GAINS FROM MIGRATION AND MARRIAGE:
THE FINAL YEARS OF THE GREAT MIGRATION, 1965-1970
A Thesis
Presented to the faculty of the Department of Economics
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
Economics
by
Max Blair Norton
SPRING
2017
© 2017
Max Blair Norton
ALL RIGHTS RESERVED
ii
GAINS FROM MIGRATION AND MARRIAGE:
THE FINAL YEARS OF THE GREAT MIGRATION, 1965-1970
A Thesis
by
Max Blair Norton
Approved by:
__________________________________, Committee Chair
Mark Siegler, Ph.D.
__________________________________, Second Reader
Ta-Chen Wang, Ph.D.
____________________________
Date
iii
Student: Max Blair Norton
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
__________________________, Graduate Coordinator
Jonathan Kaplan, Ph.D.
Department of Economics
iv
___________________
Date
Abstract
of
GAINS FROM MIGRATION AND MARRIAGE:
THE FINAL YEARS OF THE GREAT MIGRATION, 1965-1970
by
Max Blair Norton
The first-time emigration of nearly five million African Americans from the
American South between 1940 and 1970 is among the most transformative demographic
events in US history. Knowledge of the economic incentives and social mechanisms that
drove this migration is incomplete and only infrequently considered in context with other
historical US migrations. This study examines the economic and social assimilation of
black migrants into the North in light of what is known about the assimilation of earlier
European migrants to the region. Using a two-period mixed model with difference-indifferences estimators, I find evidence that even in the 1965-1970 period, as the
migration wave waned, black Southern men and women experienced a positive income
return to migration. This contrasts with findings in past literature on the period. Estimates
also indicate the existence of a marriage premium for black Southern women who
continue to work after marriage. Men do not show evidence of a general marriage
premium, but there is consistent evidence of complementarity between migration and
intermarriage with a Northern-born spouse. Women do not appear to experience this
v
complementarity. Two candidate explanations, a social capital hypothesis and an
unobserved input hypothesis, are proposed.
_______________________, Committee Chair
Mark Siegler, Ph.D.
_______________________
Date
vi
ACKNOWLEDGMENTS
This thesis culminates several years of study that were a bit of a pivot from the
humanities orientation of my undergraduate degree. I do not know how I would have managed
this without the California community colleges and the California State University system. I have
found that these institutions serve a public need for affordable, nonexclusive education, so I am
first of all grateful to the residents of California for creating and funding them, and to the staff
that make them work, especially Sharon Jordan of the CSU Sacramento Economics Department.
Many faculty members have helped me on this path. Among these, Andrew Currie, Tim
Ford, Kristin Kiesel, and Terri Sexton provided especially helpful and timely advice and support.
Jonathan Kaplan has done this and more by way of the opportunities he has created and advice he
has offered me. I also deeply appreciate the support I have received through scholarships
endowed by Robert L. Curry Jr., Chi-Ming Dana Curry, and Ayad Al-Qazzaz.
During these last few years I have also been sustained by multidimensional support from
family and friends. I am particularly grateful to my parents, Marc Norton and Gillian Blair, my
aunts, Tina Blair and Marion Blair, and to Rose Haughey.
Ta-Chen Wang’s course on the economic history of the United States planted the seed of
the idea behind this thesis. I have valued his insight as the idea developed. Finally, for guiding me
through the thesis process, for the perceptive discussions along the way, and for his support of
this effort and of my academic goals in general, I am indebted to Mark Siegler.
Any errors or fallacies advanced below are entirely my own responsibility.
vii
TABLE OF CONTENTS
Page
Acknowledgments ................................................................................................................... vii
List of Tables............................................................................................................................. x
List of Figures .......................................................................................................................... xi
Chapter
1. INTRODUCTION............................................................................................................... 1
I. The Great Migration ................................................................................................. 1
II. Migration and Assimilation in US History.............................................................. 5
2. LITERATURE REVIEW .................................................................................................. 12
I. Cultural Mechanisms and Economic Assimilation ................................................. 12
II. Selection and Destination Choice in Migration .................................................... 22
III. Methodological Issues in Migration .................................................................... 25
3. EMPIRICAL METHODOLOGY ..................................................................................... 29
I. Overview ................................................................................................................. 29
II. Reduced-Form Models .......................................................................................... 31
III. Data Description .................................................................................................. 41
IV. Exploratory Analysis ........................................................................................... 48
4. RESULTS ......................................................................................................................... 55
I. Overview ................................................................................................................. 55
II. Model One: Returns to Migration and Marriage .................................................. 55
IV. Model Two: Complementarity of Migration Destination and Spousal Origin .... 65
viii
5. CONCLUSION .................................................................................................................. 74
I. Findings ................................................................................................................... 74
II. Discussion .............................................................................................................. 75
Appendix A. Complete results tables for model two ............................................................... 79
Appendix B. Notes toward controlling for relative changes in state income........................... 85
Bibliography ............................................................................................................................. 89
ix
LIST OF TABLES
Tables
Page
1.
Specification of Model One ......................................................................................... 37
2.
Specification of Model Two: Northern Subsample ..................................................... 39
3.
Specification of Model Two: Southern Subsample ..................................................... 40
4.
Hypotheses ................................................................................................................... 41
5.
Individuals Counted in Sample by Marriage and Migration ........................................ 42
6.
Dependent Variable Descriptions ................................................................................ 44
7.
Summary Statistics for Dependent Variables .............................................................. 49
8.
Select Summary Statistics for Explanatory Variables ................................................. 53
9.
Model One: Men .......................................................................................................... 56
10.
Model One: Women ..................................................................................................... 57
11.
Model Two: Men in North (Condensed)...................................................................... 66
12.
Model Two: Men in South (Condensed)...................................................................... 70
13.
Model Two: Women in North (Condensed) ................................................................ 71
14.
Model Two: Women in South (Condensed) ................................................................ 72
15.
Model Two: Men in North (Full) ................................................................................. 79
16.
Model Two: Men in South (Full) ................................................................................. 81
17.
Model Two: Women in North (Full)............................................................................ 82
18.
Model Two: Women in South (Full)............................................................................ 84
x
LIST OF FIGURES
Figures
Page
1.
Percentage of African Americans Residing in Southern States, 1840-1990 ................. 8
2.
Relative Frequency Histograms for Income Measures, 1970 ..................................... 61
xi
1
CHAPTER 1
INTRODUCTION
I. The Great Migration
The Great Migration of African Americans from the South to the North is a
transformative episode in United States history, one whose consequences we live with
today. The nearly five million who made this move reshaped the cities, society, and
economy of the US in ways yet to be adequately understood. In the prevalent narrative,
their migration was not only flight from racial oppression in the South, but also pursuit of
greater economic opportunity in the industrial North. Thus, the episode represents this
crucial American subpopulation’s annexation of the liberty to make their own residential
location decisions in response to economic and social incentives.
The economic history of the US includes many waves of migrant relocations in
response to economic and social incentives, from Puritans fleeing religious persecution to
present-day Latin Americans and Asians drawn to US jobs. Some of these waves appear
to have merged completely into the American mainstream, both culturally and
economically. In other cases, this merging or assimilation is partial at best. Considerable
attention, academic and popular, has been given to certain parts of this story. There is a
significant body of economic and sociological research on the assimilation of European
migrants into the American mainstream. Separately, a significant stream of economic
research is devoted to the study of racial income disparity. (See Chapter 2.I.) Popular
interest in the black emigration from the South is also notable. Not as much reliable
2
knowledge exists on questions at the intersection of these issues: How did the incentives
that drove the Great Migration work? How does persistent residential segregation and
inequality by race reflect unsuccessful integration of African-American migrants into the
general US labor market? These questions echo some of the most troubling themes of
modern migration and urban policy debates.
Conditions for black Southerners in the first half of the twentieth century were so
bad that it is not hard to conceive of the Great Migration as a flight from oppression and
nothing more. Economic historians such as Wright, however, have linked the migration
to certain economic growth phenomena, including the economic convergence of the
South with the North and partial closure of the racial income gap—the latter explained in
part by rising black incomes in the South. Wright’s reading of the evidence implies that
these convergences reflected the first successful integration of the Northern and Southern
workforces in a national labor market. In this recounting, the New Deal imposition of the
first national minimum wage plays a significant role in fomenting labor market
integration.1 At the same time, though, both Boustan and Gardner have demonstrated that
competition from Southern migrants suppressed wage gains among black Northern
natives, while white natives were protected by discriminatory hiring practices. Further,
research by Eichenlaub, Tolnay, and Alexander (hereinafter ETA) conclude that black
1. Gavin Wright, Old South, New South: Revolutions in the Southern Economy (Baton
Rouge, LA: LSU Press, 1986).
3
Southern migrants achieved no economic gains in the move to the North.2 What can we
make of the confluence of these findings?
The research literature on the economics of migrant families reflects a growing
body of evidence that family structure plays a crucial role in the economic fortunes of
new migrants. In particular, exogamy—intermarriage with a partner outside of one’s
social group—is associated with higher earnings among recent migrants than endogamy,
marriage within one’s group (see Chapter 2.I). Exogamy appears to influence or at least
relate to a migrant’s integration into the economy of their destination. To my knowledge,
however, no previous work has investigated intermarriage between domestic
subpopulations in the case of internal migration in the US or elsewhere.
Discussions of racial income disparity in the US often make reference to changes
in family structure. The second half of the twentieth century saw an increase in family
instability and single parenthood that appears to have begun earlier in Northern black
communities than it did elsewhere. However, a substantial body of research shows that
black Southern migrants exhibited greater family stability than their Northern neighbors.
2. Leah P. Boustan, “Competition in the Promised Land: Black Migration and Racial
Wage Convergence in the North, 1940-1970” (NBER Working Paper No. 13813,
http://www.nber.org.proxy.lib.csus.edu/papers/w13813, 2008).
John Gardner, “The Labor Economics of the Great Migration,” (PhD diss., Carnegie
Mellon University, 2014), 37.
Suzanne C. Eichenlaub, Stewart E. Tolnay, and J. Trent Alexander, “Moving Out but Not
Up: Economic Outcomes in the Great Migration,” The American Sociological Review 75,
no. 1 (2010), 101-125.
4
Wilson finds that this stability advantage is explained primarily by selection: a systematic
difference between those who choose to migrate and those who do not.3
This raises several questions that I attempt to answer in this study. Why would
more stable people choose to uproot themselves to the North for no apparent economic
reward—or have the returns to migration to the North been underestimated in previous
research? Is there an income premium associated with marriage itself? Finally, can we
find evidence of a reward to exogamy in this domestic migration example? I explore
relevant literature to motivate the estimation of a mixed model with difference-indifferences (DD) variables and random effects. Due to data availability, I estimate this
model for the 1965-1970 period, the last five years of the migration, when returns to
migration are likely at their weakest. The results indicate moderate returns to migration,
the existence of a marriage premium exclusive to women, and a strong complementarity
between migration and exogamy exclusive to men.
The thesis proceeds as follows: The next section introduces the American
conception of migrant assimilation in depth. Chapter 2 reviews relevant literature on
three topics: quantitative measurement of assimilation, selection and destination choice in
migration, and methodological issues in the study of migration. Chapter 3 describes my
empirical strategy for answering the questions above, and Chapter 4 outlines the results.
Chapter 5 summarizes these findings, discusses implications, and suggests further
research. Two appendices follow. Appendix A includes several complete regression
3. Thomas C. Wilson, “Explaining Black Southern Migrants’ Advantage in Family
Stability: The Role of Selective Migration,” Social Forces 80, no. 2 (2001), 555-571.
5
results tables that are too large to report in Chapter 4. Appendix B discusses an idea
developed during this project for improving estimates of returns to migration, which
turned out to be methodologically inapplicable in this case.
II. Migration and Assimilation in US History
John F. Kennedy’s idea of the United States as “a nation of immigrants” owes
much of its traction to the country’s absorption of wave after wave of European migrants
between 1850 and 1913. In this period, known now as the Age of Mass Migration,
historically low trans-Atlantic travel costs and liberal US policies toward white
immigrants combined to facilitate the importation of approximately thirty million
Europeans. Immigrants of common national origin arrived together in waves: those of the
mid-nineteenth century originated in the British Isles and Germany, followed by later
waves from Scandinavia and Eastern and Southern Europe.4 The phenomenon of
clustering in specific origin countries in any given year reflects the importance of push
factors in instigating emigration from Europe. Episodic disamenities of European life,
such as the Irish potato famine and German political disorder, were clearly associated
with the onset of major waves from those countries. Once these waves began, though, the
establishment of migration networks from origin countries to the US made follow-on
migration easier and more affordable, decreasing the level of push necessary to continue
4. Ran Abramitzky and Leah P. Boustan, “Immigration in American Economic History,”
(NBER Working Paper 21882, http://www.nber.org/papers/w21882, 2016), 6.
Jeremy Atack and Peter Passell, A New Economic View of American History (New York:
W. W. Norton, 1994): 229-232.
6
the trend. The relative significance of pull factors drawing migrants to the US is subject
to competing claims, but the view that these were demonstrably more significant than
European push factors is a mainstream view in economic history. Northern industrialists
recognized that migrants were key to mitigating the nation’s labor shortage. European
arrivals were often able to find employment comparable to that held by established
Americans. 5 Recent economic research demonstrates the presence of labor market
discrimination against new arrivals, but also shows that the impact of this discrimination
lessened as migrants merged into the American mainstream.6 Thus, it was not just global
circumstance that brought the nineteenth-century migrant tide to American shores; these
migrants’ integration into the labor force was a deliberate and profitable move by
Northern industrial firms.
However, each new wave of migrants faced an entrenched expectation that it
would assimilate into American culture. Gordon identifies two main ideologies
underlying assimilation demands: on the strict side, “Anglo-conformity,” which expects
new arrivals to forsake all past ethnic identity and replace it with a pre-established AngloAmerican ideal; and, on the moderate side, the “melting pot” concept, which expects
5. Atack and Passell, American History, 233.
Milton M. Gordon, Assimilation in American Life: The Role of Race, Religion, and
National Origins (Oxford: Oxford University Press, 2010): 91-92.
Abramitzky and Boustan, “Immigration,” 3.
6. Ran Abramitzky, Leah P. Boustan, and Katherine Eriksson, “Cultural Assimilation
during the Age of Mass Migration” (NBER Working Paper No. 22381,
http://www.nber.org/papers/w22381, 2016).
7
migrants to adopt a blended but uniquely American common identity fused out of their
own and other migrants’ forsaken ethnic backgrounds. The shift over the latter half of the
nineteenth century from the Anglo-Teutonic “old immigration” to the “new immigration”
of Mediterranean and Eastern Europeans posed a challenge to both of these ideologies of
assimilation, especially the Anglo-conformity variant. An influential portion of the
established American electorate perceived the new immigrants as inherently incapable of
assimilation; anxiety over ethnic enclaves in the Northern cities where new immigrants
tended to reside was widespread.7 These anxieties, along with World War I-inspired fifth
column fears, the post-war Red Scare, and West Coast anti-Asian prejudice, instigated
the creation of the first national immigration quota system in 1921.8
7. Gordon, Assimilation, 97-102.
Lowell Gallaway, Richard Vedder, and Vishwa Shukla, “The Distribution of the
Immigrant Population in the United States: an Economic Analysis,” Explorations in
Economic History 11, no. 3 (1974): 213-226.
8. Gordon, Assimilation, 101-102.
8
Figure 1. Percentage of African Americans Residing in Southern States, 1840-1990
100%
90%
80%
92% 92% 92% 91% 90% 90% 90%
89%
85%
79% 77%
70%
60%
70%
68%
62%
60%
50%
53%
40%
30%
20%
10%
0%
1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990
That the US adopted an immigration policy that slowed its main source of new
labor of course did nothing to mitigate the undersupply of labor in the industrial North.
The new US immigration policy coincided roughly with a first wave of black migration
from South to North, supported by World War I-era industrial growth, which
preliminarily established certain black migration networks. This wave is sometimes
labeled the ‘first Great Migration,’ but it consisted of a relatively small quantity of skilled
black workers in particular occupations.9 In more general use, the Great Migration refers
to the much vaster migrant wave of nearly five million arrivals between 1940 and 1970.
9. Kenneth Chay and Kaivan Munshi, “Black Networks after Emancipation: Evidence
from Reconstruction and the Great Migration,” (working paper,
http://www.cireqmontreal.com/wp-content/uploads/2014/11/chay.pdf, 2014).
Wright, Old South, New South.
9
Two decades after being closed off from most potential new international migrant labor,
Northern industry had acquired a domestic source. Three decades later, however, the long
decline in the proportion of African Americans in the South suddenly reversed, marking
the end of the Great Migration era. Figure 1 captures the stability of the nineteenth
century, the dramatic shift of the Great Migration, and the sharp reversal in 1970.
Black Southern migrants differed in important respects from the Europeans who
preceded them in the North. The economic literature holds that throughout American
history, long-term trends in migrant incomes approximately match trends in native
incomes. European immigrants, pulled to the US by job opportunities and often entering
the Northern urban labor market in a similar position to white natives, had little catching
up to do.10 Wright underscores the importance of the contrast in the black migrant
experience. Push factors dominated: these included the elimination of low-wage,
traditionally black agricultural jobs due to the intertwined phenomena of agricultural
mechanization and New Deal policy; and the physical and psychological threats of racial
violence and Jim Crow. Also, in spite of the Northern economy’s growth, its pull on
black workers was weak relative to the pull it exerted on past white migrant groups,
according to Wright. The urban destination neighborhoods into which these migrants
arrived were marked by much greater unemployment than met their European
precursors.11 Indeed, the research of ETA characterizes the average trajectory of Southern
10. Abramitzky and Boustan, “Immigration,” 3.
11. Wright, Old South, New South.
10
black migration as “out but not up.”12 Those that did attain jobs with wages similar to
their white neighbors often worked in much more difficult and dangerous roles.13 Further,
racial segregation in the North became ingrained with a fixity that bias against white
ethnic migrants never attained. Boustan’s work confirms the popular impression than a
significant share of mid-century suburbanization can be understood as ‘white flight’ out
of black migrant destination areas.14 Nevertheless, the African Americans that left the
South for the North were positively self-selected—that is, they tended to be those with
the most education and highest earning potential. One of the motivating questions of this
study is how we can explain positive self-selection into Northward migration when, as
Gordon observes, no “pretense” of assimilability was ever advertised to black migrants
from the South.15
In contrast to the black experience, the assimilation undergone by ethnic whites
has been extensive, in spite of the anxiety and backlash that their initial arrival inspired.
