Migration and Labor Market Opportunities
by
Michael J. Greenwood
University of Colorado
Abstract. This paper traces the development of the role of economic opportunities in the study
of migration. From the earliest years of internal migration as a recognized field of study, scholars
in many social science disciplines believed that such opportunities were key determinants of
migration. However, during the late 19th and early 20th centuries, the lack of statistical measures
of income and wages at sub national levels prevented empirical testing of the economic
opportunities hypothesis. During this time much rural to urban migration was occurring, and the
presumption was that these flows were being driven by perceived urban-rural differences in
economic well-being. The first formal measures used by economists in the 1930s were regional
unemployment rates, and these rates proved to be significant determinants of migration during
the Depression, but did not always hold up to scrutiny in later years. As aggregate income
measures became increasingly available after 1960, they were incorporated in migration models,
but their empirical success also was limited. Finally, the availability of micro data that reflects
personal employment status and household income has allowed numerous advances in our
understanding of various migration phenomena and also has helped clear up many dilemmas
regarding earlier migration studies that used aggregate data.
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Introduction
The earliest work on migration recognizes the importance of economic opportunity as a
key determinant of migration, if not the single most important determinant. In his classic
nineteenth-century article, Ravenstein (1885, 181) leaves little doubt that he believed
employment and wage opportunities were the major “determinants” of migration: “In most
instances it will be found that they did so (leave their homes) in search of work of a more
remunerative or attractive kind than that afforded by the places of their birth” (parentheses are
mine). Later he wrote that “the call for labour in our centres of industry and commerce is the
prime cause of these currents of migration.” (198). Ravenstein does, however, recognize that the
motives for migration are “various.”
For many years after Ravenstein's work, very little research focused on internal U.S.
migration, which D.S. Thomas (1938) attributes to a scholarly focus on U.S. immigration during
the period often referred to as "the age of mass migration." However, with the imposition of
binding immigration quotas in 1921 and even more restrictive quotas in 1924, followed by the
Great Depression in the 1930s, immigration fell sharply and internal migration (especially rural
to urban, South to North, and East to West migration) claimed an important place in the study of
U.S. migration. Now economists began to focus on internal migration as a field of study rather
than more or less exclusively on immigration. With the economists came a much more specific
concern for the importance of economic opportunities as a major force underlying migration.
This was a concern that they carried over from their work on international migration.
During the 1930s, in a series of articles published in Oxford Economic Papers that was
one of the most empirically sophisticated studies of its time, Makower, Marschak, and Robinson
(1938, 1939, 1940) not only anticipated the gravity model of migration, but they also stressed the
importance of economic opportunities as measured by the unemployment rate: “Quite a close
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relationship was found between discrepancies in the unemployment rates and migration of labour
where allowance was made for the size of the insured population and the distance over which
migrants had to travel” (1938, 118). At about the same time, Hicks (1932, 76) was arguing that
"differences in net economic advantages, chiefly differences in wages, are the main causes of
migration."
Ravenstein was a British geographer, whereas Hicks and Makower, Marschak, and
Robinson were British economists. Understandably, their focus was primarily on Great Britain.
In the United States prominent demographer Warren Thompson was further stressing the
importance of economic opportunities: "The distribution of population always has been, and still
is, determined chiefly by the economic necessities of individuals, families, or larger groups,
although social usages, personal preferences, and group traditions have always interfered with
the free play of the economic factors in this process" (1936, 250). At about the same time,
economist Carter Goodrich, et al., was focusing on economic opportunities during the
Depression in Migration and Economic Opportunity (1936). Much rural-to-urban migration
occurred during the 1930s, and the Carter group was asking questions like would the migrants be
better off if they were back in the rural communities from which they had departed.
Another famous work by one of the all-time best migration researchers appeared just
after the Goodrich book. This was D.S. Thomas's (1938) Research Memorandum on Migration
Differentials. This contribution contains surprisingly little reference to economic differences as
main determinants of migration. However, Thomas clearly thought that such differences were
among the top determinants: "It goes without saying that there are other important factors
(among the determinants of migration) in addition to community structure, distance, and phase of
the business cycle, but we regard these three as fundamental" (1938, 6) (parentheses mine). Her
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reference to "phase of the business cycle" has to refer to economic opportunities. Her focus was
mainly on migration differentials, or selective migration, like age and sex, and it was apparently
too early for her to see how economic opportunities could play a major role in the determination
of who moves. For example, age selection is importantly determined by economic opportunities
in the sense that migration tends to occur at early ages because to postpone moving means
sacrificing the monetary returns that are discounted least.
