Economic and Social Integration of Minorities:
The Effect of WWII on Racial Segregation
Andreas Ferrara∗
University of Warwick
Preliminary draft
March 30, 2017
Abstract
This paper shows that the significant switch of black workers from low- to semiskilled occupations after WWII in the U.S. is causally related to casualties sustained
by each state and county during the war. Difference-in-differences regressions estimate that a one standard deviation increase in the casualty rate among semi-skilled
whites increases blacks’ probability of being employed in semi-skilled manufacturing jobs by 7 p.p., and raises the share of blacks in semi-skilled occupations by 1.6
p.p. Using data from the “Negro Political Participation Study” of 1961 and instrumenting the change in the share of blacks in semi-skilled jobs from 1940 to 1950
with the casualty rate, IV regressions find a significant and positive impact of the
skill upgrade of blacks on their social standing and political participation. Individuals living in counties with a stronger casualty induced skill upgrade of blacks have
a higher probability for interracial friendships and interracial friendships formed
at work, stronger preferences for integration over separation, as well as increased
political participation by and reduced repercussions for political activity of blacks.
JEL codes: J15, J24, N42
Keywords: Skill; minorities; social interaction; war.
∗
I wish to thank Sascha O. Becker, Yannick Dupraz, Price Fishback, Carola Frydman, Christoph
König, Luigi Pascali, and seminar participants at the University of Arizona and Warwick for valuable
comments and discussions.
Email: [email protected]
1
1
Introduction
Many countries, such as the United States, have a sizable share of minority groups.
Despite being natives, these minorities are oftentimes neither economically nor socially
well integrated with the rest of society.1 Less fragmented societies, however, have been
found to have better outcomes with respect to public goods provision (Alesina and La
Ferrara, 2000), innovation in production (Goff et al., 2002), or urban poverty and inequality (Ananat, 2011). One potential policy to improve social integration is to help minority
workers into employment where they are more frequently exposed to and cooperate with
majority workers. A priori the effect of such a policy is ambiguous. Increased exposure
of the two groups in the workplace may lead to more cooperation, reduced stereotyping
and thus more social interaction, but can produce the exact opposite effect when the two
groups perceive each other as rivals for employment, promotions, and wages.
This paper studies the question whether increased economic integration of minority workers leads to more social integration from a historic viewpoint using a natural
experiment provided by World War II. During the war African Americans in the U.S.
experienced a strong and persistent rise in their occupational standing (see figure 1). As
blacks moved from low- to semi-skilled occupations, a predominantly white skill group at
the time, this significantly increased the exposure of whites to blacks both at work and
in the cities to which African Americans relocated from the rural areas during and after
the war (Boustan, 2007; Boustan, 2010). While economists have paid much attention to
the effects of the war on job market outcomes of women (see Goldin, 1991; Acemoglu
et al., 2004; Goldin and Olivetti, 2013), its role in the skill upgrade of blacks and the
implications for their social outcomes have not yet been studied.
The purpose of this paper is therefore twofold. First, it offers a novel explanation
for the unprecedented shift of black workers from low- to semi-skilled occupations at
mid-century by arguing that this development was particularly pronounced in areas with
higher WWII casualty rates among semi-skilled whites. Second, it exploits the quasiexperimental nature of the casualty induced skill upgrade of blacks to estimate the effect of
economic integration on social outcomes of blacks and attitudes towards racial segregation
in the South in 1961.
1
For examples in the European context see the reports by Froy and Pine (2011), or OECD/EU
(2015).
2
Figure 1: Skill Composition of Blacks and Whites from 1900 to 2000
(a) Blacks
100
80
60
%
40
20
80
60
%
40
rs
ls
ke
na
s
en
or
er
s io
m
w
ag
es
s ks
of
le le r
an
Pr
M
Sa C
20
ft s
ra
C
rs
ls
ke
na
s
en
or
er
s io
m
w
ag
es
ks
s
ft s
of
le le r
an
ra
Pr
M
Sa C
C
0
s rs
er ke
or or
es
rs b w
t iv
r e la ic e
ra
b o rm rv
pe
La Fa Se
O
rs
s
er rke
o
or
es
w
rs b
t iv
r e la ic e
ra
b o rm rv
pe
La Fa Se
O
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
0
100
(b) Whites
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Note: Graphs are based on the public use microdata files of the 1900-2000 Decennial U.S. Censuses by Ruggles et al.
(2015). The sample includes employed males aged 16 to 65. Black, dark-gray, and light-gray shades indicate low-, semi-,
and high-skilled occupations, respectively. Skill groups are defined according to the 1950 Census Bureau occupational
classification scheme.
To test whether and how higher casualty rates among semi-skilled whites affected
the occupational standing of African Americans, I match 8.3 million Army Enlistment
Records with the WWII Honor List of Dead and Missing. The enlistment records contain
information on a soldier’s state and county of residence, ethnicity, and pre-war occupation.
This allows to construct an accurate measure of how many white, semi-skilled workers
in each state and county did not return from the war. Combining this casualty rate
measure with individual level Census data from 1900 to 2000, difference-in-differences
(DiD) estimates show that a one standard deviation increase in the the state casualty
1
1
rate among white semi-skilled workers raises the probability of being employed in a semiskilled manufacturing job for blacks by 7 p.p. The effect persists until the end of the
sample period in 2000.
Using county-level data for the U.S. South, a one standard deviation increase in
the county casualty rate is associated with a 1.6 p.p. increase in the share of blacks in
semi-skilled jobs. This result is robust to different regression specifications, as well as
observable and unobservable county characteristics. Given a 12.4% share of blacks in
semi-skilled jobs in 1940, an increase of 1.6 p.p. corresponds to an employment increase
of blacks by 12.9% in this skill group.
3
In order to test the effect of economic integration of blacks via the skill upgrade on
their social standing and attitudes towards racial segregation in the South, I utilize the
“Negro Political Participation Study” by Matthews and Prothro (1975) from 1961. The
randomly sampled 1,312 black and white Southerners were asked questions regarding
their political and personal preferences with respect to racial topics and segregation.
Instrumenting the change in the share of blacks in semi-skilled occupations from 1940 to
1950 with the casualty rate of semi-skilled whites, IV regressions show positive effects of
the casualty induced skill upgrade on the social standing of blacks. Individuals living in
counties with a more pronounced casualty-induced skill upgrade of blacks have a higher
probability for interracial friendships, and a higher propensity to preferring integration
over segregation. Such friendships are especially formed in the workplace. In addition,
affected counties see higher rates of political participation among African Americans and
fewer instances in which they experience repercussions for their political involvement by
whites.
The remainder of this paper is organized as follows. Section 2 provides a brief overview
of previous work done on black economic history in the 20th century and its relation to
World War II and racial segregation in the South. Section 3 estimates the effect of
the WWII casualty rate among semi-skilled whites on the probability of blacks being
semi-skilled manufacturing employment and on the share of blacks in semi-skilled jobs in
general. It first describes the construction of the casualty rate measure, and then estimates a long DiD regression from 1900 to 2000 using the Census micro data by Ruggles
et al. (2015). The last part of the section repeats the DiD analysis at the county level to
provide further evidence for the relationship between the skill upgrade of African Americans and the casualty rate. Section 4 uses data from the “Negro Political Participation
Study” to estimate the effect of the casualty induced skill upgrade on outcomes regarding
the social standing and political participation of blacks in the South. The final section
concludes.
2
WWII, Black Economic Progress, and Interracial Relations
Before WWII the economic situation of blacks can be summarized by the often cited
assessment of Myrdal (1944): “They own little property; even their household goods
4
are mostly inadequate and dilapidated. Their incomes are not only low but irregular.
They thus live from day to day and have a scant security for the future.” (p. 205).
This is reflected in figure 1 and puts the influx of blacks into semi-skilled jobs into
perspective. The war marks a turning point in black economic history (Collins, 2001),
however, the majority of existing work has focused on its implications for the labor supply
and occupational choice of women (see Goldin, 1991; Acemoglu et al., 2004; Goldin and
Olivetti, 2013; Jaworski, 2014).
As many young white men were drawn into the military, this opened up job opportunities for groups who had previously been excluded from certain jobs such as manufacturing occupations. This explanation also makes sense for African Americans but has
received less attention. The employment gains of blacks during the war were considerable.
One million black workers found civilian jobs during the war, mostly in manufacturing
(Flynn, 1993), in addition to the 750,000 African-American soldiers who served during
WWII (Wolfbein, 1947). Unlike women, most of whom were displaced from their jobs by
returning war veterans, blacks managed to secure their wartime employment gains.
Relating to previous work on the economic changes for blacks at mid-century, potential
explanations for this non-displacement include the increase in schooling and school quality
(Donohue et al., 2002; Aaronson and Mazumder, 2011), the temporary implementation of
anti-discrimination laws (Collins, 2001), outmigration of blacks from the South (Boustan,
2010), or veteran benefits via the G.I. Bill and skills acquired in the military (Collins,
2000; Turner and Bound, 2003). However, changes in schooling and school quality already
occurred before the war and in a more continuous fashion (Smith, 1984), while gains from
military service and fair employment laws were generally short-lived.
Collins (2000) analyzes the Palmer Survey and rejects the idea that work experience
in the armed forces significantly improved black veterans’ employment chances in the
manufacturing sector. Instead he finds that the increased mobility during the war was
a reason for the occupational upgrade of blacks. He also does not find evidence for
black workers leaving agricultural jobs in order to move into semi-skilled manufacturing
employment. However, these results are based on six cities only, none of which is located
in the South where the situation might have been different.
In a later paper Collins (2001) examines the impact of the first nationwide antidiscrimination law on black employment. The law was enacted under Roosevelt and
5
implemented via the Fair Employment Practice Committee in 1941. His careful analysis
shows a significant effect on black employment in war related industries. Given that the
committee was disbanded in 1946, it may not fully explain why and how black workers
managed to secure the occupational gains achieved during wartime. Some states enacted
their own anti-discrimination laws but it was not until 1964 that a comprehensive legal
framework was established across the whole U.S. (see Collins, 2003).
A marked improvement in schooling and school quality for blacks has been documented by Donohue et al. (2002). They show that the quality of school inputs converged
to white levels between 1930 and 1960 for which philanthropic activism such as the
Rosenwald Fund played a major role. Similar conclusions are reached by Aaronson and
Mazumder (2011). The G.I. Bill, which subsidized college attendance for veterans, also
had modest effects for blacks but only for those who were born outside the Southern
states (Turner and Bound, 2003).