Among whites, self-identification by national origin has declined and lost specificity over
time.16 Waters argues that the US classification of European ancestries as ethnicities
12. Eichenlaub, Tolnay, and Alexander, “Out but Not Up.”
13. Christopher L. Foote, Warren C. Whatley, and Gavin Wright, “Arbitraging a
Discriminatory Labor Market: Black Workers and the Ford Motor Company, 19181947,” Journal of Labor Economics 21, no. 3 (2003): 493-532.
14. Leah P. Boustan, “Was Postwar Suburbanization ‘White Flight’? Evidence from the
Black Migration,” Quarterly Journal of Economics 125, no. 1 (2010): 417-443.
15. Gordon, Assimilation, 113.
16. Ibid., 160.
11
rather than races is itself indicative of the successful assimilation of European migrants.17
By the middle of the twentieth century, the most persistent axis of incomplete
assimilation between white subgroups was the Protestant-Catholic divide, but even on
this axis assimilation was proceeding. Kennedy, whose own election as President still
symbolizes the acceptance of Catholicism into the American mainstream, postulates that
the Irish migrants of the nineteenth century prepared the ground for this integration by
converting the Catholic Church in the US from a French-speaking to an English-speaking
institution.18
In contrast, the American racial divide persists. Black and white incomes did
converge for four decades starting in 1940, but never attained parity.19 I have proposed to
study the role of marriage in the assimilation of black Southerners into the North, but first
I turn to the economic and sociological literature in search of fundamental definitions.
What is meant by assimilation into the American mainstream? In what sense should we
understand Wright’s idea that the US labor market integrated during the Great Migration?
What have economists said about assimilation?
16. (Continued.) Also, Stanley Lieberson and Mary C. Waters, “Ethnic Groups in Flux:
The Changing Ethnic Responses of American Whites,” The Annals of the American
Academy of Political and Social Science 487 (1986), 79-91.
17. Mary C. Waters, “Immigration, Intermarriage, and the Challenges of Measuring
Racial/Ethnic Identities,” American Journal of Public Health 90, no. 11 (2000): 17351737.
18. John F. Kennedy, A Nation of Immigrants (New York: Harper and Row, 1964): 39.
19. Robert A. Margo, “Government and the American Dilemma,” in Government and the
American Economy: A New History, ed. Price V. Fischback et al. (Chicago: University of
Chicago Press, 2007): 232-254.
12
CHAPTER 2
LITERATURE REVIEW
I. Cultural Mechanisms and Economic Assimilation
Sociological contributions. The anxiety over allegedly unassimilable migrants
that has surfaced repeatedly in the US reflects at least three distinct fears, two of them
concretely economic. First, there is the fear that new migrants’ competition with
established American workers will drive down wages. The economic literature indicates
that in the broad view of US history, the economy has adjusted to absorb migrants such
that, for the average established worker, migration has not driven down wages. Particular
groups, however, have borne costs due to other groups’ arrivals—including the
aforementioned impact of black Southern migrants on the wages of black native
Northerners.20 This distinction exemplifies the variation between the experiences of white
migrants during the Age of Mass Migration and those of black migrants during the Great
Migration.
Two other fears tied to unassimilability share a direct relationship. First, nativist
groups have traditionally raised fears that foreign ethnic identities imported by
immigrants will overwhelm and crowd out the cultural ideals of established Americans.
Second, some have expounded the belief that new migrants are culturally unsuited to
achieve the same levels of economic success attained by established Americans. This
20. Abramitzky and Boustan, “Immigration,” 3.
Boustan, “Competition.”
13
latter fear, that culture condemns certain immigrant groups to low incomes, is also said to
re-entrench these immigrants in their ancestral ethnic identity.21 Thus, each of these fears
can be viewed as a factor in the production of the other: the culture of unassimilated
migrants is seen as an impediment to their labor market success, and their difficulty in the
labor market is seen as an impediment to their cultural assimilation.
While discussion of assimilation ideologies is more prominent in sociology than
economics, sociologists and economists alike have taken to modeling social integration,
as well as empirically analyzing assimilation’s relationship with economic outcomes. In
both cases, human capital theory in the mode of Becker and Schultz is foundational.22
Individuals in the labor market possess particular characteristics, accumulated over years
of education, socialization, and personal growth, that make up their particular human
capital. As with physical capital, owners (individuals) can rent human capital to firms (on
the labor market) for the production of goods and services. Further, as with physical
capital, individuals can augment their human capital through investment—for instance, in
education or health. A third similarity between human and physical capital forms is that
they can be employed at different levels of efficiency. To Schultz, internal migration,
when it is driven by better employment prospects, is an investment made by individuals
21. Gordon, Assimilation, 97-102.
22. Theodore W. Schultz, “Investments in Human Capital,” The American Economic
Review 51, no. 1 (1961), 1-17.
Gary S. Becker, Human Capital (New York: Columbia University Press, 1964).
14
in order to put their human capital to more efficient use.23 In both the sociological and
economic traditions, assimilation is often understood as another type of investment that
increases the efficient deployment of human capital. The unassimilated migrant lacks the
information and cultural skills needed to put their human capital to its most productive
use; the more assimilated migrant thus fares better in the labor market. Language skills
are a prime example: a deep economic literature shows that as migrants gain fluency in
the language of their destination, their economic outcomes improve.24
Sociologists have taken great interest in the study of assimilation along cultural
axes. As reviewed in Chapter 1, some of this research tradition devotes itself to
unpacking the ideologies implicit in various strains of assimilation rhetoric. Another line
of research focuses on describing formal models of assimilation. A key model in this
literature is the segmented assimilation model, which takes Piore’s segmented labor
market theory as its basis. Piore’s basic idea, which was motivated in part by a need to
explain the lack of upward mobility for urban black workers in Northern cities, is that the
overall labor market is divided into two strata. A primary labor market offers flexibility,
mobility, and a spectrum of high-paying jobs, with increasing returns to human capital; a
secondary labor market consists of low-wage jobs that offer little return to human capital,
23. Schultz, “Investments,” 1.
24. Barry R. Chiswick and Paul W. Miller, “The Endogeneity between Language and
Earnings: International Analyses,” Journal of Labor Economics 13, no. 2 (1995): 246288.
Edward P. Lazear, “Culture and Language,” Journal of Political Economy 107, no. S6
(1999): S95-S126.
15
since all are approximately equally low paying. In an economy with a segmented labor
market, transfer from one market to the other is theorized to be difficult. Therefore the
segmentation of the market produces a high floor for primary market workers in difficult
circumstances, and imposes a low ceiling for secondary market workers even in good
circumstances.25 In this light, Boustan’s finding that the Great Migration depressed
Northern black wages measurably and white wages not at all seems to reflect a labor
market akin to Piore’s theoretical dual-segment market.26 In the segmented assimilation
model introduced by Portes and Zhou, assimilation is a process by which migrants
become entrenched in one of two labor market strata—potentially the primary, or
‘mainstream,’ market, but also possibly the secondary market. Cultural and racial
characteristics enter into the model as factors that can substantially increase the potential
for a migrant to assimilate ‘downward’ into the secondary market, rather than into the
mainstream.27
Economic assimilation. The economic literature on migration, in contrast to the
sociological literature, treats ‘assimilation’ mostly as a descriptor of the growth path of
migrant income. The concept is rather weakly defined relative to the extensive
25. Michael J. Piore, “Notes for a Theory of Labor Market Stratification,” (MIT Working
Paper No. 95,
https://dspace.mit.edu/bitstream/handle/1721.1/64001/notesfortheoryof00pior.pdf, 1972).
26. Boustan, “Competition,” 761-765.
27. Alejandro Portes and Min Zhou, “The New Second Generation: Segmented
Assimilation and Its Variants,” The Annals of the American Academy of Political and
Social Science 530 (1993): 74-96. Portes and Zhou’s model has particularly profound
implications for subsequent generations descended from original migrants.
Intergenerational phenomena, however, are outside the scope of this study.
16
definitional literature elsewhere, which the frequently invoked term ‘economic
assimilation’ implicitly acknowledges. Economic assimilation, in this tradition, is
synonymous with income convergence between migrants and natives. On a broad scale,
there is little evidence for this kind of assimilation in the history of US migration.
Incomes of immigrants to the US have tended to grow at about the same pace as those of
natives.28 This view appears to hold up overall when considering the European
immigrants of the Age of Mass Migration. Some earlier research using cross-sectional
data finds that immigrant incomes grew faster than those of natives and eventually
converged, but more recent efforts suggest that the earlier results are spuriously induced
by upwardly biased measures of migrant income growth. Estimates based on betterstructured samples indicate that the growth in migrant income was comparable to the
growth in native income during this era. See Section III of this chapter for discussion of
the methodological literature supporting the greater credibility of the latter results. The
general pattern observed above does not disprove significant variation between specific
migrant groups. Abramitzky and Boustan note significant heterogeneity in the European
migrant experience.29
Cultural factors of economic assimilation. A growing economic literature focuses
on the impact of cultural assimilation variables on income. As in other economic
assimilation work, ultimately the outcome of interest is the ratio of migrant to native
earnings, or simply the level of migrant earnings. The previously noted studies on
28. Abramitzky and Boustan, “Immigration,” 3.
29. Ibid., 24-25.
17
language adoption and migrant income are among the oldest work in this literature.
Another well-known example is the line of research linking distinctively ethnic or racial
names with negative labor market outcomes. For example, distinctively black names have
a demonstrated link with decreased earnings. Employer discrimination is well-established
as an explanation of this phenomenon, though it does not necessarily constitute a
complete explanation.30 Similarly, Abramitzky, Boustan, and Eriksson find that European
ethnic names during the Age of Mass Migration are also associated with decreased
income. It is probable that more than one mechanism produces this association. Their
research supports the hypothesis that employer discrimination is one of these
mechanisms. Another that they postulate is that ethnic names impact self-perceptions of
identity.31 Their implication is that self-perception induces self-sorting, an idea consistent
with the role that ethnicity is theorized to play in the segmented assimilation model.
A newer body of research has begun to explore the role of intermarriage in
economic assimilation. The role of exogamous marriage has long been established in the
sociological literature as a factor in the blurring of white ethnic boundaries in the US.32
This topic was only established within economics, however, in a paper published in 2005
by Meng and Gregory. Their work demonstrates that immigrants to Australia who
intermarry with native Australians receive a wage premium of 17% for men and 22% for
30. Marianne Bertrand and Sendhil Mullainathan, “Are Emily and Greg More
Employable Than Lakisha and Jamal? A Field Experiment on Labor Market
Discrimination,” The American Economic Review 94, no. 4 (2004): 991-1013.
31. Abramitzky, Boustan, and Eriksson, “Cultural Assimilation.”
32. Lieberson and Waters, “Ethnic Groups,” 81.
18
women that is neither received by natives intermarried with non-natives, nor by nonnatives intermarried with other non-natives.33 Dribe and Nystedt also identify a native
intermarriage premium of 20% received by male migrants to Sweden. (They do not look
for an impact on intermarried women.34) Both of these studies propose that the premium
exists in part because exogamy integrates these immigrants more fully and rapidly into
the host society than their endogamous or single counterparts. Consider that many recent
arrivals may be unable to deploy their human capital with high efficiency because of a
lack of information about labor demand in the host country, or because they do not know
how to signal their human capital to potential employers. The integration induced by
intermarriage may include gaining social networks or information about labor market
conventions that can help them overcome these problems.35
This conception of assimilation via intermarriage is grounded in two older strands
of economic literature. The idea of social capital was introduced in 1988 by Coleman to
describe the productivity potential that is embedded in relations between people, akin to
the potential embedded in individuals (human capital) or the potential embedded in
33. Xin Meng and Robert G. Gregory, “Intermarriage and the Economic Assimilation of
Immigrants,” Journal of Labor Economics 23, no. 1 (2005): 135-174.
34. Martin Dribe and Paul Nystedt, “Is There an Intermarriage Premium for Male
Immigrants? Exogamy and Earnings in Sweden 1990-2009,” International Migration
Review 49, no. 1 (2014): 3-35.
35. Meng and Gregory, “Intermarriage,” 136.
19
durable goods (physical capital).36 Of the various forms that social capital takes, the one
of key interest in the case of assimilation via intermarriage is the subtype sometimes
labeled network capital. This describes the potential for interpersonal networks to
facilitate informational transfer and network expansion that leads to improved efficiency,
in this example, on the labor market.37 Coleman’s original exposition of social capital
describes its primary significance as a means of augmentation and activation of human
capital.38 Subsequent research on social capital has not always emphasized this
instrumentality of social capital, but in the case of intermarriage, the description clearly
applies.
Substantial amounts of research have been published on the topic of a general
marriage premium. There is widespread evidence of a measurable premium for men
across developed countries, while the existence of a marriage premium or penalty for
women appears to vary across cultures. Selection is a potential explanation, but studies
have demonstrated the persistence of marriage effects even when selection is controlled.
Another leading explanation of the phenomenon is the family investment hypothesis,
which suggests that married couples may begin to make economic decisions with the aim
36. James S. Coleman, “Social Capital in the Creation of Human Capital,” The American
Journal of Sociology 94, Supplement: Organizations and Institutions: Sociological and
Economic Approaches to the Analysis of Social Structure (1988): S95-S120.
37. For a more recent taxonomy of the types of social capital studied by economists,
including network capital, see Partha Dasgupta, “Social Capital,” in The New Palgrave
Dictionary of Economics, ed. Steven N. Durlauf and Lawrence Blume (New York:
Palgrave MacMillan, 2008).
38. Coleman, “Social Capital,” 100.
20
of maximizing a single family utility function, rather than two individual utility functions.
This introduces the potential for specialization of labor within the family, most
commonly by having the man focus on work outside the household, while the woman
focuses on domestic labor. This pattern should result in a marriage-related income
premium for men and an income penalty for women. Indeed, these outcomes are often
empirically observed; but men’s marriage premiums often appear to be greater than
existing wages for additional hours worked in lieu of housework. And men’s premiums
are commonly detected even when penalties to women do not exist. Two nonexclusive
explanations have strong plausibility.
The first candidate is that marriage directly induces a change in individual levels
of human capital. For example, a newly married individual of either gender might be
imbued with a newfound sense of responsibility that leads them to be more productive at
work. This socially induced productivity and the family specialization concept are both
social capital mechanisms by Coleman’s definition, whereby social relations create a
more efficient deployment of human capital. Other variants of induced human capital
change are surely possible.
The second candidate explanation is that marriage and increased productivity are
both correlated with an unobserved third input that varies over time, leading to concurrent
increases in the probability of marriage and in earnings at around the same life stage. One
possible example of such an unobserved input is a change in attractiveness, which
21
research shows can generate a wage premium, and which might also aid in marriage.39
Dougherty employs a distributed fixed effect model to demonstrate that at least one, if
not both, of these candidate explanations plays a role in determining the incomes of
married American men and women observed between 1980 and 1998.40 As a
consequence of Dougherty’s research and other recent studies, the specialization
explanation has been increasingly called into question.41 It is now well-established,
though, that marriages either constitute a form of social capital that effectively augments
the human capital of the spouses, or that marriage and some unobserved earningsaugmenting input are closely correlated.
I will refer to these two candidate explanations of the marriage premium as the
social capital hypothesis and the unobserved input hypothesis. These are antecedents to
consider in developing explanations of any observed intermarriage premium.
Intermarriage premiums, though, are a phenomenon rooted in migration as well as in
marriage. Understanding the intermarriage premium in any particular migration setting
thus demands studying the general economics of that migration.
39. One study substantiating a premium for attractiveness is Daniel S. Hamermesh and
Jeff E. Biddle, “Beauty and the Labor Market,” The American Economic Review 84, no. 5
(1994): 1174-1194.
40. Christopher Dougherty, “The Marriage Earnings Premium as a Distributed Fixed
Effect,” The Journal of Human Resources 41, no. 2 (2006): 433-443.
41. Alexandra Killewald and Margaret Gough, “Does Specialization Explain Marriage
Penalties and Premiums?” The American Sociological Review 78, no. 3 (2013), 477-502.
22
II. Selection and Destination Choice in Migration
In order to contend with the fundamental economics of the Great Migration, the
first question this study asks is whether African-American Southerners earned economic
returns by migrating from the South to the North. The first step in a sound estimation
strategy for returns to migration is accounting for the role of selection bias. Otherwise, if
immigrants with high earning potential tend more often to choose to migrate—the
phenomenon of positive selection—then an estimate of gains will be biased upward;
likewise, negative selection will obscure gains. This section reviews why selection occurs
and how it influences destination choice in migration. The following section (2.III)
reviews specific methodological issues regarding the quantification of migration and
selection.
