The Effect of Social Connectedness on Crime:
Evidence from the Great Migration∗
Bryan Stuart
University of Michigan
[email protected]
Evan Taylor
University of Michigan
[email protected]
January 2015
Preliminary and Incomplete. Comments Welcome.
Abstract
The Great Migration of millions of African Americans out of the South generated considerable variation in social connectedness across destinations. We use this variation to study
the effect of social connectedness on crime from 1960-2009 across U.S. cities. Our estimates
imply that a one standard deviation increase in social connectedness leads to a 12 percent
reduction in the murder rate. Estimated effects are driven by cities with a high African American population share. Cities with different levels of social connectedness saw dramatically
different changes in crime rates over time.
∗
For helpful conversations and feedback, we thank Martha Bailey, Dan Black, John Bound, Charlie Brown, John
DiNardo, Dan Nagin, Seth Sanders, Jeff Smith, and Lowell Taylor; plus seminar participants at the University of
Michigan.
1
Introduction
For almost 300 years, the enormous variance of crime rates across different places has puzzled
social scientists and policy makers (e.g., Guerry, 1833; Quetelet, 1835). Even when accounting
for differences in observed demographic and economic characteristics across space, crime rates
vary tremendously (Glaeser, Sacerdote and Scheinkman, 1996). Motivated in part by this puzzle,
a number of papers examine the role of social connectedness in accounting for city and neighborhood crime rates (e.g., Glaeser, Sacerdote and Scheinkman, 1996; Sampson, Raudenbush and
Earls, 1997; Sampson, Morenoff and Earls, 1999). The critical challenge in assessing this hypothesis is finding plausibly exogenous variation in social connectedness across different locations.
Commonly used measures, such as participation in civic or political organizations or responses
to survey questions about cooperation, might be affected by crime rates, causing an important
endogeneity problem.
In this paper, we study the relationship between crime and social connectedness across U.S.
cities from 1960-2009. Our key contribution is use of variation in social connectedness across
otherwise similar cities driven by the twentieth century Great Migration of African Americans out
of the South. For example, consider Decatur, Illinois and Albany, New York, two cities which are
similar in the number of Southern black migrants they received, 1980 population, and 1980 African
American population share.1 Nearly forty percent of Decatur’s migrants came from Brownsville,
Tennessee, while the second and third largest sending birth towns account for roughly two percent
each of all migrants. Albany, on the other hand, did not receive a large share of its migrants
from a single birth town. The top three sending birth towns to Albany each account for about 2
percent of migrants. Qualitative evidence suggests that the concentration of Brownsville migrants
translated into stronger community ties: even in the mid 2000’s, Brownsville high school reunions
were held in Decatur (Anonymous, 1994; Smith, 2006). We measure social connectedness using
a Herfindahl-Hirschman Index of birth town to destination city population flows. Our ability to
measure these long-run population flows comes from use of the Duke SSA/Medicare dataset.
1
We provide details below.
1
Variation in social connectedness comes from the tendency to follow previous migrants from
one’s birth town, an important feature of the Great Migration (Stuart and Taylor, 2014). Importantly, we use variation in social connectedness conditional on the total number of migrants living
in a destination. Historical context supports our central assumption that this variation is plausibly exogenous with respect to unobserved determinants of crime in 1960-2009, conditional on a
variety of economic and demographic controls. Rich qualitative work suggests that early location
decisions, many made in the 1910’s, were driven by employment opportunities (Scott, 1920; Bell,
1933; Rubin, 1960; Gottlieb, 1987; Grossman, 1989). If anything, it seems plausible that individuals living in a destination with their close friends and family were less likely to have out-migrated
in response to crime, making it more difficult to find a negative effect of social connectedness on
crime. Our measure of social connectedness is positively correlated with the share of a destination’s work force employed in manufacturing, a relatively attractive sector for African American
migrants (see Stuart and Taylor, 2014). Our measure of social connectedness is not correlated with
crime rates from 1911-1914, before the Great Migration, or in a consistent manner with economic
or demographic covariates from 1960-2000.
To guide our empirical analysis, we develop a simple model in which an individual’s decision
to commit crime depends on a location-specific payoff, social connectedness, and peers’ actions.
The model highlights that the effect of social connectedness among African Americans with ties
to the South on city-level crime rates depends importantly on the nature of peer effects. The first
prediction of the model is that if social connectedness decreases crime committed by blacks with
ties to the South and cross-group peer effects are not sufficiently negative, then social connectedness decreases the equilibrium city-level crime rate. Second, under certain plausible peer effect
specifications, the effect of social connectedness on the city-level crime rate is larger in magnitude
in cities with a higher African American population share.
We find that higher social connectedness causes considerably lower crime rates. At the mean,
a one standard deviation increase in social connectedness leads to a precisely estimated 12 percent
decrease in murder, the best measured crime in FBI data. The effect of replacing social connected-
2
ness in Albany with that of Decatur is a 36.6 (6.8) percent decrease in murders, a 50.3 (5.2) percent
decrease in robberies, and a 35.8 (7.9) percent decrease in motor vehicle thefts. The elasticity of
crime with respect to social connectedness ranges from -0.03 to -0.24 across the seven index crimes
of murder, rape, robbery, assault, burglary, larceny, and motor vehicle theft, and is statistically distinguishable from zero for six of the seven crimes. The negative effect of social connectedness
on crime is driven by cities with a relatively high black population share. Our empirical estimates
broadly agree with the theoretical model’s predictions. We also find that social connectedness has
a particularly strong negative effect on murders attributed to African American youth, a modest
negative effect on murders attributed to black adults, and much smaller and imprecisely estimated
effects on murders attributed to whites.
Social connectedness has large and important effects on the evolution of crime rates from 19602009. Social connectedness appears to have little effect in the 1960’s, a period of substantial
migration and relatively low crime rates. From the 1970’s to 1990’s, a period of high crime rates,
the effect of moving from the 75th to 25th percentile of social connectedness is an increase in
murder rates of 26-56 percent. The effect of social connectedness fades in the 2000’s, a period of
relatively low crime and several decades after the end of the Great Migration.
One violation of our identification assumption is that connected groups of migrants tended
to move to cities with low unobserved determinants of crime which persist over time; this could
lead to a spurious negative correlation between social connectedness and crime. If this selection
were quantitatively important, then controlling for the crime rate from 1960-1964 would affect
estimates for crime after 1965. Controlling for the 1960-1964 crime rate has virtually no effect on
our estimates, allaying concerns about this violation. In addition, our results are robust to different
approaches of handling possibly missing data and different sample selection rules.
Our theoretical model provides a mapping between the estimated effect of social connectedness on city-level crime and the effect of social connectedness on crime committed by African
Americans with ties to the South, which is unobserved. Using a parametrized model, we find a
large range of possible values for the latter effect. This uncertainty arises because of uncertainty
3
about the magnitude of peer effects and the large influence of peer effects on this mapping.
Our study relates closely to papers which use social structure to explain neighborhood-level
crime rates within a city (e.g., Sampson, Raudenbush and Earls, 1997; Sampson, Morenoff and
Earls, 1999), drawing upon earlier work by Coleman (1990) and others. We differ from this literature in using variation in social connectedness across cities, as opposed to within a city. We also
differ in our use of plausibly exogenous variation in social connectedness from the Great Migration. Finally, our setting allows us to study the effect of social connectedness over the course of 50
years.
A number of papers examine the relationship between peers’ criminal behavior and one’s own
criminal behavior (Case and Katz, 1991; Glaeser, Sacerdote and Scheinkman, 1996; Ludwig, Duncan and Hirschfield, 2001; Calvó-Armengoi and Zenou, 2004; Silverman, 2004; Kling, Ludwig
and Katz, 2005; Ludwig and Kling, 2007; Bayer, Hjalmarsson and Posen, 2009; Sciandra et al.,
2013; Damm and Dustmann, 2014). The peer effects studied in these papers are important for
our setting, but are not our main focus. Instead, we estimate the effect of stronger social ties on
criminal activity. Our paper also relates to the literature examining the consequences of ethnic and
racial segregation (e.g., Borjas, 1995; Cutler and Glaeser, 1997; Cutler, Glaeser and Vigdor, 1999;
Alesina, Baqir and Easterly, 1999; Alesina and Ferrara, 2000; Bertrand, Luttmer and Mullainathan,
2000).
For almost a century, economists have studied the timing of the Great Migration and outcomes
for migrants, plus sending and receiving areas (e.g., Scroggs, 1917; Smith and Welch, 1989; Carrington, Detragiache and Vishwanath, 1996; Collins, 1997; Boustan, 2009, 2011; Hornbeck and
Naidu, 2014; Black et al., forthcoming). Recent work has emphasized the importance of social
interactions in understanding the Great Migration (Chay and Munshi, 2013; Stuart and Taylor,
2014). More broadly, there is enormous interest in the causes and consequences of criminal activity and incarceration in U.S. cities, especially for African Americans (e.g., Freeman, 1999; Evans,
Garthwaite and Moore, 2014; Neal and Rick, 2014)
4
2
Historical Background
In the Great Migration, nearly six million African Americans left the South from 1910 to 1970
(Census, 1979). Although migration was concentrated in certain cities, like Chicago, Detroit,
and New York, other cities also experienced dramatic changes. For example, Chicago’s black
population share increased from two to 32 percent from 1910-1970, while Racine, Wisconsin
experienced an increase from 0.3 to 10.5 percent (Gibson and Jung, 2005). Migration out of the
South increased from 1910-1930, slowed during the Great Depression decade, and then resumed
forcefully until the 1970s.
Figure 1 uses Census data to show how the number of African Americans in the North evolved
during the twentieth century.2 As seen in panel A, the number of Southern-born African American
adults living in the North increased from 300,000 in 1910 to 1.2 million in 1940, then increased to
nearly 3.5 million by 1980. Panel B shows that the number of black children living in the North
increased dramatically, from less than one million in 1940 to nearly three million in 1960 and over
four million after 1970. The number of African American children with at least one parent born in
the South follows a similar pattern as the total number of children until 1980. Relatively few of the
black children living in the North were themselves born in the South, indicating low migration by
children. As time passed, the earliest migrants, born in the South, had children born in the North,
who eventually became adults in the North and had children there. Census data allow only an
imperfect accounting for the share of African Americans with direct, first-, or second-generation
ties to the South, but this number is certainly large; in 1900, nearly 90 percent of all African
Americans lived in the South.
Factors which contributed to the mass exodus of African Americans from the South include an
increase in labor demand and decrease in immigrant labor supply in Northern cities with the onset
of World War I (Scroggs, 1917; Scott, 1920; Gottlieb, 1987; Marks, 1989; Jackson, 1991; Collins,
1997), a decrease in labor demand in agriculture due to the boll weevil’s destruction of cotton crops
2
For figure 1, the South consists of the former Confederate states, while the North consists of all other states. The
former Confederate states are Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, North Carolina, South
Carolina, Tennessee, Texas, and Virginia.
5
(Scott, 1920; Marks, 1989, 1991), and generally better economic and social opportunities outside
the South.
Migrants tended to follow vertical routes established by railroad lines, e.g., Mississippi-born
migrants predominantly moved to Illinois, and South Carolina-born migrants predominantly moved
to New York (Scott, 1920; Carrington, Detragiache and Vishwanath, 1996; Collins, 1997; Boustan,
2011; Black et al., forthcoming). Labor agents, who offered paid transportation, employment, and
housing, directed some of the earliest migrants, but their role diminished sharply after the 1920s
(Gottlieb, 1987; Grossman, 1989).3
Qualitative historical accounts and recent quantitative work indicate that social interactions
strongly affected location decisions. Qualitative work suggests that initial migrants, who often
migrated before World War I, chose their location primarily in response to economic opportunity.
Migrants connected workers from their hometown to jobs and shelter in the North, sometimes
leading to persistent population flows from the birth town to the destination city (Rubin, 1960;
Gottlieb, 1987; Stuart and Taylor, 2014). For example, Rubin (1960) finds that migrants from
Houston, Mississippi had close friends or family at two-thirds of all initial destinations. Among
these migrants, the most important destination characteristics were employment opportunities and
the presence of family (Rubin, 1960). Using data on several hundred thousand migrants, Stuart and
Taylor (2014) find that birth town-level social interactions had very large impacts on the location
decisions of African American migrants.4 These social interactions mirror vertical migration patterns, established by railroad lines, and were stronger in destination cities with more manufacturing
employment, a particularly attractive sector for black workers.
