link, we
would expect that they vary systematically with the historical distribution of slavery
and that conditioning on such a factor would reduce the coefficient of the historical
slave share.
Panel A of Table 4presents OLS estimates of Eq. (1) where the outcome is the
causal place effect of each CZ on adult income for low-income children, while
conditioning on proxies of each potentially mediating factor, respectively.
16
To ease
interpretation, standardized coefficients are again reported in brackets. Similar regres-
sions in which each factor listed in the column heads is used as an outcome are reported
in panel B to examine the extent to which they correlate with the prevalence of slavery.
To reduce concerns that the variation in the potentially mediating factors is mainly
driven by unique features of places in the South, I focus here on the subsample of CZs
that are located in the 15 slave states and include the full set of historical and modern
controls, as well as state fixed effects.
17
In panel A of Table 4,columns1–4 show that the estimated link between slavery and
mobility remains similar in magnitude when conditioning on segregation by income or
race as well as two alternative measures of income inequality (the Gini coefficient and
the share of income accruing to the top 1 %) relative to the corresponding OLS estimate
16
For brevity, I focus here on the causal place effect as an outcome, although using the alternative measures of
absolute and relative mobility produces very similar results (not reported). Additional CZ characteristics are
drawn from Chetty et al. (2014a) and are described in more detail in the notes for Table 4.
17
Results are, however, similar in the full sample of CZs (not reported).
44.0
44.5
45.0
45.5
46.0
46.5
Absolute Upward Mobility
–.2 0 .2 .4 .6
Cotton Suitability
39.0
39.5
40.0
40.5
41.0
41.5
Absolute Upward Mobility
0.2 .4 .6 .8
Cotton Suitability
a CZs with no slaves in 1860 census b CZs with slaves in 1860 census
Fig. 4 Cotton suitability and upward mobility. These figures show binned scatterplots of the residualized CZ–
level link between cotton suitability and absolute upward mobility across CZs with no (0) slaves reported in
the 1860 census (panel a) and across CZs with at least one slave reported in the census (panel b). Each group
of CZs is collapsed into 10 bins based on the residualized average suitability for cultivating cotton after
absorbing state fixed effects, and for each bin, the mean level of residual absolute upward mobility is depicted.
The sample means are added back to the residuals of each variable prior to binning and plotting. Also shown
are fitted OLS regressions based on the underlying (ungrouped) CZ–level data
Slavery and the Geography of Intergenerational Mobility 1559
Tab l e 4 Explanation of the link between slavery and mobility
Segregation Inequality Social Capital K–12 School System Family Structure
Income Race Gini Top 1 %
R&G
Index
High
School
Dropout
Rate
Tes t
Scores
Student-
Teach er
Ratio Expenditure Divorced Married
Single
Mothers
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
A. Conditional Effect
of Slavery on
Mobility (outcome:
CZ per-year exposure
effect (p= 25))
% slaves, 1860 –0.007** –0.008** –0.008** –0.009** –0.008** –0.008** –0.006** –0.008** –0.009** –0.009** –0.004** –0.001
(0.001) (0.001) (0.001) (0.002) (0.002) (0.002) (0.001) (0.002) (0.002) (0.002) (0.001) (0.001)
[–0.375] [–0.438] [–0.425] [–0.462] [–0.436] [–0.390] [–0.326] [–0.422] [–0.451] [–0.457] [–0.233] [–0.032]
Factor in column head –0.042** –0.010** –0.014** –0.006 0.060 –0.048* 0.018** –0.047 –0.001 –0.010 0.036** –0.045**
(0.007) (0.003) (0.005) (0.006) (0.049) (0.019) (0.004) (0.035) (0.001) (0.020) (0.005) (0.005)
[–0.330] [–0.194] [–0.229] [–0.065] [0.117] [–0.226] [0.296] [–0.163] [–0.108] [–0.031] [0.395] [–0.604]
B. Effect of Slavery
on Modern Outcomes
(outcome: listed in
column heads)
% slaves, 1860 0.035* 0.027 0.036 –0.030 –0.005 0.020* –0.134* 0.004 0.026 –0.010* –0.119** 0.179**
(0.014) (0.035) (0.039) (0.019) (0.004) (0.008) (0.049) (0.006) (0.126) (0.005) (0.022) (0.024)
[0.235] [0.072] [0.116] [–0.155] [–0.135] [0.219] [–0.424] [0.063] [0.009] [–0.170] [–0.555] [0.695]
Mean (SD) of Factor
in column head
4.41 14.43 46.33 11.72 –0.77 0.65 –3.02 16.37 538.10 9.97 56.44 23.36
(3.22) (7.84) (6.56) (4.12) (0.80) (1.96) (6.73) (1.48) (65.22) (1.29) (4.57) (5.50)
1560 T. Be rge r
Tab l e 4 (continued)
Segregation Inequality Social Capital K–12 School System Family Structure
Income Race Gini Top 1 %
R&G
Index
High
School
Dropout
Rate
Tes t
Scores
Student-
Teach er
Ratio Expenditure Divorced Married
Single
Mothers
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
State Fixed Effects? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Historical Controls? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Modern Controls? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Number of Observations (CZs) 269 269 269 269 269 258 268 244 269 269 269 269
Notes: Panel A re ports OLS estimates of Eq. (1), where the ma in right-hand-side vari able is the percentage of the population en umerated as slaves in 1860, and the
outcome is the causal place effect of each CZ, while conditioning on each fa ctor listed in the column heads. Panel B presents similar OLS regressions with each