Margo (1995) states the the occupational upgrade of blacks played an important role
in the Great Compression for wages of African American workers but that the reasons
for this upgrade are not well understood. Given the topical coverage of the previous
literature, there are thus two questions which have received little attention. Firstly, how
did the war affect the occupational standing of blacks? To fill this gap in the literature,
the hypothesis here is that blacks transition more frequently from low- to semi-skilled
employment in areas with higher WWII casualty rates among semi-skilled whites. Unlike
the draft rate, this is a potential channel which is unlikely to be influenced by displacement
effects after the war from returning soldiers.
Secondly, how and through what channels, if any, did WWII affect the social standing
of blacks in the post-war period? The move of blacks from rural to urban areas during and
after the war not only meant higher exposure of whites to blacks in the cities (Boustan,
2010), but also at the workplace due to the shift of blacks from low- to semi-skilled work
(see figure 1). Contemporary economists such as Wolfbein (1947) noticed already at the
time that a, “significant shift occurred from the farm to the factory as well as considerable
upgrading of Negro workers, many of whom received their first opportunity to perform
basic factory operations in a semiskilled or skilled capacity.” (p. 663). The effect of
increased exposure between the two groups is ambiguous.
6
One of the first theories on the improvement of racial relations between blacks and
whites via economic cooperation was formulated by Allport (1954). His Intergroup Contact Theory poses that prejudice and conflict can be reduced by increasing contact between two groups if there is no unequal or hierarchical relationship between the two,
and the environment of interaction is non-competitive but characterized by a common
goal. The timing of the theory coincides with a significant improvement in the economic
standing of black workers and the start of the Civil Rights movement. During the war the
increased employment of blacks in factories was met with some resistance. For instance,
the Philadelphia transit strike in 1944 was only broken up after the Army threatened
to review striking white workers’ draft exemptions (Collins, 2001). In the Intergroup
Contact Theory there may be an initial period of anxiety after the first contact between
the two groups that must be overcome. Hence the booming postwar economy provided a
potentially more non-competitive, less anxious environment for interaction in the sense
of Allport (1954).
More recent studies on increased intergroup relations find mixed results. Alesina and
La Ferrara (2000) model the conditions under which more ethnic diversity has positive
effects on social participation. Their empirical findings show that more ethnic fragmentation in U.S. cities is associated with lower levels of social capital. More racially fragmented
localities also display lower levels of public goods provision (Alesina et al., 1999). Ethnic
diversity has also been found to have positive effects on innovation in production (Goff
et al., 2002) and on urban poverty of blacks and inequality (Ananat, 2011).
Even though Alesina and La Ferrara (2005) conclude that developed countries deal
better with ethnic diversity in terms of cooperation, they also state that even within
developed countries there are varying degrees of conflict and cooperation that are related
to ethnic diversity. For instance, Colussi et al. (2016) argue that the construction of
mosques and the increased salience of Muslims during Ramadan leads to more political
extremism in German elections as well as increased numbers of politically motivated
crimes against the Muslim minority. On the other hand, Grigorieff et al. (2016) show
that expanding the majority group’s information set about minority groups (immigrants
in their case) positively affects attitudes towards these minorities. It is therefore not clear
as to whether increased exposure between two groups at the workplace has positive or
negative effects with respect to the social interaction of the two groups.
7
3
WWII Casualties and the Semi-Skilled Employment of Blacks
The discussion of how WWII casualties among white, semi-skilled workers affected the
probability for employment in semi-skilled manufacturing jobs for blacks and the share
of blacks in semi-skilled jobs more generally is presented in three parts. First, I discuss
the data sources and construction of the casualty rate among semi-skilled whites.
Second, using individual level data from the U.S. decennial Census, I show the evolution of the probability of obtaining semi-skilled manufacturing employment for blacks
between 1900 and 2000, and estimate the long-run casualty effect in a difference-indifferences regression while controlling for a wide set of controls.
Lastly, to move to a more granular spatial level, I provide county level evidence
on how the casualty rate affected the share of blacks in semi-skilled jobs using county
level aggregates from the 1940 and 1950 Census. This is limited to states in the South
as these are the only states for which employment statistics distinguish occupational
groups between black and white workers at the county level. The purpose of using
the county level data is to show that the rather coarse state level casualty measure is
not accidentally picking up some other unobserved factor which increased the share of
blacks in manufacturing in the aftermath of the war. Counties in the Southern states
are also interesting for the granular analysis given that the majority of African American
population is clustered in this geographic region.
3.1
Constructing the Casualty Rate for Semi-Skilled Whites
In order to construct a casualty rate for semi-skilled whites, I match 8.3 million soldiers
from the WWII Army Enlistment Records with 309,456 entries from the WWII Honor List
of Dead and Missing. The enlistment records were digitized by the National Archive and
Records Administration (NARA) while the casualty records were digitized specifically for
this paper. The enlistment records include a soldier’s name, Army serial number, state
and county of residence, place and terms of enlistment, the date of enlistment, grade and
service branch, nativity, year of birth, race, education, and pre-war occupation. For a
small subsample the variable holding a soldier’s height was filled in with data on their
Army General Classification Test (AGCT) score, the predecessor of the modern AFQT
(see Aaronson and Mazumder, 2011).
8
Figure 2: Number of Drafted and Fallen Soldiers by Month and Year
(a) Draft Numbers
Battle of the Bulge
Inductions per Month
400000
200000
300000
20000
500000
(b) Casualty Numbers
Okinawa
Casualties
10000
15000
D-Day
100000
5000
Battle of Anzio
Battle of Sicily
Operation Torch
0
0
Guadalcanal
1940m1
1941m1
1942m1
1943m1
1944m1
1945m1
1942m1
1943m1
1944m1
1945m1
1946m1
Note: Draft numbers (inductions) include those who enlisted voluntarily prior to when voluntary enlistment was forbidden in
1942. Both draft and casualty figures are for the Army and Army Air Force only. Panel (b) shows the number of fallen soldiers
per month together with major battles and operations involving U.S. Army and Army Air Force personnel.
The enlistment data and the casualty records cover Army and Army Air Force soldiers
whereas the Navy, Marines and Cost Guard are not included. Nonetheless the 8.3 million
individuals in the data comprise the majority of the 10 million drafted men in WWII.
The records also include enlisted men. Due to the high manpower demands by the
armed forces there was little scope to choose a service branch for drafted soldiers (Flynn,
1993). Volunteering gave more choice with respect to ones branch of service but was
forbidden early on in the war in 1942. This is before the largest battles and casualties
were sustained, so it would have been difficult to form a prior as to which branch was the
1
least dangerous. This
is shown in figure 2.
1
Deferments were obtained by fathers with dependents, workers in war-related industries and farmers, or conscientious objectors. Out of 40 million men who had been assessed
by their local draft boards only 11,896 men registered as conscientious objectors based on
religious reasons (Flynn, 1993). Out of 16 million WWII soldiers some 50,000 deserted
as compared to the 200,000 out of 2.5 million Civil War soldiers (Glass, 2013). Overall,
there is little evidence that draft evasion and avoidance were a major issue during WWII,
especially after Pearl Harbor.
Casualty and draft records were matched based on soldiers’ unique Army serial number which results in a match rate of 74.4%. Matches are imperfect because both data
sources include errors given that they have been digitized via Optical Character Recog9
nition (OCR). This is particularly true for the casualty records for which the original
scans are not of very high quality. Another 3.4% of unique casualties were identified
by matching the enlistment records with the American Battle Monuments Commission
(ABMC) burials and memorials data. These include the names and serial numbers of
buried soldiers serving in WWII. Again, this is not a complete list of casualties as not all
soldiers could be buried given that some were never retrieved after the war.
The remaining 22.3% of casualties were matched via the probabilistic string matching
algorithms provided by Wasi and Flaaen (2015). A one-to-one match was used to link each
casualty with a potential enlistment record based on name and serial number stratified
by state of residence. Names are matched via a tokenization and serial numbers via a
bigram algorithm, and the match with the highest matching score was kept. This results
in a final match rate of 94%. From a random sample of 1,000 matches the error rate was
0.6%. The OCR quality of the remaining 6% of casualty observations was too poor in
order to clearly identify whether a given match was correct. These cases were dropped
as were a small number of soldiers from Alaska and Hawaii which were not independent
states at the time.
Table 1 provides summary statistics for the enlistment records and some of the key
variables, including the indicator for whether a soldier did not return from the war. Blacks
faced both the lowest draft and casualty rates. This was due to segregation in the armed
forces and the insufficient amount of available facilities to maintain this segregation. The
other reason was that service required a minimum standard of education and illiteracy was
a main reason for rejection. In 1940, 11.5% of blacks were illiterate as compared to 2.9%
of whites (Smith, 1984). Given that the AGCT test was taken after formal schooling was
completed, the score is essentially also a function of the amount and quality of education
received and not necessarily a signal for lower ability.
Enlisted men, most of whom joined between late 1941 and the end of 1942, tended
to be younger, single, more able and educated,2 but faced the same unconditional death
probability as drafted men as shown in panel B. Also the comparison of Southerners in
panel C shows differences in terms of education, marital status, and age but not in the
share of fallen soldiers.
2
An AGCT score of 130 and higher was required to be classified in the top grade on the 5-tier
assessment scale of the Army.
10
Table 1: Summary Statistics - WWII Enlistment Records
Panel A:
Black (n = 807,116)
Age
Education
AGCT
Married
Height (in.)
Weight (lbs.)
Died
Panel B:
mean
25.03
9.29
70.19
0.23
68.21
148.42
0.019
st. dev.
5.80
1.86
19.54
0.42
3.51
17.90
0.139
White (n = 7,228,570)
min.
18
8
40
0
59
94
0
max.
49
18
187
1
82
249
1
Enlisted (n = 1,670,352)
Age
Education
AGCT
Married
Height (in.)
Weight (lbs.)
Died
Panel C:
mean
22.859
11.456
133.181
0.121
68.821
149.056
0.027
st. dev.
5.155
2.148
27.585
0.326
2.839
19.256
0.162
min.
18
8
1
0
59
90
0
Age
Education
AGCT
Married
Height (in.)
Weight (lbs.)
Died
st. dev.
5.570
2.207
25.958
0.434
2.308
19.501
0.166
min.
18
8
1
0
59
90
0
st. dev.
5.69
2.24
22.17
0.42
3.25
19.97
0.169
min.
18
8
40
0
59
88
0
max.
49
18
199
1
82
257
1
Drafted (n = 6,622,454)
max.
48
20
199
1
82
257
1
South (n = 2,249,203)
mean
22.288
10.157
90.722
0.252
68.658
148.076
0.028
mean
24.59
10.68
100.46
0.23
68.49
149.59
0.029
mean
25.156
10.306
95.777
0.256
68.328
149.311
0.029
st. dev.