Borjas’s application of the Roy model of self-selection to the migration context is
fundamental to the economic literature on migration. Nevertheless, empirical evidence
regarding the phenomenon that the model predicts is mixed. According to the logic of the
model, a utility-maximizing individual i with a certain amount of human capital will
migrate to a given destination when that destination offers a wage to those at individual
i’s level of human capital that exceeds the wage for those at individual i’s level of human
capital in i’s current location. Consequently, individuals with above-average productive
capacity are attracted to labor markets where their advantage is rewarded with more
steeply increasing returns to skill. Meanwhile, less-skilled workers select into more
equitable labor markets. This framework predicts that migrants moving to countries (or
regions) with more equitable income distributions than their home countries will tend
23
toward negative selection, and those moving to more unequal countries will be positively
selected.42
Empirical testing of the Roy model has not consistently validated the theory.
Immigration to the US during the Age of Mass Migration generally appears to follow the
trends suggested by the Roy model. In more recent migrations to the US, dating back to
approximately 1960, immigrants exhibit consistent positive self-selection, regardless of
the relative inequality of their country of origin.43 This shift is at least partly a
consequence of post-1921 US policy, which is favorable to migrants with human capital.
Such policies tend to increase positive selection into migration.44
The internal migrants of the Great Migration mostly did not face strong legal
constraints.45 Existing literature indicates that these migrants were nevertheless positively
selected. Tolnay uses Census samples to show that on average, African-American
migrants to the North possess nearly two additional years of education than do non-
42. George J. Borjas, “Immigration and Self-Selection,” in Immigration, Trade, and the
Labor Market, ed. John M. Abowd and Richard B. Freeman (Chicago: University of
Chicago Press, 1991), 29-76. Borjas’s innovation is the application of the model to
migration. The original model of occupational selection appears in Andrew D. Roy,
“Some Thoughts on the Distribution of Earnings,” Oxford Economic Papers 3, no. 2
(1951): 135-46.
43. Abramitzky and Boustan, “Immigration,” 12.
44. Catherine G. Massey, “Immigration Quotas and Immigrant Selection,” Explorations
in Economic History 60, no. 1 (2016): 21-40.
45. It was common in the 19th and 20th Centuries for Southern states to try to protect
white landowners, who were dependent on low-wage black labor, by persecuting
migrants and by restricting the flow of information about work opportunities elsewhere.
The mass emigration under discussion shows that these efforts eventually failed.
24
migrant African-American Southerners in the 1940 and 1950 samples, but that this
difference falls almost to a single year by 1980.46 This finding—positive selection, with a
declining magnitude over the course of the migration—proves robust under an alternative
measure of educational attainment proposed by Vigdor.47
The literature also indicates that migrants with more stable families and marriages
tended to select into migration to the North, and consequently experienced a ‘migrant
advantage’ in family stability over their black Northern neighbors. Wilson finds that
endogamous Southern marriages were more stable than exogamous marriages between
Southerners and Northerners.48 Indeed, despite the plausible assimilation benefits of
exogamy, many migrants used Southern social capital to improve their outcomes in the
North. Chay and Munshi show that the experiences of previous migrants strongly
influenced the destination choices of interwar African-American migrants from the same
home counties. Their assertion is that certain communities had strong social networks
that bridged Southern origin locations and Northern destination cities, dispensing
valuable information to new migrants in both places.49 This is but one example showing
46. Stewart E. Tolnay, “Educational Selection in the Migration of Southern Blacks, 18801990,” Social Forces 77, no. 3 (1998), 487-514.
47. Jacob L. Vigdor, “The Pursuit of Opportunity: Explaining Selective Black
Migration,” Journal of Urban Economics 51 (2002), 391-417.
48. Wilson, “Explaining.”
Stewart E. Tolnay, “The Great Migration and Changes in the Northern Black Family,
1940 to 1990,” Social Forces 75, no. 4 (1997): 1213-38.
49. Chay and Munshi, “Networks.”
25
that assimilation is far from the only means for migrants to collect information that
improves their labor market prospects.
III. Methodological Issues in Migration
Abramitzky and Boustan propose that methodological issues explain part of the
divergence between findings on modern and historical migration to the US, though the
extent of this explanatory power has not been directly studied. Modern migration
research generally uses pre-migration income to identify selection. Naturally, any
measure of an unobservable characteristic like migrant ability is subject to significant
measurement error, but income prior to migration makes a good proxy for the true
variable in question, potential labor productivity after migration. Individual income data
and consistent panel surveys are rare prior to the late 1960s, though, so studies of
historical migrations tend to rely on proxies for income or skill, which vary in their
credibility. Statistics that have been called upon to play this role include average income
by occupational description, educational attainment, height, literacy, and ‘age heaping,’
in which the relative frequency of self-reported round-numbered ages is taken as a
positively-correlated measure of the prevalence of innumeracy in a population.50 These
less perfect predictors of earnings introduce a significant possibility of measurement bias
into any study that uses them.
50. Abramitzky and Boustan, “Immigration,” 13, 19.
26
Another source of bias in estimation of selection can arise from experimental
design. McKenzie, Stillman, and Gibson (hereinafter MSG) provide useful benchmarking
of the impact of experimental design on reducing bias in estimates of returns to
migration. Through extensive fieldwork, they follow up with Tongans who participated
in a comprehensive survey of Pacific Islands households and subsequently participated in
lotteries for permits to permanently move to New Zealand that took place in 2002, 2003,
and 2004. The random nature of the lottery is interpreted as a quasi-experiment that
controls for self-selection, yielding a substantial positive estimate of the return to
migration. In parallel, they estimate the return to migration with various nonexperimental techniques. These techniques all overshoot the quasi-experimental estimate,
suggesting under-accounting of positive selection. A DD comparison of changes in
income between migrants and non-migrants yields a bias that is statistically different
from zero yet not large in real terms. A naïve ordinary least squares (OLS) regression
with pre-migratory income as an independent variable yields an estimate of return to
migration with a larger upward bias.51 Other researchers have shown that it is feasible to
use a DD approach to correcting for selection using binary indicators of skill, such as low
and high educational statuses, rather than continuous measures like income, which are not
as common in historical datasets.52 In this study I employ a DD approach to control bias
51. David McKenzie, Steven Stillman, and John Gibson, “How Important Is Selection?
Experimental vs. Non-Experimental Measures of the Income Gains from Migration,”
Journal of the European Economic Association 8, no. 4 (2010), 913-945.
52. Gardner, “Labor Economics of the Great Migration,” and Vigdor, “Pursuit of
Opportunity.”
27
in estimates of the returns to both migration and marriage during the late Great Migration
era. To accommodate the historical data limitations, I use imperfect dependent variables,
but they are more informative as dependent variables than the aforementioned binary
indicators. These variables are discussed in detail in Chapter 3.III.
Return migration is another issue that biases returns-to-migration estimates.
Suppose a study compares the long-term gains to migration between two distinct groups.
When a study measures relative income changes between two groups of individuals
where at least one group is migratory, the literature shows that it must account for the
possibility of return migration, or risk overestimating convergence between the groups.
Abramitzky and Boustan provide a clear accounting of this bias. While panel data is the
ideal basis for analyzing migration, because each individual’s actions are known over the
entire study period, studies of historical migration often rely on repeated cross-sections
instead. In a repeated cross-section, however, especially one based on an infrequent
survey like the US Census, return migrants bias estimates of returns to migration twice.
First, return migration is just as subject to selection bias as the original migration, though
typically in the reverse direction. In repeated cross sections, the departure of lowerachieving migrants is easily misread as increasing incomes across the entire migrant pool,
which yields an overly optimistic picture of migrants’ income convergence. Return
migration also biases estimates of migration’s effects because as successive migration
cohorts become more neutrally selected, negatively selected return accelerates.53
53. Abramitzky and Boustan, “Immigration,” 25, 63.
28
My present study benefits from working with a two-period panel, which is
described in Chapter 3. However, the period that elapses between the two periods is five
years, so it is possible that some in the sample who have migrated and returned appear as
non-migrants. Since the study period is the last years of the Great Migration, when
Vigdor and Tolnay show that positive selection has declined substantially, this is an issue
that merits attention in the interpretation of results.
29
CHAPTER 3
EMPIRICAL METHODOLOGY
I. Overview
The two preceding chapters motivate the empirical investigation of three
questions regarding the economic outcomes of African-American Southerners during the
Great Migration: (1) Can we detect returns to migration? (2) For each gender, is there
evidence of a marriage premium? (3) Is there evidence of an intermarriage premium for
exogamously married migrants in the North? This chapter articulates my empirical
strategy for answering these questions. This strategy differs from previous research by
using mixed models with DD terms and a random effects error structure. This reducedform approach controls bias and produces informative estimates of the level of selection
into behaviors of interest. I estimate the models using data from a subsample of the 1970
Census, because a random subset of that year’s respondents, unlike any other Census
respondents of the era, provided individual-level occupational information for both 1965
and 1970. The observation of individuals across two time periods is what enables the use
of DD terms. Consequently, my estimates of the returns to migration, marriage
premiums, and intermarriage premiums experienced by migrants are specific to the 19651970 period—the last five years of the Great Migration, when returns are likely
approaching neutrality.
Existing research consistently demonstrates positive selection into migration to
the North and partial income convergence between races in this era. (See 1.II and 2.I.) At
30
the same time, previous literature has found the economic returns to migrants during the
Great Migration to be negligible. Positive selection into an unrewarding emigration at a
time when Southern black wages were rising is puzzling behavior. DD estimations with
1965-1970 occupational data offer a novel and well-identified way to re-examine these
past findings. I hypothesize that the return to migration both between Southern states and
from South to North was actually significant.
Sociological research shows that 1960s black migrants to the North had more
marriage and family stability than non-migrant African Americans in both the South and
the North. Wilson finds significant evidence that this stability advantage is explained by
positive selection into migration.54 The existence of positive selection by marriage
stability suggests that marriage itself offers an economic return, but no existing literature
that I know of estimates marriage premiums specifically in the Great Migration context. I
hypothesize the existence of a marriage premium among Southern-born African
Americans in the 1965-1970 period. I aim to control for any female marriage penalty
caused by intra-family specialization (i.e., women dropping out of the workforce after
marriage) by screening the data for labor force participation. Therefore, I anticipate a
positive marriage premium for both genders. Further, I postulate that social capital
associated with exogamous marriage to a Northern native improves labor market
outcomes for new arrivals, so I anticipate the presence of an additional intermarriage
premium among recent migrants of both genders.
54. Wilson, “Family Stability.”
31
II. Reduced-Form Models
The empirical approach of this study relies on two separate reduced-form models
of desirable labor market outcomes. The outcomes are represented as index numbers
extrapolated from occupations reported in the 1970 Census. The nature of these indices
receives detailed attention in Section III of this chapter. The two models used vary in the
populations they are applied to and in independent variables they model as inputs to
individual labor market performance. However, they rely on the same econometric
approach: a mixed model with DD terms to identify self-selection and treatment effects
of interest, with a random effects error structure to control for individual-level
autocorrelation between 1965 and 1970 observations of labor market performance.
Two populations of interest. The separation of the models is necessary because
the population of interest in identifying returns to migration and general marriage
premiums is different from the population of interest in identifying an intermarriage
premium specific to recent migrants. The population of interest in studying returns to
migration and general marriage premiums comprises all black Southerners who resided in
the South in 1965. Incomes of those who moved to the North by 1970, or who married,
are compared to the incomes of those who did not engage in these behaviors, in order to
identify the associated changes in outcome. The second model involves the interaction of
marriage and migratory behaviors to look for evidence of complementarity. The main
population of interest in this case comprises all Southern-born African Americans who
resided in the North in 1970. The incomes of new arrivals who intermarried are compared
both to the incomes of new arrivals who did not intermarry, and to longer-term migrants
32
who did intermarry. For comparison, the second model is also applied to the population
of Southern-born African Americans who resided in the South in 1970.
For each of the models, I separately estimate and report results for men and
women. As shown in Chapter 4, results vary significantly between genders. Given the
apparent gender-specific patterns, mixed-gender results are not especially informative,
and are not reported.
Random effects. No individual-level labor market study can model every single
determinant of every individual’s outcomes. Thus, any given individual is likely to
overperform or underperform relative to predictions based on the included determinants,
due to the omission of other determinants. This deviation will be systematic: the error in
individual i’s 1965 earnings will be strongly correlated with the error in i’s 1970 earnings
since the same omitted determinants likely affect both years. For this reason, the models
used in this study do not satisfy the classical linear regression model assumption that
errors are independent. OLS will therefore produce systematically biased estimates, as
was evident in a set of unreported preliminary estimates. I adopt a random effects error
structure to control this bias. Time-invariant individual random effect ui is included in
both reduced-form models as a component of variance in observed outcome values
relative to the values predicted by individual i’s measured traits. That is, ui reflects the
portion of error in that individual’s labor market output that is attributable to timeinvariant deviation from the output of other individuals similar to i. A model with random
effects cannot be estimated using OLS; the preferred estimation technique is feasible
generalized least squares (FGLS). This approach does not admit the heterogeneity of
33
individual effects over time that Dougherty’s distributed fixed effect approach
accommodates.55 Given the two-period setting of this study, though, the random effects
approach is the best available technique.
Identifying selection and treatment effects. In the presence of uncontrolled
selection, straightforward regression of a labor market outcome on migration status will
tend to confound returns to migration with the premium attributable to the human capital
advantage of the average migrant relative to the average non-migrant. In the Great
Migration, empirical research consistently indicates that migrants are positively selected,
so appropriate econometric techniques are needed to control bias in returns-to-migration
estimates.56 Some past estimates of Great Migration returns, such as those by ETA, only
control for selection bias through the inclusion of an educational attainment variable in a
straightforward regression of income on migration choice. By including this education
variable, their study prevents observed educational attainment differences between
migrants and non-migrants from biasing the estimated return to migration. Unfortunately,
selection into migration also occurs along other axes, including by traits that are not
observable within the Census data on which both ETA and I rely.
The models I use here control for selection along multiple axes. The random
effects term, first of all, is likely to capture at least part of any self-selection correlated
55. Dougherty, “Marriage.”
56. Eichenlaub, Tolnay, and Alexander, “Moving Out but Not Up.”
Tolnay, “Educational Selection.”
Vigdor, “The Pursuit of Opportunity.”
34
with variables that are not present in the regression specifications. It is useful, though, to
get an actual estimate of the selection effect associated with the behaviors that we are
investigating—migration and marriage. To do so, I introduce treatment and group
dummy variables standard to DD models, denoted with a leading t or g, respectively. Use
of DD variables is a technique for modeling outcomes observed in sample data as if the
outcomes result from a random experiment with various treatment options and an
untreated control group. An individual in the sample is ‘treated’ when they adopt one of
the behaviors of interest—say, by marrying. If individual i is married in time t, then the
treatment dummy for marriage tMarriedi,t is equal to one; otherwise it is zero. With a DD
specification, the aim is to concurrently estimate the impact of this treatment and the
impact of whether an individual ever receives the treatment. In the latter case, if
individual i ever marries, then they are said to be in the marriage treatment group, and the
group dummy variable gMarriesi,t is equal to one in all periods t, (i.e., 1965 and 1970),
regardless of whether i has yet married. The estimated impact of gMarries on the
outcome variable is an estimate of selection into treatment, because it reflects the average
difference in the outcome between those who marry and those who do not. Then the
estimated effect of tMarried on the outcome variable is an estimate of the direct effect of
marriage on the outcome. Convention suggests interpreting tMarried as a causal
treatment effect of marriage on the outcome variable, though we cannot exclude the
possibility that the estimated impact of tMarried expresses some unobserved input
correlated with both marriage and labor market performance, as discussed in Chapter 2.I.
35
The estimate of the treatment effect is, in principle, unbiased by selection, as the
gMarries term holds pre-existing differences constant.
Both of the reduced-form models that I estimate in this study follow this pattern.
They incorporate DD treatment and group variables for the behaviors of interest, and a
random effects term u. I also incorporate a time effect term λt, the impact of which
reflects the component of variance between 1965 and 1970 observations that is shared
across the entire sample. Last, I employ an additional set of standard income determinant
variables whose relationship with labor market outcomes are informative—Age, Age2,
YearsOfEducation, and NumberOfChildren. Overall, this model design follows the
example of Ashenfelter and Card, who also combine DD treatment and group variables
with a random effects error structure.57 The general form of both of my models can be
expressed as:
yi,t = β0 + β1 × tAi,t + α1 × gAi,t + β2 × tBi,t + α2 × gBi,t + … + βx × xi,t + λt + ui + εi,t
β0 is an intercept term. β1, β2, … and α1, α2, … respectively estimate the treatment effects
of, and selection into, treatments A, B, and so on. βx contains the estimates for the effects
⎡ Agei,t
⎤
⎢
⎥
2
⎢ Agei,t
⎥
of the covariates in vector xi,t= ⎢
. YearsOfEducationi,t is sometimes
YearsOfEducationi,t ⎥
⎢
⎥
⎢⎣ NumberOfChildreni,t ⎥⎦
57. Orley Ashenfelter and David Card, “Using the Longitudinal Structure of Earnings to
Estimate the Effect of Training Programs,” The Review of Economics and Statistics 67,
no. 4 (1985): 648-660. Ashenfelter and Card use the method of moments estimator rather
than the FGLS estimator.
36
excluded from this covariate vector. Finally, λt, ui, εi,t, are the previously discussed
components of variance, with εi,t representing the component that is transitory and not
autocorrelated.
Model one: returns to migration and marriage. The first reduced-form model
used here is designed to answer the following questions: Can we detect returns to
migration? For each gender, is there evidence of a marriage premium? The relevant
treatments, therefore, are migration from South to North, migration within the South, and
marriage. The sample is restricted to African Americans living in the South in 1965.
Since the data used offers no means of linking individuals to pre-1965 income or
occupation, there is no evident way to use this sample to estimate longer-term returns to
migration from the South. Thus, any measurable treatment effects in this model represent
returns to migration or marriage premiums realized in a short time frame.
