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Places of Persistence: Slavery and the Geography of Intergenerational Mobility in the United States

Authors:

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 contemporary 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.
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 mobilityfamily structure, income inequality, school quality,
segregation, and social capitalcan 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) 55:15471565
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 CZlevel
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) CZlevel 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 CZlevel 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 Statesfamily structure, income inequality, school quality,
segregation, and social capitalweaken 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 childrens 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 19962000, and child incomes are measured in 20112012.
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 childrensincome
rank on parentsincome 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 childrens 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 (19801982) 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 Agricultures 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
(19801982) 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 todaya 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 19801982 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 CZlevel 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 CZlevel 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 crossCZ 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 crossCZ 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 whiteZIP 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 CZlevel 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 (3.5011.00) 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 FAOs 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
13 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,columns14 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) CZlevel 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 K12 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 K12 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. K12 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 69ofTable4, which condition on four
aspects of the K12 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 1012 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, respectivelydo 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 K12 characteristics are not
available for all CZs, which slightly reduces the number of observations in columns 68ofTable4.
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 whiteZIP 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.
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Slavery and the Geography of Intergenerational Mobility 1565
... A third strand of scholarship shows that a legacy of slavery affects contemporary socioeconomic and health outcomes. For instance, Nunn (2008) finds that twentieth-century economic development was slower in southern counties with higher levels of slavery, and Berger (2018) finds that economic mobility is lower in counties with high levels of slavery. The impact on socioeconomic status and health has been particularly detrimental to Black people. ...
... A growing body of research documents the contemporary consequences of the historical legacy of slavery. This research investigates the impact of slavery on demographic composition; socioeconomic outcomes and disparities; racial, social, and political attitudes; health disparities; and crime (Ruef and Fletcher 2003;Nunn 2008;Bertocchi andDimico 2012, 2014;O'Connell 2012;Acharya et al. 2016Acharya et al. , 2018Gouda and Rigterink 2016;Reece and O'Connell 2016;Kramer et al. 2017;Berger 2018;Gunadi 2019;Reece 2020;Uttermark 2020). Although both academics and the public have drawn connections between slavery and our criminal justice system (Alexander 2010; DuVernay 2016), only one previous quantitative study addresses this empirically, finding a bivariate association between status as a former slave state and contemporary executions (Vandiver et al. 2007). ...
... The first key finding is that counties with a substantial legacy of slavery are more likely to detain people charged with crimes before trial and to sentence individuals to incarceration; they also sentence individuals to a longer time behind bars. These findings are consistent with much of the prior scholarship that finds that a legacy of slavery has negative average effects (Gouda and Rigterink 2016;Berger 2018;Gunadi 2019;Uttermark 2020). It is also consistent with descriptive empirical evidence on mass incarceration: the 10 states with the highest incarceration rates are all former slave states (Sentencing Project, n.d.). ...
... However, with the spatial concentration of economic gains within countries and the associated social problems in lagging regions, there has been a reappraisal of the importance of place-based policies to achieve shared and inclusive growth (Shambaugh & Nunn, 2018). A recent set of studies has focused on the regional dimension of social mobility and its importance for inclusive development (Berger, 2018;Berger & Engzell, 2019;Connor & Storper, 2020;Corak, 2020). This paper is a contribution to the recent literature on the importance of regional differences in economic outcomes for public policy prescriptions. ...
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... As people navigate major structural change, they can undergo upward or downward mobility. On the other hand, their preparation for navigating such creative destruction depends in part on deeply-rooted local structures that vary considerably across the country (25)(26)(27)(28)(29)(30)(31). We might say that in the former case, regional ISM is restructured by economy-wide forces such as technological change, while in the latter case, regional differences reflect persistent local selection and shaping of those forces through childhood environments. ...
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Significance Intergenerational social mobility in the United States has declined over the last century, sparking a national debate about how to improve equality of opportunity. By analyzing data spanning the 20th century, we demonstrate strong temporal patterns operating across regions. Some areas of the United States have witnessed significant declines in social mobility, while others have had persistent low levels all along. Thus, the contemporary national picture is shaped by both powerful forces of change that reduce intergenerational mobility in some regions and deeply entrenched long-term forces generating persistence in others. It follows that improving social mobility will be challenging, as policy would need to respond to both forces and do so according to their varying mixture across different regions.