The Carter study was conducted primarily at the University of Pennsylvania, so it is
perhaps not surprising that one of the primer migration studies of the 1950s and 1960s was
conducted at this University as well. Led by S. Kuznets and D.S. Thomas, the University of
Pennsylvania group published Population Redistribution and Economic Growth in the United
States, 1870-1950 (1957, 1960, 1964). This research also emphasized the importance of
economic opportunities: "the distribution of a country's population at any given time may be
viewed as a rough adjustment to the distribution of economic opportunities" (Kuznets and
Thomas, 1957, 2). Thus, from the very beginning of migration research as a recognized
discipline for study, economic opportunities were viewed as important determinants of
migration, and perhaps as the single most important set of determinants, and this view was held
by scholars in several social science disciplines.
In the sense that it can be valued, either directly in the market or indirectly through
imputation, almost anything may be viewed as an "economic opportunity" (Greenwood, 1997).
Thus, for example, location-specific amenities, such as desirable (or undesirable) aspects of
climate, have "values" that are reflected in labor and/or land markets. However, in this paper my
emphasis is on more traditional measures of economic opportunities. These measures include (1)
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wages and incomes, and (2) job opportunities as reflected in employment, employment growth,
unemployment rates, and "crowding out."
Although many models that concern less-developed countries are similar in their
formulation to counterparts for developed countries (Todaro, 1976), my focus in this paper is
specifically on developed countries. Several very good survey articles are available on migration
in less-developed countries: Lucas (1997), Mazumdar (1987), Williamson (1989), and Yap
(1977). Especially in the context of less-developed countries, the so-called "new economics of
migration" provides certain new and different perspectives on economic opportunities. In
traditional approaches to migration, individuals who are presumed to be utility maximizers make
the decision to migrate or not, but in this new theory, migration decisions are made by larger
groups such as families. Remittances play a key role in the sense that a (family) member may be
sent away for the express purpose of sending funds back "home." Thus, economic opportunities
are viewed in a somewhat different sense in this approach.
Measuring Economic Opportunities
In the earliest empirical studies of internal migration, economic opportunities did not play
a key role because statistical measures of such opportunities simply were not available at sub
national (spatial) levels. The best alternative appears to have been a focus on migration to cities,
where economic opportunities were presumably seen as superior to those in the rural areas from
which the migrants were coming. This orientation is clearly apparent in Ravenstein's (1889)
second paper and in numerous papers discussed in Thomas (1938).
Since those early days when almost no regional measures of economic well-being were
available for inclusion in migration models, numerous measures have been developed and used
to reflect economic opportunities. As noted above, county unemployment rates were used to
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study internal migration in Great Britain during the 1930s. By 1960, Easterlin (1960) had
developed estimates of U.S. regional and state per capita income back to 1840, as well as
estimates of service income per worker at the state level. The former estimates were
subsequently used in studies of historical U.S. internal migration (e.g., Gallaway and Vedder,
1971). In the U.S. various Census measures that reflected statewide mean or median income
were being employed to study interstate migration. Such measures along with aggregate
unemployment rates also were employed to study other geographic configurations like sub-state
areas. Not only were such measures used to analyze primary migration, but they also were used
to study secondary moves (like return and on-ward migration).
During the 1960s and 1970s, studies that used aggregate place-to-place migration
measures or that studied in- and out-migration or net migration often adopted income,
unemployment rates, contemporaneous employment growth (in simultaneous-equation models),
and lagged employment growth (to avoid simultaneity problems). Such studies frequently used
these variables defined for places of origin (to reflect forces that might push potential migrants
out or encourage them to stay) and for places of destination (to reflect forces that would attract or
pull migrants or, alternatively, discourage them from coming) (Lee, 1966). In other instances,
ratios of destination to origin variables were adopted, but these measures in the then frequently
estimated double-log, modified gravity models constrained the coefficients on the origin and
destination variables to be the same except for sign.