5.809
2.244
22.773
0.436
3.414
20.066
0.167
min.
18
8
1
0
59
88
0
max.
49
20
199
1
82
257
1
Non-South (n = 6,043,984)
max.
46
20
199
1
82
256
1
mean
24.844
10.680
99.825
0.220
68.364
149.657
0.028
st. dev.
5.819
2.280
22.727
0.414
3.293
19.989
0.166
min.
18
8
1
0
59
88
0
max.
49
20
199
1
82
257
1
Note: Summary statistics for data from drafted soldiers in the Army or Army Air Force between 1940 and 1946. AGCT
is the Army General Classification Test, an ability test administered during the draft examinations. This measure is only
available for a subset of men drafted in 1943. The similarities in the minimum values for the AGCT, education levels, and
height across groups are due to the minimum requirements imposed by the Army on the draft. The indicator for a soldier’s
death equals one for those who were killed in combat or who died due to all other reasons such as battle and non-battle
injuries, accidents, self-inflicted wounds or diseases.
Occupations in the enlistment records were classified by the Army in three-digit groups
using the Dictionary of Occupational Titles of 1939. This classification scheme includes
a division into high-, semi-, and low-skilled occupations according to which semi-skilled
workers here are defined (see table A2). Using the Enlistment Records merged with
the casualty information, the casualty rate for white semi-skilled workers in each region
r ∈ {state, county} is then constructed by summing over the I casualties and J soldiers
in each region and computing the rate as,
PI
casualty rater = Pi=1
J
1(white semi-skilled casualties)ir
j=1
1(white semi-skilled drafted)jr
11
· 100
(1)
Figure 3: Spatial Distribution of the Casualty Rate among Semi-Skilled Whites
(a) State Level
3.38 - 4.25
3.09 - 3.38
2.78 - 3.09
2.33 - 2.78
1.67 - 2.33
(b) County Level
4.10 - 22.22
3.16 - 4.10
2.38 - 3.16
1.16 - 2.38
0.00 - 1.16
Note: The semi-skilled casualty rate is defined as the number of fallen soldiers
with a semi-skilled pre-war occupation divided by the number of drafted semi-skilled
workers times one hundred. State and county boundaries are as of 1940 excluding
Alaska and Hawaii. Polygon shades refer to quintiles of the casualty rate distribution
with the percentage ranges being reported in the legend. Darker shades show higher
quintiles of the casualty rate among semi-skilled whites.
Rather than computing the casualty rate over all semi-skilled workers, this measure
takes only those into account who were actually missing during the war and thus exposed
1
to the risk of dying. The second motivation for using the number of drafted semi-skilled
workers as the denominator rather than the semi-skilled workers of draft eligible age is
that i) the draft age was changed multiple times and ii) workers in war related industries
had a higher chance of receiving deferments. Without further knowledge on what exact
sub-industries and occupations in the Census received such deferments, this will lead to
an inaccurate measure of the number of workers who were actually at risk of being killed.
12
Using all semi-skilled workers as denominator also makes the assumption that a 50 year
old worker is a substitute for a 20 year old draftee. The denominator of (1) represents
those whites who were missing during the war and therefore needed replacement, whilst
the denominator represents those who were potentially replaced but did not return, i.e.
those who did not displace their wartime replacement.
The spatial distribution of the semi-skilled casualty rate by state is shown in figure 3
at the state- and county-level. More variation is available when considering the casualty
rate measure at the county level. The county level variation cannot be exploited in the
public use micro samples of the Census given that county of residence is not provided
after 1940 due to the 72-year rule. The spatial pattern of the casualty rate distribution is
not random and nowhere in the following statistical analysis will such an assumption be
made. The implication though is that plain OLS is unlikely to be sufficient for estimating
the casualty effect on black semi-skilled employment.
3.2
Difference-in-Differences Estimates Using State-Level
Casualty Rates
The DiD strategy employed in this section uses the state-level casualty rate together
with the microdata files of the decennial U.S. Censuses between 1900 and 2000 which are
made available by Ruggles et al. (2015). The estimation sample includes African American male employed manufacturing workers aged 16 to 65 of the non-institutionalized
population who are not currently attending school. The focus on manufacturing is due
to the large proportion of semi-skilled work available in this sector. According to the
1950 Census, 46.5% of all semi-skilled employment and 30.7% of the total private sector
employment was in manufacturing. Individuals with missing occupation or industry information were dropped. Classification of workers and industries follows the 1950 Census
Bureau industrial and occupational classification schemes. As in Acemoglu et al. (2004),
some of the Census samples such as the 1950 1% file require the use of sampling weights
which are employed here to account for the underrepresentation of individuals living in
large households. The concerned samples are listed in appendix A.
The Census data include several variables of interest to be utilized as control variables
in the statistical analysis. These are the completed years of education attained, age, skill
13
and industry group, as well as veteran, marital, urban residency, and inter-state migration
status. The latter is an indicator which equals one if an individual’s current residence
state is not the same as their birth state. Given that several factors changed for African
Americans at mid-century such as the increase in education (Smith, 1984; Donohue et
al., 2002; Aaronson and Mazumder, 2011), stronger inter-state mobility, and service in
the armed forces that potentially provided training and practical skills (Wolfbein, 1947;
Collins, 2000), or benefits from the G.I. Bill for Veterans (Bound and Turner, 2003), this
is an attempt to take these factors into account.
In order to address further aggregate factors that had an impact on the employment of
black workers in skilled and semi-skilled occupations, I supplement the Census data with
several other data sources. Fishback and Cullen (2013) document slight employment
effects in manufacturing related to WWII spending across U.S. counties. War related
spending comes from the 1947 edition of the County and City Data Book published
by the U.S. Census Bureau. Information on state specific anti-discrimination laws was
taken from Collins (2003). An indicator variable for these regulations equals one for
implementing states in the nearest Census year after the law was enacted and is zero
otherwise.
Another concern is technological change in the agricultural sector. Olmstead and
Rhode (2001) document a doubling in the number of tractors between 1940 and 1950,
increasing from 1.5 to 3 million tractors. With blacks being the main farm labor group
affected, the improvement in farm technologies might act as a push factor that shifts labor
from farms to factories. This is particularly important given that Olmstead and Rhode
(2001) notice a comparatively stronger diffusion of the tractor in the Southern states
during and after WWII. To address this concern, I use data from the 1900 and 2000
agricultural Census by Haines et al. (2016) to control for the average number of tractors
per farm, the total share of acres in farming, the share of cash- and share-tenants to
account for differential skill in agriculture, and the average value of farm machinery per
farm.
The difference-in-differences regression to be estimated is as follows:
Pr(semi-skilled = 1)ist = αs +λt +
X
0
βt casualty rates + Xist
γ + αs t + ηist
t6=1940
14
(2)
where the outcome is an indicator for whether a black individual i, in state s, in decade
t is employed in a semi-skilled manufacturing job. This type of jobs refers to the Census
classifications of operatives and craftsmen. In this specification the WWII casualty rate
among white semi-skilled workers is interacted with decade fixed effects. To uniquely
identify the coefficients of interest, a baseline period must be omitted which here was
chosen to be 1940, i.e. the last pre-treatment period. For ease of interpretation the
casualty rate is standardized to have unit variance. The treatment coefficients βt thus
measure the average percentage point increase in the outcome in each period (relative to
1940) for a one standard deviation increase in the casualty rate.
Time-invariant factors leading to cross-state variation in the employment probabilities
are captured by the state fixed effects αs while time-varying shocks common to all states
are accounted for by the decade fixed effects λt . In addition, state-specific linear time
trends αs t control for systematic long run trends in the employment probability for blacks
in semi-skilled manufacturing jobs across states. The vector Xist consists of individual
and state-level covariates. These include age, an education score variable,3 a quartic in
potential job market experience, and indicators for marital, veteran, inter-state migration,
and urban residence status. The indicator for inter-state migration equals one if an
individual does not live in their state of birth. Even though the treatment here need
not be random, it still has to be exogenous from the viewpoint of the individual worker.
Otherwise the casualty rate coefficients potentially capture additional migration effects.
The inter-state migration dummy attempts to account for such possible treatment status
manipulation.
The state level controls include the interactions of decade fixed effects with the WWII
draft rate, and with the log of WWII related spending. The draft rate is the number of
soldiers inducted for service over the number of men in the draft-eligible age range of 18
to 49. Also included are state-specific fair employment laws before 1964. Time-variant
covariates are the share of workers employed in the manufacturing sector, the share of
acres used in agricultural production, the average value of machinery per farm, the share
of cash- and share-tenants, and the average number of tractors per farm. Differentiating
agricultural employment into share- and cash-tenants takes care for differential skilllevels in the agricultural sectors while the average number of tractors per farm accounts
3
A consistent measure of years of schooling is only available from 1940 onwards. In prior Censuses
only literacy is reported.
15
Figure 4: Share of Blacks in Manufacturing in High- and Low-Casualty Rate States
(a) Split by Median of the Casualty Rate
0
0
Share of blacks in manufacturing
2
4
8
6
Share of blacks in manufacturing
4
2
6
8
10
10
(b) Split by Quartiles of the Casualty Rate
1900
1910
1920
1930
1940
Below Median Casualties
1950
1960
1970
1980
1990
2000
1900
1910
1920
1st Quartile
Above Median Casualties
1930
1940
1950
2nd Quartile
1960
1970
1980
3rd Quartile
1990
2000
4th Quartile
Note: Figures based on the 1900-2000 Census files by Ruggles et al. (2015) for workers employed in the manufacturing
sector. Sample splits are based on above- and below median casualty rate states in panel (a), and on quartiles of the
casualty rate in panel (b). The casualty rate refers to the share of fallen white semi-skilled workers relative to the total
number of drafted white semi-skilled workers. The last pre-war sample is 1940.
for potential push factors via technology driven labor release.
The main identifying assumption is that the probability of employment in semi-skilled
manufacturing jobs for African Americans evolved in a parallel way over time across states
with higher and lower casualty rates during the war. Additionally, no other omitted but
significant and time-varying determinant of the outcome must be correlated with the
casualty rate and change as a result of the war. Panel (a) of figure 4 shows the average
share of blacks in manufacturing
for above- and below-median casualty
rate states. These
1
1
move in a parallel fashion in the pre-war period and diverge after the war which lends
graphical evidence for the parallel trends assumption. Despite the ease of interpretation
and visibility, the median split does not describe the full picture. Panel (b) therefore splits
states according to the casualty rate quartiles. No effect is visible for states with the lowest
casualty rates and the increase in the post-war semi-skilled employment probability for
blacks in manufacturing is monotonically increasing in the treatment intensity.