The experience of John McCord, born in Pontotoc, Mississippi, captures many important features of early black migrants’ location decision.5 In search of higher wages, nineteen-year-old McCord traveled in 1912 to Savannah, Illinois, where a fellow Pontotoc-native connected him with
a job. Two years later, McCord moved to Beloit, Wisconsin after hearing of opportunities there.
3
Gottlieb (1987) finds evidence of labor agents bringing Southern blacks to work in Pittsburgh only in the years
1916-19 and 1922-23. Grossman (1989) argues that the role of labor agents in Chicago had diminished by 1917.
4
See also Chay and Munshi (2013).
5
The following paragraph comes from work by Bell (1933), as discussed by Knowles (2010).
6
Within a week, he started as a janitor at Fairbanks Morse and Company, a manufacturer. After two
years in Beloit, McCord spoke to his manager about returning home for a vacation. The manager,
needing more labor, asked McCord to recruit workers during his trip. McCord returned with 18
unmarried men, all of whom worked for Fairbanks Morse and Company. Thus began a persistent
flow of African Americans from Pontotoc to Beloit: among individuals born from 1916-1936, 14
percent of migrants from Pontotoc lived in Beloit’s county at old age.
3
3.1
A Simple Model of Crime and Social Connectedness
Set-up
There are two generations of agents. For simplicity, we consider a static model in which each
member of the younger generation makes a single decision about whether to commit crime or not,
while members of the older generation do not commit crime. There are three types of agents:
blacks with ties to the South (τ = bs), blacks without ties to the South (τ = bn), and all others
(τ = w). Members of the older generation have a tie to the South if they were born there. Members
of the younger generation have a tie to the South if at least one of their parents, members of the
older generation, was born in the South. We index members of the younger generation by i and
older generation by o.
Let Ci = 1 if person i commits crime and Ci = 0 otherwise. Suppose the probability that
person i of type bs commits crime is
E[Ci |τi = bs, j(i) = j] = αbs +
X
bs
γi,o,j
+ g bs (C−i ),
(1)
o
where j(i) denotes the birth town of i’s parents. The probability of committing crime depends
on three things. First, all individuals of type bs face the common element αbs , which captures
all non-social determinants of crime (e.g., police presence, employment opportunities, climate).
bs
Second, the intergenerational influence of older agent o on individual i is γi,o,j
, reflecting the
role of parents and other adults in discouraging crime. Finally, peer influences are captured by
7
g bs (C−i ). The functional form assumptions embedded in equation (1) help motivate the measure of
social connectedness used in our empirical analysis, but are not necessary for the main theoretical
results.
We model intergenerational influence (or social connectedness) as a function of whether the
bs
bs
parents of person i share a common birth town with person o. In particular, we let γi,o,j
= γH
if
bs
the agents share a birth town, j(i) = j(o), and γi,o,j
= γLbs otherwise, where j(o) denotes the birth
town of o. The aggregate intergenerational influence experienced by person i is a weighted average
bs
of the high connectedness effect (γH
) and the low connectedness effect (γLbs ),
X
bs
γi,o,j
o
bs
Nj,0
bs
+
= bs γH
N0
bs
Nj,0
1 − bs
N0
!
γLbs
(2)
bs
where Nj,0
is the number of elders of type bs from birth town j which live in a city, and N0bs =
P bs
j Nj,0 is the total number of elders. If the strength of intergenerational influence depends on
bs
, lead to differences
birth town connections, then previous migration decisions, embedded in Nj,0
in expected crime rates for agents from different birth towns.
The probability that a randomly chosen person of type bs commits crime is
E[Ci |τi = bs] =
X
j
bs
Nj,1
N1bs
!
E[Ci |τi = bs, j(i) = j],
bs
is the number of type bs youth with a connection to birth town j, and N1bs =
where Nj,1
(3)
P
j
bs
Nj,1
bs
bs
is the total number of youth. If we assume that (Nj,0
/N0bs ) = (Nj,1
/N1bs )∀j, then we obtain
bs
E[Ci |τi = bs] = αbs + γLbs + (γH
− γLbs )HHIbs + ḡ bs (C−i ),
(4)
P
bs
bs 2
where HHIbs =
j (Nj,0 /N0 ) is the Herfindahl-Hirschman Index and the average peer effect
P
bs
bs bs
is ḡ bs (C−i ) =
j (Nj,1 /N1 )g (C−i, ). Similar relationships exist for African American youth
without ties to the South and white youth.
HHI emerges as a natural way to measure intergenerational influence, or social connectedness,
8
bs
at the city level. The direct effect of higher HHI on the type bs crime rate is γH
− γLbs . One
bs
reasonable case is γH
< γLbs < 0, so that elders discourage youth from committing crime, and
the effect is stronger among youth and elders linked through a common birth town. Because peer
effects can augment (or weaken) the intergenerational effect, we now examine the equilibrium of
this model.
3.2
Equilibrium and Main Results
We use HHI to measure social connectedness and assume that peer effects depend on the average
crime rate, leading to the following equilibrium,
C̄ bs = F bs (αbs , HHIbs , C̄ bs , C̄ bn , C̄ w )
(5)
C̄ bn = F bn (αbn , HHIbn , C̄ bs , C̄ bn , C̄ w )
C̄ w = F w (αw , HHIw , C̄ bs , C̄ bn , C̄ w )
where C̄ τ is the average crime rate among group τ youth, and F τ is an unknown function. The
equilibrium crime rate vector C̄ all ≡ [C̄ bs C̄ bn C̄ w ]0 is a fixed point of equation (5).
We are interested in the effect of HHIbs on equilibrium crime rates. It is straightforward to
show that
bs
0
dC̄ all
−1
bs
=
(I
−
J)
∂F
/∂HHI
0
0
,
dHHIbs
(6)
where I is the 3 × 3 identity matrix and J, a sub-matrix of the Jacobian of equation (5), captures
the role of peer effects,

bs
bs
bs
bn
bs
w

∂F /∂ C̄
∂F /∂ C̄ 
 ∂F /∂ C̄


bn
bs
J =
∂F bn /∂ C̄ bn ∂F bn /∂ C̄ w 
∂F /∂ C̄
.


w
bs
w
bn
w
w
∂F /∂ C̄
∂F /∂ C̄
∂F /∂ C̄
9
We assume the equilibrium is stable, which essentially means that peer effects are not too
large.6 For example, if ∂F bs /∂ C̄ bs > 1, and there are no cross-group peer effects, then a small
increase in the crime rate among group bs leads to an equilibrium where all members of group bs
commit crime.7 In a stable equilibrium, a small change in any group’s crime rate does not lead to
a different equilibrium.
The effect of social connectedness on crime depends importantly on the nature of peer effects.
We now discuss key results which hold for what we think are plausible models of peer effects,
given our priors and existing empirical evidence.8 The first result is that the negative effect of
social connectedness on crime extends to a setting with peer effects, as long as the equilibrium is
stable and cross-group peer effects – i.e., off-diagonal elements of J – are non-negative.
Result 1. dC̄ all /dHHIbs ≤ 0 if ∂F bs /∂HHIbs < 0, the equilibrium is stable, and cross-group
peer effects are non-negative.
In a stable equilibrium with non-negative cross-group peer effects, the crime-reducing effect
of social connectedness among Southern blacks is not counteracted by higher crime among other
groups. Hence, equilibrium crime rates weakly decrease in Southern African American HHI. With
negative cross-group peer effects, the reduction in crime rates among Southern blacks could lead
to higher crime by other groups.
Most of our empirical work examines the city-level crime rate, C̄, which is a weighted average
of three group-specific crime rates,
C̄ = P b [P s|b C̄ bs + (1 − P s|b )C̄ bn ] + (1 − P b )C̄ w ,
(7)
where P b is the black population share and P s|b is the share of the black population with ties to
the South. Result 1 is a sufficient, but not necessary, condition to ensure that the city-level crime
rate decreases in Southern black HHI. There exist situations in which cross-group peer effects are
6
The technical assumption underlying stability is that all eigenvalues of J lie in the interval [0, 1).
A small decrease in the crime rate leads to an equilibrium where no members of the group commit crime.
8
Throughout, when we refer to different models of peer effects, we mean different parametrizations of J.
7
10
negative, but an increase in HHIbs leads to a decrease in the equilibrium city-level crime rate.
The second result states that the effect of social connectedness on the city-level crime rate decreases (or, increases in magnitude) with the black population share for certain peer effect models.
Result 2. dC̄/dHHIbs decreases with P b if the equilibrium is stable and cross-group peer effects
are non-negative and sufficiently small.
Assume that the effect of HHIbs on crime does not depend on the black population share,
yielding9
bn
bs
dC̄ w
d2 C̄
s|b dC̄
s|b dC̄
=
P
+
(1
−
P
)
−
dP b dHHIbs
dHHIbs
dHHIbs dHHIbs
(8)
Two jointly sufficient conditions for result 2 are (a): dC̄ bs /dHHIbs < dC̄ w /dHHIbs and (b):
dC̄ bn /dHHIbs < dC̄ w /dHHIbs . If Southern black social connectedness leads to greater crime
reductions among both groups of black youth, relative to white youth, then the total effect is larger
(in magnitude) in cities with a higher share of black youth. The nature of peer effects determines
whether condition (a) and (b) are satisfied.
As a simple example, suppose that there are no cross-group peer effects between black and
white youth (J13 = J23 = J31 = J32 = 0). Because there are no cross-group effects between black
and white youth, an increase in HHIbs does not affect the expected crime rate among white youth,
ensuring that condition (a) holds. For condition (b) to hold, an increase in HHIbs must lead to a
decrease in crime among non-Southern black youth, which will be true if peer effects between the
two groups of black youth are non-negative. As shown in appendix A, the formal conditions are a
stable equilibrium and J21 > 0.
To summarize, we expect that higher social connectedness among African Americans with ties
to the South will lead to lower city-level crime rates (result 1). We also expect that the effect will
If we relaxed this assumption, it is not clear whether we would expect, say, dC̄ bs /dHHIbs to become more or less
negative in response to higher P b . The effect might decrease in magnitude (move towards zero) if the higher black
population share diluted existing community ties. Alternatively, the effect might increase in magnitude if the higher
black population share reinforced community ties. The former case makes result 2 less likely to hold, while the latter
case makes it more likely.
9
11
be stronger in cities with a higher black population share (result 2). The relationship between the
direct effect of HHIbs on crime by African Americans with ties to the South and the aggregate
effect of HHIbs on city-level crime depends on the nature of peer effects, which we examine in
section 7.
4
Data
We use three different data sets in our empirical analysis. First, we measure annual city-level crime
using FBI Uniform Crime Report (UCR) data for 1960-2009, available from ICPSR. UCR data
contain voluntary monthly reports from individual police agencies, which we aggregate at the cityyear level. Our measure of crime is the number offenses reported to police. We focus on the seven
index crimes: murder and non-negligent manslaughter (“murder”), forcible rape (“rape”), robbery,
assault, burglary, larceny, and motor vehicle theft. Murder is the best measured crime, with robbery and motor vehicle theft also relatively well-measured (Blumstein, 2000; Tibbetts, 2012). The
UCR data also contain annual population measures from the Census Bureau.10 Starting in 1980,
information on the age, sex, and race of offenders are available for index crimes resulting in arrest;
we refer to this as “ASR” data below. We construct annual crime counts by summing across the
different months in a year, as in Chalfin and McCrary (2014).11 Because missing observations are
indistinguishable from true zeros, we drop any city-year in which any of the three property crimes
(burglary, larceny, and motor vehicle theft) equal zero. We also show that our results are robust to
treating zeros as Chalfin and McCrary (2014) do.
Second, we use the Duke SSA/Medicare dataset to construct our measure of social connectedness. The data contain sex, race, date of birth, date of death (if deceased), and the ZIP code of
residence at old age (death or 2001, whichever is earlier) from the Medicare Part B Master Beneficiary Records. In addition, the data contain individuals’ birth town from the SSA NUMIDENT
10
There is considerable measurement error in annual city-level population. Our results are not sensitive to replacing
observed population with predicted values from a flexible city-specific regression model.