Before the availability of data sets like the Census Public Use Microdata Samples
(PUMS), researchers were constrained to the use of aggregate measures of income and
unemployment rates. Generally, they had no other options. In studies of aggregate migration, an
unavoidable problem with such measures is that area averages may have little relevance to actual
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or even potential migrants (unless everyone is regarded as a potential migrant). I next turn to a
discussion of some of the issues tied to the use of these aggregate measures.
Unemployment Rates
As a regional characteristic, unemployment rates presumably reflect the tightness of the
regional labor market. Thus, relatively higher unemployment rates characterize regions with
labor markets that should encourage out-migration and discourage in-migration. The opposite is
true of regions with relatively low unemployment rates. As a personal characteristic,
unemployment reflects a situation in which the individual's opportunity cost of migrating is
lower and his incentive to find a job anywhere, importantly in other regions as well as in his
current region of residence, is higher.
The earliest study of which I am aware that uses unemployment rates in a formal
regression analysis is the Makower, Marshak, and Robinson study (1939) noted above. These
economists had data from the Oxford Employment Exchange that indicated the number of
persons who entered the unemployment insurance system in specific counties other than Oxford
and who were residing in Oxford in 1936. Although their information included such personal
characteristics as sex, age, industry of employment both before and after the move, and county of
origin, they aggregated the data to the county level, presumably because at that early date they
did not know how to analyze micro data. Makower, Marshak, and Robinson defined what they
called the "relative unemployment discrepancy" as "the ratio of the difference between the
unemployment rate in the county (or Division) and the unemployment rate in the whole country,
to the unemployment rate in the whole country" (1939, 81). Their regression results indicated
that "there was a very clear correspondence between variations in the relative unemployment of
the county and variations in the gains and losses by migration" (1939, 82). The work of these
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authors was important for reasons that go well beyond their use of unemployment rates in a
regression analysis. They were the first researchers of whom I am aware that formally estimated
a gravity model of spatial interaction, although they did not refer to their model as such. They
were the first to actually estimate a distance elasticity of migration, which they called the
coefficient of spatial friction (1938).
Focusing on approximately the same period as Makower, Marshak, and Robinson, but for
the United States, Bogue, Shryock, and Hoermann (1957) also provide an early regression
analysis that incorporates (male) unemployment rates. They employ Census data to study 19351940 (gross in-, gross out-, and net-) migration flows (defined as rates) for metropolitan versus
nonmetropolitan state sub-regions. They provide regression results for migration from both
metropolitan and nonmetropolitan sub-regions to both metropolitan and nonmetropolitan subregions. In this analysis, unemployment rates fail to be positive and statistically significant only
for total migration from nonmetropolitan areas and for migration from nonmetropolitan areas to
other nonmetropolitan areas (but the signs remain positive). In their regressions for in-migration
and net migration, the signs on all unemployment-rate coefficients are negative, as anticipated,
and highly significant (which for these authors is 5 percent).
The early multiple-regression analyses of Makower, Marshak, and Robinson and Bogue,
Shryock, and Hoermann are noteworthy because they were conducted at a time before the
availability of computers. They also are noteworthy because their authors obtained expected
signs and statistically significant coefficients on unemployment-rate variables. For many years
and for many studies after these early efforts, the results on unemployment-rate variables were
not to be so uniformly "correct." In fact, in modified gravity models the findings associated with
unemployment-rate variables were among the most consistently troublesome in terms of signs
8
and significance levels. ("Modified gravity models" are models in which absolute migration from
i to j, or the rate of migration from i to j, is a function of the basic variables of the gravity model
(distance from i to j, population of i, population of j) and additional variables, such income in i
and in j, unemployment rate in i and in j, and numerous other possible variables.)
Many examples are available of studies that obtain unexpected signs or insignificant
coefficients on an unemployment-rate variable (Greenwood, 1975a). As indicated in Greenwood
(1975a, 403), "the failure of unemployment rates to appear to influence migration in the expected
direction and/or with the expected relative magnitude has been attributed to the simultaneousequations bias inherent in single-equation, multiple regression models. This bias is likely to be
particularly marked in those studies that employ explanatory variables defined for the end of the
period to analyze migration that occurred over the period, because migration is itself likely to
influence end-of-period economic conditions." In Greenwood (1975b), I examine these
hypotheses with U.S. Census data on 1955-1960 and 1965-1970 metropolitan in- and outmigration, using explanatory variables defined for the beginning-of-period, end-of-period, and,
alternatively, changes over the period. The models are estimated by ordinary least squares and by
two-stage least squares. For the most part, no matter how the unemployment-rate variables are
defined and no matter how they estimated, the coefficients tend with few exceptions to be
statistically insignificant. The major exception is for metropolitan in-migration from nonmetropolitan areas, for which the sign is almost always negative and significant. When the
absolute change in unemployment is included as endogenous in a simultaneous-equations model,
for 1955-1960 migration, this variable tends to have the expected positive sign in the outmigration regressions and the expected negative sign in the in-migration regressions, and in both
cases the variable is significant. However, for 1965-1970 migration the results are not so clean.