... Such work is already underway, with recent papers documenting the role of education and labor markets (e.g. Rothstein 2017), crime (Sharkey & Torrats-Espinosa 2017), pollution (O'Brien et al. 2018;Manduca & Sampson 2019), and history (Berger 2018) in accounting for place-based differences in economic opportunity. More broadly, ongoing efforts to account for recent trends in mortality may find the opportunity gap framework useful when examining spatial patterns in the rise of 'deaths of despair' resulting from suicide, accidents, or drugs and alcohol (Woolf et al. 2018). ...
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In this study, we introduce the ‘racial opportunity gap’ as a place-based measure of structural racism for use in population health research. We first detail constructing the opportunity gap using race-sex specific estimates of intergenerational economic mobility outcomes for a recent cohort. We then illustrate its utility in examining spatial variation in the racial mortality gap. First we demonstrate a correlation between the racial opportunity gap and the racial mortality gap across U.S. counties; where the gap in the adult earnings of black and white children born to families at the same income level is greater so, too, is the gap in mortality. Second, we show in a multivariable framework that the racial opportunity gap is associated with the racial mortality gap net of differences in the socioeconomic composition of the two groups. In so doing, we aim to provide population health researchers with a new empirical tool and analytic framework for examining the role of structural racism in generating racial health disparities.
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To assess whether an indicator of structural racism—the legacy of slavery—impacts racial inequality in poverty among individuals within the same family structure. Family structure is a dominant explanation for racial inequality in poverty. This overemphasis on an “individual‐level” variable results in relatively less attention to the role of structural factors. Yet, structural factors, like the legacy of slavery, may be key to understanding how race and family structure intersect in the context of poverty. The authors use data from the Luxembourg Income Study, the American Community Survey, and the Historical 1860 Census. Multilevel models link individual‐ and state‐level data and separate models focus on counties. The authors assess whether a proxy of the legacy of slavery (i.e., the historical concentration of enslaved people in 1860) relates to Black–White inequality in poverty among single mother households and among married with children households in the US South. There is an impact of the legacy of slavery on Black–White inequality in poverty even within‐family type, but the legacy of slavery appears to be more consequential for married with children households than single mother households. Among married parent households, the racial gap in poverty is more pronounced where the legacy of slavery is stronger. Results suggest the link between family structure and racial inequality in poverty is overstated and more indirect, operating—at least partially—through structural racism. This study challenges the perception of marriage as an anti‐poverty mechanism. It also underscores the limitations of viewing family structure from an individual‐level perspective when explaining racial inequality. Local manifestations of structural racism are part of how family structure matters for inequality in poverty.
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The uneven geographic distribution of colleges in the United States endows students with uneven access to colleges depending on where they live. To examine the implication of this for student welfare, we estimate a model of high school students’ college choices, allowing for rich heterogeneity in students’ preferences for college attributes. We use data on students’ enrollment decisions and application decisions—i.e., the sets of colleges to which they applied—to identify the distribution of students’ preferences, and find that place indeed matters: the expected value of applying to college differs dramatically across states and across counties within a state.
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While American poverty research has devoted greater attention to poverty in the Northeast and Midwest, poverty has been persistently higher in the U.S. South than in the other regions. Thus, this study investigates the enduring question of why poverty is higher in the South. Specifically, it demonstrates the role of power resources as an explanation for this regional disparity, yet also considers family demography, economic structure, and racial/ethnic heterogeneity. Using six waves (2000–2016) of U.S. Census Current Population Survey data from the Luxembourg Income Study (N = 1,157,914), this study employs a triangulation of analytic techniques: (1) tests of means and proportion differences, (2) multilevel linear probability models of poverty, and (3) binary decomposition of the South/non-South poverty gap. The comparison of means associated with the power resource hypothesis yields the largest substantive differences between the South and the non-South. In the multilevel models, adjusting for power resources yields the largest declines in the South coefficient. Binary decomposition results indicate power resources are the second most influential factor explaining the South/non-South poverty gap. Overall, power resources are an important source of the South/non-South poverty gap, though economic structure and other factors certainly also play a role. Results also suggest an important interplay between power resources and race. Altogether, these results underscore the importance of macrolevel characteristics of places, including political and economic contexts, in shaping individual poverty and overall patterns of inequality.