11
We follow Chalfin and McCrary (2014) in decreasing the number of murders for year 2001 in New York City by
2,753, the number of victims of the September 11 terrorist attack.
12
file. The Duke data are ideally suited to measuring long-run birth town-to-destination city population flows, which form the basis of our social connectedness measure. We focus on individuals
born from 1900-1936 in the former Confederate states.12
Finally, we use Census city data books from 1960, 1970, 1980, 1990, and 2000 to measure a
variety of city-level covariates, described in further detail below. These data are only available for
cities with at least 25,000 residents in years 1960, 1980, and 1990; we apply the same restriction
for years 1970 and 2000. To be in our sample, a city must have received at least 25 Southern-born
African American migrants and have crime and demographic data for a given year. We focus on
cities in the Northeast, Midwest, and West Census regions. We use Federal Information Processing
System (FIPS) place definitions throughout. In our main analysis, we exclude the 14 cities with
1980 population greater than 500,000, as we found considerable measurement error in murder
counts for these cities.13 Furthermore, our measure of social connectedness does not vary much
across the largest 14 cities (see figure 2, discussed below).
5
Empirical Approach
5.1
Estimation
Motivated by the model in section 3, we estimate the effect of social connectedness on city-level
crime rates (result 1) and whether this effect is stronger in cities with a higher African American
population share (result 2). We measure social connectedness with a Herfindahl-Hirschman Index,
P
2
HHIk =
j (Nj,k /Nk ) , where Nj,k is the number of migrants from birth town j which move
P
to destination city k, and Nk ≡ j Nj,k is the total number of migrants. HHIk is a natural way
to measure social connectedness, as shown in section 3, and approximately equals the probability
12
Coverage rates are very high for African Americans born in the South from 1916-1936 (Black et al., forthcoming;
Stuart and Taylor, 2014). We focus on African American migrants born from 1900-1936, as the older cohorts were
important in establishing persistent migration patterns which generate variation in social connectedness.
13
In particular, for 1980-1989, we constructed annual murder counts using the UCR data, which are not broken
down by age, race, or sex, and the ASR data, which are. In principle, both data sets should yield the same number of
murders in a city. However, there are substantial discrepancies in the largest cities, as shown in appendix figure A.1.
13
that two randomly chosen migrant-residents of city k share a birth town.14 Because our regression
models include log HHI, log number of migrants, and log city population, our empirical results
are identical when using city population as the denominator of HHI. In addition, to isolate social
connectedness arising from a single birth town, we use the leading term of HHIk , which equals the
squared migrant share of the top sending town, i.e., (N1,k /Nk )2 , where N1,k = maxj {Nj,k }.
We view HHI as a useful proxy for the unobserved level of social connectedness. Variation in
HHI, conditional on the number of migrants, is driven by migrants coming from fewer birth towns.
Qualitative work provides strong evidence that birth town social ties had substantive, long-lasting
effects on life in destination cities. For example, a news account from 2002 states, “Fort Wayne
is Marion, Alabama, once removed” (Crowder and Spencer, 2002). The Duke data show a strong
flow of migrants from Marion, Alabama to Fort Wayne, Indiana (Stuart and Taylor, 2014, Table
2). A news account from 1983 states that roughly 1,000 of Erie, Pennsylvania’s 11,600 African
American residents once lived in Laurel, Alabama, while nearly half had family connections to
Laurel, leading an Erie resident to say, “I’m surrounded by so many Laurelites here, it’s like a
second home” (Anonymous, 1983).15 In the Duke data, a higher share of Erie residents were born
in Laurel than in Erie. The same article describes an active Erie chapter of a Laurel high school
alumni association; migrants from Brownsville, Tennessee established a similar organization in
Decatur, Illinois, as described in the introduction.
We estimate the following equation,
0
Yk,t = exp[ln(HHIk )δ + ln(Nk )θ + Xk,t
β] + k,t ,
14
(9)
The probability that two randomly chosen migrants living in city k come from the same birth town is
X
P[j(i) = j(i0 )] =
P[j(i) = j(i0 )|j(i0 ) = j] P[j(i) = j]
j
=
X Nj,k − 1 Nj,k
j
Nk − 1 Nk
15
≈ HHIk
The article also states that, in the 1930’s and 1940’s, a group of ministers traveled between the two locations,
providing information on job opportunities in Erie.
14
where Yk,t is the number of crimes in city k in year t, HHIk and Nk are defined above, Xk,t is
a vector of observed covariates which potentially explain the level of crime in a city, and k,t
captures unobserved determinants of crime. We describe Xk,t in detail below. We estimate Poisson
regression models because a considerable number of cities report no murders in a given year. When
estimating equation (9), we cluster standard errors at the city level to allow for autocorrelation in
unobserved determinants of crime.16
Table 1 displays summary statistics for the 19,471 city-years in our sample. Around 19 percent
of city-years have zero murders. The average population size is 92,000. The average number of
Southern black migrants born from 1900-1936, measured in our Duke data, is 667; this number is
not directly comparable to Census population counts because the Duke data contain only one-third
of individuals from these birth cohorts alive in 1960.17 On average, the top sending birth town
accounts for six percent of all migrants observed in the Duke data.
5.2
Identification
The key parameter of interest in equation (9) is δ, which can be interpreted as the elasticity of
the crime rate with respect to HHI, our measure of social connectedness, because we include log
population as a covariate. If social connectedness reduces city-level crime, consistent with result 1
of the model, we expect δ < 0. A sufficient condition to identify δ is
k,t ⊥
⊥ HHIk |(Nk , Xk,t )
(10)
Condition (10) states that, conditional on the number of migrants which move to k and the vector
of observed covariates, HHI is independent of unobserved determinants of crime.18
16
We also experimented with standard errors clustered at the metropolitan statistical area level. The resulting standard error on δ̂ is slightly smaller than when clustering at the city level.
17
The 1960 Census counts 5.7 million African Americans born in the South from 1900-1936. The Duke data
contain 2.9 million individuals with these characteristics, but birth place and destination are identified for 1.9 million
individuals.
18
Note that condition (10) does not guarantee identification of the other parameters (θ, β). For example, identification of θ requires plausibly exogenous variation in the the total number of migrants to each city; Boustan (2011)
provides one possible strategy for such an approach.
15
Two features of the Great Migration inform the plausibility of condition (10). The first is
the process by which certain destinations initially received significant numbers of migrants from
the same birth town. Rich qualitative work suggests that early location decisions, many made
in the 1910’s, were driven by employment opportunities and had a considerable effect on later
migrants’ location decisions (Scott, 1920; Bell, 1933; Rubin, 1960; Gottlieb, 1987; Grossman,
1989).19 In other work, we show that town-based social networks had an enormous impact on
location decisions, and that these social interactions were strongest in destinations with attractive
jobs (Stuart and Taylor, 2014). Appendix table A.1 shows that HHI, measured in the latter part
of the twentieth century, is not correlated with homicide rates from 1911-1914, a period with low
migration to the North. Although underpowered, using data from only 31 of the largest cities, the
results are consistent with the qualitative evidence which indicates that early location decisions of
connected migrants were not driven by crime.
The second issue is the degree to which connected migrants moved out of a city in response
to unobserved crime shocks, relative to unconnected migrants. Condition (10) allows the level
of migration to respond to unobserved determinants of crime, so that Nk depends on k,t . The
key requirement is that connected migrants do not respond differently to k,t than non-connected
migrants. If, as seems plausible, connected migrants are infra-marginal with respect to their location decision, then connected migrants would out-migrate less than non-connected migrants in
reponse to higher k,t , leading to a higher measure of HHI in cities with worse unobserved crime
shocks. This would bias our estimate of δ upwards, making it more difficult to conclude that social
connectedness reduces crime. Moreover, as shown in table 2, nearly 90 percent of African American migrants born from 1900-1936 stayed in the same county for the 5 year periods 1955-1960,
1965-1970, 1975-1980, 1985-1990, and 1995-2000. From 1965 on, between 60 and 80 percent of
migrants stayed in the same house over a 5 year period. Low mobility rates among these migrants
19
For example, Scott (1920) writes, “The tendency was to continue along the first definite path. Each member of
the vanguard controlled a small group of friends at home, if only the members of his immediate family. Letters sent
back, representing that section of the North and giving directions concerning the route best known, easily influenced
the next groups to join their friends rather than explore new fields. In fact, it is evident throughout the movement that
the most congested points in the North when the migration reached its height, were those favorite cities to which the
first group had gone” (p. 69).
16
allay some concerns that out-migration after 1960 biases estimates of δ.
The plausibility of condition (10) depends on the set of observed covariates Xk,t . The “demographic” covariates we include are log population; percent black; percent female; percent age 5-17,
18-64, and 65 and older; percent at least 25 years old with a high school degree; percent at least
25 years old with a college degree; and log city area. The economic covariates we include are log
median family income; the unemployment rate; the labor force participation rate; and manufacturing employment share.20 With only a few exceptions, discussed in the note to table 4, we observe
the above covariates separately for each decade from 1960-2000. In explaining crime in year t,
we only use covariates corresponding to the decade in which t lies. We allow coefficients for all
covariates besides log population to vary across decades. In addition, we include state-by-year
indicator variables to account for time-varying shocks to crime or reporting behavior.
Table 3 reports regressions of log HHI on the log number of migrants and a variety of city-level
covariates across decades, for 234 destination cities observed in every decade from 1960 to 2009.
Column 1 shows that the log number of migrants explains 71 percent of the variation in log HHI,
and log HHI and the log number of migrants are negatively correlated. Adding state indicator
variables, in column 2, explains more of the variation in HHI but does not affect the relationship
between HHI and the number of migrants. Column 3 adds a variety of demographic and economic
covariates measured in 1960. The coefficient on manufacturing employment share is positive and
statistically significant at the 1 percent level, consistent with the results in Stuart and Taylor (2014).
Of the other 12 covariates, two are significant at the 10 percent, but not 5 percent, level. Columns
4-7 display results from using covariates measured in 1970, 1980, 1990, and 2000. Across all
decades, HHI and manufacturing employment share are positively correlated. Otherwise, there is
little evidence that HHI correlates regularly with observed determinants of crime.21
To better understand the variation used to identify δ, figure 2 plots log HHI and the log number
20
Stuart and Taylor (2014) find that manufacturing employment share strongly predicts the strength of social interactions in location decisions among Southern black migrants.
21
An exception is the strong, negative correlation between HHI and the percent of population age 0-4 in 1990. We
intend to examine this in the future using restricted access Census data, which will allow us to focus on fertility of
African American residents.
17
of Southern black migrants. Our identification strategy exploits variation in HHI conditional on
the number of migrants in a city, which is variation in the vertical dimension of figure 2. The
negative correlation arises because a large number of sending towns were necessary to generate
a large number of migrants, due to the small size of Southern birth towns relative to destination
cities. There is very little variation in HHI conditional on the number of migrants for cities with
1980 population above 500,000.
Figure 3 shows that most of the variation in social connectedness is driven by a single sending
town. Sixty-seven percent of the variation in log HHI is explained by the leading term of log HHI,
which equals the log squared percent of migrants from the top sending town. This is consistent
with the finding of qualitative work that connected groups of migrants resulted when the right
pioneer migrant got the right job at the right time (Scott, 1920; Bell, 1933).
6
Empirical Results
6.1
The Effect of Social Connectedness on Crime Rates
Table 4 illustrates the relationship between social connectedness and the murder rate. To assess
the sensitivity of our results to different covariates, we first focus on the 234 cities consistently
observed across all five decades; we examine the full sample below. When controlling for demographic covariates and state-by-year dummies in column 1, our point estimate (standard error) of δ
is -0.193 (0.052). Adding economic covariates in column 2 yields similar results, -0.200 (0.045).
The estimate is attenuated somewhat when replacing state-by-year with region-by-year indicators,
as seen in column 3.22 Column 4 shows that when only using the leading term of HHI, the estimate
is -0.066 (0.022). In sum, we find a statistically significant, negative relationship between social
connectedness, measured by HHI or its leading term, and the murder rate in cities from 1960-2009.
We prefer the specification in column 2, which controls for demographic and economic covariates
and includes state-by-year indicator variables.