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An alternative explanation for unanticipated findings on unemployment-rate variables
was provided by Lansing and Mueller (1967). As stated in my 1975 article, they argue that
"unemployment tends to be highest among the least mobile groups in the labor force--among
persons in blue-collar occupations, among those with low skill and educational levels, and
among the young....the unemployed tend to be workers who ordinarily would not consider
migration as one of their options" (1975a, 403). The Lansing and Mueller hypothesis clearly
relates to personal unemployment. However, Current Population Survey data have for many
years indicated that the unemployed are more likely to migrate than the employed. Of course,
such cross-tabs do not control for many other personal characteristics such as those noted by
Lansing and Mueller.
The solution to much of the mystery associated with unemployment-rate variables
awaited the availability of micro data. At the time my 1975 survey was written, only three of the
251 articles referenced in the paper utilized micro data. Soon after the publication of this article,
the micro-data revolution began in earnest. DaVanzo (1978), who used micro data from the
Panel Study of Income Dynamics (PSID), provided important new insights into the
unemployment-rate puzzle. Quoting from my 1985 survey, DaVanzo "shows that families whose
heads are looking for work are more likely to move than families whose heads are not looking.
Moreover, the unemployed are more likely to move than the employed. Higher area
unemployment rates encourage the out-migration of those who are unemployed, but exert little
influence on those who have a job" (1985, 532). This last finding has important implications for
studies of (aggregate) migration that employ aggregate regional characteristics, including the
regional unemployment rate. Even in the DaVanzo study, only a fraction of the unemployed
actually migrate. The unemployed constitute a small fraction of the labor force and a much
10
smaller fraction of the population, and the unemployed who actually migrate are a smaller
fraction still. In aggregate studies, the numbers of individuals who actually migrate due to
unemployment may simply be too small to be reflected in the empirical results.
Navratil and Doyle (1977) use micro data in combination with aggregate data to directly
address the question of the influence of aggregation on elasticities estimated in migration
models. They study 1965-1970 migration of white males, white females, black males, and black
females, with 82 county-groups contained within specific states serving as the observation base
(with about 220,000 individuals). In one model they use aggregate proxies for personal
characteristics (like group-specific age and group-specific unemployment rate) in combination
with general area characteristics (like the unemployment rate). In a second model they replace
the group-specific characteristics with personal characteristics (such as a dichotomous variable
for unemployed versus employed), and they retain the general area characteristics. The general
unemployment rate is negative and significant only for white females when the first type of
model is estimated and only for white males in the second (probit) type of model. Among the
aggregate personal characteristics, the unemployment rates are never significant, but when they
are replaced with a dummy variable reflecting personal employment status, the unemployment
variable is positive and highly significant for all four groups. Thus, individuals who were
unemployed at the beginning of the migration interval (1965) were more likely to have migrated
between 1965 and 1970, but in no way was such a finding possible to obtain with aggregate data
alone.
Income, Earnings, and Wages
For economists, from the earliest studies, income or wage differences were considered to
be the most basic of the determinants of migration. This position was strongly held with regard
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to both internal and international migration. Hicks's reference to the main drivers of migration
noted above ("chiefly differences in wages") is a good example of the dominant position of wage
or income differences in the thinking of economists. However, the empirical evidence on income
and/or wages has not been uniformly in support of this hypothesis. Whereas some results
strongly support the position, other evidence is more mixed. Moreover, one of the basic ideas in
the neoclassical model is that migration should itself cause wage differences to narrow and
migration to diminish over time, other factors held constant. However, the empirical results
regarding this hypothesis also have been mixed.
