Declining Migration within the US: the Role of the Labor Market
Raven Molloy
Federal Reserve Board of Governors
Christopher L. Smith
Federal Reserve Board of Governors
Abigail Wozniak
University of Notre Dame, NBER and IZA
Preliminary: Not for Circulation or Citation
I. Introduction
Declines in internal migration since the mid-2000s have attracted much attention because
they coincided with a dramatic housing market contraction and deep economic recession (Batini
et. al. 2010, Frey 2009, Kaplan and Schulhofer-Wohl 2010). However, in earlier work we
demonstrated that these declines appear to be the continuation of a longer-run trend (Molloy,
Smith and Wozniak 2011). Specifically, internal migration within the United States has fallen
continuously since the 1980s, reversing the upward trend that occurred over the rest of the 20th
century. In this paper, we assess several explanations for the secular decline in migration,
focusing on factors that may have played a role throughout the entire thirty year period. It is of
course possible that multiple factors came together to depress migration in different sub-periods
of the three decades that are our focus. However, in the search for explanations, it seems efficient
to begin by examining ideas that can account for a large portion of the time period in question.
We begin by summarizing the contributions of a number of demographic and
socioeconomic factors to the change in migration from the 1980s to the 2000s in a simple
Oaxaca decomposition framework. We find sharp differences across long-distance (inter-county
1
or inter-state) migration and migration over shorter distances (within county). For intra-county
migration, compositional changes in age, homeownership, and other observable characteristics
explain a large portion (nearly 80 percent) of the decline since 1980. By contrast, we find that
changes in age and other demographics only explain a small part of the decline in long-distance
migration. Instead, the results point to a substantial drop in the probability of migration that is
common among all of the demographic and socioeconomic groups in the model.
Motivated by the relative difficulty for demographic and socio-economic characteristics
to explain long-distance moves, we proceed to investigate more carefully other explanations for
declining long-distance moves. A number of factors suggest that the labor market has played a
key role. First, the decline in migration was more pronounced for labor force participants.
Second, it has also been sharper for longer distance moves (i.e. across states or metropolitan
areas), which tend to be related to labor market reasons more often than shorter distance moves
(i.e. within county). Third, other measures of churning in the labor market, such as quits and
employer-to-employer flows, have also trended down during this period. Using individual-level
and state-level data, we present evidence that labor market transitions, such as employerswitching and occupation-switching, and geographic mobility are correlated.
In our view, the descriptive evidence suggests that an explanation for the long-run decline
in migration should have two broad features. First, the mechanism for the long-run decline is
likely to be found in the labor market, as opposed to the housing market or in compositional
changes within the population. And second, the mechanism is likely to have led to a decline in
labor market turnover more generally.
One reasonable candidate for such a mechanism is a rise in returns to specific human
capital. The remainder of the paper assembles evidence on how returns to several measures of
2
specific human capital may have changed over time. We use several waves of the National
Longitudinal Surveys to compare returns to months of employment on the current job across
multiple cohorts. We find that young workers in the later 1980s, early 1990s, and early 2000s
receive a return to job experience that is nearly double that for similar workers in the 1970s. We
also use the Displaced Worker Survey to examine returns to industry experience. Again, we find
returns to this type of specific human capital that are much higher in the mid-2000s than in
earlier periods.
The evidence assembled so far is preliminary, but examining the role of changing returns
to specific human capital in declining migration seems a promising avenue for future research.
The migration decline poses an interesting puzzle since some trends over the last three decades,
such as the use of the internet by employers and job seekers, seem likely to have raised
migration rates. We hope in future work to expand our investigation into specific human capital
as a mechanism leading to the observed widespread slowing in labor market churning as well as
to more fully account for the historic decline in migration within the US.
II. How much of the decline in migration can be explained by demographic and socioeconomic trends?
A natural explanation for the decline in migration is changing demographic or socioeconomic trends, as they have slowly been shifting in favor of groups with lower mobility rates.
Two prominent trends in this regard are the shifts toward higher shares of homeowners and older
adults. Because demographics are commonly thought to be the primary drivers of declines in
migration, we begin with an analysis of these factors. If demographic shifts explain the bulk of
3
the decline, then there is no puzzle remaining. If, however, these factors fail to explain a
preponderance of the trend in migration, then another explanation is needed.
To assess the importance of a large number these factors in a single framework, we use
an Oaxaca decomposition to examine the change in migration from the 1980s to the 2000s, first
for within-county migration and secondly for interstate migration. For each time period, we
estimate an OLS regression of the probability of migration on a number of characteristics (to be
discussed below). Then we apportion the change in the probability of moving into a portion
attributable to the change in the quantity of each independent variable (i.e. the change in the
characteristics of the population), a portion attributable to the change in the estimated coefficient
of each independent variable (i.e. the change in the propensity to move of the population with a
given characteristic), and a portion attributable to the interaction between quantities and
coefficients. Specifically:



Y00 s  Y80 s  X 00 s  X 80 s  80 s    00 s   80 s  X 80 s  X 00 s  X 80 s
where

00 s
  80 s 
is the average probability of moving in the decade denoted by tt,
the independent variables in the same period, and
[2]
is the average of
is the vector of estimated coefficients from
the regression of Y on X using a single decade of data. Because the CPS is a small sample, we
estimate the OLS regressions using pooled data from the 1981-1989 and 2002-2010 time
periods.1 Including multiple years in each sample period also allows us to smooth through any
cyclical changes in migration that might affect the comparison of any two short time periods.