Table 5 shows that the manner in which we control for the number of Southern black migrants,
22
We use four Census regions.
18
or the size and social connectedness of other population groups, has relatively small effects on
the relationship between HHI and crime. Column 1 contains the estimates in column 2 of table 4
to facilitate comparisons. As seen in column 2 of table 5, not controlling for the log number of
Southern black migrants leads to an estimate of δ of -0.306 (0.045).23 In column 3, we control for
the log number of Southern black migrants, as before, and add four covariates for log HHI and the
log number of native white and foreign migrants. The point estimate on African American migrant
log HHI is -0.146 (0.047). In column 4, we replace the log number of Southern black migrants
variable with ten indicator variables, one for each decile of the Southern black migrant distribution.
Controlling more flexibly for the number of migrants has very little effect on the results.
Table 6 extends the analysis to all index crimes. Across six of the seven crimes (all except
larceny), we find a negative and statistically significant relationship between social connectedness
and offenses reported to police. These regressions include 488 cities, some of which are not in the
sample across all years. As seen in column 1, our estimate of the elasticity of the murder rate with
respect to HHI is -0.153 (0.036). Besides murder, we emphasize results for robbery and motor
vehicle theft, which are relatively well measured. The estimates for robbery and motor vehicle
theft are -0.235 (0.035) and -0.149 (0.041), relatively close to the result for murder. In sum, we
find very strong evidence that higher social connectedness decreases crime, consistent with result
1 of the model.24
To better understand the magnitudes implied by the estimates in table 6, we consider two
examples in which we imagine replacing a city’s HHI with that from a more connected, but still
comparable city. We first consider Middletown, OH and Beloit, WI, which are comparable in
terms of the 1980 population, percent black, and the number of migrants received.25 However,
HHI in Beloit (0.057) is over four times as large as in Middletown (0.014). The effect of replacing
Middletown’s HHI with that of Beloit is a 19.3 (4.1) percent decrease in murders, a 28.1 (3.6)
23
For identification purposes, we strongly prefer the specification which controls for the log number of migrants.
We estimate this regression to demonstrate that the strong relationship between HHI and the number of migrants does
not account for the negative coefficient on log HHI.
24
Appendix table A.2 displays results for all covariates from the regressions in table 6.
25
For Middletown and Beloit, the 1980 population is 35,207 and 43,719; the 1980 percent black is 11.3 and 12.0;
and the number of Southern black migrants is 376 and 407.
19
percent decrease in robberies, and an 18.9 (4.7) percent decrease in motor vehicle thefts. The
second example we consider is even more extreme: HHI in Decatur, IL (0.118) is almost twenty
times larger than that of Albany, NY (0.006).26 The effect of replacing Albany’s HHI with that
of Decatur is a 36.6 (6.8) percent decrease in murders, a 50.3 (5.2) percent decrease in robberies,
and a 35.8 (7.9) percent decrease in motor vehicle thefts. These examples demonstrate that social
connectedness potentially has very large effects on crime rates.
6.2
Relationship between Social Connectedness Effect and Black Population Share
Besides predicting a negative effect of HHI on crime, the model in section 3 also predicts a stronger
effect in cities with a higher African American population share. Table 7 estimates the baseline
specification separately for different terciles of percent black, as measured in 1960. Across increasing levels of percent black, the estimated effect of HHI on the murder rate is -0.026 (0.136),
-0.049 (0.044), and -0.189 (0.069). A similar pattern exists for other crimes, including robbery
and motor vehicle theft. Across all crimes, point estimates for the lowest percent black tercile are
indistinguishable from zero, while point estimates for the highest percent black tercile are negative
and statistically significant.27
Figure 4 shows the effect of moving from the 25th to 75th HHI percentile for different percent
black terciles. In particular, we estimate a separate Poisson model for each percent black tercile.
Using the value of covariates associated with the average crime rate for each tercile, we plot two
different predicted murder rates: one for the 75th percentile (HHI = 0.028) and one for the 25th
percentile (HHI=0.008). There is no substantive effect of HHI at the lowest percent black tercile.
At the middle tercile, increasing HHI across the interquartile range leads to 0.9 fewer murders per
100,000 population (relative to a base of around 5 murders per 100,000); the effect is 3.2 fewer
murders per 100,000 population at the highest percent black tercile (relative to a base around 10
26
For Decatur and Albany, the 1980 population is 94,081 and 101,727; the 1980 percent black is 14.6 and 15.9; and
the number of Southern black migrants is 760 and 874.
27
However, standard errors for estimates in the lowest percent black tercile are quite large, and we cannot reject
equality of coefficients in the low and high terciles for murder (z = −1.07) or robbery (z = −1.43), but can for motor
vehicle theft (z = −2.37).
20
murders per 100,000). As seen in table 7, the effects at the two lowest terciles are indistinguishable
from zero, while the effect at the highest tercile is different from zero. In sum, the negative effect
of HHI on crime appears to be driven by cities with high levels of percent black, consistent with
result 2 of the model, though relatively large standard errors temper this conclusion.
6.3
Additional Results: Age and Race of Offender, Temporal Pattern
To this point, we have presented results for city-level crime. Table 8 shows results from 19801989 data on the age and race of offenders for murders resulting in arrest.28 Column 1 shows
that the elasticity of the aggregate murder rate with respect to HHI is -0.176 (0.059), similar to
the result from the 1960-2009 UCR data. Columns 2-5 examine how Southern black migrant
social connectedness affects crimes committed by black and white adults and juveniles. Social
connectedness has the strongest effect on murders committed by black juveniles, -0.747 (0.180),
and a sizable effect on murders committed by black adults, -0.276 (0.080). Point estimates for
whites are considerably lower, -0.117 and -0.018, and indistinguishable from zero, though the
standard errors are relatively large. It appears that the relationship between Southern black HHI and
crime is strongest for African Americans, consistent with a causal effect of social connectedness
on city-level crime.
To facilitate discussion of the temporal pattern of the effect of HHI on crime, figure 5 plots
the time series of crime rates for all seven index offenses and murder for our sample. The murder rate was relatively flat from 1960-1964, then increased dramatically from 1965-1980 before
falling from 1981-1984. From 1985-1993, murder increased again, before declining sharply from
1993-2000 and remaining relatively flat thereafter. The pattern of the index crime rate is broadly
similar.29
28
ASR data quality appears to fall considerably in the 1990s. For example, from 1980-1992, on average, 33 Illinois
cities report valid ASR crime data (as determined by non-zero reports of burglary, larceny, and assault). There are
11 such cities in 1993, and 10 in 1994. After 1995, only Chicago reports valid crime data. On the other hand, from
1989 to 1990, there are 52 more cities for which we have crime data. To balance the decline in data quality with the
changing sample composition, we focus on 1980-1989.
29
Patterns from our sample are comparable to national patterns of UCR data. The time series of crime rates can
depend on the data source (e.g., Boggess and Bound, 1997).
21
Panel A of figure 6 examines the effect of HHI on the evolution of murder rates. In particular,
for each five year period from 1965-1969 to 2005-2009, we estimate a Poisson regression model
and take the level of covariates associated with the average crime rate. As discussed in more detail
below, we control for the average crime rate from 1960-1964 in these regressions.30 We then plot
the murder rate associated with the 75th and 25th percentiles of HHI. By construction, the two
series intersect for the period from 1960-1964. There is virtually no effect of HHI from 19651969. In the 1970’s, a period of rising murder rates, the less connected city sees a considerably
larger increase in murder. Murder rates remain 26-56 percent higher for the less connected city
through the mid 1990s. After 1995, when murder rates declined nationally, the less connected
city sees a sharper decline, until murder rates converge in the 2000’s. Panel B shows qualitatively
similar results for the motor vehicle theft rate.
Individuals most likely to commit murder in 1970 were born around 1950 to mothers born
around 1925.31 The individuals affected by social connectedness in the 1970’s are children of postwar migrants and grandchildren or great-grandchildren of the earliest group of migrants. What
could explain the insignificant effect of HHI on crime in the late 1960’s? First, there was substantial migration from South to North in the 1950’s and 1960’s, as seen in figure 1, which could
explain the lack of effect if exposure to a stable community throughout one’s life was important in
reducing crime. Another possibility is that the individuals committing crime in the 1960’s, a period of relatively low crime, were infra-marginal and not susceptible to intergenerational influence.
One explanation for the diminishing effect of HHI in the 2000’s is that connectedness dissipated
with time, as older generations of migrants died and younger generations moved to different locations. Another possibility is that the individuals who selected into crime during a low crime period
were not susceptible to social influence. Our results do not distinguish between these different
explanations.
30
31
Figure A.2 displays results when we do not control for the average crime rate from 1960-1964.
Among black males, the highest offending rate for murder is between ages 18-24 (Fox, 2000).
22
6.4
Robustness
If, contrary to our identification assumption (10), connected groups of migrants tended to locate
in cities with low unobserved determinants of crime, and these unobserved determinants of crime
persisted over time, then our estimate of δ is biased downwards. Figure 7 examines this concern
by reporting estimates from models with and without controlling for the 1960-1964 log average
crime rate.32 Estimates of the effect of HHI on murder and motor vehicle theft are not affected by
controlling for the 1960-1964 crime rate, providing no evidence against our identification assumption.
In panel A of appendix table A.3, we include the 14 largest cities which are excluded from
the main analysis. The elasticity of HHI with respect to the murder rate increases slightly from
-0.153 (0.036) to -0.145 (0.036). There are other minor changes, but the qualitative conclusions
from table 6 remain. In panel B, we estimate negative binomial models for the sample excluding
the largest cities. Point estimates for all crimes tend to increase. For example, the murder rate
elasticity increases to -0.086 (0.033), but is still negative and statistically significant. We prefer the
Poisson model because it requires fewer assumptions to generate consistent estimates of δ (e.g.,
Wooldridge, 2002). The key assumptions are condition (10) and a properly specified conditional
mean function.
Appendix table A.4 displays results when we exclude arguably extreme crime counts, which
could be due to measurement error. Following Chalfin and McCrary (2014), panel A displays
results when we exclude years which are less than 1/6 or greater than six times the mean number
of crimes for each city. Panel B applies a similar rule, but for each city’s median number of crimes.
In both cases, results are similar to those from table 6.
Finally, our primary sample only contains cities with at least 25 migrants, measured in the
Duke data, and at least 25,000 residents, measured in Census data. When we only include cities
with at least 50,000 residents, the murder rate elasticity decreases from -0.153 (0.036) to -0.190
(0.040). When we additionally limit the sample to cities with at least 50 migrants, the murder rate
32
Controlling for the average log crime rate is unattractive because many cities report zero murders in a given year.
23
elasticity is -0.209 (0.042). When we restrict attention to cities with at least 50,000 residents and
at least 100 migrants, the estimate is -0.230 (0.043).33 Our key conclusions do not depend on this
sample selection rule.
7
Connection between Empirical Estimates and Model
We now use the model of section 3 to connect the estimated effect of HHI on city-level crime to
the effect of HHI on crime committed by blacks with ties to the South. Equation (6) implies that
the elasticity of the city-level crime rate with respect to Southern black HHI can be written
δ = sbs εbs P b (P s|b m11 + (1 − P s|b )m21 ) + (1 − P b )m31 ,
(11)
where δ ≡ (dC̄/dHHIbs )(HHIbs /C̄) is the parameter estimated in our regression models, sbs ≡
C̄ bs /C̄ is the share of crime committed by African Americans with ties to the South, εbs ≡
(dC̄ bs /dHHIbs )(HHIbs /C̄) is the elasticity of Southern black crime with respect to HHI, P b is
the black population share, and P s|b is the share of blacks with ties to the South. Peer effects are
captured by
(1 − J22 )(1 − J33 ) − J23 J32
det(M )
J23 J31 + J21 (1 − J33 )
=
det(M )
J21 J32 + J31 (1 − J22 )
=
det(M )
m11 =
m21
m31
(12)
where M = I − J, m = M −1 , and the elements of m are given by equation (12).
Our central estimate of δ, reported in table 6 for murder, is -0.153 (0.036). We set the black
population share P b = 0.1 and the fraction of the black population with ties to the South P s|b =
0.6.34 We do not observe the share of crimes committed by blacks with ties to the South. In
33
The number of cities in our sample falls from 488 to 186, 158, and 134 with these restrictions.