The specification in (2) is interesting for two reasons. First, it presents a formal test
for the identifying assumption which is given by the pre-1940 βt coefficients. The casualty
rate measure should not significantly affect the outcome before there are any casualties.
This would cast doubt on the identification strategy and hint towards pre-existing trends
in the share of blacks across high- and low-casualty rate states. Compared to the visual
inspection, the regression based test for pre-trends has the advantage of conditioning
16
-.1
Casualty Rate Effect βt
.1
0
.2
Figure 5: Long-Run Difference-in-Differences Results
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
Note: Coefficient plot for regressions of the semi-skilled manufacturing employment indicator
on the WWII casualty rate of semi-skilled white workers interacted with decade fixed effects.
Coefficients are in terms of a one s.d. increase in the casualty rate. The estimation sample
uses 142,850 individual African American workers employed in manufacturing from the 19002000 decennial U.S. Census files by Ruggles et al. (2015). The regression includes decade and
state fixed effects, and state-specific linear time trends. Individual level controls include age,
education, a quartic in potential job market experience, and indicators for skill group, marital,
veteran, inter-state migration, black, and urban residence status. State level controls include
interactions of decade fixed effects with the draft rate and WWII related military spending per
capita, as well as information on state-specific anti-discrimination laws, the total employment
share in manufacturing, and the number of tractors per acre. Error bars show 95% confidence
intervals. Standard errors are clustered at the state level.
on covariates as well rather than observing the unconditional evolution of the outcome.
Secondly, the coefficients with the post-1940 decade interactions measure i) the casualty
rate effect on the outcome and ii) the fading-out time (or persistence, if any) of the
treatment.
Figure 5 shows coefficient plots from regression (2). Coefficients are reported in terms
of a one standard deviation increase in the casualty rate. Standard errors are clustered
at the state level to account for heteroscedasticity and serial correlation of the residuals.
The vertical line marks the last pre-treatment period in 1940. Before the war none of the
coefficients are statistically significant meaning that there are no systematic differences
in the employment probabilities for blacks 1in states with differing casualty rates. After
the war occurs, a one standard deviation increase in the casualty rate among semi-skilled
whites leads to an average increase of 7 p.p. in the employment probability for blacks
in semi-skilled jobs. All coefficients are significant at the 5% level until the end of the
17
sample period. While the effect seems to be continually increasing over time, none of the
later coefficients are significantly different the effect in 1950.
3.3
Difference-in-Differences Estimates Using County Data for
the Southern States
Despite the rich set of controls, one concern with the previous estimation strategy is
that the rather coarse state level casualty rate might be picking up time-varying factors
which are correlated with both the casualty rate and the outcome. The Census files
for the Southern states report county level aggregates for occupational groups such as
operatives and craftsmen for blacks and whites separately. This makes it possible to take
the analysis to a more granular level. The second interesting feature of this data is that
it is located in the part of the U.S. where the majority of African Americans reside. Data
is available for 17 states and Washington D.C. with a total of 1,387 counties using the
state and county boundaries of 1940.4
Figure 6 shows the spatial distribution of the casualty rate among semi-skilled whites
in the Southern counties in panel (a). Compared to the state level measure, the county
level casualty rate provides much more variation with both a larger maximum and standard deviation, but with a similar mean. Table A3 displays the summary statistics of the
casualty rate for each level of aggregation. Comparing Southern and non-Southern counties, the distribution functions are similar at the center but differ in the tails. Southern
counties have more extreme values while non-Southern counties have a higher number
of zeroes. The estimated density functions of the casualty rate in the Southern counties
compared to all non-Southern counties are displayed in panel (b).
The DiD specification using the county aggregates is
share of blacksct = αc + d1950 + βcasualty ratec · d1950 + Xct0 γ + ct
(3)
where the outcome is the share of blacks in semi-skilled occupations, i.e. craftsmen
and operatives, αc are county fixed effects, d1950 is an indicator for 1950 which is also
interacted with the county casualty rate. The vector of controls includes the interactions
4
States included are Alabama, Arkansas, Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North and South Carolina, Oklahoma, Tennessee, Texas, Virginia, and West Virginia.
18
Figure 6: County Level Casualty Rates
(a) Spatial Distribution (Southern counties)
0
4.10 - 22.22
3.16 - 4.10
2.38 - 3.16
1.16 - 2.38
0.00 - 1.16
Distribution of Casualty Rates (County Level)
.1
.2
.3
(b) County Casualty Rate Densities
0
5
10
15
20
25
Non-Southern States
Southern States
Note: Panel (a) displays the casualty rates among semi-skilled whites in Southern counties. Polygon colors indicate
quintiles of the casualty rate distribution with the percentage ranges of each quintile being reported in the legend. Panel
(b) shows the estimated casualty rate densities for counties in Southern and non-Southern states.
between the 1950 indicator and: the draft rate, the average casualty rate in the counties
adjacent to county c to control for spatial spillover effects, the log of WWII spending
per capita, the share of acres flooded by the Mississippi in 1927 to control for a major
migration event for black workers (Hornbeck and Naidu, 2014), as well as the number of
slaves in 1860 and the number of lynchings of blacks between 1900 and 1930 per thousand
blacks to account for anti-black sentiment.
Further time-variant controls include the number of Rosenwald schools per one thou1
sand blacks to account for the impact of improved education (Aaronson and Mazumder,
1
2011), the share of manufacturing
and agricultural employment, the share of black males,
the share of women in manufacturing, the value of agricultural output and the number
of tractors per acre to control for technological change in agriculture, the share of acres
in cotton production, the median values of education and family income, as well as the
Republican’s vote shares in the 1940 and 1950 Congress elections.
Summary statistics for 1940 and 1950 are reported in table A4. Table 2 reports
the results from estimation of (3) for various specifications. The addition of controls
does not significantly affect the estimated increase in the share of blacks in semi-skilled
occupations of 1.6-1.7 p.p. for a one s.d. increase in the casualty rate. The interaction of
the covariates with the 1950 indicator reduces the estimated treatment effect, though the
selection procedure of Belloni et al. (2014) in column 4 discards most of these interactions.
19
Table 2: County Level Difference-in-Differences Results, 1940-1950
Outcome: share of blacks in semi-skilled jobs
Casualty Rate
Controls
Controls × Time
LASSO-Selection
Observations
Within R2
Oster’s δ
(1)
1.669
(0.367)***
No
No
No
2,771
0.029
1.125
(2)
1.604
(0.451)***
Yes
No
No
2,544
0.072
16.300
(3)
1.135
(0.527)**
Yes
Yes
No
2,544
0.104
2.272
(4)
1.577
(0.384)***
Yes
Yes
Yes
2,691
0.042
1.837
Note: Difference-in-differences regressions of the share of blacks in semi-skilled occupations in each county on the county
casualty rate interacted with the 1950 dummy. The estimation sample contains 1,387 counties in Southern states in 1940
and 1950. Coefficients are expressed in terms of a one standard deviation increase in the casualty rate. All regressions
include county fixed effects and an indicator for 1950. Controls include: draft rate × d1950 , the average casualty rate of
counties adjacent to a given county, the share of manufacturing and agricultural employment, the share of black males, the
share of women in manufacturing, the value of agricultural output per acre, number of tractors per thousand acres, median
education, median family income, log WWII spending per capita, the share of agriculture acres in cotton production, share
of acres flooded by the Mississippi in 1927 × d1950 , the number of lynchings of blacks between 1900-1930 per thousand
blacks, the number of Rosenwald schools per thousand blacks, the vote share of the Republican Party in the 1940 and
1950 Congress elections, and the number of slaves in 1860 × d1950 . Monetary values are deflated to 2010 dollars. The
LASSO-selection model uses the variable selection procedure for testing model stability and improving inference by Belloni
et al. (2014). Standard errors are clustered at the county level. Significance levels are denoted by * p < 0.10, ** p < 0.05,
*** p < 0.01.
Their doubly-robust selection method first regresses the outcome on all covariates,
their squares, and cross-term interactions, then regresses the treatment on the same
set of variables. Those covariates which are significant predictors in either of the two
regressions as judged by LASSO or other high quality selection methods are kept. Lastly,
the outcome is regressed on the treatment variable and the selected variables from the
previous step. The casualty rate effect from the doubly-robust DiD regression produces
a coefficient which is statistically indistinguishable from the original specification.
A sensitivity analysis for the coefficient stability of the casualty rate coefficient with
respect to the observable is shown in table 3 and a robustness check with respect to
the unobservables is shown in table 2 by reporting Oster’s (2016) δ statistic. Including
the most influential control variables separately or jointly does not significantly affect the
casualty rate coefficient. All of the top row coefficients from column 2 to 9 are within half
a standard error of the uncontrolled estimate in column 1. Given that the coefficient of
interest remains stable in all of these specifications, this is evidence that omitted variable
bias is of little concern.
However, this line of reasoning makes the assumption that coefficient stability with
respect to the observables is informative on the relationship between the casualty rate
20
21
2771
1,387
0.029
2,766
1,387
0.035
2,765
1,387
0.037
1.139***
(0.169)
(3)
1.473***
(0.371)
2,771
1,387
0.034
-0.139***
(0.034)
(4)
1.722***
(0.370)
2,771
1,387
0.031
0.200**
(0.087)
(5)
1.706***
(0.360)
2,771
1,387
0.029
0.020
(0.036)
(6)
1.670***
(0.367)
2,771
1,387
0.031
-0.399
(0.458)
(7)
1.677***
(0.374)
-0.167
(0.592)
2,725
1,376
0.030
(8)
1.693***
(0.391)
(9)
1.573***
(0.383)
-0.206***
(0.080)
1.467***
(0.175)
-0.171***
(0.031)
0.368***
(0.102)
-0.034
(0.031)
-0.496
(0.525)
-0.167
(0.493)
2,716
1,376
0.058
Note: Difference-in-differences regressions of the share of blacks in semi-skilled occupations in each county on the county casualty rate interacted with the 1950 dummy. The estimation
sample contains up to 1,387 counties in Southern states in 1940 and 1950. Coefficients are expressed in terms of a one standard deviation increase in the casualty rate. All regressions include
county fixed effects and an indicator for 1950. Standard errors are clustered at the county level. Significance levels are denoted by ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
Observations
Counties
R-squared
Rosenwald schools
Tractors per acre
% female manufact.
% black males
% agriculture emp.