The fully specified form of model one is given in Table 1. Note one difference
between the returns-to-migration estimators β1 and β2 and the marriage premium
estimator β3. Since this sample excludes Southerners who have already migrated to
the North in 1965, β1 and β2 are well-identified. However, the sample includes many
already-married observations in 1965. The resulting marriage premium estimate may be
confounded with long-term marriage impacts, or with selection into earlier marriage,
potentially introducing unknown biases. Intuition suggests that both of these factors
might be expected to bias the estimate upward. My conclusions reflect uncertainty
regarding the consistency of this estimator and an assumption that it is probably biased
upward by a moderate amount.
37
Table 1. Specification of Model One
Treatment variables
Migratory
treatments
yi,t = β0
+ β1 × tMigratedNorthi,t
+ β2 × tMigratedWithinSouthi,t
Marriage
treatment
Group variables
+ α1 × gMigratesNorthi,t
+ α2 × gMigratesWithinSouthi,t
+ β3 × tMarriedi,t
+ α3 × gMarriesi,t
+ βx × xi,t + λt + ui + εi,t
It is also important to recall that MSG empirically test several non-experimental
methods of estimating returns to migration and rank DD as the best method available
outside the specific context of their study. Their results nevertheless indicate that we
should put some emphasis on the possibility that DD estimates of returns to migration
retain some upward bias.58 MSG’s DD model does not incorporate random effects,
though. They speculate that the main sources of remaining bias are probably time-varying
unobserved characteristics (analogous to Dougherty’s distributed fixed effects). Our
random effects terms may capture a portion of such characteristics, though as previously
noted, they are not especially suited to traits that vary over time.
Model two: the intermarriage premium. Chapter 2.I explains the motivation
behind testing for the existence of a premium earned by recent migrants to the North who
intermarry with Northern natives. The section concludes by naming two candidate
58. McKenzie, Stillman, and Gibson, “Selection?” 932.
38
explanations we might consider if we detect such a premium: a social capital hypothesis
and an unobserved input hypothesis. A key condition for plausibility of an intermarriage
premium, and in particular for the social capital hypothesis, is that it should only exist, or
at least be measurably greater, for those migrants that have recently arrived in the North.
Since long-term migrants are presumably better assimilated into Northern labor markets
than recent arrivals, the social capital hypothesis is not a compelling explanation for an
intermarriage premium that is similar for both recent and long-term migrants.
The question to answer with model two, then, is whether recent arrivals in the
North who intermarry experience wage gains relative to others who intermarry. To test
this notion, the population we study must include long-term migrants to the North in
addition to those who arrive between 1965 and 1970. I define a long-term migrant to the
North as anyone born in the South and living in the North in both 1965 and 1970. This
expands the range of migratory treatments that are found in the data: we can now observe
migration within the North, and long-term migrants who are sedentary during the study
period. Return migrants are also included in the population of interest. This model treats
each of these behaviors as a determinant of labor market performance. It also
distinguishes between exogamous marriage, endogamous marriage, and not marrying.
Further, to directly examine the question of interest, the interactions of marriage and
migratory behaviors are required. The model necessarily involves many more state
variables than were required in the first model, as Table 2 illustrates.
39
Table 2. Specification of Model Two: Northern Subsample
Treatment variables
Migratory
treatments
Group variables
yi,t = β0 + β1 × tNewArrivali,t
+ α1 × gNewArrivali,ti,t
+ β2 × tReturnedMigranti,t
+ α2 × gReturnMigranti,ti,t
+ β3 × tMigratedWithinNorthi,t
Marriage
treatments
+ α3 × gMigratesWithinNorthi,ti,t
+ β4 × tIntermarriedi,t
+ α4 × gIntermarriesi,ti,t
+ β5 × tMarriedi,t
Interactions of
migration and
marriage
+ α5 × gMarriesi,ti,t
+ β6 × tNewArrivali,t × tIntermarriedi,t
+ α6 × gNewArrivali,t× gIntermarriesi,t
+ β7 × tNewArrivali,t × tMarriedi,t
+ α7 × gNewArrivali,t× gMarriesi,t
+ β8 × tReturnedMigranti,t × tIntermarriedi,t
+ α8 × gReturnMigranti,t × gIntermarriesi,t
+ β9 × tReturnedMigranti,t × tMarriedi,t
+ α9 × gReturnMigranti,t × gIntermarriesi,t
+ β10 × tMigratedWithinNorthi,t × tIntermarriedi,t
+ α10 × gMigratesWithinNorthi,t × gIntermarriesi,t
+ β11 × tMigratedWithinNorthi,t × tMarriedi,t
+ α11 × gMigratesWithinNorthi,t × gMarriesi,t
+ βx × xi,t + λt + ui + εi,t
40
Table 3. Specification of Model Two: Southern Subsample
Treatment variables
Group variables
Migratory
treatment
yi,t = β0 + β1 × tMigratedWithinSouthi,t
Marriage
treatments
+ β2 × tIntermarriedi,t
+ α1 × gMigratesWithinSouthi,ti,t
+ α2 × gIntermarriesi,ti,t
+ β3 × tMarriedi,t
Interactions of
migration and
marriage
+ α3 × gMarriesi,ti,t
+ β4 × tMigratedWithinSouthi,t × tIntermarriedi,t
+ α4 × gMigratesWithinSouthi,t × gIntermarriesi,t
+ β5 × tMigratedWithinSouthi,t × tMarriedi,t
+ α5 × gMigratesWithinSouthi,t × gMarriesi,t
+ βx × xi,t + λt + ui + εi,t
To simplify reporting without losing precision, I first apply this model only to
individuals who reside in the North at some point during the study period. Then, I apply
the same model with a modified set of migratory treatments to those who remain in the
South throughout the study period. The fully specified model for the Southern subsample
is given in Table 3. Table 4 summarizes the hypotheses outlined in this chapter’s
overview following the notation developed above.
41
Table 4. Hypotheses.
Model Coefficient Associated variable
Effect
Null
Alternative Expectation
1.
β1
tMigratedNorth
Return to
migration to
North
β1 = 0
β1 > 0
Reject null
1.
β2
tMigratedWithinSouth
Return to
migration within
South
β2 = 0
β2 > 0
Reject null
1.
β3
tMarried
Marriage
premium
β3 = 0
β3 > 0
Reject null
2.
(North)
β6
tNewArrival ×
tIntermarried
Intermarriage
premium for
recent migrants
β6 = 0
β6 > 0
Reject null
No particular hypothesis is associated with Model 2, Southern subsample.
III. Data Description
Sample description. The primary source of data for this study is a 1970 US
Census sample from the Integrated Public Use Microdata Series (IPUMS-USA).59
Individuals of interest to the study are all African Americans born in the South who are of
working age during the entire 1965-1970 period, defined as being born no earlier than
1911 and no later than 1946, so that individuals are older than 18 and younger than 60 for
the entire study period. Only individuals who are in the labor force in 1970, and neither in
college nor in the military in 1965, are included. Existing literature on the economics and
sociology of the Great Migration defines the South as the states of the Confederacy, i.e.,
Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, North Carolina, South
59. Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek,
Integrated Public Use Microdata Series: Version 6.0 (machine-readable database,
Minneapolis: University of Minnesota, 2015).
42
Carolina, Tennessee, Texas, and Virginia, plus Kentucky and Oklahoma, and I maintain
this convention here.60 I separately analyze trends within each gender, because patterns
vary sufficiently that mixed-sample analysis does not provide additional information.
Table 5 reports the number of individuals meeting these criteria found in the IPUMSUSA 1970 Form 1 1% sample, categorized by marriage and migration behavior.
Table 5. Individuals Counted in Sample by Marriage and Migration
New arrivals
in exogamous
marriages
Long-term N in
exogamous
marriages
Total
exogamous
marriages
Endogamous
marriages
Total marriages
Men
14
2,557
2,571
11,944
14,515
Women
10
938
948
5,645
6,593
Total
24
3,495
3,519
17,589
21,108
Sedentary
Within
South
Long
Term
North
Within
North
⊂ LT
Return
New
Arrival
Total
Men
10,136
151
6,835
213
104
314
17,540
Women
6,865
64
4,515
146
77
198
11,719
Total
17,001
215
11,350
359
181
512
29,259
Sedentary: Resident of same Southern state in 1965 and 1970. Within South: Resident of
different Southern states in 1965 and 1970. Long Term North: Resident of North in 1965 and
1970 (Southern-born). Within North: Resident of different Northern states in 1965 and 1970
(subset of Long Term North). Return: Resident of North in 1965 and South in 1970. New
Arrival: Resident of South in 1965 and North in 1970.
Dependent variables. I use the 1970 Form 1 sample because, uniquely among
Great Migration-era Census forms, this questionnaire elicited data on respondents’ work
activity five years prior (1965) in addition to their work activity at the time of response.
As a result, the sample provides two-period panel data for a small number of
60. Eichenlaub, Tolnay, and Alexander, “Moving Out but Not Up.”
43
occupational variables, permitting the application of the DD approach to the five-year
period at the end of the three-decade migratory wave. 1965 income is not among these
variables, but I follow the literature on historical migration by using occupational
information to proxy for income. (Chapter 2.III discusses the merits of this approach.)
IPUMS-USA includes several standardized variables that index occupations according to
various labor market outcome scales. In lieu of studying the impact of marriage and
migration on income directly, I study their impact on four of these index variables: two
designed as proxies for income, and two designed as proxies for less tangible notions of
social prestige. Each index is an extrapolation from reported occupation alone, so these
indices really serve as four different views of the same underlying information. Even so,
taking four angles on the data creates space for interpretation and a certain amount of
robustness checking. Thus, the data source has a significant effect on both the time period
and dependent variables selected for this study.
Why consider proxies of intangible social standing? The historical setting
involves significant changes in both social and economic patterns. The Great Migration is
concurrent with Northern industrial growth; with income convergence between South and
North, and between white and black; and with the decline of the Jim Crow regime and a
rise in urban disamenities in heavily black neighborhoods of Northern cities. If we seek
to understand the incentives that shaped the migration, it is informative to look at both
economic and social incentives. Literature on the Great Migration does not reveal a
consensus approach to quantifying nonmonetary incentives. The indices in IPUMS-USA,
44
however, are a straightforward means of estimating and comparing monetary and
nonmonetary outcomes.
Table 6. Dependent Variable Descriptions
Measures of income
Interpretation
erscor50
Occupational earnings Percentile rank of occupational median income
score
relative to other occupations.
occscore
Measures of status
presgl
sei
Occupational income
score
Occupational median income in hundreds of dollars
per year.
Siegel Prestige Score
Unitless ranking of occupational prestige, from 0
(least prestige) to 99.9 (most prestige)
Duncan Socioeconomic Unitless composite ranking of occupational
Index
prestige, income, and education level, from 1
(lowest status) to 96 (highest status)
All measures are extrapolations from occupation alone.
Table 6 serves as a brief reference to the four dependent variables studied. The
two measures of social prestige are the Siegel Prestige Score (presgl) and the Duncan
Socioeconomic Index (sei). These scores are unitless quantifications of social status on
approximate 0-100 scales. Presgl is solely a measure of prestige, while sei is a composite
measure of income, educational attainment, and prestige. Some mobility researchers
argue that composite measures such as sei are misleading because the relative weights of
the dimensions, i.e., income, education, and prestige, are arbitrarily assigned.61 I include
sei, however, because it enables direct comparison between my results and the results
61. Ruggles, et al., “User Note on Composite Measure of Occupational Standing,”
IPUMS, https://usa.ipums.org/usa/chapter4/sei_note.shtml.
45
presented by ETA. That said, presgl, as a ‘dimension-specific’ index—i.e., it measures
prestige alone—is less controversial, and as we will see, more telling.
Including two proxy measures of prestige is a useful robustness check. Though
the list of occupations underlying presgl and sei are identical, they often do not provide
consistent ordinal rankings of these occupations. The reasons for this inconsistency can
be inscrutable. For instance, loom fixers are a relatively specific class of worker, likely
working mainly in factories in regions with textile industries. While textile industry
wages are generally low, loom fixers may have specialized skills that confer greater
prestige than some of the other jobs within the industry. Their presgl is 30.4, but the
composite sei is only 10. Contrast this with millers, whose sei of 19 exceeds that of loom
fixers, but whose presgl of 25.2 falls below loom fixers. Precise interpretation is elusive:
millers seem to be a broader category, and potentially higher-paid; but also, as
agricultural workers, they may be perceived as having lower prestige than mid-ranked
factory workers. These inconsistencies offer a benefit, though, in that statistically
significant effects that are observable on both variables are good evidence of a real effect
on prestige.
I take a parallel approach to measuring effects on income, using two standardized
indices as proxies. As with presgl and sei, these are direct extrapolations from reported
occupations, but their units are practical. An individual’s Occupational Earnings Score
(erscor50) is the percentile rank of the median income in the occupation that that
individual reports. Their Occupational Income Score (occscore) is the median income of
their reported occupation in hundreds of dollars. Since the use of these indices eliminates
46
variance in earnings within any given occupation, they are not perfect measures of labor
market outcomes. (This holds for the prestige indices as well.) Another drawback is that
they do not vary across states or within the time period of study. Judging by their
occscore, a loom fixer in Indiana in 1970 and a loom fixer in Alabama in 1965 have the
same income. Still, occupational change is a major factor in income growth, especially
among migrants, so increases in these measures are a good basis for inferences about
income growth.
As with prestige, the use of two measures of income provides a useful robustness
check. The income measures also give inconsistent ordinal rankings of occupations at
times: the occscore for millers is 25, or $2,500 per year, while for bill collectors it is 27,
or $2,700 per year; but erscor50 ranks millers at the 49.7th percentile in median income,
and places bill collectors at only the 36.1th percentile. The origin of this inconsistency is
understandable in the case of income: the two scores index the same list of occupations,
but the median income data they represent come from different samples and different
years. As with the prestige measures, given the inconsistency, significant effects that are
observable on both variables are good evidence of an effect on actual income.
Explanatory variables. The principal variables of interest are derived from points
of information on each individual in the 1970 IPUMS sample:
•
1965 state of residence,
•
1970 state of residence,
•
whether the individual is married in 1970,
•
age at first marriage, and
47
•
if married, whether the spouse is a Northerner by birth.
Using these reference points, with the simplifying assumption that all 1970 marriages are
first marriages, I classify each individual into the categories defined in Table 5.
Other demographic variables are extracted directly from IPUMS. All non-black
observations are removed from the sample. Age, as noted above, is a screen for labor
force eligibility, and is also used as an explanatory variable, along with its square. Each
model is estimated separately for each gender. Other explanatory variables are years of
education and number of children.
Most related studies use some kind of regional average income term to correct for
regional cost-of-living differences. The design of this study omits such a term. Due to the
aforementioned invariance of the dependent variables across state lines and time, there is
no within-occupation income variance to explain as cost-of-living adjustment. Indeed, no
real variance in incomes within occupations is represented in the data used here, either.
Consequently, the income gains that I will argue are returns to migration are only returns
realized through occupational change.
The occupation-level invariance of the dependent variables has a second
consequence for the design of this study. In earlier iterations of this study, I have argued
that some existing Great Migration literature uses flawed regression specifications and
consequently estimates the marginal rather than average return to migration. I have also
proposed the use of a state-level per-capita income growth rate variable to correct the
issue. I still hold that previous research designs produce an unintended downward bias on
their estimates of the average return to migration. However, the growth rate approach is
48
inapplicable to the present study because of the lack of outcome variance across space
and time within occupations. Appendix B provides further discussion of the marginal
returns problem and the proposed growth rate variable.
IV. Exploratory Analysis
Table 7 gives basic summary statistics for each migrant group for the four indices
in 1965 and 1970, as well as wage income in 1970, for comparison. These indices are
certainly imperfect, but the more accurate representation of income, between its two
proxies, is occscore, whose index values here and in all subsequent reporting are
transformed into 1970 dollars using the GDP deflator.62 In addition to being
systematically lower than directly reported income, occscore also shows considerably
less variance than income, a trait that follows from occscore’s flattening of the wage
distribution within occupations. One consequence is that income differences between
various migratory groups appear more striking in income terms than in occscore terms.
The lowest-earning migrant groups, sedentary Southerners and return migrants, have
occscore means 0.15 standard deviations below the whole-sample mean; their actual
incomes are 0.27 standard deviations below the whole-sample mean. Long-term Northern
migrants that move within the North have an average income 0.47 standard deviations
above the whole-sample mean, while their average occscore is only 0.21 standard
deviations above the mean.
62. US Bureau of Economic Analysis, “Table SA1 – Personal Income Summary:
Personal Income, Population, Per Capita Personal Income,” accessed 2017,
https://www.bea.gov/iTable/index_regional.cfm.
49
Table 7. Summary Statistics for Dependent Variables
Entire sample: n=29,259
Mean, 1965
Mean, 1970
wage income
4,512
(3,226)
occscore
3,177
3,311
(1,231)
(1,284)
erscor50
33
36
(22)
(24)
presgl
28
30
(12)
(13)
sei
21
23
(18)
(19)
Table section 1 of 3
Median, 1965 Median, 1970
4,050
Sedentary: n=17,001
wage income
occscore
erscor50
presgl
sei
Within-South: n=215
wage income
Mean, 1965
2,990
(1,239)
31
(22)
27
(12)
19
(18)
Mean, 1965
Mean, 1970
3,621
(2,720)
3,122
(1,299)
33
(24)
29
(12)
21
(18)
3,149
3,464
28
35
28
28
15
18
Median, 1965
Median, 1970
3,250
3,149
3,307
28
28
22
28
14
15
Mean, 1970
Median, 1965 Median, 1970
4,058
4,050
(3,054)
occscore
3,288
3,497
3,149
3,621
(1,433)
(1,479)
erscor50
37
40
28
35
(25)
(26)
presgl
30
31
28
28
(13)
(13)
sei
22
25
15
18
(20)
(20)
Sample standard deviation reported in parentheses below means. To facilitate comparison with income,
occscore index values are transformed into 1970 dollars.