From Census data, the average black population share for our sample of cities is 0.067, 0.082, 0.109, 0.117, and
0.128 for 1960-2000. From the Duke data, the average share of African American migrants born in the South is 0.597.
34
24
the ASR data, half of the murders resulting in arrest are attributed to African Americans. If we
assume that blacks with and without ties to the South commit equal amounts of crime, then sbs =
(0.6)(0.5) = 0.3; we believe this overestimates sbs , leading to a lower bound on εbs .
We make a number of simplifying assumptions regarding peer effects. First, we assume that
own-group peer effects are equal across all three groups.35 Second, we assume that cross-group
effects are symmetric. Third, we assume that cross-group effects between whites and both groups
of African Americans are the same. These assumptions can be written as (1) J11 = J22 = J33 , (2)
J12 = J21 , and (3) J13 = J31 = J23 = J32 .
We summarize here what existing estimates imply about J and provide more detail in appendix
B. Damm and Dustmann (2014) estimate the effect of municipality crime rates on criminal convictions for refugees age 15-21 in Denmark. They find that a one percentage point increase in the local
conviction rate leads to a 0.32 percentage point increase in the annual conviction rate, suggesting
on-diagonal values of J close to 0.3. Their estimates also suggest off-diagonal elements of J near
0. Estimates from Case and Katz (1991), who have data on Boston youth from 1989, suggest
on-diagonal values of J close to 0.1. Ludwig and Kling (2007) find no evidence that neighborhood violent crime rates affect violent crime arrests among Moving to Opportunity experimental
participants age 15-25. Finally, estimates in Glaeser, Sacerdote and Scheinkman (1996) suggest
on-diagonal elements of J close to 0.5 for murder and close to 1 for robbery and motor vehicle
theft, among other crimes.
The above estimates are not necessarily comparable to each other or our setting, as they rely
on different contexts, identification strategies, data sources, and crime definitions. We posit that
reasonable values of on-diagonal elements of J (own-group peer effects) are between 0 and 0.5,
and off-diagonal elements of J (cross-group peer effects) are likely small, but could be sizable for
African Americans with and without ties to the South.
We present seven different parametrizations of J in table 9.36 In column 1, with no peer effects
35
We are aware of no evidence suggesting that own-group peer effects differ for white versus black youth.
The chosen values of J are feasible given the stable equilibrium assumptions made. In addition, result 2 holds for
all of these parametrizations.
36
25
J = 0, the implied elasticity of the Southern black crime rate with respect to HHI is εbs = 8.500 (2.000).37 Column 2 adds moderate own-group peer effects of 0.25, but no cross-group peer
effects, yielding -6.375 (1.500). Column 3 allows for cross-group peer effects between Southern
and non-Southern blacks of 0.2; the resulting elasticity is -5.028 (1.183). When allowing for crossgroup peer effects between white and black youth of 0.1, the elasticity is considerably attenuated
to -1.521 (0.358). Column 5 allows for a higher level of own-group peer effects, 0.5, but no crossgroup effects; the resulting elasticity is -4.250 (1.000). Column 6 adds relatively high cross-group
effects among African Americans of 0.4, yielding -0.998 (0.235). Finally, column 7 adds small
cross-group effects between whites and blacks; the elasticity is attenuated even further to -0.133
(0.031).
The implied effect of HHI on crime committed by blacks with ties to the South is extremely
sensitive to the assumed structure of peer effects. With no cross-group peer effects, the implied
elasticity decreases by 50 percent when own-group peer effects increase from 0 to 0.5. Allowing
for positive cross-group effects further decreases the implied elasticity. At the mean, a one standard
deviation increase in HHI is an 80 percent increase in HHI (table 1), which implies a decrease in the
city-level crime rate of 12 percent and a decrease in the crime rate of blacks with ties to the South
between 680 and 11 percent. Given the existing empirical evidence, we place some emphasis on
the results in columns 3 and 4, which imply decreases in the Southern black crime rate of 402 and
122 percent. Uncertainty about appropriate peer effect magnitudes limits any conclusion about the
effect of HHI on crime committed by Southern blacks.
8
Conclusion
This paper studies the effect of social connectedness on crime across U.S. cities from 1960-2009.
The key contribution is use of variation in social connectedness across otherwise similar cities
driven by the Great Migration. Our measure of social connectedness is driven by the tendency
to follow the location decisions of previous migrants from one’s birth town. We find that higher
37
Standard errors are calculated using the Delta method.
26
social connectedness causes considerably lower crime rates. At the mean, a one standard deviation
increase in social connectedness leads to a precisely estimated 12 percent decrease in murder. We
also find suggestive evidence that the effect of social connectedness is stronger in cities with a
higher African American population share. Our empirical results broadly agree with the predictions of our theoretical model.
Social connectedness has large and important effects on the evolution of crime rates from 19602009. Social connectedness appears to have little effect in the 1960’s, a period of substantial
migration and relatively low crime rates. From the 1970’s to 1990’s, a period of high crime rates,
the effect of moving from the 75th to 25th percentile of social connectedness is an increase in
murder rates of 26-56 percent. The effect of social connectedness fades in the 2000’s, a period of
relatively low crime and several decades after the end of the Great Migration.
Several mechanisms could underlie the negative effect of social connectedness on crime. In
future work, we hope to examine the effect of social connectedness on other outcomes of interest,
including educational attainment, marriage, and fertility.38
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Table 1: Summary Statistics, Crime and Social Connectedness, 1960-2009
Mean
S.D.
Q1
Offenses reported to police per 100k population
Murder
6.659
9.527
1.655
Rape
29.142
30.067
9.986
Robbery
210.835
250.265
65.814
Assault
1,126.425 1,090.556
288.811
Burglary
1,237.043 1,002.575
662.547
Larceny
32,44.092 1,952.688 2,002.587
Motor Vehicle Theft
581.007
522.095
258.130
Population
91,904.023 93,349.786
39,258
S. Black Migrant HHI
0.020
0.016
0.008
Log S. Black Migrant HHI
-4.227
0.783
-4.857
Top Sending Town Share
0.061
0.040
0.036
S. Black Migrants
666.761
1425.218
54
Q3
Fraction
Zero
8.532
39.621
264.716
1,609.119
1,618.099
4,202.917
736.334
102,652
0.028
-3.579
0.074
624
0.191
0.071
0.003
0.005
0.000
0.000
0.000
-
Notes: Each observation is a city-year. HHI and migrant counts are calculated among all individuals born
in the former Confederacy states from 1900-1936. Data on rape is only available starting in 1964. Sample
is restricted to cities with population less than 500,000 in 1980. N = 19,471
Sources: FBI UCR, Duke SSA/Medicare dataset
30
Table 2: Mobility Rates, Blacks born in the Confederacy from 1900-1936, living in the North
Compared to 5 years ago, percent living in
Same State
Census
Year
(1)
Same
House
(2)
Same
County
(3)
Different
County
(4)
1960
1970
1980
1990
2000
39.00
57.40
75.45
78.25
80.21
47.86
30.24
19.81
15.05
2.57
3.27
2.11
2.10
Unknown
(5)
Different
State
(6)
Abroad
(7)
0.70
19.09
-
10.02
5.15
2.56
2.62
2.23
0.56
0.34
0.08
0.04
0.41
Notes: In 1970, 2.91% moved to unknown place. For 1990 and 2000, column 3
equals the percent living in the same PUMA.
Sources: Census IPUMS
31
Table 3: Social Connectedness among Black Migrants and City Covariates
Dependent variable: Log HHI, Southern black migrants
Year of Covariates
Log number S. black migrants
(1)
(2)
1960
(3)
1970
(4)
1980
(5)
1990
(6)
2000
(7)
-0.442***
(0.018)
-0.441***
(0.021)
-1.877***
(0.102)
-2.395***
(0.178)
x
234
0.746
-0.454***
(0.034)
0.035
(0.065)
0.276
(0.538)
1.347
(2.455)
-5.153
(3.442)
-4.077
(2.654)
-1.500
(2.295)
-0.401
(0.764)
1.645*
(0.885)
-0.031
(0.048)
0.027
(0.455)
5.230*
(2.699)
0.356
(0.437)
1.301***
(0.364)
-0.477
(5.039)
x
234
0.772
-0.461***
(0.037)
0.029
(0.074)
0.109
(0.403)
-1.051
(3.047)
3.093
(4.746)
1.892
(4.289)
3.750
(3.750)
-0.415
(0.768)
0.746
(0.631)
0.008
(0.056)
-0.078
(0.440)
6.460
(3.973)
1.482
(1.166)
1.029**
(0.407)
-4.251
(5.715)
x
234
0.764
-0.448***
(0.040)
0.013
(0.082)
0.155
(0.397)
-0.476
(4.296)
5.093
(6.476)
3.356
(4.583)
7.003
(4.977)
-1.505*
(0.803)
0.618
(0.534)
0.014
(0.056)
0.014
(0.381)
0.345
(1.836)
0.421
(1.508)
1.072**
(0.459)
-5.887
(4.504)
x
234
0.761
-0.405***
(0.043)
-0.028
(0.090)
-0.086
(0.336)
-0.928
(4.287)
13.044**
(6.155)
9.550**
(4.482)
12.174***
(4.393)
-0.431
(0.611)
0.658
(0.411)
0.018
(0.062)
-0.702**
(0.284)
-0.311
(2.289)
1.493
(1.485)
1.492***
(0.486)
-4.643
(5.074)
x
234
0.763
-0.403***
(0.037)
0.044
(0.081)
-0.162
(0.276)
0.006
(3.174)
6.025
(7.319)
7.055
(5.671)
9.315*
(5.209)
-0.442
(0.640)
0.289
(0.413)
-0.031
(0.061)
-0.213
(0.219)
1.907
(2.215)
-0.494
(0.638)
1.724***
(0.464)
-7.075
(6.736)
x
234
0.769
Log population
Percent black
Percent female
Percent age 5-17
Percent age 18-64
Percent age 65+
Percent 25+ with HS
Percent 25+ with College
Log area, square miles
Log median family income
Unemployment rate
Labor force participation rate
Manufacturing employment
share
Constant
State dummies
Observations
Adjusted R2
234
0.707
Notes: There are 234 city-level observations. Sample restricted to cities with population less than 500,000 as of 1980.
Heteroskedastic-robust standard errors in parentheses. * p < 0.1; ** p < 0.05; *** p < 0.01
Sources: FBI UCR, Census City databook, Duke SSA/Medicare dataset
32
Table 4: Murder and Social Connectedness among Black Migrants, 1960-2009
Dependent variable: number of murders reported to police
(1)
(2)
(3)
(4)
Log HHI, S.
black migrants
Log number S.
black migrants
Log squared population
share, top sending town
Demographic covariates
Economic covariates
State-Year Dummies
Region-Year Dummies
Log-likelihood
-0.193***
(0.052)
0.150***
(0.031)
-0.200***
(0.045)
0.156***
(0.030)
-0.133**
(0.055)
0.148***
(0.032)
x
x
x
x
x
x
x
-29,441
-28,905
x
-31,507
0.219***
(0.028)
-0.066***
(0.022)
x
x
x
-29,004
Notes: Results estimated by a Poisson regression model. There are 11,568 city-year observations and 234 cities in all columns. Sample restricted to cities with population less
than 500,000 as of 1980. Demographic covariates include log population (annual), percent black (1960, 1970, 1980, 1990, 2000), percent age 5-17, 18-64, and 65+ (1960, 1970,
1980, 1990, 2000), percent female (1970, 1980, 1990, 2000), percent of population at least
25 years old with a high school degree (1960, 1970, 1980, 1990), percent of population at
least 25 years old with a college degree (1960, 1970, 1980, 1990), and log of area in square
miles (1960, 1970, 1980, 1990, 2000). Economic covariates include log median family
income (1960, 1970, 1980, 1990), unemployment rate (1960, 1970, 1980, 1990, 2000),
labor force participation rate (1960, 1970, 1980, 1990, 2000), and manufacturing employment share (1960, 1970, 1980, 1990). For decades in which a covariate is not available,
we use the relevant covariate from the adjacent decade. Standard errors, clustered at the
city level, are in parentheses. * p < 0.1; ** p < 0.05; *** p < 0.01
Sources: FBI UCR, Duke SSA/Medicare dataset, Census City databook
33
Table 5: Robustness, Murder and Social Connectedness among Black Migrants, 1960-2009
Dependent variable: number of murders reported to police
(1)
(2)
(3)
Log HHI, S.