Economic historians have developed a great deal of evidence that wage differences (or
wage gaps) between the United States and various European countries were primary
determinants of migration between Europe and America during much of the nineteenth and the
early twentieth centuries. For example, Hatton and Williamson (1998) show that wage
differences were significant drivers of such migration, but their importance declined as the wage
gaps narrowed as the nineteenth century progressed. Thus, at least with respect to historical U.S.
immigration from Europe, empirical findings are consistent with wage gaps between the U.S.
and the European source countries providing a major impetus to migration, these gaps narrowing
due importantly to the equilibrating effects of mass migration from Europe to the Americas, and
in turn emigration from northern and western Europe to the Americas diminishing as the
nineteenth century progressed.
Early studies of internal migration did not include wages or income, presumably because
no measure was available. For example, Makower, Marshak, and Robinson include only the
relative unemployment rate (along with distance and population) in their regression. Bogue,
Shryock, and Hoermann employ a variable they refer to as "level of living index" (1957, 74).
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Even D.S. Thomas's famous Migration Differentials book contains limited reference to income
or wages. In a later paper (1958) discussed below, she uses real per capita gross national product.
Contrary to historical studies of U.S. immigration from Europe, modified gravity models
of internal U.S. migration (that became popular during the 1960s) frequently yielded unexpected
signs and/or statistically insignificant coefficients on origin and destination income variables,
especially on origin variables. Negative signs were typically expected on origin-income variables
and positive signs on their destination counterparts, since higher income was expected to
discourage out-migration and encourage in-migration. Many examples are available.
Proponents of the equilibrium hypothesis (that wage or income differences are compensating
differences that reflect the values of location-specific amenities) claim to have provided an
explanation for the unexpected signs and insignificant coefficients on income variables.
However, even in the presence of various amenity variables, many models continued to yield
unexpected findings.
One of the most understudied aspects of migration research is the temporal relationship
between cyclical economic activity and migration. Although the topic has been of interest and
concern for many years, good temporal data on migration prevented any in-depth analyses of the
relationship. One of the Oxford studies of Makower, Marschak, and Robinson (1939) is the
earliest of which I am aware that addresses the issue. Their data covered the period 1923-1937.
They conclude that "the data of the Oxford study suggested that mobility increased with
prosperity during the period 1933-7...While it suggests that mobility was reduced during the
slump of 1931, it confirms the rise in mobility during the recovery. Thus mobility fluctuates in
harmony with the trade cycle. It was found, further, that 'short-distance' movements were less
sensitive to the slump than 'long-distance' movements" (1939, 94). They attribute the cyclicality
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of migration to out-of-pocket expenses: "in times of...prolonged unemployment, people find it
more difficult to raise the money necessary for migrating" (1938, 118).
Another early study that attempted to uncover the relationship between cyclical activity
and interstate migration was conducted by D.S. Thomas (1958). She and her research team at the
University of Pennsylvania had developed fairly detailed state-specific net migration estimates
by age and sex for each intercensal decade from 1870 to 1950. As indicated above, in her
famous monograph on differentials in migration, she had noted that "phase of the business cycle"
was one key to understanding migration differentials, but then she provided little or no empirical
support for her hypothesis. With more detailed migration estimates, she now returned to this
relationship. Her measure of economic activity was novel. She fit "six successive thirty-one year
linear trends to annual data on gross national product per capita, in constant prices, beginning
with the first year of each decade, cumulating the absolute deviations from each trend over each
decade, and expressing their algebraic sums as percentages of corresponding cumulative trend
values" (1958, 317). She then classified decades between 1880 and 1940 as relatively high
versus relatively low in terms of economic activity. Her basic conclusion was that "young males,
seeking economic betterment, (showed) a correspondingly greater intensity of migration during
high than during low activity periods" (1958, 319) (parentheses mine).
Later, Greenwood, Hunt, and McDowell (1986) used annual (1959-1975) data from the
One Percent Continuous Work History Sample of the U.S. Social Security Administration to
study the linkage between employment change and migration. They conclude that in an average
year two additional local jobs attract about one additional employed migrant. However, like
Thomas, they also find that the migrant-attractive power of an additional jobs behaves cyclically,
rising during upswings and falling during downswings. They speculate that when the costs of
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migration are relatively high, such as during cycle troughs, a greater degree of migrant self
selection occurs and thus migrant quality in terms of human capital rises. The opposite occurs
during cyclical upswings. More recently, Saks and Wozniak (2011) examine long-distance
migration over the business cycle. Using a variety of data sets, they too conclude that migration
is procyclical, which they attribute to greater net benefits to moving during cyclical upturns.