50
Table 7. Summary Statistics for Dependent Variables
Long-term to North: n=11,350
Mean, 1965 Mean, 1970
wage income
5,845
(3,473)
occscore
3,454
3,581
(1,164)
(1,217)
erscor50
36
39
(22)
(24)
presgl
30
32
(13)
(13)
sei
24
27
(19)
(20)
Within-North: n=359
(Subset of long-term to North)
wage income
occscore
erscor50
presgl
sei
Return: n=181
wage income
Mean, 1965
3,610
(1,315)
39
(24)
32
(14)
28
(22)
Mean, 1965
Mean, 1970
6,037
(3,622)
3,839
(1,344)
43
(24)
36
(13)
34
(22)
Table section 2 of 3
Median, 1965 Median, 1970
5,850
3,621
3,621
35
35
28
29
18
18
Median, 1965
Median, 1970
6,050
3,621
3,621
35
35
30
35
18
24
Mean, 1970 Median, 1965 Median, 1970
3,663
3,050
(2,982)
occscore
3,225
3,217
3,149
3,050
(1,231)
(2,982)
erscor50
33
34
28
34
(23)
(23)
presgl
29
32
28
28
(13)
(15)
sei
23
25
15
18
(20)
(21)
Sample standard deviation reported in parentheses below means. To facilitate comparison with income,
occscore index values are transformed into 1970 dollars.
51
Table 7. Summary Statistics for Dependent Variables
New Arrivals: n=512
Mean, 1965
Mean, 1970
wage income
5,058
(2,884)
occscore
3,209
3,541
(1,164)
(1,049)
erscor50
35
39
(23)
(22)
presgl
29
32
(13)
(15)
sei
23
25
(20)
(21)
Table section 3 of 3
Median, 1965 Median, 1970
5,050
3,149
3,621
28
35
28
28
15
18
Sample standard deviation reported in parentheses below means. To facilitate comparison with income,
occscore index values are transformed into 1970 dollars.
Hence, while occscore tends to preserve the relative positions of the various
migrant types in 1970, it does not capture the full extent of variation between types. This
is a symptom of the elimination of same-occupation interregional variance discussed in
Section III of this chapter: observed differences in the data reflect distinct occupational
options available to different migrant types, rather than improved real wages in one
region relative to another. Estimates of returns to migration based on occscore and other
occupationally defined proxies may well be underestimates if real wages for a given
occupation are higher in the North than in the South. While we cannot use the present
data for verification, this is quite plausible, since Southern income lagged Northern
income overall for over a century. (See Chapter 2.I.) Another likelihood I cannot directly
test or correct is that eliminating within-occupation income variance disproportionately
truncates the upper end of the income distribution. If we assume that the Northern
industrial economy was likely to produce greater income inequality than that produced by
52
the South’s labor-glut dynamics, then the income indices are likely to understate the
Northern income advantage.
An individual’s erscor50 indicates the percentile rank of their occupational
median income relative to other occupations, so it is not surprising that the means and
medians for this Southern African American sample fall well below the 50th percentile
overall. Variance in erscor50 relative to the 1970 mean is larger than in the case of
income or occscore. The means increase between 1965 and 1970 for all subgroups. The
medians, however, remain constant for sedentary Southerners and long-term migrants in
the North, including the subgroup of those who move between Northern states between
1965 and 1970, implying growing inequality within these two groups. Meanwhile,
medians increase substantially for the entire sample, for those who move within the
South, for recent migrants to the North, and even for return migrants leaving the North.
This provides preliminary support for an immediate positive return to migration or
substantial positive selection into migration.
Similar patterns are apparent in the status indices. Migrants of all types have a
head start in presgl on sedentary Southerners, though less so among return migrants. This
suggests positive selection into migration North according to prestige, and negative
selection into returning to the South: the three-point spread between long-term migrants
who stay North and those who return is about equivalent to the prestige difference
between a deliveryman on the higher end and a miller on the lower end. All subgroups
see comparable growth in their mean sei during the period except for migrants within the
North. This index is a composite measure that synthesizes prestige, income, and
53
educational attainment into a single unitless ranking. All individuals are over 18 and not
attending college during our sample period, so these gains do not reflect educational
increases, though they might reflect more efficient deployment of pre-existing
educational capital through occupational change. The group of migrants moving around
within the North begins with a substantial sei and presgl advantage and also sees strong
growth in presgl—a good example of how matching trends in two related measures can
increase our impression of the robustness of a pattern. It seems quite likely that there is
strong positive selection into migration within the North.
Table 8. Select Summary Statistics for Explanatory Variables
WithinLT to
WithinSedentary
South
North
North
Age
41.0
34.8
41.6
37.2
(1970 mean)
Children
3.0
2.4
2.2
1.9
(1970 mean)
Years of education
10.0
11.2
11.2
12.2
(1970 mean)
% Female
40.4%
29.8%
39.8%
40.7%
% Married (1970)
90.9%
93.0%
92.1%
89.4%
% Married to
6.9%
3.3%
16.4%
17.6%
Northerner (1970)
Return
New
Arrivals
All
36.2
33.7
41.0
2.0
2.4
2.7
11.6
11.2
10.5
42.5%
88.4%
38.7%
89.1%
40.1%
91.3%
5.5%
4.7%
6.9%
Table 8 reports select summary statistics for explanatory variables. A first point of
reference is the variation in mean years of education among the migrant groups:
relatively low attainment in the sedentary group further supports the expectation of
positive selection into migration, while the strikingly high mean among within-North
migrants supports the idea that this is a relatively privileged group. Notably, though,
return migrants also have relatively high educational attainment. If it turns out that there
54
is negative selection into return migration, then it seems likely that this selection is along
non-educational traits.
The demographic profiles of the various groups are another point of interest.
Typical within-South and South-to-North migrants are significantly younger than
sedentary Southerners and long-term Northern migrants. The within-South group
includes fewer women than any other group, perhaps reflecting that this group is made up
substantially of a male-dominated mobile labor force. In contrast, it appears that women
are more common among return migrants than among other groups, though the
implication of this pattern is not clear.
Marriage rates are high in all groups. Marriage to a Northern-born spouse is
uncommon in the South, but not unheard of; in contrast, more than 16% of long-term
migrants to the North marry Northerners by 1970. Return migrants’ rates of intermarriage
resemble the intermarriage rates of those that remained in the South.
The next chapter reviews the estimates obtained by the strategy described in this
chapter, in light of the context we have now established.
55
CHAPTER 4
RESULTS
I. Overview
This chapter analyzes the estimates generated by the reduced-form models and
data described in Chapter 3. I aim to answer the following questions: What were the
returns captured by Southern African Americans who migrated to the North, and within
the South, during the last five years of the Great Migration? What were the general
marriage premiums within this population? Was there an additional premium for
exogamous marriage to Northern natives, particularly among new arrivals in the North?
The research design only enables investigation of gains realized through occupational
change, not intra-occupational gains; but the design admits analysis of gains in social
prestige as well as in income. Tables 4 and 6 serve as references for my specific
hypotheses and the dependent index variables, respectively. Since all the hypotheses
indicated in Table 4 are one-tailed, regression results tables in this Chapter and in
Appendix A indicate statistical significance at one-tailed significance levels.
II. Model One: Returns to Migration and Marriage
Tables 9 and 10 present the results of six initial regressions on the male and
female subsamples, respectively.
56
Table 9. Model One: Men
(1M)
erscor50
(2M)
occscore†
(3M)
presgl
(4M)
sei
(5M)
occscore†
(6M)
presgl
tMigratedToNorth
1.240
(1.027)
193.0***
(45.88)
-0.618
(0.510)
0.177
(0.664)
195.6***
(45.87)
-0.579
(0.510)
tMigratedToSouth
0.366
(1.464)
114.8**
(65.41)
0.149
(0.728)
1.380*
(0.947)
116.1**
(65.40)
0.169
(0.728)
gMigratesToNorth
0.954
(1.245)
16.11
(57.05)
1.85***
(0.626)
2.52***
(0.899)
-57.47
(54.89)
0.880*
(0.592)
4.352***
(1.769)
153.4**
(81.09)
2.42***
(0.890)
2.617**
(1.277)
98.03
(77.98)
1.688**
(0.840)
tMarried
0.453
(0.589)
24.93
(26.46)
0.218
(0.293)
0.595*
(0.390)
4.133
(26.23)
-0.0407
(0.290)
gMarries
7.086***
(0.874)
374.4***
(39.92)
2.67***
(0.439)
3.74***
(0.621)
304.2***
(38.66)
1.732***
(0.419)
Children
-0.34***
(0.0652)
-25.6***
(3.001)
-0.22***
(0.0329)
-0.55***
(0.0477)
-11.2***
(2.903)
-0.032
(0.0311)
78.20***
(2.405)
1.025***
(0.0257)
Incl***
Incl***
gMigratesWithinSouth
Years of education
Time, Age, Age^2, Constant
Incl***
Incl***
Incl***
Incl***
Observations
21,202
21,202
21,202
21,202
21,202
21,202
R-squared within
0.0352
0.0418
0.0356
0.0393
0.0422
0.0354
R-squared between
0.0183
0.0250
0.0141
0.0226
0.1128
0.1427
R-squared overall
0.0212
0.0276
0.0178
0.0249
0.1016
0.1244
ρ
0.6566
0.6871
0.6649
0.7246
0.6614
0.6241
Standard errors in parentheses. One-tailed significances indicated by asterisks: ***p<0.01, **p<0.05,
*p<0.1. †Occscore reported in 1970 dollars.
The first four regressions vary only in the dependent variable. All four omit years
of education as an explanatory variable; the fifth and sixth regressions replicate the
occscore and presgl regressions with education included. (Hereinafter, the symbols (1M)
and (1F) represent the results of the first specification on the two respective genders, and
57
so on for each specification.) In the specifications that omit education, the impact of
education will be expressed at least in part as a selection effect via the group terms (those
with a leading g). Adding education as a term reduces the magnitude of these group term
estimates, illuminating how much of a role education plays in overall selection.
Table 10. Model One: Women
(1F)
erscor50
(2F)
occscore†
(3F)
presgl
(4F)
sei
(5F)
occscore†
(6F)
presgl
tMigratedToNorth
0.319
(1.056)
152.0**
(65.38)
0.481
(0.623)
0.623
(0.895)
169.9***
(65.42)
0.666
(0.623)
tMigratedToSouth
-1.154
(1.832)
-68.86
(113.5)
-1.436*
(1.081)
-3.240**
(1.553)
-55.68
(113.6)
-1.322
(1.081)
gMigratesToNorth
6.290***
(1.492)
274.7***
(85.32)
4.859***
(0.996)
7.595***
(1.483)
133.3**
(77.50)
2.691***
(0.824)
1.042
(2.587)
-15.86
(147.9)
1.549
(1.728)
2.583
(2.572)
-88.10
(134.3)
0.474
(1.427)
tMarried
2.617***
(0.777)
128.6***
(47.47)
1.616***
(0.468)
2.161***
(0.675)
72.30*
(46.57)
0.758**
(0.453)
gMarries
1.216
(1.042)
102.0**
(61.21)
1.416**
(0.669)
2.141**
(0.986)
63.82
(57.47)
0.777*
(0.588)
gMigratesWithinSouth
Children
Years of education
Time, Age, Age^2, Constant
-1.306*** -65.49*** -0.940*** -1.464*** -29.15*** -0.393***
(0.0812)
(4.609)
(0.0546)
(0.0814)
(4.212)
(0.0454)
148.5*** 2.332***
(3.452)
(0.0375)
Incl***
Incl***
Incl***
Incl***
Incl***
Incl***
Observations
14,254
14,254
14,254
14,254
14,254
14,254
R-squared within
0.0154
0.0180
0.0295
0.0298
0.0180
0.0307
R-squared between
0.0602
0.0661
0.0626
0.0666
0.2597
0.3923
R-squared overall
0.0548
0.0597
0.0594
0.0632
0.2271
0.3579
ρ
0.7488
0.7189
0.8038
0.8174
0.6576
0.7127
Standard errors in parentheses. One-tailed significances indicated by asterisks: ***p<0.01, **p<0.05,
*p<0.1. †Occscore reported in 1970 dollars.
58
Taking regression (2F) as an example, the coefficient for gMigratesToNorth is
274.7. This term estimates the direction and size of selection into migration to the North:
it reveals that women who become migrants to the North possess unobserved traits that
cause them to earn $274.70 more per year on average than women who do not migrate to
the North, independent of any treatment effect of migration. The inclusion of
gMigratesToNorth corrects for the omission of these unseen traits from the specification,
which would otherwise give the estimated treatment effect (indicated with a leading t:
tMigratedToNorth) an upward bias. Specification (5F) is alike, but includes years of
education as an explanatory variable. The estimated impact of an additional year of
education is an income increase of $148.50 annually, significant at the 1% level.
Meanwhile, the estimate for gMigratesToNorth falls to 133.3, suggesting that
approximately half of all selection into migration to the North is associated with preexisting educational differences.
Migration. With regard to assessing the existence of returns to migration, the
estimates of interest are the treatment effects, the coefficients on tMigratedToNorth and
tMigratedWithinSouth. To establish context, I first discuss selection into migration. The
estimates for gMigratesToNorth in (1F) – (6F) are all large, positive, and statistically
significant. This is clear evidence that women of the era selected positively into migration
to the North. The unobserved qualities according to which this selection occurred are
positively correlated with both income and occupational prestige. However, in the case of
men, selection into migration to the North is only apparent in the regressions on status
variables, presgl and sei. While women who selected migration to the North tended to
59
exhibit above-average earnings and occupational prestige, men who selected into this
behavior exhibited above-average prestige but average earnings while working in the
South.
Selection into migration between Southern states has a different profile. Estimated
coefficients for gMigratesWithinSouth in regressions (1M) – (4M) and (6M) are
consistently large and significant, so migrant men within the South tended to come from
higher-earning, higher-status occupations than the average. In the case of regressions (1F)
– (6F), however, the same coefficients are not statistically distinct from zero, nor do they
suggest any consistent trend among women who migrate within the South.
To speculate, the differing gender patterns may reflect that in the late 1960s,
individual women were still less likely to participate in the labor force at all. Those with
the drive to work (an unobservable trait) may have foreseen better opportunities in the
North, while women who never intended to participate heavily in the formal labor force
would have had less incentive to move. Men, who did not opt out of the labor force at the
same level as women, also may not have faced as deep a dearth of gender-appropriate job
opportunities in the South as did women. As previously discussed, Southern incomes in
general and Southern black incomes in particular were rising during the period of this
study. It fits this context that men did not select into interregional migration on the basis
of their earning potential alone. That they continued to do so on the basis of prestige
likely reflects a distinction in some unobserved dynamic between the Northern and
Southern labor markets: potentially the types of jobs available in the two regions, or
community norms established through education. Specifications (5M) and (6M) support
60
this idea. In the case of (5M), the coefficient on years of education is rather large—
$78.20 additional annual income per additional year of education—and statistically
significant at the 1% level. Meanwhile, in (5M) and (6M), the coefficients of
gMigratesToNorth and gMigratesWithinSouth fall in value when compared to (2M) and
(3M), respectively, and the coefficient of gMigratesWithinSouth loses statistical
significance in (5M). This suggests that differences in education were a primary axis
along which selection occurred among men of the era. Recall, also, that changes in wages
across regions but within occupations are not visible in the data. It is possible that this
obscures positive selection into migration by earnings potential.
Turning to treatment effects, I see mixed evidence regarding returns to migration.
There is little evidence of significant status gain by men who move to the North in (3M),
(4M), and (6M). However, in (2M), the coefficient on tMigratedToNorth offers
substantial evidence that we should reject the null hypothesis of no returns to migration to
the North as measured by occscore, and conclude with high confidence that men did gain
income through occupational change associated with migration North. According to this
estimate, men that make such a move between 1965 and 1970 earn $193.00 more per
year, on average, than sedentary Southern men—a gain of over 5% of average 1970
income as measured by occscore. Although the (1M) estimate for tMigratedToNorth’s
impact on erscor50 is not statistically distinguishable from zero, the size of the estimate
alone suggests that migrants to the North may have earned more than non-migrants, all
else constant. There is, nevertheless, an inexorable possibility that this is random noise.
61
Figure 2. Relative Frequency Histograms for Income Measures, 1970
62
The countervailing postulations of upward bias due to the findings in MSG and
downward bias due to truncation of the intra-occupational wage distribution in the
dependent variables make it difficult to say definitively that this estimate is biased in one
direction or another. Figure 2 offers additional some perspective. The three histograms in
this figure illustrate the divergence between the actual distribution of wage income in
1970 in our sample and the distributions of the income measures intended to stand in for
the income distribution. Although neither index is an ideal proxy, the distribution of
occscore better recalls the actual distribution of income. Also, as noted in Chapter 3, the
variance of erscor50 is greater than that of actual income, even though they are meant to
represent the same distribution. In this light, especially given the strong evidence from
(2M), failing to reject the null hypothesis because of the large standard error in (1M)
looks overly cautious. Thus, even taking into account potential bias, the evidence that
men made a real gain by migrating north is rather strong.