Outcome: share of blacks in semi-skilled jobs
(1)
(2)
Casualty rate
1.669***
1.675***
(0.367)
(0.368)
Draft rate
-0.162**
(0.068)
Adjacent cas. rate
Table 3: County Difference-in-Differences Sensitivity Analysis
and the unobservables. In order to test whether unobservables might be a threat to
identification, I compute Oster’s (2016) δ statistic. Oster (2016) considers a standard
linear regression model Y = βX + W1 + W2 + , where W1 = Ψwo is a vector of observable
controls and W2 is an index of unobservables. She then defines the selection relationship
1 ,X)
as δ Cov(W
=
V ar(W1 )
Cov(W2 ,X)
V ar(W2 )
and solves for δ (the degree to which selection on unobservables
is less than or larger than selection on observables) which would be required to produce
β = 0. Assuming that W1 and W2 can fully explain variation in the casualty rate, i.e.
Rmax = 1 in a regression of the casualty rate on W1 and W2 , a reasonable threshold for
the previous results in table 2 to be considered robust is δ ≥ 1, meaning that the selection
on unobservables would need to be at least as important as selection observables in order
to yield a coefficient of zero for the casualty rate.
The δ statistics in table 2 are all larger than this threshold even though it decreases in
columns 3 and 4 given that the interactions of the controls with the 1950 dummy mainly
add noise to the regression. The LASSO procedure on the other hand produces a sparse
model by construction yielding a lower R2 value that is then used in computing the δ.
The assumption in Belloni et al. (2014) is that all relevant variables are available in a
larger set of controls but that the econometrician does not know which these variables
are. The LASSO selection and the Oster test do not contradict each other but rather
operate under different assumptions.
As another check on the parallel trends assumption, figure 7 shows the coefficient plot
from a DiD regression including the year 1930, keeping 1940 as the base year for comparison. There are no systematic differences in the share of blacks in semi-skilled occupations
across counties with differing WWII casualty rates among semi-skilled whites before the
war. Also it does not alter the estimated casualty rate effect after the war. Another
point to notice is that the estimated casualty rate effect is not exaggerated in the Southern context compared to the U.S. as a whole. The distribution of casualty rates between
South and non-South are very similar (see figure 6) and there is no difference in soldiers’
observable characteristic (table 1 panel C). Southerners were also not more patriotic or
enthusiastic with regards to service, hence the similarities in the death probabilities is
also reflected in the voluntary enlistment rates (see figure A1).
22
-.5
0
Casualty Rate Effect β
.5
1
1.5
2
Figure 7: County Level DiD, 1930-1950
1930
1940
1950
Note: Difference-in-differences regressions of the share of blacks in semi-skilled occupations
in each county on the county casualty rate interacted with decade fixed effects. The estimation sample contains 1,387 counties in Southern states in 1930, 1940 and 1950. Coefficients
are expressed in terms of a one standard deviation increase in the casualty rate. All regressions include county fixed effects and decade fixed effects where the indicator for 1940
is omitted. Controls include: draft rate × decade FE, the average casualty rate of counties
adjacent to a given county, the share of manufacturing and agricultural employment, the
share of black males, the share of women in manufacturing, the value of agricultural output
per square acre, number of tractors per acre, median education, median family income, log
WWII spending per capita, the share of agriculture acres in cotton production, share of acres
flooded by the Mississippi in 1927 × decade FE, the number of lynchings of blacks between
1900-1930 per thousand blacks, the number of Rosenwald schools per thousand blacks, the
vote share of the Republican Party in the 1940 and 1950 Congress elections, and the number
of slaves in 1860 × decade FE. Monetary values are deflated to 2010 dollars. Standard errors
are clustered at the county level and error bars show the 95% confidence intervals for each
coefficient.
4
The Skill Upgrade and Social Status of Blacks in the PostWWII South
During and after the war, African Americans migrated from rural to urban areas
(Boustan, 2010). The skill upgrade from low- to semi-skilled occupations added to the
increased exposure of whites to black workers who were now not only living in the same
areas but were also working in the same jobs. Theoretically, such an increase in exposure
to the minority group and the associated increase
in salience can have opposing effects.
1
Grigorieff et al. (2016) find that information provision can improve the attitudes of individuals belonging to the majority group towards immigrants and that such improvements
can persist over time. Colussi et al. (2016) on the other hand show that increased salience
of a minority group close to an election date can also increase political extremism and
23
racially motivated crimes. They exploit the distance between election dates and the
month of Ramadan using German data.
To test whether the increased economic integration via the casualty induced skill upgrade of black workers also improved their social standing, I utilize the “Negro Political
Participation Study” of 1961 by Matthews and Prothro (1975). This data set is combined with the previous county level information for the share of blacks in semi-skilled
occupations and the casualty rate. The study was conducted in the former confederacy
for a random sample of 618 black and 694 white adults in 1961.5 The initial purpose was
to collect information on political participation of African Americans in the South and
the reasons for (non-)participation.
Interviewers, however, not only asked questions strictly related to the U.S. political
system or political preferences but also touched upon topics concerning the issues of race
and segregation. Examples of such questions are whether an individual frequently spoke
about racial or political topics to his friends, family, or fellow workers, whether they had
friends of the opposite color and, if so, how they met them, their preferences concerning
segregation as well as the perceived segregationist tendencies in their area of residence.
Standard information on each individual’s state and county of residence, age, marital
status, education, veteran status, rural/urban status, family income, employment status
together with information on occupation and industry, and gender were also included.
For the analysis here I coded responses to questions regarding the social integration
and status of blacks into binary variables. A complete list of the specific questions and the
coding scheme for the outcome variables is provided in table A5. Social integration here
refers to any question concerning non-market interactions between blacks and whites, or
attitudes towards people from the opposite race. The outcomes considered relate to social
interaction between blacks and whites such as friendships, whether these are formed at
work, and if they discuss general and political issues with people from the other group.
Given that there is no universally accepted measure of social integration, the choice of
outcomes here is an attempt to capture at least some aspects that one might associate
with social inclusion and status.
5
This includes the states of Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, North and
South Carolina, Tennessee, Texas, and Virginia.
24
In addition, I also consider outcomes for political integration by looking at individual
and residence area wide opinions towards integration/segregation, political participation,
and whether blacks get punished for political activity. The choice of these outcomes is
motivated by the importance of representation in the political system. If a minority does
not or cannot participate in the pluralistic process of decision making within a society
then this minority is likely not well integrated.
Despite the relatively small sample size, this data set provides a unique opportunity to
study the social standing of African Americans in the South during a time when the Civil
Rights movement was at its peak but before the race riots between 1963 and 1970, as well
as the major legislative and judicative reforms against segregation. Major desegregation
laws were only enacted later such as the Civil Rights Act of 1964, the Voting Rights
Act of 1965, the Fair Housing Act of 1968, or Supreme Court rulings such as Loving vs.
Virginia 1967 which invalidated anti-miscegenation laws that were still in force in the
South. The only exception is the Supreme Court case of Brown vs. Board of Education
of Topeka in 1954 wherein segregation at public schools was declared unconstitutional.
Regressing outcomes related to African Americans’ social standing on the share of
blacks in semi-skilled occupations is unlikely to provide unbiased and consistent estimates. The reason for this is a reverse causality problem given that an individual’s
economic status may affect her social status, but the opposite might be true when better
job opportunities arise from an increase in social contacts. In order to address this endogeneity issue, I instrument the change in the share of blacks in semi-skilled jobs from
1940 to 1950 with the casualty rate among semi-skilled whites. The corresponding first
and second stage regressions are,
0
∆share of blackscs = ψs + βcasualty ratec + Xics
γ + ρcs
0
social outcomeics = ψs + φ∆share of blackscs + Xics
δ + ics
(4)
(5)
where ∆share of blackscs is the change in the share of blacks in semi-skilled jobs from
1940 to 1950 in county c in state s. In the absence of detailed firm-level data, the influx
of blacks into semi-skilled work acts as an intent-to-treat measure of increased exposure
of whites to blacks at work. In principle it is still possible that blacks and whites are
perfectly segregated across firms. In this case an increase of blacks in this skill group
25
should not have an effect on their social relations with whites. The casualty rate is defined
0
as before, ψs are state fixed effects, ρcs and ics are stochastic error terms, and Xics
is a
vector of individual and county level controls. Individual controls include gender, race,
age, years of completed education, family income, the size of the respondent’s household,
marital status, location of the dwelling to differentiate between rural, suburban, and
urban residence status, the county and the percentage of blacks in the county an individual
grew up in, employment status, the number of years an individual has spent in their
current county of residence, place size, whether they vote for the Democratic party,
veteran status, and occupation.
The county level controls include the percentage of black residents, the median age
and education, the share of people born in other counties, the share of manufacturing
employment in the county, the WWII draft rate, as well as the previous variables on
lynchings between 1900 and 1930, Rosenwald schools and the number of slaves in 1860.
Summary statistics for the individual controls by race are reported in table A6 and summary statistics for the outcomes are shown in table A7 for black and white respondents,
respectively.
A threat to identification here would be if the WWII casualty rate among semi-skilled
whites would somehow directly affect the social integration or political participation outcomes in 1961. For instance, white war veterans may think more or less of blacks due
to the service of blacks. This could be because African Americans were fighting less
frequently which might lead to negative sentiment, or because they served in the same
area under similar conditions which could result in a more positive feeling. This is why
the regressions control for veteran status and the county draft rate in order to account
for such effects. Also inter-county migration is controlled for by the years an individual
lived in their current county and the share of people born outside a given county.
Due to missing values in key variables such as education, dwelling location, place size,
employment status, and occupation the sample shrinks to 1,034 individuals which are
located in only 22 counties. The number of available counties is too small as that the
conventional cluster robust variance-covariance estimator could yield unbiased estimates
of the standard errors. In addition to the usual cluster robust standard errors I also report
the small-cluster size corrected standard errors produced by the wild cluster bootstrap-t
procedure by Cameron et al. (2008).