factor listed in the column heads as the outcome, with the mean (SD) of each outcome report ed in the bottom of the table. Income and racial segregation is measured
as a rank-order index estimated at the census-tract level and a multigroup Theil index based on four gr oups (black, Hispanic, other, and white) , respectively, both
based on data from the 2000 census and both scaled by a factor of 100 for presentational purposes. Inequality is measured by the Gini coefficient of income (× 100),
and the share of in come accruing to the top 1 % based on tax records for the sample used to derive mobility rates. Social capital is measured by the Rupasingha and
Goetz (2008) standardiz ed index that combines measures of voter turn out rates, the shar e of people returning their census forms, and participation in community
organizations. K–12 school ing characte ristics are: a residual of high school dropout rates (× 100) and mean math and English standardized test scores after regressing
them on household income pe r capita in 2000, the average student-teacher ratio in public sc hools, and the average expenditure per student (× 100) basedondata from
the NCES CCD and the George Bush Global Report Card. Family structure is measured by the percentage of the population aged 15 and older who are divorced and
married (and not separated), respectively, and the percentage of all households with children that are headed by a single mother based on the 2000 census. See Chetty
et al. (2014a) for a further description of these variables and see the notes to Table 2for a description of the additi onal contro ls. Standardized coefficients are
reported in brackets, and robust standard errors clustered at the state level are reported in parentheses.
*p< .05; **p<.01
Slavery and the Geography of Intergenerational Mobility 1561
without these additional covariates (see Table 2,column5).
18
Similarly, controlling for
differences in the level of social capital as reflected in the index of Rupasingha and
Goetz (2008) does not seem to affect the link between slavery and upward mobility
(column 5 of Table 4).
19
An emphasis on the role of the educational system in shaping mobility outcomes is
broadly supported by the results in columns 6–9ofTable4, which condition on four
aspects of the K–12 school system: dropout rates and test scores adjusted for parental
income, and the average student-teacher ratio and expenditure per student in public
schools.
20
Yet, although output-based measures of school quality (dropout rates and test
scores) are significantly worse in areas with more prevalent slavery (panel B, columns 6
and 7), it does not seem to account for the reduced-form link between slavery and upward
mobility (panel A).
A final factor that has been highlighted as a key determinant of mobility patterns is
differences in family structure. Columns 10–12 of Table 4condition on three measures
of the stability of family structures: the percentage of the population aged 15 and older
that are divorced and married, respectively, as well as the percentage of households
with an own child present and headed by single mothers. When conditioning on the
share of female single-parent households in column 12, the association between slavery
and upward mobility loses its statistical significance, and the coefficient is effectively
reduced to 0, which is consistent with a significant part of the lingering effect of slavery
working through more fragile family structures in these places. A link between slavery
and subsequently more fragile family structures is further reinforced by the estimates
presented in panel B of Table 4, which show that a 1 SD increase in slavery is
associated with a 0.555 SD decrease in the share married and an even larger (0.695
SD) increase in the share of children living in single-parent households (columns 11
and 12).
21
Although it is beyond the scope of this study to identify how slavery affected
18
Although such comparisons should be made with care, it is interesting to note that slavery has a larger effect
on contemporary mobility rates (in SD terms) relative to influential factors such as income inequality or
segregation. Similarly, alternative measures of (income) segregation derived by Chetty et al. (2014a)—such as
the extent to which the bottom and top income quartiles are isolated from families at higher and lower income
levels, respectively—do not affect the estimated link between slavery and mobility (not reported).
19
Similarly, controlling for alternative measures of social capital used by Chetty et al. (2014a), such as the fraction of
religious individuals or violent crime rates, does not alter the link between slavery and mobility (not reported).
20
A similar analysis of the pot ential role for higher education reflected in the number of Title IV institutions per
capita, the average in-state fees/tuition for first-time undergraduates, or the income-adjusted college graduation
rates in a CZ leaves the link between slavery and mobility virtually unaffected, suggesting that this relationship
does not reflect differencesin the provision of higher education (not reported). The K–12 characteristics are not
available for all CZs, which slightly reduces the number of observations in columns 6–8ofTable4.