We also have reasonable evidence that male migrants experienced a return to
migration within the South. The (2M) coefficient on tMigratedWithinSouth, in particular,
permits us to state with 95% confidence that these intraregional migrants increased their
earnings. We also see evidence at the 90% confidence level that such migrants saw a
return in sei-measured status in (4M). Given the assumption of educational attainment
invariance between 1965 and 1970 and the lack of evidence for prestige gains from
intraregional movement (as demonstrated by (3M) and (6M)), it seems likely that the
change in this hybrid measure of prestige, education, and income reflects the income
increase captured in (2M). The unwieldiness of this interpretation illustrates the
63
arbitrariness that has led some researchers to question the utility of composite indices like
sei.
Although (2F) shows good evidence of a return for women who migrate to the
North, the evidence for within-South migration differs significantly from the male results.
In (1F) – (6F), the coefficients on tMigratedWithinSouth, the estimates of the impact of
within-South migration, are consistently negative, though only statistically significant in
the case of the status measure regressions (3F) and (4F). Holding education levels
constant in regression (6F) causes this coefficient to lose statistical significance, though
in the unreported regression on sei that includes education as a regressor, the coefficient
retains its significance, and the value change relative to (4F) is insubstantial. While the
statistical power of these results appears to be rather weak, they suggest that intraregional
migration may have had a detrimental effect on women’s labor market outcomes in both
income and status terms. Considering that the modal woman’s occupation was a private
household worker, this may be related to the difficulty of re-establishing such an
occupation in a new community.
Marriage. The results suggest positive selection into marriage among both men
and women. In nearly all of the reported regressions, positive and significant coefficients
for gMarries indicate a higher starting point for earnings and occupational prestige
among those who marry than among those who do not. In the case of erscor50 among
women (i.e., (1F)) the coefficient does not achieve statistical significance, but it retains
the expected sign, showing the same trend as other specifications do. The introduction of
education as a regressor causes the relationship between gMarries and occscore among
64
women to lose statistical significance (5F), but this merely indicates that the bulk of
female selection into marriage is correlated with higher educational attainment.
What then of the returns (tMarried)? In the case of men, they are only significant
with regard to a single dependent variable, sei, and only weakly so (4M); otherwise the
coefficient is indistinguishable from zero. Unexpectedly, returns to marriage appear to be
stronger among women, though (2F) puts these returns at 5.4% of average 1970
income—far from the 20% found in other marriage premium research. Chapter 3.II also
discussed an important issue regarding the identification of the marriage treatment group:
since some marriages exist in the pre-treatment period, 1965, the estimated marriage
premium is partially confounded with the advantage of having been married for an
extended period. Nevertheless, the significant and large coefficients across all six
regressions strongly suggest that marrying has a positive relationship with income and
occupational prestige. This marriage premium needs not necessarily be an effect directly
caused by marriage—I cannot rule out a time-varying unobserved input akin to
Dougherty’s distributed fixed effect. It also bears note that losses associated with
childbearing overwhelm any marriage advantage after the second child in both genders.
Finer analysis of returns to marriage is the aim of the next section.
65
IV. Model Two: Complementarity of Migration Destination and Spousal Origin
Tables 11 and 12 report condensed results of model two estimates of selection and
treatment effects related to marriage, exogamous marriage, the varieties of migratory
behavior exhibited among those in our sample who lived in the North in 1965 or 1970 or
both, and the interactions of these marriage and migratory choices. Tables 13 and 14
report parallel regression results for the corresponding choices and interactions exhibited
among those who lived in the South in both 1965 and 1970. (I make the assumption,
surely imperfect, that individuals who lived in the South in both years did not migrate and
return during the intervening period.) Note that I report these regressions on the Northern
and Southern subsamples separately with clarity in mind; the large number of migratory
behaviors and interactions makes reporting the results of regressions involving all
possible migratory behaviors unwieldy. That said, estimating a single regression
including both Northern and Southern residents, with group indicators for each type of
migratory and all the corresponding marriage interactions that these indicators generate,
can yield the same results. I therefore discuss results in Tables 11 and 13 with the
understanding that all treatments are implicitly interacted with being in the Northern
migrant group; likewise, Table 12 and 14 results are implicitly interacted with
membership in the remains-in-South group. For example, the coefficients on tMarried in
specifications (7M) – (12M) estimate the treatment effect of marriage on a migrant to the
North, relative to Northern migrants who have not married.
66
Table 11. Model Two: Men in North (Condensed)
(7M)
(8M)
Some variables hidden
erscor50 occscore†
gReturnMigrant
-12.69** -765.2***
(6.816)
(322.1)
(9M)
presgl
-5.230*
(3.599)
(10M)
sei
-7.718*
(5.458)
(11M)
occscore†
-646.6**
(310.5)
(12M)
presgl
-3.374
(3.344)
gMigratesWithinNorth
9.953**
(4.872)
263.0
(230.3)
4.703**
(2.574)
7.891**
(3.904)
141.0
(222.0)
2.811
(2.391)
gMigratesWithinNorth
× gIntermarries
3.772
(3.899)
142.1
(184.2)
5.229***
(2.059)
7.681***
(3.122)
85.45
(177.6)
4.353**
(1.913)
tMarried
0.842
(0.897)
43.51
(40.54)
0.852**
(0.448)
1.607***
(0.651)
28.16
(40.22)
0.607*
(0.441)
tIntermarried
0.717
(1.551)
57.51
(69.96)
-0.0415
(0.773)
0.731
(1.121)
57.40
(69.50)
-0.0413
(0.762)
tMigratedWithinNorth
× tMarried
-1.799
(4.266)
-165.1
(190.4)
-0.405
(2.098)
-2.588
(3.018)
-171.4
(190.4)
-0.494
(2.097)
tMigratedWithinNorth
× tIntermarried
0.257
(3.219)
86.82
(143.7)
-0.245
(1.583)
-0.626
(2.278)
89.55
(143.7)
-0.208
(1.583)
tNewArrival
× tMarried
-0.480
(3.835)
-264.9*
(171.2)
0.965
(1.886)
2.398
(2.714)
-271.0*
(171.2)
0.879
(1.885)
15.82***
(5.135)
731.2***
(229.2)
5.533**
(2.526)
5.714*
(3.634)
728.0***
(229.2)
5.480**
(2.524)
89.35***
(3.479)
1.391***
(0.0373)
Incl***
Incl***
tNewArrival
× tIntermarried
Years of education
Time, Age, Age^2, Const
Incl***
Incl***
Incl***
Incl***
Observations
14,506
14,506
14,506
14,506
14,506
14,506
R-squared within
0.0437
0.0474
0.0512
0.0536
0.0478
0.0519
R-squared between
0.0189
0.0186
0.0211
0.0229
0.1002
0.1781
R-squared overall
0.0232
0.0232
0.0256
0.0272
0.0918
0.1585
ρ
0.6519
0.6892
0.6980
0.7280
0.6659
0.6506
See Table 15 for full results. Standard errors in parentheses. One-tailed significances indicated by
asterisks: ***p<0.01, **p<0.05, *p<0.1. †Occscore reported in 1970 dollars.
The central hypothesis motivating these estimates is the existence of a
complementarity: an intermarriage premium conditional on new arrival in the North. The
coefficient estimates for tNewArrival × tIntermarried in specifications (7M) – (12M)
67
provide strong evidence for such a complementarity among men: recent migrants who
intermarry experience greater earnings both relative to recent migrants who do not
intermarry, and relative to long-term migrants to the North who intermarry. Based on the
estimated coefficient in (9M), we reject the null hypothesis of no interaction effect on
occupational prestige at the 5% significance level. The evidence is also statistically and
practically large in its income effects. By intermarrying, a recent arrival makes a
significant income gain compared to a recent arrival who marries endogamously:
regression (7M) indicates a relative 16.537 percentile point climb in the income
distribution, and (8M) indicates a $788.71 relative annual income increase. The same
recent arrival who intermarries experiences significantly higher income gains attributable
to the marriage than does a long-term migrant to the North who marries a Northern
spouse: (7M) indicates a 15.340 percentile point gain, and (8M) indicates a $466.30
annual gain. Thus we see a consistent positive complementarity expressed across all four
dependent variables. In contrast, intermarriage alone does not have a significant effect.
Chapter 2.I proposes that such a result might be explained by an unobserved
input, or by a social capital effect. Other results of these regressions do not prove
sufficient to disentangle these competing hypotheses, but they facilitate exploration of
both. It is certainly possible that some unobserved personal trait correlated with both
recent arrival and spousal choice is expressing itself via this interaction term: the
unobserved input hypothesis. This premise becomes especially plausible if we can
conceive of such a trait that seems likely to vary at the individual level over time. One
possibility is whether an individual lives in an urban or rural area. Those who move from
68
a rural area to an urban area during the study period are quite possibly moving from
South to North, and are also likely to be increasing their prospects for exogamous
marriage. Indeed, urban status is an important and standard income determinant, which I
would have included in the model if 1965 values were available. Consider, however, that
Southern migrants to the North are highly likely to settle in urban destinations. Since
urbanization is presumably very common in the sample, but the combination of new
arrival and intermarriage is rare, it seems unlikely that the strong effect of the interaction
of these behaviors is attributable primarily to urbanization. A beauty explanation in the
vein of Hamermesh and Biddle might fit, but the data needed to test it is in this case
essentially uncollectable. We are left with the undeniable possibility of an explanatory
unobserved input, but no persuasive hypothesis concerning what it might be.
The hypothesis that intermarriage constitutes valuable social capital is also at least
quite plausible given these results. We can ask what sort of social capital mechanism this
might be: Access to locally specific labor market information? Alternatively, general
socialization relevant to navigating the Northern labor market? Examples of the more
general sort might include learning how to engage in signaling games with employers or
shedding a Southern accent.
Some of the estimated terms can inform our attempt to distinguish between these
possibilities. Consider that longer-term migrants who move from place to place within
the North during the study period might be thought good candidates for the local
employer information access effect. We might then hypothesize that the interaction
tMigratedWithinNorth × tIntermarried will be significant, but this hypothesis is not
69
supported. We do not have information, though, on the relative timing of the marriages
and the within-region moves, so we cannot reject the local information effect.
Now, consider the concept of general Northern socialization, and note that the
coefficients for gMigratesWithinNorth and gMigratesWithinNorth × gIntermarries
suggest positive selection into these behaviors. One might believe that the positively
selected traits work as substitutes for the social capital benefit bestowed by intermarriage,
negating the further impact of intermarriage and explaining the insignificant treatment
effect tMigratedWithinNorth × tIntermarried. The personal traits associated with
earnings potential that are also associated with a propensity to move within the North and
intermarry are probably social skills general to the North, indirectly providing support for
the Northern socialization effect.
Table 12 offers an additional perspective on this question. In these results for the
subgroup remaining in the South, intermarriage has a weakly significant treatment effect
even for non-migrants. Further, the complementarity between within-South migration
and exogamous marriage is even more pronounced than the complementarity between
intermarriage and new arrival in the North. This might fit with a social capital mechanism
related to general socialization—not socialization into the Northern labor market,
specifically, but perhaps into a more geographically widespread, high-earning labor
market segment. This argument is weaker than it first appears, though, because—unlike
the Northern case—the interaction of migrating within the South and marrying
exogamously is almost certainly expressing some of the impact of the omitted
urbanization variable. The extent to which this biases the interaction effect is potentially
70
large. We are thus left with a spectrum of potential explanations for the presence of an
intermarriage premium particular to certain migrant groups, but no clear correct
explanation.
Table 12. Model Two: Men in South (Condensed)
(13M)
(14M)
Some variables hidden
erscor50 occscore†
gMarries
6.925*** 365.3***
(0.897)
(41.01)
(15M)
presgl
2.591***
(0.449)
(16M)
sei
3.551***
(0.634)
(17M)
occscore†
294.7***
(39.75)
(18M)
presgl
1.658***
(0.429)
gIntermarries
-1.764
(5.102)
310.4*
(230.8)
4.210**
(2.549)
12.70***
(3.435)
203.7
(227.4)
2.852
(2.495)
tMarried
0.533
(0.604)
30.46
(27.24)
0.232
(0.302)
0.630*
(0.400)
9.410
(27.02)
-0.0229
(0.298)
tIntermarried
11.08**
(4.924)
313.0*
(221.7)
1.750
(2.457)
-2.282
(3.238)
332.9*
(220.2)
1.978
(2.431)
tMigratedWithinSouth
× tIntermarried
24.63***
(7.997)
1,090***
(358.6)
12.61***
(3.985)
15.79***
(5.174)
1,093***
(358.6)
12.66***
(3.985)
77.74***
(2.445)
1.008***
(0.0261)
Incl***
Incl***
Years of education
Time, Age, Age^2, Const
Incl***
Incl***
Incl***
Incl***
Observations
20,574
20,574
20,574
20,574
20,574
20,574
R-squared within
0.0359
0.0388
0.0376
0.0408
0.0391
0.0375
R-squared between
0.0202
0.0298
0.0165
0.0255
0.1161
0.1414
R-squared overall
0.0228
0.0311
0.0200
0.0275
0.1043
0.1240
ρ
0.6659
0.6951
0.6702
0.7290
0.6703
0.6311
See Table 16 for full results. Standard errors in parentheses. One-tailed significances indicated by
asterisks: ***p<0.01, **p<0.05, *p<0.1. †Occscore reported in 1970 dollars.
It is evident, inspecting the coefficients of gReturnMigrant among men (Table
11), that there is significant negative selection into return migration. This suits our
standard expectations regarding selection into migration. Therefore, the wide time spread
of our data points may cause some of the estimates given here to have an upward bias.
71
Nevertheless, the coefficients of primary concern are quite large, so the evidence for the
fundamental result is still strong.
Table 13. Model Two: Women in North (Condensed)
(7F)
(8F)
(9F)
Some variables hidden
erscor50 occscore†
presgl
gReturnMigrant
-1.769
-266.7
-9.288**
(6.239)
(367.8)
(4.268)
gIntermarries
(10F)
sei
-14.37**
(6.408)
(11F)
occscore†
-146.5
(343.7)
(12F)
presgl
-7.365**
(3.702)
6.525***
(2.422)
391.1***
(137.7)
4.668***
(1.556)
7.779***
(2.285)
254.9**
(134.8)
2.311*
(1.488)
gNewArrival
× gIntermarries
1.469
(6.478)
55.83
(381.7)
0.302
(4.427)
0.523
(6.645)
8.617
(356.9)
-0.462
(3.845)
tMarried
0.855
(1.087)
32.82
(61.40)
2.314***
(0.690)
2.590***
(1.009)
15.03
(60.58)
1.949***
(0.671)
tIntermarried
-1.231
(2.354)
-165.2
(132.9)
-0.350
(1.493)
-1.179
(2.182)
-166.8
(131.3)
-0.216
(1.456)
tNewArrival
× tIntermarried
-2.714
(5.266)
-96.88
(294.6)
-1.946
(3.290)
-0.449
(4.786)
-84.73
(294.5)
-1.766
(3.288)
149.1***
(5.144)
2.411***
(0.0550)
Incl***
Incl***
Years of education
Time, Age, Age^2, Const
Incl***
Incl***
Incl***
Incl***
Observations
9,580
9,580
9,580
9,580
9,580
9,580
R-squared within
0.0190
0.0183
0.0577
0.0615
0.0189
0.0574
R-squared between
0.0517
0.0647
0.0614
0.0678
0.2041
0.3310
R-squared overall
0.0464
0.0580
0.0609
0.0670
0.1775
0.2932
ρ
0.6677
0.7006
0.7229
0.7398
0.6574
0.6316
See Table 17 for full results. Standard errors in parentheses. One-tailed significances indicated by
asterisks: ***p<0.01, **p<0.05, *p<0.1. †Occscore reported in 1970 dollars.
The results in Table 13 do not show evidence for a similar intermarriage-new
arrival complementarity among women, as the coefficients for tNewArrival ×
tIntermarried are consistently negative yet statistically insignificant. Neither does Table
14, pertaining to women remaining in the South, indicate any particular advantage to
72
exogamous marriage in the South, as we find for men. Women exhibit strong positive
selection into exogamous marriage to Northerners, but selection into the interaction of the
behaviors is neutral.
Table 14. Model Two: Women in South (Condensed)
(13F)
(14F)
(15F)
Some variables hidden
erscor50 occscore†
presgl
gMarries
0.620
67.06
1.106*
(1.076)
(63.58)
(0.689)
(16F)
sei
1.726**
(1.014)
(17F)
occscore†
41.36
(59.83)
(18F)
presgl
0.617
(0.608)
gIntermarries
11.36**
(5.187)
452.5*
(312.0)
10.62***
(3.235)
15.32***
(4.725)
93.79
(299.8)
5.720**
(2.977)
tMarried
3.045***
(0.806)
154.9***
(49.47)
1.758***
(0.485)
2.387***
(0.700)
93.91**
(48.57)
0.886**
(0.470)
tIntermarried
-3.544
(4.719)
-10.13
(290.9)
-4.250*
(2.823)
-2.087
(4.070)
39.21
(286.7)
-4.236*
(2.763)
tMigratedWithinSouth
× tMarried
-0.0560
(5.436)
95.97
(338.7)
2.154
(3.212)
2.057
(4.617)
86.74
(339.0)
2.049
(3.211)
tMigratedWithinSouth
× tIntermarried
3.265
(10.59)
222.2
(659.5)
8.688*
(6.261)
-1.556
(9.001)
233.8
(659.7)
9.191*
(6.252)
147.5***
(3.523)
2.300***
(0.0380)
Incl***
Incl***
Education
Time, Age, Age^2, Const
Incl***
Incl***
Incl***
Incl***
Observations
13,858
13,858
13,858
13,858
13,858
13,858
R-squared within
0.0163
0.0180
0.0296
0.0298
0.0178
0.0305
R-squared between
0.0564
0.0617
0.0587
0.0634
0.2524
0.3836
R-squared overall
0.0516
0.0559
0.0560
0.0604
0.2214
0.3504
ρ
0.7547
0.7256
0.8071
0.8200
0.6669
0.7198
See Table 18 for full results. Standard errors in parentheses. One-tailed significances indicated by
asterisks: ***p<0.01, **p<0.05, *p<0.1. †Occscore reported in 1970 dollars.