Black Migrants
Log number of S. black migrants
Log HHI and number,
native whites and foreign-born
No control for number of S. black migrants
10 dummies for number of S. black migrants
Log-likelihood
-0.200***
(0.045)
x
-0.306***
(0.045)
-0.146***
(0.047)
x
x
(4)
-0.190***
(0.046)
x
-28,905
-29,134
-28,803
x
-28,662
Notes: Results estimated by a Poisson regression model. The dummy variables for number of Southern black
migrants correspond to deciles. Regressions include covariates from column 2 of table 4. See note to Table 4 for
description of variables and sample. Standard errors, clustered at the city level, are in parentheses. * p < 0.1; **
p < 0.05; *** p < 0.01
Sources: FBI UCR, Duke SSA/Medicare dataset, Census City databook
Table 6: Crime and Social Connectedness among Black Migrants, 1960-2009
Dependent variable: number of offenses reported to police
Log HHI, S.
black migrants
Log number S.
black migrants
Log-likelihood
City-year observations
Cities
Murder
(1)
Rape
(2)
Robbery
(3)
Assault
(4)
Burglary
(5)
Larceny
(6)
Motor
Vehicle Theft
(7)
-0.153***
(0.036)
0.156***
(0.022)
-44,761
19,471
488
-0.070**
(0.034)
0.071***
(0.026)
-88,820
18,279
488
-0.235***
(0.035)
0.153***
(0.027)
-341,731
19,471
488
-0.133***
(0.041)
0.076***
(0.029)
-1,668,135
19,471
488
-0.074***
(0.024)
0.058***
(0.018)
-1,112,264
19,471
488
-0.028
(0.031)
0.042*
(0.024)
-2,588,533
19,471
488
-0.149***
(0.041)
0.048*
(0.029)
-1,034,348
19,471
488
Notes: Results estimated by a Poisson regression model. Regressions include covariates used in column 2 of table 4.
Standard errors, clustered at the city level, are in parentheses. * p < 0.1; ** p < 0.05; *** p < 0.01
Sources: FBI UCR, Duke SSA/Medicare dataset, Census City databook
34
Table 7: Crime and Social Connectedness, by Percent Black Tercile, 1960-2009
Dependent variable: number of offenses reported to police
Percent
Black
Tercile
Low
Medium
High
Murder
(1)
Rape
(2)
Robbery
(3)
-0.026
(0.136)
-0.049
(0.044)
-0.189***
(0.069)
-0.134
(0.140)
-0.151
(0.092)
-0.210***
(0.074)
Assault
(4)
0.025
-0.170
(0.165)
(0.135)
-0.182*** -0.172**
(0.067)
(0.085)
-0.232*** -0.264***
(0.072)
(0.077)
Burglary
(5)
Larceny
(6)
Motor
Vehicle Theft
(7)
0.021
(0.086)
-0.104**
(0.049)
-0.155***
(0.041)
-0.016
(0.081)
0.000
(0.046)
-0.180**
(0.071)
0.193
(0.175)
-0.173**
(0.083)
-0.276***
(0.092)
Notes: The percent black cutoffs are 0.021 and 0.073. Regressions include covariates used in column 3 of table 4.
Percent black is measured in 1960. Standard errors, clustered at the city level, are in parentheses. * p < 0.1; **
p < 0.05; *** p < 0.01
Sources: FBI UCR, Duke SSA/Medicare dataset, Census City databook
Table 8: Social Connectedness and Murder, by Race and Age of Offender, 1980-1989
Dependent variable: number of murder arrests of age/race group
Black
White
All
(1)
Adult
(2)
Log HHI, S. black migrants
-0.176***
(0.059)
Log number S. black migrants 0.091**
(0.044)
Log-likelihood
-9,260
-0.276***
(0.080)
0.524***
(0.064)
-5,238
Juvenile
(3)
Adult
(4)
-0.747*** -0.117
(0.180)
(0.074)
0.390*** 0.056
(0.135)
(0.056)
-1,676
-6,911
Juvenile
(5)
-0.018
(0.178)
0.119
(0.100)
-2,305
Notes: Poisson regressions include covariates used in column 3 of table 4. Standard errors, clustered at
the city level, are in parentheses. There are 3,586 city-year observations and 418 cities in the sample. *
p < 0.1; ** p < 0.05; *** p < 0.01
Sources: FBI UCR, Duke SSA/Medicare dataset, Census City databook
Table 9: Connection between Model and Empirical Estimates
(1)
Peer effect parametrization
J11 = J22 = J33
0
J12 = J21
0
J13 = J31 = J23 = J32
0
Elasticity of crime by blacks -8.500
with ties to South, εbs
(2.000)
(2)
(3)
(4)
(5)
(6)
(7)
0.25
0
0
-6.375
(1.500)
0.25
0.2
0
-5.028
(1.183)
0.25
0.2
0.1
-1.521
(0.358)
0.5
0
0
-4.250
(1.000)
0.5
0.4
0
-0.998
(0.235)
0.5
0.4
0.1
-0.133
(0.031)
Notes: Table 9 presents results of applying equations (11) and (12) for each peer effect specification. The estimate
of δ is -0.153 (0.036). Standard errors calculated using the Delta method.
35
0
2
Population, millions
4
6
8
10
Figure 1: Number of African Americans in the North
1900
1910
1920
1930
1940
1950
Year
1960
1970
1980
1990
2000
1980
1990
2000
Adults in North
Adults in North, born in South
0
1
Population, millions
2
3
4
5
(a) Adults
1900
1910
1920
1930
1940
1950
Year
1960
1970
Children in North
Children in North, parents born in South
Children in North, born in South
(b) Children
Note: The South here consists of former Confederate states, and the North consists of all other states. The different
groups are mutually exclusive.
36
Log HHI, Southern black migrants
-3
-2
-7
-6
-5
-4
Figure 2: Social Connectedness and Number of Southern Black Migrants
Linear fit: -0.40 ( 0.01), R2 = 0.69
4
6
8
10
Log number of Southern black migrants
25-75k, 0-.05
75-150k, 0-.05
150-500k, 0-.05
25-75k, .05-.15
75-150k, .05-.15
150-500k, .05-.15
25-75k, .15-1
75-150k, .15-1
150-500k, .15-1
>500k, 0-.05
>500k, .05-.15
>500k, .15-1
12
Notes: Figure contains cities in sample in 1980. Cities are classified by their 1980 population and African American
population share.
37
Log HHI, Southern black migrants
-4
-6
-3
-5
-2
Figure 3: Top Sending Town Accounts for Most of Variation in HHI
Linear fit: 0.58 ( 0.02), R2 = 0.67
-8
-6
-4
Log Squared Percent of Migrants from Top Sending Town
25-75k, 0-.05
75-150k, 0-.05
150-500k, 0-.05
25-75k, .05-.15
75-150k, .05-.15
150-500k, .05-.15
25-75k, .15-1
75-150k, .15-1
150-500k, .15-1
>500k, 0-.05
>500k, .05-.15
>500k, .15-1
-2
Notes: Figure contains cities in sample in 1980. Cities are classified by their 1980 population and African American
population share.
38
4
Average crime per 100k population
8
12
6
10
14
Figure 4: Social Connectedness and Murder Rate, by Percent Black Tercile
1
2
Percent black tercile, 1960
HHI at 75th percentile
3
HHI at 25th percentile
Notes: To construct figure 4, we estimate a separate Poisson model for each percent black tercile, and take the value
of covariates associated with the average crime rate for each tercile. We then plot two different predicted crime rates:
one for the 75th percentile (HHI = 0.028) and one for the 25th percentile (HHI=0.008). Poisson regression models are
estimated using the covariates in column 3 of table 4. The percent black cutoffs are 0.021 and 0.073.
39
10
20
05
20
00
20
95
19
19
90
85
19
80
19
75
19
70
19
65
19
19
60
4
8
10
6
Murders per 100,000 Population
12
UCR Index Offenses per 100,000 Population
2000
4000
6000
8000
10000
Figure 5: Evolution of Crime Rates Over Time
Year
Index Offenses
Murder
Notes: Index offenses include murder, rape, robbery, aggravated assault, burglary, larceny theft, and motor vehicle
theft. Sample is limited to cities in our analysis sample with less than 500,000 residents in 1980.
40
4
Average crime per 100k population
8
6
10
12
Figure 6: Social Connectedness and the Evolution of Crime Rates Over Time
1960
1965
1970
1975
1980 1985
Year
HHI at 75th percentile
1990
1995
2000
2005
HHI at 25th percentile
200
Average crime per 100k population
800
1000
400
600
(a) Murder
1960
1965
1970
1975
1980 1985
Year
HHI at 75th percentile
1990
1995
2000
2005
HHI at 25th percentile
(b) Motor Vehicle Theft
Notes: For each five year period from 1965-1969, 1970-1974, etc., we estimate a Poisson regression model and take
the level of covariates associated with the average crime rate. Besides the covariates used in column 2 of table 4, we
control for the log average crime rate from 1960-1964. We then plot the murder rate associated with the 75th and 25th
percentiles of HHI. By construction, the two series intersect for the period from 1960-1964.
41
-.6
Coefficient on Log HHI
-.4
0
-.2
.2
Figure 7: Importance of Controlling for Average 1960-1964 Crime Rates
1965
1970
1975
1980
1985
Year
1990
Control for 1960-64 crime
1995
2000
2005
No control
-.6
Coefficient on Log HHI
-.4
0
-.2
.2
(a) Murder
1965
1970
1975
1980
1985
Year
1990
Control for 1960-64 crime
1995
2000
2005
No control
(b) Motor Vehicle Theft
Figure 7 shows point estimates and 95-percent confidence intervals from estimating a separate regression for years
1965-1969, 1970-1974, and so on, for models with covariates used in column 2 of table 4, with and without also
controlling for the log average crime rate from 1960-1964.
42
Online Appendix
A
Additional Theoretical Details
As noted in the text, two jointly sufficient conditions for result 2 are (a): dC̄ bs /dHHIbs < dC̄ w /dHHIbs
and (b): dC̄ bn /dHHIbs < dC̄ w /dHHIbs . Assuming that ∂F bs /∂HHIbs < 0, conditions (a) and (b)
are equivalent to m11 > m31 and m21 > m31 (these variables are defined in section 7). Simple
algebra demonstrates that condition (a) is satisfied if and only if
(1 − J22 )(1 − J33 − J31 ) > J32 (J21 + J23 ),
(A.1)
which is true if cross-group peer effects are small enough. Similarly, condition (b) is satisfied if
and only if
J21 (1 − J33 − J32 ) > J31 (1 − J22 − J23 ).
(A.2)
If black peers have stronger effects on each other than Southern blacks and whites, J21 > J31 ≥ 0,
inequality (A.2) is satisfied if 1 − J33 − J32 > 1 − J22 − J23 , which reduces to J22 > J33 under the
assumption that J32 = J23 .
If there are no cross-group peer effects between black and white youth, J13 = J23 = J31 =
J32 = 0, then
1 − J22
(1 − J11 )(1 − J22 ) − J12 J21
J21
=
(1 − J11 )(1 − J22 ) − J12 J21
=0
m11 =
(A.3)
m21
(A.4)
m31
(A.5)
The stable equilibrium assumption implies that J22 ∈ [0, 1) and (1 − J11 )(1 − J22 ) > J12 J21 ,
ensuring that condition (a) holds. Condition (b) additionally requires J21 > 0.