21
An alternative explanation for the higher share of single-parent households in areas with more prevalent slavery
may be that this partly reflects a historical concentration of African Americans in these CZs. Indeed, the raw
correlation between the share of single-parent households and the share of black residents is 0.93, and controlling
for black shares renders the link between slavery and mobility statistically insignificant. However, the link
between the CZ-level share of black residents and upward mobility turns positive albeit not statistically significant
when I simultaneously control for the share of single-parent households, whereas the latter remains an
economically and statistically significant correlate of lower upward mobility. Chetty et al. (2014a) similarly
documented this relationship and argued that it provides suggestive evidence that the link between race and
mobility is likely to work through indirect channels (e.g., differences in family structure) rather than through a
direct effect of race on mobility. This possibility is consistent with the evidence described earlier showing that
mobility rates are also lower in predominately “white”ZIP codes in areas with more prevalent slavery and the
results reported throughout this article showing that most of these effects seemingly arise at the community level.
1562 T. Be rge r
the evolution of family structures over the twentieth century, it is interesting to note that
these results are broadly consistent with prior research that has emphasized how the
legacy of slavery shaped family structures after the Civil War and the historical
continuity of family patterns (e.g., Gordon and McLanahan 1991; Miller
forthcoming;Morganetal.1993;Ruggles1994).
Conclusions
A key contribution of this article is to document that the present-day geography of
intergenerational mobility in the United States largely reflects the historical distribution
of slavery, with substantially less upward mobility in areas with a higher share of slaves
by the outbreak of the Civil War. Based on a variety of empirical strategies, the
evidence suggests that this relationship is causal. Exploiting differences reported by
Chetty and Hendren (2016a,2016b)inobservedmobilityratesforchildrenwhose
families move across CZs to identify the place-based component of upward mobility
suggests that this relationship does not arise mainly from sorting of families across CZs;
rather it reflects a causal effect of place.
As a starting point to understand why slavery still shapes the geography of oppor-
tunity in the United States, I examine whether the five broad factors highlighted by
Chetty et al. (2014a) as the most important correlates of mobility can account for the
documented link between slavery and upward mobility. More fragile family structures
in areas that had more prevalent slavery is seemingly the most important for under-
standing why these places produce significantly worse mobility outcomes today.
Although these results are suggestive, they should be interpreted carefully because of
the extremely challenging task of identifying the wide variety of causal transmission
mechanisms that may link slavery to present-day differences in mobility. Further work
is necessary for understanding how these differences emerged and the extent to which
they link the past to the present.
Acknowledgments I am very grateful for comments and suggestions by the editors and three anonymous
referees that substantially improved this article. Any remaining errors are my own.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and repro-
duction in any medium, provided you give appropriate credit to the original author(s) andthe source,provide a
link to the Creative Commons license, and indicate if changes were made.
References
Aaronson, D., & Mazumder, B. (2008). Intergenerational economic mobility in the United States, 1940 to
2000. Journal of Human Resources, 43, 139–172.
Acharya, A., Blackwell, M., & Sen, M. (2016). The political legacy of American slavery. Journal of Politics,
78, 621–641.
Altonji, J. G., Elder, T. E., & Taber, C. R. (2005). Selection on observed and unobserved variables: Assessing
the effectiveness of Catholic schools. Journal of Political Economy, 113, 151–184.
Slavery and the Geography of Intergenerational Mobility 1563
Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton,
NJ: Princeton University Press.
Bertocchi, G., & Dimico, A. (2014). Slavery, education, and inequality. European Economic Review, 70, 197–
209.
Black, S. E., & Devereux, P. J. (2011). Recent developments in intergenerational mobility. Handbook of Labor
Economics, 4, 1487–1541.
Chetty, R., Grusky, D., Hell, M., Hendren, N., Manduca, R., & Narang, J. (2017). The fading American
dream: Trends in absolute income mobility since 1940. Science, 356, 398–406.
Chetty, R., & Hendren, N. (2016a). The impacts of neighborhoods on intergenerational mobility I: Childhood
exposure effects (NBER Working Paper No. 23001). Cambridge, MA: National Bureau of Economic
Research.
Chetty, R., & Hendren, N. (2016b). The impacts of neighborhoods on intergenerational mobility II: County-
level estimates (NBER Working Paper No. 23002). Cambridge, MA: National Bureau of Economic
Research.
Chetty, R., Hendren, N., Kline, P., & Saez, E. (2014a). Where is the land of opportunity? The geography of
intergenerational mobility in the United States. Quarterly Journal of Economics, 129, 1553–1623.
Chetty, R., Hendren, N., Kline, P., Saez, E., & Turner, N. (2014b). Is the United States still a land of
opportunity? Recent trends in intergenerational mobility. American Economic Review: Papers and
Proceedings, 104, 141–147.