Moreover, similar to the earlier finding of Thomas, they argue that younger workers are
especially procyclical in their migration behavior, presumably due to better economic conditions
during cycle upswings.
In the end, we should recognize that the "net wage" or the "net income differential" is
critical. Such a net value is corrected for state and local tax differences as well as differences in
the values of publically provided goods and services. In the U.S., state income taxes vary from
none to significant percentages of taxable income, and many benefits also differ greatly across
states and localities. Such benefits include differences in per student expenditures on K-12
education, as well as assistance for needy families in the form of food stamps, housing, and other
services. A number of studies have addressed the importance of various types of state and/or
local taxes and/or public expenditures in migration decisions, and I will not treat this literature in
any detail here. For example, Charney (1993) surveys the literature on public expenditures and
taxes in the United States, and Day (1992) does so for Canada.
Employment Opportunities
Among those variables that reflect economic opportunities, the most consistently
significant are those that proxy the availability of jobs. This condition tends to be true whether
employment opportunities are measured as contemporaneous employment change (in
simultaneous-equation models), lagged employment change, or employment rate.
15
Several studies of historical migration, as well as a number of those dealing with
contemporary migration, assert that migrants crowd out others in the sense that the migrants
encourage the out-migration of prior residents or discourage the in-migration of potential new
residents. Presumably, underlying this phenomenon is competition for jobs, although cultural and
other differences between immigrants and natives also could be responsible in part. The
historical studies have focused on the manner in which the location of immigrants in northern
U.S. cities influenced South to North migration of the native born. During the late nineteenth and
early twentieth centuries, as immigration from Europe surged, migration from South to North
ebbed, and when immigration ebbed, this internal flow surged (B. Thomas, 1973). Whereas the
B. Thomas study was descriptive and inferential, Collins (1997) shows empirically essentially
the same phenomenon. Thus, historically, broad regional growth patterns were significantly
affected by immigration and by immigrant settlement patterns. Even during the Great
Depression, when immigration was very low and immigrants were not a major issue, internal
migrants to U.S. cities caused the out-migration of longer-term residents. Boustan and Fishback
(2010) show that during the 1930s for every 10 new migrants 1.9 residents departed; moreover,
another 2.1 individuals were unable to find a relief job and 1.9 more moved from full-time to
part-time work.
A number of studies have examined the location of the foreign born and the internal
migration of the native born in the United States. The basic conclusion of much of this work is
nicely summarized in the early study by Filer (1992, p. 267): "It is clear that there is a strong
relation between the arrival of immigrants in a local labor market and the mobility patterns of
native workers. The higher the concentration of recent immigrants in an area, the less attractive
16
that area appears to have been for native workers." Filer's focus is on Standard Metropolitan
Statistical Areas and 1975-80 net internal migration flows.
More recent studies (e.g., Kritz, Gurak, and Lee, 2011) examine various groups of
internal migrants and more recent periods (such as 1985-90 and 1995-2000), but many of these
studies arrive at essentially the same conclusion. For example, Frey and Liaw (2005) use
microdata and logit analysis to study both in and out interstate migration patterns for 1995-2000
and, after controlling for numerous personal and area characteristics, conclude that "Our results
generally show no race-specific flight of whites alone from (states with large numbers of lowskilled immigrants), but rather show an accentuated out-migration of all race-ethnic groups from
states with ... high levels of foreign-born immigration" (p. 246, parentheses mine). Moreover,
they find that "for every 100 new low-skilled immigrants to California there would be a net outmigration of fifty-one low-skilled domestic migrants" (p. 213). Similarly, Borjas (2006), using
data from the 1960, 1970, 1980, 1990, and 2000 Censuses and focusing on various skill groups,
finds a powerful effect of immigrants on native internal migration amounting to two fewer
natives wishing to live in a state if 10 more immigrants settle there. The effect is somewhat
greater at the metropolitan level, and more in line with the estimates of Frey and Liaw.