26
27
(IV)
0.016
(0.007)∗∗
[0.009]∗
0.526
3.042
(OLS)
0.014
(0.004)∗∗∗
[0.007]∗∗
0.343
4.082
(IV)
0.024
(0.004)∗∗∗
[0.007]∗∗∗
0.343
6.997
You Pro Integration
(OLS)
0.017
(0.004)∗∗∗
[0.007]∗∗
0.526
3.232
(IV)
0.007
(0.002)∗∗∗
[0.004]∗
0.048
13.814
(OLS)
0.015
(0.005)∗∗∗
[0.009]∗
0.703
2.134
(IV)
0.034
(0.007)∗∗∗
[0.008]∗∗∗
0.703
4.836
Area Pro Integration
(OLS)
0.006
(0.002)∗∗∗
[0.003]∗
0.048
11.897
Friend (Work)
(OLS)
0.003
(0.005)
[0.006]
0.621
0.483
(IV)
0.012
(0.003)∗∗∗
[0.004]∗∗∗
0.113
10.619
(IV)
0.016
(0.007)∗∗
[0.009]∗
0.621
2.576
Ever voted
(OLS)
0.009
(0.003)∗∗∗
[0.004]∗∗
0.113
7.964
Talk about Politics (Work)
(IV)
0.015
(0.004)∗∗∗
[0.007]∗∗
0.205
7.317
(OLS)
-0.012
(0.002)∗∗∗
[0.004]∗∗∗
0.074
-16.216
(IV)
-0.012
(0.003)∗∗∗
[0.004]∗∗∗
-0.074
16.216
Punished for Polit. Activity
(OLS)
0.011
(0.004)∗∗∗
[0.007]
0.205
5.366
Talk to opposite Race
Note: The estimation sample is kept constant in all regressions with 1,034 individuals in 22 counties from Southern states using data from the “Negro Political Participation Study” of 1961
(Matthews and Prothro, 1975). The change in the share of blacks in semi-skilled employment from 1940 to 1950 (∆share of blackscs ) in county c and state s is instrumented with the WWII
casualty rate of semi-skilled whites. The first stage coefficient of the instrument is 3.052 with a standard error of 0.438 (first stage F statistic = 48.65). Individual level controls include
gender, race, age, years of completed education, family income, the size of the respondent’s household, marital status, location of the dwelling (rural, rural non-farm, suburban, and urban),
the county an individual grew up in, the percentage of blacks in the county an individual grew up in, employment status, the number of years an individual has spent in their current county
of residence, place size, whether they vote for the Democratic party, veteran status, and occupation. The county level controls include the percentage of black residents, the median age and
education, the share of people born in other counties, the share of manufacturing employment in the county, the WWII draft rate, as well as the number of lynchings between 1900 and 1930,
Rosenwald schools and the number of slaves in 1860. Standard errors are clustered at the county level and are reported in parentheses. Standard errors corrected for the small cluster size
using the wild cluster bootstrap-t procedure by Cameron et al. (2008) are reported in squared brackets. Significance levels are denoted by ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
Outcome mean
Effect size as % of mean
∆share of blackscs
Outcome mean
Effect size as % of mean
∆share of blackscs
Friend
Table 4: OLS and Instrumental Variables Regression Results
Results from OLS and IV linear probability models, using the first and second stages
defined in (4) and (5), respectively, are reported in table 4. The instrument is strong with
a first-stage F statistic of 48.65. Results show a significant effect of the casualty-induced
skill upgrade of blacks more than a decade earlier on measures of the social integration of
blacks. An increase in the share of blacks is associated with a 1.6 p.p. higher probability of
having an interracial friendship, and with a 0.7 p.p. higher probability of such a friendships
being formed at work. The coefficients appear relatively small in terms of effect size but
starting at the mean value of each variable, a one β increase in the treatment translates
into an increase in the average probabilities of respondents having an interracial friendship
by 3% and of having an interracial friendship that was formed at work by 13.8%.
In this context, only a positive or a null effect seem plausible. Only in extreme cases
one would expect a reduction in the number of interracial friendships due to an influx of
black workers into the skill group. Such a case might occur when there is a single black
worker in a firm who made friends with whites due to a lack of alternatives. Breaking
up those earlier friendships in order to connect with the newly arrived black coworkers
would then be the only reason as to why a negative effect could be estimated here.
The OLS and IV estimates are very close suggesting that reverse causality or omitted
variables are not be overly problematic for these relationships. The estimated coefficients
remain significant even after correcting for the small number of clusters. In general, the
small cluster correction does not inflate standard errors and t-statistics beyond conventional values of statistical significance.
The probability to converse with people from the opposite race on public issues and to
discuss political and racial topics at work are increased for individuals living in counties
with a stronger casualty-induced skill upgrade of blacks. Given that income, occupation,
migration and residence effects are controlled for, this strengthens the argument that an
increase in the share of semi-skilled blacks had a separate effect through the increased
exposure of whites to blacks in the workplace.
Individuals living in counties with a more sizable casualty-induced skill upgrade of
blacks have a significantly higher probability of being in favor of integration and report
more frequently that people in their area of residence favor integration. They also have a
higher probability for having ever voted and a lower likelihood for reporting repercussions
28
against blacks for political activity. Although the effect on voting is modest relative to
the outcome mean, the effect on the punishment for political activity variable for blacks
is sizable with an effect size of 16% with respect to the average. The skill upgrade thus
not only seems to have improved the social standing of blacks regarding their relationship
with the white majority group but also improved their opportunities for participation in
the political process without harassment.
In the case of the integration and voting outcomes the IV and OLS results differ. This
suggest that respondents make larger errors or misreport such variables more frequently
thus inducing measurement error, or that omitted variables play a more important role
in the relationship between the black skill upgrade and these outcomes.
In general, the results in table 4 provide evidence for a unanimously positive effect of
increased economic integration on social integration and political outcomes. There are two
caveats. First, the estimated coefficients are a mixture of coefficients from the black and
white subsamples. For almost all of the outcomes there is a significant difference in means
between the two groups which may not be fully captured by the race indicator included
in the regressions. To see which group drives the results for each outcome variable, tables
A8 and A9 repeat the analysis in the black and white subsamples, respectively. Inference
in this case becomes virtually useless due to the sample size being cut in half but the
magnitude of the coefficients is still informative on the influence of each group on the
estimated coefficients in table 4.
While the direction of the treatment effect is the same in both samples except for
the voting outcome in the sample of whites, the magnitudes are mainly driven by black
respondents. In terms of effect size relative to the mean, black and white results are
similar for the estimated probability increases for having an interracial friendship at
work, and for whether individuals favor integration. This does not counter the previous
results given that also for whites all of the social interaction outcomes with the opposite
race remain positive.
Secondly, the controls related to occupation, employment, and family income are also
potentially outcomes of the treatment. This would make them bad controls. Therefore
table A10 shows OLS and IV results without these covariates to provide evidence that
the previous findings are not sensitive to the inclusion or exclusion of these variables.
29
5
Conclusion
The 1940s have seen an unprecedented rise in the occupational standing of African
Americans in the United States. Previous work has mainly focused on the effect of the
war on the labor market outcomes of women whereas its effect on blacks has received considerably little attention. Equally less well known are the consequences of the improved
occupational status of blacks on their social standing in the post-WWII era. The war not
only increased exposure of whites to blacks due to migration but also due to the influx
of blacks into the skill group which previously was mainly dominated by whites. The
implications of this development is not immediately obvious given that it can increase or
decrease the social integration of minority group workers. The two main contributions of
this paper are the following. First, it provides a novel explanation for the skill upgrade
of blacks by showing that areas with higher WWII casualty rates among semi-skilled
whites experienced a greater increase in the share of blacks in semi-skilled occupations.
Using a newly constructed measure of the WWII casualty rate among semi-skilled whites
together with individual- and county-level data from the Census, difference-in-differences
regressions show a significant increase in the share of blacks in manufacturing and in
semi-skilled jobs in general.
Second, it shows that the casualty induced improvement in the economic standing
of blacks had significant and positive effects on their social standing and political participation in the U.S. South in 1961. I use the “Negro Political Participation Study”
of 1961 by Matthews and Prothro (1975) to construct measures of social and political
inclusion. Using the casualty rate as an instrument for the change in the share of blacks
in semi-skilled occupations from 1940 to 1950 to cope with the reverse causality issue
between economic and social standing, results provide evidence for a positive effect of the
skill upgrade of blacks on their social outcomes. The natural experiment made available
by the war shows how the increased exposure of the majority to the minority group via
an economic improvement of the minority can have positive effects on societal outcomes
such as integration and political participation. This finding contributes to the literature
describing positive effects on social outcomes which are connected to less fragmentation.
30
References
Aaronson, D. and Mazumder, B. (2011) “The Impact of Rosenwald Schools on Black Achievement”, Journal of Political Economy, Vol. 119(5), pp. 821-888
Acemoglu, D., Autor, D.H., and Lyle, D. (2004) “Women, War, and Wages: The Effect of
Female Labor Supply on the Wage Structure at Midcentury”, Journal of Political Economy,
Vol. 112(3), pp. 497-551
Alesina, A. Baqir, R., and Easterly, W. (1999) “Public Goods and Ethnic Divisions”, Quarterly
Journal of Economics, Vol. 114(4), pp. 1243-1284
Alesina, A. and La Ferrara, E. (2000) “Participation in Heterogeneous Communities”, Quarterly
Journal of Economics, Vol. 115(3), pp. 847-904
Alesina, A. and La Ferrara, E. (2005) “Ethnic Diversity and Economic Performance”, Journal
of Economic Literature, Vol. 43(3), pp. 762-800
Allport, G.W. (1954) “The Nature of Prejudice”, Addison-Wesley, Cambridge, MA
Ananat, E.O. (2011) “The Wrong Side(s) of the Tracks: The Causal Effects of Racial Segregation
on Urban Poverty and Inequality”, American Economic Journal: Applied Economics 3(2),
pp. 34-66
Belloni, A., Chernozhukov, V., and Hansen, C. (2014) “High-Dimensional Methods and Inference on Structural and Treatment Effects”, Journal of Economic Perspectives, Vol. 28(2),
pp. 29-50
Boustan, L.P. (2007) “Black Migration, White Flight: The Effect of Black Migration on Northern Cities and Labor Markets”, Journal of Economic History, Vol. 67(2), pp. 484-488
Boustan, L.P. (2010) “Was Postwar Suburbanization “White Flight”? Evidence from the Black
Migration”, Quarterly Journal of Economics, Vol. 125(1), pp. 417-443
Cameron, A.C., Gelbach, J.B., and Miller, D.L. (2008) “Bootstrap-based improvements for
inference with clustered errors”, Review of Economics and Statistics, Vol. 90(3), pp. 414-427
Collins, W.J. (2000) “African-American Economic Mobility in the 1940s: A Portrait from the
Palmer Survey”, Journal of Economic History, Vol. 60(3), pp. 756-781
Collins, W.J. (2001) “Race, Roosevelt, and Wartime Production: Fair Employment in World
War II Labor Markets”, American Economic Review, Vol. 91(1), pp. 272-286
Collins, W.J. (2003) “The Labor Market Impact of State-Level Anti-Discrimination Laws, 19401960”, Industrial and Labor Relations Review, Vol. 56(2), pp. 244-272
Colussi, T., Isphording, I.E., and Pestel, N. (2016) “Minority Salience and Political Extremism”,
IZA Discussion Paper No. 10417
Donohue, J.J. III, Heckman, J.J., and Todd, P.E. (2002) “The Schooling of Southern Blacks:
The Roles of Legal Activism and Private Philanthropy”, Quarterly Journal of Economics,
Vol. 117(1), pp. 225-268
Fishback, P. and Cullen, J.A. (2013) “Second World War spending and local economic activity
in US counties, 193958”, Economic History Review, Vol. 66(4), pp. 975-992
Flynn, G.Q. (1993) “The Draft, 1940-1973”, University Press of Kansas, Lawrence, KS
31
Froy, F. and Pyne, L. (2011) “Ensuring Labour Market Success for Ethnic Minority and Migrant
Youth”, OECD Local Economic and Employment Development, Working Paper 2011/09,
OECD Publishing, Paris
Glass, C. (2013) “Deserter: A Hidden Story of the Second World War”, The Penguin Press,
New York, NY
Goff, B.L., McCormick, R.E., Tollison, R.D. (2002) “Racial Integration as an Innovation: Empirical Evidence from Sports Leagues”, American Economic Review, Vol. 92(1), pp. 16-26
Goldin, C. (1991) “The Role of World War II in the Rise of Women’s Employment”, American
Economic Review, Vol. 81(4), pp. 741-756
Goldin, C. and Olivetti, C. (2013) “Shocking Labor Supply: A Reassessment of the Role of
World War II on Womens Labor Supply”, American Economic Review P&P, Vol. 103(3),
pp. 257-262
Grigorieff, A., Ubfal, D., and Roth, C. (2016) “Does Information Change Attitudes Towards
Immigrants? Representative Evidence from Survey Experiments”, IZA Discussion Paper No.