B
Details on Peer Effect Parametrization
Damm and Dustmann (2014) estimate the effect of municipality crime rates on convictions for
criminal behavior of refugees in Denmark. For males, they find that a one percentage point increase
in the local crime rate leads to a 7-13 percent increase in the probability of conviction between ages
15-21 (Table 3, also see p. 1820). Given an average conviction rate of 46 percent, this translates
into a 3-6 percentage point increase in the probability of conviction; we will take the midpoint of
4.5. For females, there is no effect. Note that these results pertain to a 7 year period. Combining
the sex effects and creating an annual rate, a one percentage point increase in the local crime rate
leads to a 4.5 / (7 · 2) ≈ 0.32 percentage point increase in the annual conviction rate. This suggests
diagonal elements of J close to 1/3. (ET multiplied this by 3, not sure why. These numbers are
for ever committing a crime, not the number of crimes.). Damm and Dustmann (2014) find that,
beyond the impact of the overall local conviction rate, the conviction rate of co-nationals has an
i
additional impact while the conviction rate of immigrants from other countries does not (Table
7). This suggests that cross-group peer effects might be small, though the nature of cross-group
interactions could differ considerably across the setting studied by Damm and Dustmann (2014)
and here.
Case and Katz (1991) report that a one percent increase in the neighborhood crime rate leads to
a 0.118 percent increase in a Boston youth’s self-reported propensity of committing a crime within
one year (Table 10). These are evaluated at the sample mean. 17 percent of youths commit a crime
within the past year. This suggests a value of diagonal of J close to 0.1.
In their preferred estimates, Ludwig and Kling (2007) find no evidence that neighborhood violent crime rates affect violent crime arrests among MTO participants age 15-25 (Table 4). They
also note that “... estimates for the overall effects of MTO mobility assignments are not directly informative about whether crime is contagious, because MTO moves change multiple neighborhood
characteristics simultaneously, which could have offsetting effects” (p. 494).
In the model of Glaeser, Sacerdote and Scheinkman (1996), there are two types of agents
arranged along circle. Fixed agents are not affected by neighbors when deciding whether to commit
crime, while compliers perfectly imitate their neighbor. In this model, a small increase in the
rate of crime has no effect on fixed agents, but has a one-for-one effect on compliers; hence, the
diagonal elements of J equal the probability that an agent is a complier. Using the notation of
Glaeser, Sacerdote and Scheinkman (1996), the probability that an agent is a fixed type is π. In
Table IIA, the authors report estimates of f (π) = (2 − π)/π. The diagonal element of J is thus
1 − π = 1 − 2/(1 + f (π). Using UCR murder data cross cities for 1970 and 1985, Glaeser,
Sacerdote and Scheinkman (1996) report estimates of f (π) between 2 and 4.5, implying estimates
of the diagonal of J between 1/3 and 2/3. Because the authors do not differentiate between different
population groups, this implicitly represents a population weighted average of J11 , J22 , and J33 .39
For simplicity, we take this evidence to suggest that J11 = J22 = J33 = 1/2 for murder. For
robbery and motor vehicle theft, the authors estimate f (π) in the range of 37-155 and 141-382,
suggesting diagonal elements of J very close to 1.
39
For this same reason, this paper does not provide evidence on off-diagonal elements of J.
ii
Table A.1: Early Twentieth Century Homicide Rates and Social Connectedness
Dependent variable: log HHI, Southern black migrants
(1)
(2)
Log homicide rate, 1911
0.074
(0.139)
0.074
(0.105)
0.01
41
-0.185***
(0.059)
0.39
41
Log homicide rate, 1912
Log homicide rate, 1913
Log homicide rate, 1914
Log number, Southern black migrants
R2
Cities
p-value
(3)
-0.048
(0.300)
-0.199
(0.289)
0.237
(0.269)
0.136
(0.261)
-0.186**
(0.074)
0.45
31
.87
Notes: The last row contains the p-value from the F-test that all log homicide rate
coefficients equal zero. Heteroskedastic-robust standard errors in parentheses. *
p < 0.1; ** p < 0.05; *** p < 0.01
iii
Table A.2: Crime and Social Connectedness, 1960-2009, Full Results
Log HHI, S. black migrants
Log number S. black migrants
Log population
Percent black, 1960
Percent black, 1970
iv
Percent black, 1980
Percent black, 1990
Percent black, 2000
Percent age 5-17, 1960
Percent age 18-64, 1960
Percent age 65+, 1960
Percent age 5-17, 1970
Percent age 18-64, 1970
Percent age 65+, 1970
Murder
(1)
Rape
(2)
Robbery
(3)
Assault
(4)
Burglary
(5)
Larceny
(6)
Motor
Vehicle Theft
(7)
-0.153***
(0.036)
0.156***
(0.022)
0.936***
(0.054)
2.163***
(0.707)
1.954***
(0.231)
1.628***
(0.169)
1.563***
(0.208)
1.911***
(0.225)
-1.185
(7.839)
8.473
(7.551)
-8.324
(6.133)
-6.536**
(2.934)
-3.689
(2.684)
-3.872*
(2.270)
-0.070**
(0.034)
0.071***
(0.026)
0.825***
(0.041)
2.764***
(0.605)
2.502***
(0.245)
1.526***
(0.159)
0.681***
(0.210)
0.099
(0.229)
-13.086*
(7.508)
3.790
(6.727)
-15.295***
(5.568)
-9.224***
(2.972)
-4.750*
(2.527)
-6.965***
(2.201)
-0.235***
(0.035)
0.153***
(0.027)
1.113***
(0.052)
2.327***
(0.564)
1.533***
(0.225)
1.188***
(0.193)
0.741***
(0.198)
0.435*
(0.229)
0.181
(6.558)
7.659
(5.650)
-2.585
(5.578)
-7.348**
(3.007)
-3.575
(2.728)
-4.031*
(2.394)
-0.133***
(0.041)
0.076***
(0.029)
0.865***
(0.049)
3.030***
(0.638)
0.924***
(0.296)
0.599**
(0.264)
0.193
(0.236)
-0.108
(0.215)
-13.048**
(6.376)
-0.251
(5.687)
-16.191***
(4.532)
-7.082
(4.435)
-7.855*
(4.093)
-5.103
(3.257)
-0.074***
(0.024)
0.058***
(0.018)
0.938***
(0.030)
1.121*
(0.628)
0.947***
(0.166)
0.335**
(0.138)
0.095
(0.163)
0.162
(0.172)
0.141
(4.529)
6.360
(4.473)
-6.200
(4.008)
-3.900**
(1.810)
-4.782***
(1.574)
-2.849**
(1.445)
-0.028
(0.031)
0.042*
(0.024)
0.865***
(0.042)
0.304
(0.522)
0.107
(0.252)
-0.144
(0.237)
-0.075
(0.295)
-0.395
(0.258)
-5.845*
(3.549)
-2.227
(3.290)
-7.613***
(2.559)
-2.303
(2.203)
-3.039
(1.935)
-1.652
(1.643)
-0.149***
(0.041)
0.048*
(0.029)
1.259***
(0.052)
1.066*
(0.626)
1.214***
(0.266)
0.887***
(0.234)
0.615**
(0.280)
0.894***
(0.238)
4.297
(4.284)
7.497**
(3.753)
-4.324
(4.397)
-0.127
(3.292)
1.906
(2.720)
-0.910
(2.690)
Table A.2: Crime and Social Connectedness, 1960-2009, Full Results
Percent age 5-17, 1980
Percent age 18-64, 1980
Percent age 65+, 1980
Percent age 5-17, 1990
Percent age 18-64, 1990
v
Percent age 65+, 1990
Percent age 5-17, 2000
Percent age 18-64, 2000
Percent age 65+, 2000
Percent female, 1960
Percent female, 1970
Percent female, 1980
Percent female, 1990
Percent female, 2000
Murder
(1)
Rape
(2)
Robbery
(3)
Assault
(4)
Burglary
(5)
Larceny
(6)
Motor
Vehicle Theft
(7)
-8.708***
(2.882)
-9.819***
(2.143)
-5.293**
(2.371)
-18.609***
(4.275)
-15.241***
(2.986)
-11.575***
(3.403)
-5.029
(4.997)
-6.084
(3.780)
-4.481
(3.867)
11.316
(8.645)
0.771
(1.964)
-1.944
(2.050)
-3.906
(2.642)
4.504
(2.936)
-11.155***
(2.909)
-8.592***
(2.070)
-8.116***
(2.305)
-9.317**
(4.031)
-7.697***
(2.657)
-6.764**
(3.006)
-9.105*
(4.970)
-6.173
(4.076)
-6.461*
(3.774)
16.723**
(7.681)
2.040
(2.046)
-1.061
(2.333)
-2.217
(2.806)
2.342
(2.299)
-3.798
(3.998)
-4.218
(2.829)
-0.670
(3.211)
-7.868*
(4.054)
-4.805*
(2.544)
-3.855
(3.056)
-3.025
(4.128)
-2.287
(3.441)
-1.707
(3.154)
1.939
(5.920)
-0.611
(2.200)
-1.874
(2.551)
0.203
(3.662)
-0.285
(2.515)
-13.352***
(4.584)
-11.983***
(3.256)
-8.644**
(3.591)
-8.890**
(4.335)
-7.927***
(3.069)
-6.828**
(3.236)
0.133
(3.964)
-1.382
(3.090)
0.335
(2.967)
9.052
(6.084)
-5.422*
(2.862)
-4.234
(3.058)
-1.387
(2.504)
4.193**
(1.890)
-6.536**
(2.676)
-6.362***
(1.866)
-4.252**
(1.881)
-4.868*
(2.595)
-6.174***
(1.835)
-3.739**
(1.888)
6.327*
(3.329)
5.205*
(2.674)
5.767**
(2.529)
13.710**
(6.393)
-0.447
(1.351)
1.422
(1.578)
0.498
(2.113)
-0.938
(1.570)
0.774
(3.974)
-0.498
(2.283)
2.534
(3.615)
0.789
(3.248)
-0.083
(2.478)
1.799
(2.154)
3.028
(3.969)
2.289
(3.105)
3.151
(3.027)
6.250
(4.130)
-0.615
(1.468)
-2.984
(2.111)
-1.828
(2.128)
0.585
(1.787)
11.741***
(4.218)
9.055***
(3.157)
10.310***
(3.326)
5.983
(5.096)
5.925*
(3.155)
5.448
(3.741)
9.048*
(5.082)
9.302**
(4.032)
7.886**
(3.729)
6.715
(5.565)
0.710
(2.582)
-1.328
(3.108)
3.999
(4.038)
-1.551
(2.850)
Table A.2: Crime and Social Connectedness, 1960-2009, Full Results
Percent 25+ with HS, 1960
Percent 25+ with HS, 1970
Percent 25+ with HS, 1980
Percent 25+ with HS, 1990
Percent 25+ with HS, 2000
vi
Percent 25+ with College, 1960
Percent 25+ with College, 1970
Percent 25+ with College, 1980
Percent 25+ with College, 1990
Percent 25+ with College, 2000
Log area, square miles, 1960
Log area, square miles, 1970
Log area, square miles, 1980
Log area, square miles, 1990
Murder
(1)
Rape
(2)
Robbery
(3)
Assault
(4)
Burglary
(5)
Larceny
(6)
Motor
Vehicle Theft
(7)
-1.335
(1.172)
-2.526***
(0.572)
-2.334***
(0.534)
-1.902***
(0.467)
-1.417***
(0.494)
3.665
(2.685)
-0.387
(0.773)
0.400
(0.483)
-0.317
(0.414)
0.066
(0.444)
-0.037
(0.075)
0.046
(0.053)
0.107**
(0.052)
0.103**
(0.048)
-0.136
(1.456)
-1.419***
(0.490)
-0.411
(0.471)
1.573***
(0.464)
2.740***
(0.529)
5.580***
(1.926)
1.244**
(0.598)
0.091
(0.481)