This literature is related to an old issue in migration. What is the migrant-attractive power
of a job? Perhaps the earliest study to directly address this question is Muth’s (1971) “Migration:
Chicken or Egg?” study. He found that three more jobs attracted two more employed net
migrants. This was the direct effect of an additional job and does not take into account the
indirect and induced effects that result from the migrant’s influence on jobs. However, this is
only one possible outcome regarding the direct effect of employment on migration. Consider the
following relationship: M = f(ΔE), where M refers to employment migration and ΔE refers to
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change in employment. The possibilities are: a. one more job attracts one more employed
migrant, or ∂M/∂E = 1.0; b. another job attracts no migrants or ∂M/∂E = 0; and c. another job
attracts some fraction of a migrant (or, say, 100 jobs attract between 1 and 99 migrants), or
0 < ∂M/∂E < 1.0. This last case reflects Muth’s finding that, for example, 100 jobs would attract
67 employed migrants. This is the most likely case. In case b, local residents take all of the
incremental jobs, as could be the case for less-skilled jobs such as might be available at
McDonalds. On the contrary, in case a, where migrants fill all the incremental jobs, the jobs may
be highly specialized, such as airplane mechanics. Little research has been done on the migrant
attractive power of different types (occupations) of jobs.
Although the most common finding is the one-to-one relationship, depending upon the
specific region, other findings are evident for U.S. regions (Greenwood and Hunt, 1984). Thus,
some crowding out appears to occur, but it is not a universal phenomenon. Even with respect to
U.S. immigrants, some studies deny the existence of such a crowding-out relationship. Butcher
and Card (1991) argue that this general conclusion is limited to New York, Los Angeles, and
Miami. Based on their use of CPS data for the 1980s, they conclude that for 21 other cities
"native in-migration flows during the 1980s were positively correlated with inflows of recent
immigrants" (1991, 294). Similarly, Kritz and Gurak (2001) find little support for the hypothesis
that native men migrate away from states with heavy immigrant concentrations over the 1985-90
period. Regarding net migration, 1975-80 and 1985-90, Wright, Ellis, and Reibel (1997) reach a
similar finding, as does Card (2001).
Conclusions
Early studies acknowledged the importance of economic opportunities as a key
determinant of migration, but due to lack of data reflecting such opportunities these studies
18
provided little empirical support for the hypothesis. A strong focus on rural to urban migration
was evident in very early migration research, and the general assumption presumably was that
economic opportunities in cities were sufficiently better than in rural areas to generate a strong
flow of migrants from one type of area to the other. D.S. Thomas's (1938) effort in the 1930s to
summarize and synthesize the migration literature is a good example of this lack of data.
Although she mentions the importance of the business cycle in determining the volume and age
composition of migration, she never specifically addresses what she might have referred to, but
did not, as "income differentials" and none of the many studies she cites introduce a measure of
income or wages in an analysis of migration.
The first study that formally introduced a measure of economic opportunity was
conducted in the late 1930s and used an unemployment measure to study intercounty migration
in Britain. Beginning in the 1950s, as publicly provided aggregate income measures (such as
median and mean income for states and more narrowly defined local areas) became more
commonly available, such measures were introduced into migration models, which would have
been judged severely lacking in their absence. Studies from this period are reviewed in
Greenwood (1975a, 1985). Although it is fair to argue that these studies did not always find
strong support for the hypothesis that income or wage differences were among the most
important determinants of migration, on balance such measures did hold up reasonably well to
empirical scrutiny.
Several recent studies have identified "crowding out" as reason for internal U.S.
migration. This phenomenon relates one group absorbing local jobs and thereby displacing
others from their positions, and thus causing the displaced individuals to migrate from the area.
Most frequently, new immigrants are seen as crowding out natives, but the same relationship has
19
been observed in the past as immigrants discouraged native out migration from the South to the
North, and during the Depression rural to urban migrants crowded urban dwellers from their
cities of residence.
As micro data became more widely available during the 1980s and beyond, later studies
incorporated individual and household income data to allow the further and deeper study of the
importance of income in the analysis of migration. For example, now spousal incomes are
available that allow the study of family migration. This genre of work is reviewed in Cushing
and Poot (2004), Ghatak, Levine, and Price (1996), and Greenwood (1997). Thus, the empirical
implementation of measures of economic opportunities in migration models was highly
dependent upon the development of various measures of economic well-being, at first from the
census and later from various special surveys. Without question, economic opportunities are now
central to virtually any model reflecting human migration. Moreover, among economic
opportunity measures, the availability of jobs stands out as the single most consistent variable to
which migrants respond.
20
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