Clark, G. (2014). The son also rises: Surnames and the history of social mobility. Princeton, NJ: Princeton
University Press.
Collins, W. J., & Wanamaker, M. H. (2015). Up from slavery? Intergenerational mobility in the shadow of Jim
Crow (NBER Working Paper No. 23395). Cambridge, MA: National Bureau of Economic Research.
Corak, M. (2013). Income inequality, equality of opportunity, and intergenerational mobility. Journal of
Economic Perspectives, 27(3), 79–102.
Dahl, M. W., & DeLeire, T. (2008). The association between children’s earnings and fathers’lifetime
earnings: Estimates using administrative data (Discussion Paper No. 1342-08). Madison: University of
Wisconsin–Madison, Institute for Research on Poverty.
Dell, M. (2010). The persistent effects of Peru’s mining “mita.”Econometrica, 78, 1863–1903.
Gordon, L., & McLanahan, S. (1991). Single parenthood in 1900. Journal of Family History, 16, 97–116.
Haines,M.R.(2005).Historical, demographic, economic, and social data: The United States, 1790–2000
[Data sets from ICPSR 2896]. Ann Arbor, MI: Inter-University Consortium for Political and Social
Research (ICPSR).
Hornbeck, R., & Naidu, S. (2014). When the levee breaks: Black migration and economic development in the
American south. American Economic Review, 104, 963–990.
Lagerlöf, N.-P. (2005). Geography, institutions and growth: The United States as a microcosm. Unpublished
manuscript, Department of Economics, York University, Toronto, Ontario, Canada.
Lee, C.-I., & Solon, G. (2009). Trends in intergenerational income mobility. Review of Economics and
Statistics, 91, 766–772.
Long, J., & Ferrie, J. (2013). Intergenerational occupational mobility in Great Britain and the United States
since 1850. American Economic Review, 103, 1109–1137.
Manson, S., Schroeder, J., van Riper, D., & Ruggles, S. (2017). IPUMS National Historical
Geographic Information System: Version 12.0 [Data set]. Minneapolis: University of Minnesota.
https://doi.org/10.18128/D050.v12.9
Mazumder, B. (2005). Fortunate sons: New estimates of intergenerational mobility in the United States using
social security earnings data. Review of Economics and Statistics, 87, 235–255.
Miller, M. C. (Forthcoming). Destroyed by slavery? Slavery and African American family formation
following emancipation. Demography.
Mitchener, K. J., & McLean, I. W. (2003). The productivity of US states since 1880. Journal of Economic
Growth, 8, 73–114.
Morgan, S. P., McDaniel, A., Miller, A. T., & Preston, S. H. (1993). Racial differences in household and
family structure at the turn of the century. American Journal of Sociology, 98, 799–828.
Nunn, N. (2008a). The long-term effects of Africa’s slave trades. Quarterly Journal of Economics, 123, 139–
176.
Nunn, N. (2008b). Slavery, inequality, and economic development in the Americas. Cambridge, MA: Harvard
University Press.
Nunn, N. (2009). The importance of history for economic development. Annual Review of Economics, 1, 65–
92. https://doi.org/10.1146/annurev.economics.050708.143336
1564 T. Be rge r
Nunn, N., & Wantchekon, L. (2011). The slave trade and the origins of mistrust in Africa. American Economic
Review, 101, 3221–3252.
Olivetti, C., & Paserman, M. D. (2015). In the name of the son (and the daughter): Intergenerational mobility
in the United States, 1850–1940. American Economic Review, 105, 2695–2724.
Ruggles, S. (1994). The origins of African-American family structure. American Sociological Review, 59,
136–151.
Rupasingha, A., & Goetz, S. J. (2008). US county-level social capital data, 1990–2005 [Data set]. University
Park: Northeast Regional Center for Rural Development, Pennsylvania State University.
Sacerdote, B. (2005). Slavery and the intergenerational transmission of human capital. Review of Economics
and Statistics, 87, 217–234.
Sokoloff, K. L., & Engerman, S. L. (2000). History lessons: Institutions, factors endowments, and paths of
development in the New World. Journal of Economic Perspectives, 14(3), 217–232.
Solon, G. (1992). Intergenerational income mobility in the United States. American Economic Review, 82,
393–408.
Solon, G. (1999). Intergenerational mobility in the labor market. Handbook of Labor Economics, 3, 1761–
1800.
Tolbert, C. M., & Sizer, M. (1996). U.S. commuting zones and labor market areas: A 1990 update (ERS Staff
Paper No. 9614). Washington, DC: U.S. Department of Agriculture, Economic Research Service, Rural
Economy Division.
Slavery and the Geography of Intergenerational Mobility 1565