10419
Haines, M., Fishback, P., and Rhode, P. (2016) “United States Agriculture Data, 1840 - 2012”,
Study No. ICPSR35206-v3, Inter-university Consortium for Political and Social Research
2016-06-29, Ann Arbor, MI
Hornbeck, R. and Naidu, S. (2014) “When the Levee Breaks: Black Migration and Economic
Development in the American South”, American Economic Review, Vol. 104(3), pp. 963990
Jaworski, T. (2014) “You’re in the Army Now: The Impact of World War II on Women’s
Education, Work, and Family”, Journal of Economic History, Vol. 74(1), pp. 169-195
Margo, R.A. (1995) “Explaining Black-White Wage Convergence, 1940-1950”, Industrial and
Labor Relations Review, Vol. 48(3), pp. 470-481
Matthews, D. and Prothro, J. (1975) “Negro Political Participation Study, 1961-1962”, Study
No. ICPSR07255-v3, Inter-university Consortium for Political and Social Research 2006-0815, Ann Arbor, MI
Myrdal, G. (1944) “An American Dilemma: The Negro Problem and Modern Democracy”,
Harper & Brothers Publishers, New York, NY
OECD/European Union (2015) “Indicators of Immigrant Integration 2015: Settling In”, OECD
Publishing, Paris
Olmstead, A.L. and Rhode, P.W. (2001) “Reshaping the Landscape: The Impact and Diffusion
of the Tractor in American Agriculture, 1910-1960”, Journal of Economic History, Vol. 61(3),
pp. 663-698
Oster, E. (2016) “Unobservable Selection and Coefficient Stability: Theory and Evidence”,
Journal of Business and Economic Statistics, in print. DOI: 10.1080/07350015.2016.1227711
Ruggles, S., Genadek, K., Goeken, R., Grover, J., and Sobek, M. (2015) Integrated Public
Use Microdata Series: Version 6.0 [Machine-readable database], Minneapolis: University of
Minnesota
Smith, J.P. (1984) “Race and Human Capital”, American Economic Review, Vol. 74(4), pp.
685-698
32
Turner, S. and Bound, J. (2003) “Closing the Gap or Widening the Divide: The Effects of the
G.I. Bill and World War II on the Educational Outcomes of Black Americans”, Journal of
Economic History, Vol. 63(1), pp. 145-177
Wasi, N. and Flaaen, A. (2015) “Record linkage using Stata: Preprocessing, linking, and reviewing utilities”, The Stata Journal, Vol. 15(3), pp. 672-697
Wolfbein, S.L. (1947) “Postwar trends in Negro employment”, Monthly Labor Review, Dec.
1947, pp. 663-665
33
Appendices
Table A1: Census Samples and Coverage
Census Year
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
Population Coverage
5%
1%
1%
1%
1%
1%
1%
1%
5%
5%
5%
Sample Size (in 000s)
3,853
923
1,051
1,216
1,352
1,922
1,800
2,030
11,343
12,501
14,081
Additional Notes
weighted sample
weighted sample
weighted sample
weighted sample
weighted sample
Note: Weighted samples require the use of sample weights. All samples are otherwise randomly drawn from the population. Samples used here are state samples as compared to other available sample types such as metro, urban/rural, or
neighborhood samples. The table refers to the individual level decennial U.S. Census files made available by Ruggles et al.
(2015)
Table A2: Census Skill and Occupational Definitions
Skill Group
1 - Skilled
1 - Skilled
1 - Skilled
2 - Semi-skilled
2 - Semi-skilled
2 - Semi-Skilled
3 - Unskilled
3 - Unskilled
3 - Unskilled
Occupational Group
1 - Professionals/Technical
2 - Managers, Officials, Proprietors
3 - Clerical and Kindred
4 - Sales
5 - Craftsmen
6 - Operatives
7 - Personal Service Workers
8 - Farm Laborers
9 - Laborers
Occupation codes
000 - 099
200 - 290
300 - 390
400 - 490
500 - 595
600 - 690
700 - 790
800 - 890
900 - 970
Note: Occupational codes correspond to the 1950 Census Bureau occupational classification scheme. Workers without
occupational responses (codes 980-999) are not included as these individuals were dropped from the sample.
Table A3: Summary Statistics for Casualty Rates by Level of Aggregation
State
All counties
Southern counties
Non-Southern counties
obs.
49
3,074
1,387
1,687
mean
2.918
2.826
3.133
2.573
st. dev.
0.585
2.083
2.227
1.922
min.
1.667
0.000
0.000
0.000
max.
4.254
22.222
22.222
14.286
Note: Summary statistics for casualty rates of semi-skilled whites in U.S. states and counties. Southern counties here are
those located in Alabama, Arkansas, Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North and
South Carolina, Oklahoma, Tennessee, Texas, Virginia, and West Virginia, or Washington D.C.
34
0
Enlistment Share of Total Entries
20
80
40
60
100
Figure A1: Enlistment Rates in Southern and Non-Southern States, 1940-1945
1940
1941
1942
1943
South
1944
1945
1946
Non-South
Note: Enlistments are share of total entries into the Army and Army Air Force from July 1940
to December 1945 in Southern and Non-Southern states. Voluntary enlistment was forbidden in
December 1942 for men aged 18 to 38. After this date those who were older than 38 could enlist
but had to demonstrate that they were physically and mentally fit for service. The spikes in mid1940 and late 1941 correspond to the first draft lottery (during peacetime) and the attack on Pearl
Harbor.
35
Table A4: Summary Statistics - County Level Data (South only)
1940
% semi-skilled black
Casualty rate
Casualty rate (adjacent counties)
Draft rate
% manufacturing employment
% females in manufacturing
% agricultural employment
Value of output per acre
Tractors per 1,000 acres
% farm land in cotton production
% of pop. w. high school degree
Median family income
WWII military spending per capita
Acres flooded in 1927
Lynchings per 1,000 blacks
Rosenwald schools per 1,000 blacks
Republican vote share
No. of slaves in 1860
1950
mean
12.771
st. dev.
17.841
8.800
11.069
47.448
133.653
0.743
5.433
15.696
8,379.733
11.340
10.263
20.565
142.816
0.866
6.756
6.796
4,161.051
0.620
0.640
13.012
13.836
1.295
19.938
mean
14.563
1.407
3.142
26.310
15.025
13.278
34.783
382.115
2.659
5.292
19.578
15,966.760
6.138
5.057
0.348
0.741
10.825
1,374.540
st. dev.
15.140
1.000
1.137
6.293
11.141
9.376
18.171
3,422.151
1.887
8.542
8.770
6,462.303
23.309
28.879
3.793
1.608
18.955
2,114.181
Note: Summary statistics for counties in Southern states. Southern counties here are those located in Alabama, Arkansas,
Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North and South Carolina, Oklahoma, Tennessee,
Texas, Virginia, and West Virginia, or Washington D.C. The casualty rate measures, draft rate, military spending, flooded
acres, and number of slaves are time-invariant and only available via interaction with a dummy for the year 1950.
36
Table A5: Interview Questions and Outcome Coding Scheme
I Friend: “Have you ever known a white (colored) person well enough that you would talk to him
as a friend?”
Coded 1 for 1 (Yes), and 0 otherwise.
I Friend (work): “How did you come to think of this person as a friend?”
Coded 1 for all relationships formed at work (incl. going to school or university together), and 0
otherwise.
I Racial topics (work): “What public problems do you talk about with people where you work?”
Coded 1 for values from 50 to 65 (racial topics), and 0 otherwise.
I Talk opposite race: “Do you ever talk about problems with any white/colored people? If yes,
who are they?”
Coded 1 for all values except 55 (No, never talk) and 98 (Not applicable), and 0 otherwise.
I You Pro integration: “Are you in favor of integration, strict segregation, or something in
between?”
Coded 1 for 2 (Integration), and 0 otherwise.
I Area Pro integration: “In general how many of the Negroes (white people) in this area would
you say are in favor of integration?”
Coded 1 for values 1 (All) or 2 (Most), and 0 otherwise.
I Voted: “Have you ever voted?”
Coded 1 for 1 (Yes), and 0 otherwise.
I Political activity punished: “What happened to Negro(es) because of political activity?”
Coded 1 for all values except 90 (Other), 91 (Inap.), 97 (Refused answer), 98 (Not applicable),
and 0 otherwise.
Note: Questions from the “Negro Political Participation Study” and codes for the outcome variables which are coded from
the corresponding questions as binary variables. Outcomes are in bold font, questions from the survey are in quotation
marks, followed by the coding scheme for the binary variables. The codebook is freely available at http://www.icpsr.
umich.edu/icpsrweb/ICPSR/studies/7255 for ICPSR study number 7255.
37
Table A6: Summary Statistics - Individual Characteristics by Race
Black (n = 523)
Male
Age
Years of education
Family income
Veteran
Years in county
% blacks in birth county
Rural
Rural, non-farm
Suburban
City/town
mean
0.382
46.319
4.952
2183.078
0.124
35.050
43.222
0.205
0.069
0.117
0.610
st. dev.
0.486
15.883
3.248
1864.756
0.330
19.425
16.309
0.404
0.253
0.321
0.488
min.
0
5
1
500
0
0
5
0
0
0
0
max.