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We show that the neighborhoods in which children grow up shape their earnings, college attendance rates, and fertility and marriage patterns by studying more than 7 million families who move across commuting zones and counties in the United States. Exploiting variation in the age of children when families move, we find that neighborhoods have significant childhood exposure effects: the outcomes of children whose families move to a better neighborhood-as measured by the outcomes of children already living there-improve linearly in proportion to the amount of time they spend growing up in that area, at a rate of approximately 4% per year of exposure. We distinguish the causal effects of neighborhoods from confounding factors by comparing the outcomes of siblings within families, studying moves triggered by displacement shocks, and exploiting sharp variation in predicted place effects across birth cohorts, genders, and quantiles to implement overidentification tests. The findings show that neighborhoods affect intergenerational mobility primarily through childhood exposure, helping reconcile conflicting results in the prior literature. © The Author(s) 2018. Published by Oxford University Press on behalf of the President and Fellows of Harvard College. All rights reserved.
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This study introduces a new sample that links people and families across 1860, 1880, and 1900 census data to explore the intergenerational impact of slavery on black families in the United States. Slaveholding—the number of slaves owned by a single farmer or planter—is used as a proxy for experiences during slavery. Slave family structures varied systematically with slaveholding sizes. Enslaved children on smaller holdings were more likely to be members of single-parent or divided families. On larger holdings, however, children tended to reside in nuclear families. In 1880, a child whose mother had been on a farm with five slaves was 49 % more likely to live in a single-parent household than a child whose mother had been on a farm with 15 slaves. By 1900, slaveholding no longer had an impact. However, children whose parents lived in single-parent households were themselves more likely to live in single-parent households and to have been born outside marriage.
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Aspiring to do better than one's parents The American dream promises that hard work and opportunity will lead to a better life. Although the specifics of what constitutes a better life vary from generation to generation, one constant is that children expect to do better—or at least to have a good chance at doing better—than their parents. Chetty et al. show that this dream did come true for children born in the middle of the 20th century, but only for half of children born in 1984 (see the Policy Forum by Katz and Krueger). A more even distribution of economic growth, rather than more growth, would allow more children to fulfill their dreams. Science , this issue p. 398 ; see also p. 382
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This study utilizes regression discontinuity to examine the long-run impacts of the mita, an extensive forced mining labor system in effect in Peru and Bolivia between 1573 and 1812. Results indicate that a mita effect lowers household consumption by around 25% and increases the prevalence of stunted growth in children by around six percentage points in subjected districts today. Using data from the Spanish Empire and Peruvian Republic to trace channels of institutional persistence, I show that the mita's influence has persisted through its impacts on land tenure and public goods provision. Mita districts historically had fewer large landowners and lower educational attainment. Today, they are less integrated into road networks, and their residents are substantially more likely to be subsistence farmers.
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We show that contemporary differences in political attitudes across counties in the American South in part trace their origins to slavery's prevalence more than 150 years ago. Whites who currently live in Southern counties that had high shares of slaves in 1860 are more likely to identify as a Republican, oppose affirmative action, and express racial resentment and colder feelings toward blacks. We show that these results cannot be explained by existing theories, including the theory of contemporary racial threat. To explain the results, we offer evidence for a new theory involving the historical persistence of political attitudes. Following the Civil War, Southern whites faced political and economic incentives to reinforce existing racist norms and institutions to maintain control over the newly freed African American population. This amplified local differences in racially conservative political attitudes, which in turn have been passed down locally across generations. © 2016 by the Southern Political Science Association. All rights reserved.
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How much of our fate is tied to the status of our parents and grandparents? How much does this influence our children? More than we wish to believe. While it has been argued that rigid class structures have eroded in favor of greater social equality, The Son Also Rises proves that movement on the social ladder has changed little over eight centuries. Using a novel technique-tracking family names over generations to measure social mobility across countries and periods-renowned economic historian Gregory Clark reveals that mobility rates are lower than conventionally estimated, do not vary across societies, and are resistant to social policies. The good news is that these patterns are driven by strong inheritance of abilities and lineage does not beget unwarranted advantage. The bad news is that much of our fate is predictable from lineage. Clark argues that since a greater part of our place in the world is predetermined, we must avoid creating winner-take-all societies. Clark examines and compares surnames in such diverse cases as modern Sweden, fourteenth-century England, and Qing Dynasty China. He demonstrates how fate is determined by ancestry and that almost all societies-as different as the modern United States, Communist China, and modern Japan-have similarly low social mobility rates. These figures are impervious to institutions, and it takes hundreds of years for descendants to shake off the advantages and disadvantages of their ancestors. For these reasons, Clark contends that societies should act to limit the disparities in rewards between those of high and low social rank. Challenging popular assumptions about mobility and revealing the deeply entrenched force of inherited advantage, The Son Also Rises is sure to prompt intense debate for years to come.