-0.505
(0.372)
-1.025**
(0.486)
0.301***
(0.071)
0.269***
(0.040)
0.277***
(0.038)
0.188***
(0.040)
-0.049
(0.938)
-1.821***
(0.565)
-1.496**
(0.655)
-1.337**
(0.526)
-0.706
(0.545)
0.303
(2.218)
-0.294
(0.758)
0.227
(0.590)
-0.033
(0.369)
-0.037
(0.436)
-0.108
(0.083)
-0.134**
(0.053)
-0.099*
(0.055)
-0.122**
(0.053)
-0.132
(1.076)
-3.071***
(0.606)
-1.172*
(0.669)
1.122**
(0.490)
1.554***
(0.453)
2.998
(2.115)
2.032***
(0.676)
0.239
(0.687)
-0.604*
(0.354)
-0.244
(0.414)
0.075
(0.070)
0.124***
(0.046)
0.127***
(0.044)
0.114***
(0.042)
0.172
(0.941)
-0.834***
(0.322)
-1.416***
(0.336)
0.840**
(0.359)
1.450***
(0.363)
3.963**
(1.877)
1.458***
(0.362)
0.857***
(0.322)
0.710***
(0.274)
-0.072
(0.329)
0.051
(0.053)
0.067***
(0.024)
0.094***
(0.026)
0.088***
(0.029)
-0.347
(0.786)
-0.139
(0.382)
-1.352**
(0.549)
0.532
(0.416)
1.013***
(0.383)
4.125***
(1.343)
1.470***
(0.382)
1.368***
(0.394)
0.964***
(0.273)
0.735**
(0.320)
0.072
(0.046)
0.095**
(0.038)
0.139***
(0.034)
0.126***
(0.036)
-1.302
(0.807)
-2.531***
(0.630)
-1.203*
(0.626)
-1.137*
(0.633)
-0.645
(0.593)
3.218
(2.351)
0.191
(0.786)
-1.284*
(0.711)
-1.430***
(0.536)
-2.089***
(0.600)
-0.159***
(0.061)
-0.212***
(0.047)
-0.171***
(0.051)
-0.046
(0.051)
Table A.2: Crime and Social Connectedness, 1960-2009, Full Results
Log area, square miles, 2000
Log median family income, 1960
Log median family income, 1970
Log median family income, 1980
Log median family income, 1990
vii
Log median family income, 2000
Unemployment rate, 1960
Unemployment rate, 1970
Unemployment rate, 1980
Unemployment rate, 1990
Unemployment rate, 2000
Labor force participation rate, 1960
Labor force participation rate, 1970
Labor force participation rate, 1980
Murder
(1)
Rape
(2)
Robbery
(3)
Assault
(4)
Burglary
(5)
Larceny
(6)
Motor
Vehicle Theft
(7)
0.076
(0.049)
-2.346***
(0.823)
-0.354
(0.294)
-0.742***
(0.215)
-0.500*
(0.261)
-1.285***
(0.185)
1.675
(6.951)
-0.775
(1.704)
1.616
(1.317)
6.890***
(2.141)
-1.346
(1.571)
0.370
(2.358)
0.920
(1.120)
2.930***
(1.013)
0.128***
(0.040)
-1.448*
(0.855)
-0.977***
(0.291)
-1.548***
(0.241)
-1.972***
(0.237)
-2.163***
(0.194)
11.853**
(5.074)
-2.006
(1.637)
2.145*
(1.141)
0.995
(1.710)
-1.439
(1.370)
-2.653
(1.787)
1.209
(0.901)
3.443***
(0.950)
-0.184***
(0.048)
-1.083
(0.690)
-0.238
(0.364)
-0.945***
(0.340)
-1.001***
(0.311)
-1.229***
(0.147)
8.602**
(4.223)
0.911
(2.179)
-0.623
(1.518)
2.575*
(1.443)
-2.334*
(1.277)
3.354
(2.240)
2.542**
(1.243)
3.153**
(1.348)
0.096**
(0.041)
-1.587**
(0.725)
-0.057
(0.367)
-0.462
(0.359)
-1.346***
(0.252)
-1.758***
(0.173)
7.455*
(4.155)
1.344
(2.117)
2.919*
(1.528)
0.677
(1.632)
0.503
(0.930)
1.940
(1.949)
3.695***
(1.375)
3.177**
(1.500)
0.068**
(0.028)
-1.946***
(0.606)
-0.741***
(0.198)
-0.379*
(0.216)
-1.220***
(0.161)
-1.341***
(0.149)
5.890
(4.794)
-0.556
(1.291)
2.198**
(0.986)
3.168**
(1.244)
2.198**
(1.079)
0.926
(1.646)
2.058***
(0.616)
2.165***
(0.668)
0.109***
(0.039)
-1.059***
(0.408)
-0.791***
(0.198)
-0.812***
(0.236)
-1.460***
(0.187)
-1.228***
(0.155)
5.256*
(3.028)
0.062
(1.294)
2.799***
(0.899)
-0.898
(1.646)
1.869*
(1.121)
1.596
(1.210)
1.881**
(0.740)
4.094***
(1.129)
-0.122***
(0.043)
-1.045*
(0.617)
0.686*
(0.375)
0.089
(0.356)
-0.215
(0.375)
-0.645***
(0.222)
4.755
(3.775)
1.344
(2.232)
0.982
(1.761)
2.165
(2.530)
-0.839
(1.091)
1.677
(1.769)
1.075
(1.257)
1.408
(1.361)
Table A.2: Crime and Social Connectedness, 1960-2009, Full Results
Labor force participation rate, 1990
Labor force participation rate, 2000
Manufacturing employment share, 1960
Manufacturing employment share, 1970
Manufacturing employment share, 1980
viii
Manufacturing employment share, 1990
Manufacturing employment share, 2000
City-year observations
Log-likelihood
Cities
Notes: See note to table 6.
Murder
(1)
Rape
(2)
Robbery
(3)
Assault
(4)
Burglary
(5)
Larceny
(6)
Motor
Vehicle Theft
(7)
2.570***
(0.986)
0.593
(0.422)
-0.011
(0.749)
-0.042
(0.297)
0.562*
(0.300)
0.268
(0.348)
0.254
(0.389)
19,471
-44761
488
3.078***
(1.001)
1.103***
(0.362)
1.330*
(0.782)
0.412
(0.298)
0.069
(0.276)
0.233
(0.358)
1.034**
(0.435)
18,279
-88820
488
3.141**
(1.391)
1.136***
(0.377)
0.922*
(0.522)
0.116
(0.339)
0.223
(0.376)
0.349
(0.374)
0.036
(0.364)
19,471
-341731
488
2.017**
(0.950)
1.360***
(0.291)
1.849***
(0.616)
0.072
(0.426)
-0.112
(0.456)
0.214
(0.415)
0.682
(0.420)
19,471
-1668135
488
2.240***
(0.741)
0.214
(0.293)
0.372
(0.538)
-0.022
(0.196)
-0.327
(0.254)
0.330
(0.312)
0.615**
(0.314)
19,471
-1112264
488
2.890***
(0.832)
1.238***
(0.315)
0.097
(0.367)
-0.267
(0.234)
-0.905**
(0.413)
-0.332
(0.409)
0.348
(0.305)
19,471
-2588533
488
1.412
(1.509)
0.312
(0.469)
0.165
(0.508)
-0.532*
(0.318)
0.030
(0.451)
-0.008
(0.454)
-0.134
(0.502)
19,471
-1034348
488
Table A.3: Robustness to Sample and Model, Crime and Social Connectedness among Black Migrants, 1960-2009
Dependent variable: number of offenses reported to police
Murder
(1)
Rape
(2)
Robbery
(3)
Burglary
(5)
Larceny
(6)
Motor
Vehicle Theft
(7)
-0.120***
(0.027)
0.026
(0.021)
-1,504,904
20,160
502
-0.102***
(0.030)
0.008
(0.023)
-3,056,894
20,160
502
-0.225***
(0.040)
0.007
(0.031)
-1,361,620
20,160
502
-0.010
(0.032)
0.054**
(0.021)
-155341
19,471
488
-0.082*
(0.046)
0.069**
(0.031)
-125045
19,471
488
Assault
(4)
A: Poisson Model, Including Big Cities
Log HHI, Southern black migrants
ix
-0.145***
(0.036)
Log number Southern black migrants 0.186***
(0.026)
Log-likelihood
-50,484
City-year observations
20,160
Cities
502
-0.149***
(0.036)
0.070**
(0.028)
-108,014
18,913
502
-0.178*** -0.189***
(0.039)
(0.042)
0.182*** 0.117***
(0.030)
(0.031)
-462,988 -2,322,973
20,160
20,160
502
502
B: Negative Binomial Model, Excluding Big Cities
Log HHI, Southern black migrants
-0.086***
(0.033)
Log number Southern black migrants 0.160***
(0.022)
Log-likelihood
-42151
City-year observations
19,471
Cities
488
-0.030
(0.033)
0.087***
(0.023)
-63021
18,279
488
-0.096**
(0.040)
0.194***
(0.028)
-101753
19,471
488
-0.058
(0.037)
0.084***
(0.027)
-132359
19,471
488
-0.023
(0.027)
0.063***
(0.019)
-135772
19,471
488
Notes: Compare these results to panel A of table 6. Regressions include covariates used in column 3 of table 4. Standard errors, clustered at the city level,
are in parentheses. * p < 0.1; ** p < 0.05; *** p < 0.01
Sources: FBI UCR, Duke SSA/Medicare dataset, Census City databook
Table A.4: Robustness to Crime Data, Crime and Social Connectedness among Black Migrants, 1960-2009
Dependent variable: number of offenses reported to police
Murder
(1)
Rape
(2)
Robbery
(3)
Assault
(4)
Burglary
(5)
Larceny
(6)
Motor
Vehicle Theft
(7)
-0.024
(0.031)
0.046*
(0.024)
-2,340,694
19,327
488
-0.144***
(0.041)
0.050*
(0.029)
-984,318
19,209
488
-0.024
(0.031)
0.046*
(0.024)
-2,328,663
19,303
488
-0.153***
(0.041)
0.046
(0.029)
-957,348
19,251
488
A: Trim Crime Counts Below 1/6 or Above 6 times City Mean
Log HHI, Southern black migrants
x
-0.107***
(0.033)
Log number Southern black migrants 0.160***
(0.021)
Log-likelihood
-37,709
City-year observations
15,552
Cities
487
-0.064* -0.231*** -0.126***
(0.035)
(0.035)
(0.041)
0.071*** 0.157*** 0.080***
(0.026)
(0.026)
(0.029)
-73,979
-306,933 -1,485,669
16,193
18,424
15,769
488
488
488
-0.070***
(0.024)
0.059***
(0.018)
-1,017,245
19,320
488
B: Trim Crime Counts Below 1/6 or Above 6 times City Median
Log HHI, Southern black migrants
-0.144***
(0.032)
Log number Southern black migrants 0.151***
(0.021)
Log-likelihood
-37,285
City-year observations
16,133
Cities
488
-0.075** -0.234*** -0.127***
(0.035)
(0.035)
(0.042)
0.067*** 0.155*** 0.082***
(0.026)
(0.026)
(0.030)
-72,946
-303,605 -1,483,895
16,306
18,441
15,746
487
488
488
-0.083***
(0.022)
0.053***
(0.017)
-885,429
19,303
488
Notes: For each city, we calculate the mean and median number of crimes across 1960-2009. Results in panel A exclude city-year observations which are
below 1/6 of the city mean or above 6 times the city mean, as in Chalfin and McCrary. Results in panel B instead use the median. Regressions include
covariates used in column 3 of table 4. Standard errors, clustered at the city level, are in parentheses. * p < 0.1; ** p < 0.05; *** p < 0.01
Sources: FBI UCR, Duke SSA/Medicare dataset, Census City databook
0
Total Number of Murders, ASR
500
1000
1500
2000
Figure A.1: Comparison of crime from different data sets
0
500
Total Number of Murders, UCR
1980
1989
1000
1984
45 degree line
0
Total Number of Murders, ASR
50
100
150
200
250
(a) All cities
0
50
100
150
Total Number of Murders, UCR
1980
1989
200
1984
45 degree line
(b) Cities with 1980 population below 500,000
The UCR data contain the total number of murders per police agency. To construct a similar measure from the ASR
data, we calculate the sum of murders committed by adult whites, adult blacks, adult other races, juvenile whites,
juvenile blacks, and juvenile other races.
xi
-.5
Coefficient on Log HHI
0
.5
Figure A.2: Social Connectedness and Crime Across Years
1960
1965
1970
1975
1980 1985
Year
1990
1995
2000
2005
1990
1995
2000
2005
-.6
-.4
Coefficient on Log HHI
0
-.2
.2
(a) Murder
1960
1965
1970
1975
1980 1985
Year
(b) Motor Vehicle Theft
Figure A.2 shows point estimates and 95-percent confidence intervals from estimating a separate regression for years
1960-1964, 1965-1969, and so on.
xii