1
85
14
11000
1
89
85
1
1
1
1
White (n = 511)
Male
Age
Years of education
Family income
Veteran
Years in county
% blacks in birth county
Rural
Rural, non-farm
Suburban
City/town
mean
0.450
45.669
7.323
4929.061
0.237
29.638
24.452
0.227
0.114
0.131
0.528
st. dev.
0.498
15.684
3.637
3178.278
0.426
21.130
17.935
0.419
0.318
0.338
0.500
min.
0
5
1
500
0
0
5
0
0
0
0
max.
1
89
14
11000
1
83
85
1
1
1
1
Note: Summary statistics for black and white respondents from the “Negro Political Participation Study” of 1961 by
Matthews and Prothro (1975). Statistics produced for individuals from the final estimation sample with non-missing
values in the outcome and control variables. Family income is coded in income bins while for the summary statistics the
midpoint of each interval was recorded as the dollar values for the corresponding bin.
38
Table A7: Summary Statistics - Outcome Variables by Race
Friend
Friend (work)
Racial topics (work)
Talk opposite race
You pro integration
Area pro integration
Voted
Political activity punished
Black (n = 523)
White (n = 511)
mean
0.467
0.086
0.111
0.237
0.642
0.583
0.398
0.147
mean
0.585
0.010
0.115
0.172
0.037
0.825
0.849
st. dev.
0.499
0.281
0.314
0.426
0.480
0.494
0.490
0.355
st. dev.
0.493
0.099
0.320
0.378
0.189
0.380
0.358
Difference
diff.
0.117***
-0.076***
0.005
-0.065***
-0.605***
0.242***
0.452***
s.e.
0.030
0.013
0.020
0.025
0.023
0.023
0.027
Note: Binary outcomes regarding the social and political integration and standing of blacks for black and white respondents
of the “Negro Political Participation Study”. Only individuals in the final estimation sample were used to produce these
summary statistics. Differences in means and the corresponding standard errors were estimated with t-tests. Significance
levels at 10%, 5%, and 1% are denoted by *, **, ***, respectively. The questions for whether whites in the South are
prejudiced and for whether there have ever been any repercussions for political activity of blacks were only asked to African
American respondents.
39
40
(IV)
0.047
(0.026)∗
[0.041]
0.467
10.064
(OLS)
-0.011
(0.049)
[0.058]
0.642
-1.713
(IV)
0.052
(0.033)
[0.045]
0.642
8.010
You Pro Integration
(OLS)
0.036
(0.035)
[0.043]
0.467
7.709
(IV)
0.019
(0.017)
[0.024]
0.086
22.093
(OLS)
-0.093
(0.018)∗∗∗
[0.035]∗∗∗
0.583
-15.952
(IV)
0.050
(0.032)
[0.024]∗∗
0.583
8.576
Area Pro Integration
(OLS)
0.027
(0.037)
[0.044]
0.086
31.395
Friend (Work)
(OLS)
-0.049
(0.043)
[0.058]
0.398
-12.311
(IV)
0.041
(0.025)
[0.015]∗∗∗
0.111
36.937
(IV)
0.064
(0.060)
[0.047]
0.398
16.080
Ever voted
(OLS)
0.070
(0.020)∗∗∗
[0.046]
0.111
63.063
Talk about Politics (Work)
(IV)
0.057
(0.030)∗
[0.040]
0.237
24.051
(OLS)
0.001
(0.021)
[0.021]
0.147
0.680
(IV)
-0.045
(0.026)∗
[0.026]∗
0.147
-30.612
Punished for Polit. Activity
(OLS)
0.110
(0.033)∗∗∗
[0.072]
0.237
46.414
Talk to opposite Race
Note: The estimation sample is kept constant in all regressions with 523 black individuals in 22 counties from Southern states using data from the “Negro Political Participation Study” of
1961 (Matthews and Prothro, 1975). The change in the share of blacks in semi-skilled employment from 1940 to 1950 (∆share of blackscs ) in county c and state s is instrumented with the
WWII casualty rate of semi-skilled whites. Individual level controls include gender, age, years of completed education, family income, the size of the respondent’s household, marital status,
location of the dwelling (rural, rural non-farm, suburban, and urban), the county an individual grew up in, the percentage of blacks in the county an individual grew up in, employment
status, the number of years an individual has spent in their current county of residence, place size, whether they vote for the Democratic party, veteran status, and occupation. The county
level controls include the percentage of black residents, the median age and education, the share of people born in other counties, the share of manufacturing employment in the county,
the WWII draft rate, as well as the number of lynchings between 1900 and 1930, Rosenwald schools and the number of slaves in 1860. Standard errors are clustered at the county level
and are reported in parentheses. Standard errors corrected for the small cluster size using the wild cluster bootstrap-t procedure by Cameron et al. (2008) are reported in squared brackets.
Significance levels are denoted by ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
Outcome mean
Effect size as % of mean
∆share of blackscs
Outcome mean
Effect size as % of mean
∆share of blackscs
Friend
Table A8: OLS and Instrumental Variables Regression Results - Black Sample
41
(IV)
0.008
(0.007)
[0.010]
0.585
1.368
(OLS)
0.006
(0.002)∗∗
[0.003]∗
0.037
16.216
(IV)
0.004
(0.001)∗∗∗
[0.002]∗
0.037
10.811
You Pro Integration
(OLS)
0.018
(0.007)∗∗
[0.010]∗
0.585
3.077
(IV)
0.004
(0.001)∗∗∗
[0.001]∗∗∗
0.010
36.383
(OLS)
-0.004
(0.005)
[0.005]
0.826
-0.484
(IV)
0.008
(0.009)
[0.013]
0.826
0.969
Area Pro Integration
(OLS)
0.002
(0.001)∗∗
[0.001]
0.010
21.314
Friend (Work)
(OLS)
0.005
(0.005)
[0.006]
0.849
0.589
(OLS)
0.005
(0.004)
[0.005]
0.115
4.348
(IV)
-0.005
(0.009)
[0.013]
0.849
-0.589
Ever voted
(IV)
0.008
(0.004)∗
[0.006]
0.115
6.957
Talk about Politics (Work)
(IV)
0.006
(0.005)
[0.007]
0.172
3.488
(OLS)
(IV)
Punished for Polit. Activity
(OLS)
-0.001
(0.005)
[0.004]
0.172
-0.581
Talk to opposite Race
Note: The estimation sample is kept constant in all regressions with 511 white individuals in 22 counties from Southern states using data from the “Negro Political Participation Study” of
1961 (Matthews and Prothro, 1975). The change in the share of blacks in semi-skilled employment from 1940 to 1950 (∆share of blackscs ) in county c and state s is instrumented with the
WWII casualty rate of semi-skilled whites. Individual level controls include gender, age, years of completed education, family income, the size of the respondent’s household, marital status,
location of the dwelling (rural, rural non-farm, suburban, and urban), the county an individual grew up in, the percentage of blacks in the county an individual grew up in, employment
status, the number of years an individual has spent in their current county of residence, place size, whether they vote for the Democratic party, veteran status, and occupation. The county
level controls include the percentage of black residents, the median age and education, the share of people born in other counties, the share of manufacturing employment in the county,
the WWII draft rate, as well as the number of lynchings between 1900 and 1930, Rosenwald schools and the number of slaves in 1860. Standard errors are clustered at the county level
and are reported in parentheses. Standard errors corrected for the small cluster size using the wild cluster bootstrap-t procedure by Cameron et al. (2008) are reported in squared brackets.
Significance levels are denoted by ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
Outcome mean
Effect size as % of mean
∆share of blackscs
Outcome mean
Effect size as % of mean
∆share of blackscs
Friend
Table A9: OLS and Instrumental Variables Regression Results - White Sample
42
(IV)
0.017
(0.007)∗∗
[0.009]∗
0.526
3.232
(OLS)
0.013
(0.004)∗∗∗
[0.008]∗
0.343
3.790
(IV)
0.026
(0.004)∗∗∗
[0.006]∗∗∗
0.343
7.580
You Pro Integration
(OLS)
0.013
(0.005)∗∗∗
[0.007]∗
0.526
2.471
(IV)
0.004
(0.002)∗
[0.004]
0.048
7.454
(OLS)
0.008
(0.006)
[0.013]
0.703
1.138
(IV)
0.030
(0.010)∗∗∗
[0.014]∗∗
0.703
4.267
Area Pro Integration
(OLS)
0.006
(0.001)∗∗∗
[0.002]∗∗
0.048
11.499
Friend (Work)
(OLS)
-0.002
(0.004)
[0.006]
0.621
-0.322
(IV)
0.016
(0.004)∗∗∗
[0.004]∗∗∗
0.113
14.159
(IV)
0.010
(0.006)∗
[0.007]
0.621
1.610
Ever voted
(OLS)
0.009
(0.003)∗∗∗
[0.006]
0.113
7.964
Talk about Politics (Work)
(IV)
0.015
(0.003)∗∗∗
[0.005]∗∗∗
0.205
7.317
(OLS)
-0.010
(0.003)∗∗∗
[0.006]∗
0.074
-13.514
(IV)
-0.011
(0.003)∗∗∗
[0.006]∗
0.074
-14.865
Punished for Polit. Activity
(OLS)
0.007
(0.003)∗∗
[0.006]
0.205
3.415
Talk to opposite Race
Note: The estimation sample is kept constant in all regressions with 511 white individuals in 22 counties from Southern states using data from the “Negro Political Participation Study” of
1961 (Matthews and Prothro, 1975). The change in the share of blacks in semi-skilled employment from 1940 to 1950 (∆share of blackscs ) in county c and state s is instrumented with the
WWII casualty rate of semi-skilled whites. Individual level controls include gender, age, years of completed education, family income, the size of the respondent’s household, marital status,
location of the dwelling (rural, rural non-farm, suburban, and urban), the county an individual grew up in, the percentage of blacks in the county an individual grew up in, employment
status, the number of years an individual has spent in their current county of residence, place size, whether they vote for the Democratic party, veteran status, and occupation. The county
level controls include the percentage of black residents, the median age and education, the share of people born in other counties, the share of manufacturing employment in the county,
the WWII draft rate, as well as the number of lynchings between 1900 and 1930, Rosenwald schools and the number of slaves in 1860. Standard errors are clustered at the county level
and are reported in parentheses. Standard errors corrected for the small cluster size using the wild cluster bootstrap-t procedure by Cameron et al. (2008) are reported in squared brackets.
Significance levels are denoted by ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
Outcome mean
Effect size as % of mean
∆share of blackscs
Outcome mean
Effect size as % of mean
∆share of blackscs
Friend
Table A10: OLS and Instrumental Variables Regression Results - Without Income and Employment Controls