One final observation relates to the earlier general marriage premium hypothesis.
Table 14 confirms the existence of a general marriage premium for women, as found
earlier, among those remaining in the South. The premium appears more weakly among
women in the North, and only appears in relation to prestige outcomes, not income (Table
73
13). Apparently, the women’s marriage premium is predominantly a Southern
phenomenon.
Tables 11, 12, 13, and 14 present the results of the regressions discussed above in
condensed form, for clarity. Tables 15, 16, 17, and 18 report full results tables for these
same regressions. These results reveal various other significant effects, but with less
apparent bearing on the questions this study aims to answer. Finding explanations for
some of these effects—such as an apparent complementarity experienced by women
between within-North migration and marriage to any spouse—will require additional
data. I relegate these complete tables to Appendix A for the sake of organization and
clarity.
74
CHAPTER 5
CONCLUSION
I. Findings
This paper uses publicly available 1970 IPUMS-USA data to consider questions
about the relationship between internal migration, marriage, and labor market outcomes
for Southern-born African Americans in the late 1960s. The tables in Chapter 4 and in
Appendix A report the results of several mixed-model regressions with DD variables and
a random effects error structure designed to illuminate these dynamics. Tables 11 and 13
provide evidence of a modest positive return to migration to the North for both genders.
These returns exist over and above positive selection into migration, which appears to be
associated particularly strongly with education and social prestige, rather than earnings
potential alone. Men also experienced a return to migration within the South, but returns
to women for such migration were nonexistent or weakly negative. Tables 11 and 13 also
indicate that only women experience a general marriage premium, although bearing more
than one child erases the gain. By comparing Tables 13 and 14, we see that this marriage
premium is mostly a Southern phenomenon. Tables 11 and 12 show that for men, there is
a strong complementarity between exogamy and recent migration—whether within the
South or to the North. Women exhibit positive selection into exogamy, but they do not
measurably benefit from this complementarity.
75
II. Discussion
The origin of the idea of 1970 as the end of the Great Migration lies in the
reversal of net migration between the North and South in that year, as Figure 1 depicts.
These findings, though, demonstrate that particular groups of black migrants—women
who remain in the labor force, for instance—continued to benefit from migration at least
until 1970. Further, these groups clearly exhibit self-selection, indicating the exercise of
the liberty to choose their residential location as a means of putting their specific human
capital to more efficient use. In contrast, the black population largely did not use
geographic relocation in this manner prior to the twentieth century, as shown by the
remarkably stable concentration of the African-American population in the South before
World War I. There is no reason to believe, though, that in 1970 this population ceased to
continue using migration in a deliberate, economically self-interested manner. Indeed, if
they had, the continuing convergence of income by race through the 1970s would be hard
to explain. The net flow reversal, then, does not reflect some retreat from the change that
occurred within the black population earlier in the century, but rather reflects an ongoing
realignment of economic and social incentives. What the post-1970 incentives are is not
the focus of this paper, though we have considered the rise of racial issues in Northern
cities as one example. Other ideas are not hard to come by: the growing preference for
Sun Belt suburban living is one. What the reversal does not represent is re-entry into the
low-wage manual farm economy of the Southern past. If the 1940-1970 era represents the
first nationwide labor market integration, one aspect of this integration is the AfricanAmerican population’s attainment of some measure of geographic self-agency—a self-
76
agency previously withheld, but already widespread in other parts of the American
workforce. The end of the Great Migration, then, is not the end of economically selected
black migration, but the end of a particular historical alignment of conditions that
generated a mass unified wave from South to North.
In spite of the concurrent rise of the New South, this study also shows specific
persistent differences in economic opportunity in the North and the South. This is
especially clear in the case of women. The IPUMS-USA data reveals that the modal
black Southern woman of 1965 spent the following five years working in a private
household setting. Establishing social status within the community appears to have been
economically valuable to this woman: marriage was rewarded with increased income,
while moving to a new Southern destination was potentially penalized. In contrast,
women with particularly high levels of human capital moved to the North and adopted
new occupations. In this new setting, those with the highest human capital were more
likely to intermarry, but social establishment through marriage was not demonstrably
valued in the labor market. These are some of the particular incentive structures under
which individuals optimized via migratory choice.
The findings of this study also include strong evidence of an intermarriage
premium awarded to migrant men—one curiously strong in the South as well as the
North. Developing an identification strategy that can either demonstrate or rule out the
hypothesis that an unobserved input is responsible is a challenge for research that builds
on this effort. Urbanization is a strong candidate to be this unobserved input in the case of
within-South migrants who intermarry, but it is perhaps less likely to explain the
77
intermarriage premium of new arrivals in the North. Another unobserved input could
quite possibly explain the phenomenon in the North.
Alternatively, intermarriage might be a form of social capital that benefits
migrants undergoing economic assimilation. If we accept the premises of a dual labor
market and segmented assimilation model, we can see intermarriage as social capital that
increases the likelihood that a new arrival will assimilate into the mainstream labor
market, rather than downwardly assimilate. Further, if future research rejects the
unobserved input hypothesis in the case of the Southern intermarriage premium, we
might take this as evidence of a national high-wage labor market segment that select
black men manage to assimilate into by learning generally valuable conventions through
exogamy. On the other hand, if urbanization does appear to explain the Southern
premium, then the assimilation value of exogamy in the North might relate to the transfer
of more locally specific information.
These results strongly suggest productive avenues for future research aimed at
identifying the shift in national dynamics that occurred in the 1970s, and at understanding
the divergent opportunities for women in the North and South. Perhaps most of all, they
suggest the value of continuing effort to distinguish between the various candidate
explanations of the intermarriage premium. An answer to that question might have
concrete implications for designing migration policy in a way that promotes mobility and
decreased social segregation.
Methodologically, this study demonstrates that accepted findings of the literature
on the Great Migration are sensitive to econometric specification, and that improved
78
research design may change some of the accepted facts about the trajectory of the
migration. There is undoubtedly further room for improvement. Linking Census records
across decades would improve upon this study by permitting the extension of this
approach to other decades, making observations of metropolitan status available,
permitting use of directly reported income data, and capturing the evolution of the
phenomena of interest over a longer time span.
79
APPENDIX A
Complete results tables for model two
Table 15. Model Two: Men in North (Full)
(7M)
(8M)
erscor50 occscore†
gNewArrival
-1.767
-388.2**
(4.511)
(213.2)
(9M)
presgl
-1.181
(2.383)
(10M)
sei
-5.556*
(3.614)
(11M)
occscore†
-296.4*
(205.6)
(12M)
presgl
0.267
(2.214)
gReturnMigrant
-12.69**
(6.816)
765.2***
(322.1)
-5.230*
(3.599)
-7.718*
(5.458)
-646.6**
(310.5)
-3.374
(3.344)
gMigratesWithinNorth
9.953**
(4.872)
263.0
(230.3)
4.703**
(2.574)
7.891**
(3.904)
141.0
(222.0)
2.811
(2.391)
gMarries
4.924***
(1.336)
202.7***
(62.50)
0.422
(0.697)
-1.666*
(1.047)
196.6***
(60.64)
0.330
(0.656)
gIntermarries
1.167
(1.599)
48.20
(72.81)
1.439**
(0.806)
1.958**
(1.181)
-16.86
(71.93)
0.424
(0.786)
gMigratesWithinNorth
× gMarries
-7.682*
(5.214)
-160.7
(246.5)
-4.147*
(2.754)
-5.927*
(4.178)
-60.10
(237.6)
-2.587
(2.559)
gMigratesWithinNorth
× gIntermarries
3.772
(3.899)
142.1
(184.2)
5.229***
(2.059)
7.681***
(3.122)
85.45
(177.6)
4.353**
(1.913)
gReturnMigrant
× gMarries
10.97*
(7.235)
647.2**
(341.9)
5.853*
(3.821)
8.534*
(5.794)
521.5*
(329.7)
3.889
(3.550)
gReturnMigrant
× gIntermarries
-15.23**
(8.814)
-777.9**
(416.4)
-2.762
(4.653)
-3.485
(7.056)
-835.3**
(401.5)
-3.655
(4.324)
gNewArrival
× gMarries
-0.656
(4.708)
118.0
(222.5)
1.627
(2.487)
5.174*
(3.772)
29.51
(214.5)
0.240
(2.310)
gNewArrival
× gIntermarries
-7.336
(6.172)
-417.3*
(291.5)
-3.941
(3.258)
-3.620
(4.939)
-343.4
(281.2)
-2.786
(3.028)
-0.00195
(3.674)
386.6***
(164.0)
-2.451*
(1.807)
-3.351*
(2.600)
399.6***
(164.0)
-2.266
(1.806)
tReturnMigrant
-2.128
(5.631)
57.08
(251.4)
-1.575
(2.769)
-2.498
(3.985)
66.97
(251.3)
-1.434
(2.768)
tMigratedWithinNorth
1.837
(3.988)
230.8*
(178.0)
1.446
(1.961)
5.256**
(2.822)
240.4*
(178.0)
1.583
(1.960)
tNewArrival
80
Table 15. Model Two: Men in North (Full) continued
(7M)
(8M)
(9M)
erscor50 occscore†
presgl
(10M)
sei
(11M)
occscore†
(12M)
presgl
tMarried
0.842
(0.897)
43.51
(40.54)
0.852**
(0.448)
1.607***
(0.651)
28.16
(40.22)
0.607*
(0.441)
tIntermarried
0.717
(1.551)
57.51
(69.96)
-0.0415
(0.773)
0.731
(1.121)
57.40
(69.50)
-0.0413
(0.762)
tMigratedWithinNorth
× tMarried
-1.799
(4.266)
-165.1
(190.4)
-0.405
(2.098)
-2.588
(3.018)
-171.4
(190.4)
-0.494
(2.097)
tMigratedWithinNorth
× tIntermarried
0.257
(3.219)
86.82
(143.7)
-0.245
(1.583)
-0.626
(2.278)
89.55
(143.7)
-0.208
(1.583)
tReturnMigrant
× tMarried
-0.993
(5.978)
-253.2
(266.9)
0.464
(2.940)
-0.433
(4.230)
-259.4
(266.8)
0.376
(2.939)
tReturnMigrant
× tIntermarried
11.34*
(7.334)
599.6**
(327.4)
4.516
(3.607)
6.115
(5.189)
595.0**
(327.3)
4.449
(3.605)
tNewArrival
× tMarried
-0.480
(3.835)
-264.9*
(171.2)
0.965
(1.886)
2.398
(2.714)
-271.0*
(171.2)
0.879
(1.885)
tNewArrival
× tIntermarried
15.82***
(5.135)
731.2***
(229.2)
5.533**
(2.526)
5.714*
(3.634)
728.0***
(229.2)
5.480**
(2.524)
Children
-0.204**
(0.0995)
-11.7***
(4.732)
-0.26***
(0.0530)
-0.49***
(0.0807)
-0.170
(4.567)
-0.0781*
(0.0490)
89.35***
(3.479)
1.391***
(0.0373)
Incl***
Incl***
Years of education
Time, Age, Age^2, Const
Incl***
Incl***
Incl***
Incl***
Observations
14,506
14,506
14,506
14,506
14,506
14,506
R-squared within
0.0437
0.0474
0.0512
0.0536
0.0478
0.0519
R-squared between
0.0189
0.0186
0.0211
0.0229
0.1002
0.1781
R-squared overall
0.0232
0.0232
0.0256
0.0272
0.0918
0.1585
ρ
0.6519
0.6892
0.6980
0.7280
0.6659
0.6506
Standard errors in parentheses. One-tailed significances indicated by asterisks: ***p<0.01, **p<0.05,
*p<0.1. †Occscore reported in 1970 dollars.
81
Table 16. Model Two: Men in South (Full)
(13M)
(14M)
erscor50 occscore†
gMigratesWithinSouth
3.273
191.4
(8.151)
(374.2)
gMarries
(15M)
presgl
5.307*
(4.089)
(16M)
sei
7.147
(5.857)
(17M)
occscore†
-109.3
(360.0)
(18M)
presgl
1.399
(3.867)
6.925***
(0.897)
365.3***
(41.01)
2.591***
(0.449)
3.551***
(0.634)
294.7***
(39.75)
1.658***
(0.429)
gIntermarries
-1.764
(5.102)
310.4*
(230.8)
4.210**
(2.549)
12.70***
(3.435)
203.7
(227.4)
2.852
(2.495)
gMigratesWithinSouth
× gMarries
1.130
(8.355)
-80.89
(383.6)
-3.138
(4.191)
-4.910
(6.003)
183.1
(369.0)
0.292
(3.963)
gMigratesWithinSouth
× gIntermarries
-6.620
(10.05)
750.3*
(461.6)
-0.731
(5.043)
-2.164
(7.228)
650.4*
(443.8)
-2.029
(4.766)
tMigratedWithinSouth
-11.08**
(6.643)
-543.2**
(297.9)
-0.342
(3.310)
-0.438
(4.298)
-539.6**
(297.8)
-0.285
(3.310)
tMarried
0.533
(0.604)
30.46
(27.24)
0.232
(0.302)
0.630*
(0.400)
9.410
(27.02)
-0.0229
(0.298)
tIntermarried
11.08**
(4.924)
313.0*
(221.7)
1.750
(2.457)
-2.282
(3.238)
332.9*
(220.2)
1.978
(2.431)
tMigratedWithinSouth
× tMarried
11.15*
(6.806)
651.7**
(305.2)
0.0681
(3.392)
1.341
(4.404)
648.9**
(305.2)
0.0234
(3.392)
tMigratedWithinSouth
× tIntermarried
24.63***
(7.997)
1,090***
(358.6)
12.61***
(3.985)
15.79***
(5.174)
1,093***
(358.6)
12.66***
(3.985)
Children
-0.35***
(0.0660)
-25.7***
(3.040)
-0.22***
(0.0331)
-0.54***
(0.0480)
-11.2***
(2.943)
-0.0289
(0.0314)
77.74***
(2.445)
1.008***
(0.0261)
Incl***
Incl***
Years of education
Time, Age, Age^2, Const
Incl***
Incl***
Incl***
Incl***
Observations
20,574
20,574
20,574
20,574
20,574
20,574
R-squared within
0.0359
0.0388
0.0376
0.0408
0.0391
0.0375
R-squared between
0.0202
0.0298
0.0165
0.0255
0.1161
0.1414
R-squared overall
0.0228
0.0311
0.0200
0.0275
0.1043
0.1240
ρ
0.6659
0.6951
0.6702
0.7290
0.6703
0.6311
Standard errors in parentheses. One-tailed significances indicated by asterisks: ***p<0.01, **p<0.05,
*p<0.1. †Occscore reported in 1970 dollars.
82
Table 17. Model Two: Women in North (Full)
(7F)
(8F)
erscor50 occscore†
gNewArrival
-2.292
-321.7*
(3.682)
(217.1)
(9F)
presgl
-1.648
(2.519)
(10F)
sei
-0.534
(3.783)
(11F)
occscore†
-218.4
(202.8)
(12F)
presgl
-0.0271
(2.184)
gReturnMigrant
-1.769
(6.239)
-266.7
(367.8)
-9.288**
(4.268)
-14.37**
(6.408)
-146.5
(343.7)
-7.365**
(3.702)
gMigratesWithinNorth
7.481*
(4.963)
315.3
(292.6)
5.914**
(3.395)
8.094*
(5.098)
447.9*
(273.4)
8.028***
(2.944)
gMarries
0.453
(1.372)
72.83
(79.40)
-1.481*
(0.909)
-2.445**
(1.350)
99.36*
(76.00)
-0.964
(0.829)
6.525***
(2.422)
391.1***
(137.7)
4.668***
(1.556)
7.779***
(2.285)
254.9**
(134.8)
2.311*
(1.488)
gMigratesWithinNorth
× gMarries
-6.000
(5.305)
-231.6
(312.7)
-4.201
(3.629)
-5.516
(5.449)
-503.9**
(292.3)
-8.589***
(3.148)
gMigratesWithinNorth
× gIntermarries
-3.356
(4.936)
-52.33
(291.0)
-4.074
(3.377)
-2.160
(5.070)
56.08
(271.9)
-2.305
(2.928)
gReturnMigrant
× gMarries
0.522
(6.708)
64.97
(395.4)
8.627**
(4.589)
14.47**
(6.889)
-61.24
(369.5)
6.572**
(3.979)
gReturnMigrant
× gIntermarries
23.95**
(11.58)
582.4
(682.4)
14.02**
(7.918)
16.04*
(11.89)
-32.48
(638.0)
4.087
(6.872)
gNewArrival
× gMarries
4.238
(4.002)
173.0
(235.9)
4.295*
(2.738)
4.000
(4.112)
3.505
(220.5)
1.581
(2.374)
gNewArrival
× gIntermarries
1.469
(6.478)
55.83
(381.7)
0.302
(4.427)
0.523
(6.645)
8.617
(356.9)
-0.462
(3.845)
tNewArrival
0.214
(2.929)
59.02
(163.9)
1.228
(1.830)
-1.610
(2.662)
77.78
(163.8)
1.583
(1.829)
tReturnMigrant
-2.773
(5.046)
-169.2
(282.3)
1.735
(3.151)
5.798
(4.585)
-156.2
(282.2)
1.982
(3.152)
tMigratedWithinNorth
-6.543*
(3.995)
-457.5**
(223.5)
-3.348*
(2.495)
10.59***
(3.630)
-442.7**
(223.4)
-3.069
(2.495)
tMarried
0.855
(1.087)
32.82
(61.40)
2.314***
(0.690)
2.590***
(1.009)
15.03
(60.58)
1.949***
(0.671)
tIntermarried
-1.231
(2.354)
-165.2
(132.9)
-0.350
(1.493)
-1.179
(2.182)
-166.8
(131.3)
-0.216
(1.456)
tMigratedWithinNorth
× tMarried
8.344**
(4.264)
555.0***
(238.6)
5.471**
(2.663)
13.20***
(3.874)
545.8**
(238.5)
5.306**
(2.664)
gIntermarries
83
Table 17. Model Two: Women in North (Full) continued
(7F)
(8F)
(9F)
erscor50 occscore†
presgl
(10F)
sei
(11F)
occscore†
(12F)
presgl
tMigratedWithinNorth
× tIntermarried
-0.859
(3.971)
-51.84
(222.2)
-0.0661
(2.480)
1.731
(3.608)
-45.43
(222.1)
0.0251
(2.480)
tReturnMigrant
× tMarried
0.239
(5.421)
-92.14
(303.3)
-1.432
(3.385)
-7.860*
(4.925)
-96.79
(303.1)
-1.506
(3.386)
tReturnMigrant
× tIntermarried
2.708
(9.412)
515.8
(526.6)
-1.907
(5.878)
-0.130
(8.551)
522.8
(526.4)
-1.789
(5.880)
tNewArrival
× tMarried
-0.105
(3.181)
62.43
(178.0)
-1.819
(1.987)
0.975
(2.891)
51.08
(177.9)
-2.020
(1.987)
tNewArrival
× tIntermarried
-2.714
(5.266)
-96.88
(294.6)
-1.946
(3.290)
-0.449
(4.786)
-84.73
(294.5)
-1.766
(3.288)
-0.78***
(0.122)
-38.5***
(7.238)
-0.60***
(0.0844)
-1.03***
(0.127)
-15.9***
(6.744)
-0.24***
(0.0722)
149.1***
2.411***
(5.144)
(0.0550)
Incl***
Incl***
Children
Years of education
Time, Age, Age^2, Const
Incl***
Incl***
Incl***
Incl***
Observations
9,580
9,580
9,580
9,580
9,580
9,580
R-squared within
0.0190
0.0183
0.0577
0.0615
0.0189
0.0574
R-squared between
0.0517
0.0647
0.0614
0.0678
0.2041
0.3310
R-squared overall
0.0464
0.0580
0.0609
0.0670
0.1775
0.2932
ρ
0.6677
0.7006
0.7229
0.7398
0.6574
0.6316
Standard errors in parentheses. One-tailed significances indicated by asterisks: ***p<0.01, **p<0.05,
*p<0.1. †Occscore reported in 1970 dollars.
84
Table 18. Model Two: Women in South (Full)
(13F)
(14F)
erscor50 occscore†
gMigratesWithinSouth
2.264
-70.66
(7.281)
(418.2)
(15F)
presgl
0.308
(4.851)
(16F)
sei
2.486
(7.217)
(17F)
occscore†
-202.4
(380.1)
(18F)
presgl
-1.650
(4.023)
gMarries
0.620
(1.076)
67.06
(63.58)
1.106*
(0.689)
1.726**
(1.014)
41.36
(59.83)
0.617
(0.608)
gIntermarries
11.36**
(5.187)
452.5*
(312.0)
10.62***
(3.235)
15.32***
(4.725)
93.79
(299.8)
5.720**
(2.977)
gMigratesWithinSouth
× gMarries
-1.625
(7.799)
60.23
(448.0)
1.197
(5.196)
-0.361
(7.731)
131.8
(407.2)
2.275
(4.309)
gMigratesWithinSouth
× gIntermarries
0.270
(15.15)
-177.7
(871.2)
0.569
(10.09)
3.878
(15.00)
-29.83
(792.8)
2.622
(8.379)
tMigratedWithinSouth
-1.191
(5.078)
-162.2
(316.4)
-3.543
(3.000)
-4.980
(4.312)
-142.0
(316.7)
-3.355
(2.999)
3.045***
(0.806)
154.9***
(49.47)
1.758***
(0.485)
2.387***
(0.700)
93.91**
(48.57)
0.886**
(0.470)
tIntermarried
-3.544
(4.719)
-10.13
(290.9)
-4.250*
(2.823)
-2.087
(4.070)
39.21
(286.7)
-4.236*
(2.763)
tMigratedWithinSouth
× tMarried
-0.0560
(5.436)
95.97
(338.7)
2.154
(3.212)
2.057
(4.617)
86.74
(339.0)
2.049
(3.211)
tMigratedWithinSouth
× tIntermarried
3.265
(10.59)
222.2
(659.5)
8.688*
(6.261)
-1.556
(9.001)
233.8
(659.7)
9.191*
(6.252)
-1.31***
(0.0819)
-66.1***
(4.668)
-0.94***
(0.0549)
-1.46***
(0.0817)
-30.3***
(4.274)
-0.403***
(0.0459)
147.5***
(3.523)
2.300***
(0.0380)
Incl***
Incl***
tMarried
Children
Education
Time, Age, Age^2, Const
Incl***
Incl***
Incl***
Incl***
Observations
13,858
13,858
13,858
13,858
13,858
13,858
R-squared within
0.0163
0.0180
0.0296
0.0298
0.0178
0.0305
R-squared between
0.0564
0.0617
0.0587
0.0634
0.2524
0.3836
R-squared overall
0.0516
0.0559
0.0560
0.0604
0.2214
0.3504
ρ
0.7547
0.7256
0.8071
0.8200
0.6669
0.7198
Notes: Standard errors in parentheses. One-tailed significances indicated by asterisks: ***p<0.01,
**p<0.05, *p<0.1. †Occscore reported in 1970 dollars.
85
APPENDIX B
Notes toward controlling for relative changes in state income
Existing work on the Great Migration generally uses direct reports of income to
estimate returns to migration. Naturally, directly reported data more accurately reflect
individuals’ actual incomes than do the occupation-extrapolated proxies used in this
study. I use these proxies mainly because they are available in two time periods per
individual, but their use has a subtle consequence that precludes two pitfalls: (1) regional
price variation, and (2) endogeneity between income growth and migration flows.
The first issue is simpler. The proxy indices have no intra-occupational variance:
a carpenter in Alabama in 1965 is as well off as a carpenter in 1970 in Alabama or New
York—no better or worse. While this is certainly an oversimplification of reality, and
introduces a bias that we must reflect on, it also means that cost-of-living adjustments are
superfluous. Other studies using actual income observations introduce per-capita or
median state income as an explanatory variable, an imperfect but adequate correction.
The second problem has received no attention that I have encountered. Consider a
hypothetical difference-in-differences study that estimates returns to migration using real
income observations. Assume that some correction is adopted to solve the first problem.
The aim of DD estimation is to find the average effect of migration on individual income
growth, given that two similar individuals will experience similar income growth in the
absence of migration. This latter condition is a critical assumption of the DD model, but
arguably fails in the Great Migration context. Recall from Chapter 2 that a preponderance
86
of literature suggests that emigration and growth in the New South are endogenous. In
such a setting, the DD estimator is almost surely inconsistent.
I argue that in this case, the likely effect of the inconsistency is to bias the DD
estimator downward. Suppose the population includes individuals m and n, similar in
every respect, except that m migrates to the North and n remains in the South. Over five
years, m and n each experience some amount of income growth: for simplicity’s sake, say
it is the same for both. Then the DD estimate of the return to m’s migration is the
difference between her income growth and n’s income growth: zero. Now suppose that
the same scenario occurs, but m does not migrate. The identifying DD counterfactual
assumption is that m’s income growth will now precisely match n’s, but due to
endogeneity, m’s decision to migrate in the first scenario increased n’s income in the
South by some amount that n will not gain now that m remains. The difference between
m and n’s income growth in the new scenario is still zero, but in fact, m’s income in the
new scenario grows less than it did in the first scenario. The DD estimate of m’s return to
migration is, therefore, too low; the endogeneity between m’s decision and n’s income
conceals the presence of the return.
This problem will corrupt the estimation of a reduced-form model like the one I
use with a large sample and real income dependent variables. If the number of migrants is
very large, as in the Great Migration case, we might consider the assumption that each
individual migrant’s marginal impact on Southern earnings is minimal. Making this
assumption about the average migrant’s impact, however, is not necessarily justifiable.
The estimate will consequently reflect something more like the marginal return to
87
migration than the average return. Also consider that the endogeneity bias will be similar
even in a simpler regression model like the one used in ETA. At the tail end of a largescale labor market shift, we should not necessarily be surprised to find that the marginal
migrant’s return is minimal—this simply suggests the labor market is approaching some
sort of equilibrium. However, it does not persuasively show that the average migrant
gained nothing by their move.
My reliance on proxy measures reduces the impact of this endogeneity problem.
As emigration from the South proceeded and made way for economic change, growth
came by at least two means: reduced labor supply led to increased wages, and industrial
reorganization led to occupational changes. Since occscore and erscor50 report the same
wage outcomes in 1965 as they do in 1970, endogenously generated wage changes are
not present in the data. The proxies thus control one significant source of bias, even
though some endogeneity may remain.
Disentangling endogenous occupational change is a difficult problem, but as far
as endogenous wage change, I propose a means of managing the issue in a study with the
same setup as mine, but relying on real income observations. This approach consists of
the construction of a new control variable
ΔOriginStateIncomei,t
=
OriginStateIncomei,t − OriginStateIncomei,t=0
OriginStateIncomei,t=0
that represents the proportional growth in per-capita income in the 1965 state of residence
of individual i from 1965 to the time of the present observation. This expression resolves
to zero whenever t = 0. When t = 1, it measures the proportional change between per
88
capita income in the state in 1965 and 1970. In a reduced-form DD model of income that
includes this variable and estimates returns to migration ceteris paribus, that estimate is
now made holding Southern income growth constant. Such an estimate will better reflect
the actual concept of interest: the income gain experienced by the average Northern
migrant in contrast to the counterfactual that the average Northern migrant had not
migrated.
89
Bibliography
Abramitzky, Ran, and Leah P. Boustan. “Immigration in American Economic History.”
NBER Working Paper 21882, 2016. http://www.nber.org/papers/w21882
Abramitzky, Ran, Leah P. Boustan, and Katherine Eriksson. “Cultural Assimilation
during the Age of Mass Migration.” NBER Working Paper No. 22381, 2016.
http://www.nber.org/papers/w22381
Ashenfelter, Orley, and David Card. “Using the Longitudinal Structure of Earnings to
Estimate the Effect of Training Programs.” The Review of Economics and
Statistics 67, no. 4 (1985): 648-660.
Atack, Jeremy, and Peter Passell. A New Economic View of American History. New
York: W.W. Norton, 1994.
Becker, Gary S. Human Capital. New York: Columbia University Press, 1964.
Bertrand, Marianne, and Sendhil Mullainathan. “Are Emily and Greg More Employable
Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination.”
The American Economic Review 94, no. 4 (2004): 991-1013.
Boustan, Leah P. “Competition in the Promised Land: Black Migration and Racial Wage
Convergence in the North, 1940-1970.” NBER Working Paper 13813, 2008.
http://www.nber.org.proxy.lib.csus.edu/papers/w13813
———. “Was Postwar Suburbanization ‘White Flight’? Evidence from the Black
Migration.” Quarterly Journal of Economics 125, no. 1 (2010): 417-443.
Borjas, George J. “Immigration and Self-Selection.” In Immigration, Trade, and the
Labor Market, edited by John M. Abowd and Richard B. Freeman. Chicago:
University of Chicago Press, 1991.
Chay, Kenneth, and Kaivan Munshi. “Black Networks after Emancipation: Evidence
from Reconstruction and the Great Migration.” Working paper, 2014.
http://www.cireqmontreal.com/wp-content/uploads/2014/11/chay.pdf
Chiswick, Barry R., and Paul W. Miller. “The Endogeneity between Language and
Earnings: International Analyses.” Journal of Labor Economics 13, no. 2 (1995):
246-288.
90
Coleman, James S. “Social Capital in the Creation of Human Capital.” The American
Journal of Sociology 94, Supplement: Organizations and Institutions: Sociological
and Economic Approaches to the Analysis of Social Structure (1988): S95-S120.
Dasgupta, Partha. “Social Capital.” In The New Palgrave Dictionary of Economics,
edited by Steven N. Durlauf and Lawrence Blume. New York: Palgrave
MacMillan, 2008.
Dougherty, Christopher. “The Marriage Earnings Premium as a Distributed Fixed
Effect.” The Journal of Human Resources 41, no. 2 (2006): 433-443.
Dribe, Martin, and Paul Nystedt. “Is There an Intermarriage Premium for Male
Immigrants? Exogamy and Earnings in Sweden 1990-2009.” International
Migration Review 49, no. 1 (2014): 3-35.
Eichenlaub, Suzanne C., Stewart E. Tolnay, and J. Trent Alexander. “Moving Out but
Not Up: Economic Outcomes in the Great Migration.” The American Sociological
Review 75, no. 1 (2010): 101-125.
Foote, Christopher L., Warren C. Whatley, and Gavin Wright, “Arbitraging a
Discriminatory Labor Market: Black Workers and the Ford Motor Company,
1918-1947.” Journal of Labor Economics 21, no. 3(2003): 493-532.
Gallaway, Lowell, Richard Vedder, and Vishwa Shukla. “The Distribution of the
Immigrant Population in the United States: an Economic Analysis.” Explorations
in Economic History 11, no. 3 (1974): 213-226.
Gardner, John. “The Labor Economics of the Great Migration.” PhD diss., Carnegie
Mellon University, 2014.
Gibson, Campbell, and Kay Jung. “Historical Census Statistics on Population Totals by
Race, 1790 to 1990, and by Hispanic Origin, 1970 to 1990, for the United States,
Regions, Divisions, and States.” U.S. Census Bureau Population Division
Working Paper No. 56, 2002.
http://mapmaker.rutgers.edu/REFERENCE/Hist_Pop_stats.pdf
Gordon, Milton M. Assimilation in American Life: The Role of Race, Religion, and
National Origins.1964. Reprint, Oxford: Oxford University Press, 2010.
Hamermesh, Daniel S., and Jeff E. Biddle, “Beauty and the Labor Market.” The
American Economic Review 84, no. 5 (1994): 1174-1194.
Kennedy, John F. A Nation of Immigrants. New York: Harper and Row, 1964.
91
Killewald, Alexandra, and Margaret Gough. “Does Specialization Explain Marriage
Penalties and Premiums?” The American Sociological Review 78, no. 3 (2013):
477-502.
Lazear, Edward P. “Culture and Language.” Journal of Political Economy 107, no. S6
(1999): S95-S126.
Lieberson, Stanley, and Mary C. Waters. “Ethnic Groups in Flux: The Changing Ethnic
Responses of American Whites.” The Annals of the American Academy of
Political and Social Science 487 (1986): 79-91.
Margo, Robert A. “Government and the American Dilemma.” In Government and the
American Economy: A New History, edited by Price V. Fishback et al., 232-254.
Chicago: University of Chicago Press, 2007.
Massey, Catherine G. “Immigration Quotas and Immigrant Selection.” Explorations in
Economic History 60, no. 1 (2016): 21-40.
McKenzie, David, Steven Stillman, and John Gibson. “How Important Is Selection?
Experimental vs. Non-Experimental Measures of the Income Gains from
Migration.” Journal of the European Economic Association 8, no. 4 (2010): 913945.
Meng, Xin, and Robert G. Gregory. “Intermarriage and the Economic Assimilation of
Immigrants.” Journal of Labor Economics 23, no. 1 (2005): 135-174.
Piore, Michael J. “Notes for A Theory of Labor Market Stratification.” MIT Working
Paper No. 95, 1972.
https://dspace.mit.edu/bitstream/handle/1721.1/64001/notesfortheoryof00pior.pdf
Portes, Alejandro, and Min Zhou. “The New Second Generation: Segmented
Assimilation and Its Variants.” The Annals of the American Academy of Political
and Social Science 530 (1993): 74-96.
Roy, Andrew Donald. “Some Thoughts on the Distribution of Earnings.” Oxford
Economic Papers 3, no. 2 (1951): 135-46.
Ruggles, Steven, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek.
Integrated Public Use Microdata Series: Version 6.0. Machine-readable database,
2015. Minneapolis: University of Minnesota. http://doi.org/10.18128/D010.V6.0.
Schulz, Theodore W. “Investments in Human Capital.” The American Economic Review
51, no. 1 (1961): 1-17.
92
Tolnay, Stewart E. “The Great Migration and Changes in the Northern Black Family,
1940 to 1990.” Social Forces 75, no. 4 (1997): 1213-1238.
———. “Educational Selection in the Migration of Southern Blacks, 1880-1990.” Social
Forces 77, no. 3 (1998): 487-514.
US Bureau of Economic Analysis, “Table SA1 - Personal Income Summary: Personal
Income, Population, Per Capita Personal Income.”
https://www.bea.gov/iTable/index_regional.cfm
Vigdor, Jacob L. “The Pursuit of Opportunity: Explaining Selective Black Migration.”
Journal of Urban Economics 51 (2002): 391-417.
Waters, Mary C. “Immigration, Intermarriage, and the Challenges of Measuring
Racial/Ethnic Identities.” American Journal of Public Health 90, no. 11 (2000):
1735-1737.
Wilson, Thomas C. “Explaining Black Southern Migrants’ Advantage in Family
Stability: The Role of Selective Migration.” Social Forces 80, no. 2 (2001): 555571.
Wright, Gavin. Old South, New South: Revolutions in the Southern Economy. Baton
Rouge, LA: LSU Press, 1986.