The regressions include year indicators so that the coefficients are identified from variation in the
independent variables within a given year. The coefficient estimates from the underlying OLS
regressions are reported Appendix Table 1. We normalize the coefficients so that they reflect
1
The CPS did not include the migration question in 1985, so the 1980s sample includes 8 years of data. To be
symmetric, the 2000s sample also spans 9 years but omits the fifth year (2006).
4
deviations from the average propensity to move in each sample period rather than deviations
from the propensity to move of a reference category. In this way, the results are not sensitive to
which characteristics are chosen as the reference category.2
Table 1 shows the results of the Oaxaca decomposition for intra-county migration.
Column 5 shows the contributions of changes to quantities. The explanatory variables can
account for most of the 1.6 percentage point drop in migration; changes in the distribution of the
explanatory variables, and changes in the coefficients for the variables can each explain 0.7 to
0.9 percentage point of the decline (the contribution of the interactions is positive, and the
constant is -0.3 percentage point, indicating that less than 20 percent of the decline in intracounty migration is unexplained by these variables). Of note, the rise in homeownership
contributes 0.4 percentage point to the decline in migration (homeowners are less likely to move
than renters), the age distribution of the population contributes another 0.45 pp (the share of
young people, who are more likely to move, falls), and the education composition of the
population contributes 0.35 percentage point (a shift towards more educated, who are more likely
than the less-educated to move short distances).
Column 6 of the table reports the contribution of changes in the coefficients. Because we
have normalized the coefficients, these contributions can be interpreted as the deviation in the
propensity to move of a given category from the average propensity to move. For example, the
probability of moving among young people has fallen more than average, contributing 0.4
percentage point to the drop in aggregate migration. Other notable results are that the propensity
to move of people with at least a college degree has fallen more than average, as it has for
individuals living without kids and individuals who are currently employed. In sum, the
2
Oaxaca decompositions also depend on the weights that are used—for example, whether the change in a
characteristic is weighted by its 1980s coefficient, its 2000s coefficient, or an average of the two. The results
reported below are robust to various weighting schemes.
5
changing demographic and socio-economic composition of the population, along with changes in
the propensity to move short distances for each of these groups, can explain the bulk of the
decline in intra-county migration.
In contrast to the results for short-distance migration, we are less successful at explaining
long-distance moves with these variables. Table 2 repeats the same Oaxaca decomposition
exercise as above, except with inter-state migration as the dependent variable. The overall
decline in inter-state migration from the 1980s to the 2000s is 0.9 percentage point. The fifth
column of Table 2 shows the contributions of the changes in quantities. Of note, the rise in
homeownership contributes 0.1 pp to the decline in migration, the age distribution of the
population contributes 0.1 pp, and the distribution of race/ethnicity in the population contributes
0.1 pp (a shift from white, non-Hispanic to Hispanics, who are more likely to move). Although
these factors cumulate to about 0.3 pp—roughly one third of the drop in migration between these
two periods—changes in the quantities of other characteristics, such as the increase in
educational attainment, have mostly offset the negative effect of these factors. In total, changes
in the demographic and socio-economic distribution of the population—in the absence of other
changes—is predicted by this decomposition to have pushed up the inter-state migration rate by
about 0.3 percentage point.
Turning to the contribution of changes in the coefficients (column 6), the changes in
interstate migration do not appear to be related to any particular demographic or socioeconomic
group. Rather, the constant contributes -1.4 pp to the decline in migration. In other words, the
propensity to migrate of an individual with average characteristics fell substantially. This result
tells us that, by and large, the decrease in interstate migration was common across all
6
demographic and socio-economic groups in the model.3 Nevertheless, we do find minor roles for
a few factors: the propensity to move of renters has fallen more than average ,as it has for young
people, married individuals, white non-Hispanic people, for individuals who do not live in dualearner households, and to a lesser degree, for individuals in metropolitan areas. Individuals in
the South Atlantic, West South Central and Mountain Census Divisions have also become a bit
less likely to move than average.
In sum, the Oaxaca decompositions demonstrate that the vast majority (80 percent) of the
downward trend in intra-county migration is explained after accounting for a variety of
demographic and socioeconomic factors. On the other hand, interstate migration is little
explained by these factors. These findings are robust to calculating the decomposition in several
ways and to using other time periods. Because labor market transitions (from employment to
non-employment, or the reverse; or changes in employers or career paths) are more likely to be
associated with longer-distance moves, it seems possible that these two phenomena may be
related, and in the next section we turn to labor-market related explanations in an attempt to
better understand the puzzling decline in longer-distance migration.4
III. Labor-market related explanations for the decline in migration
There are several reasons to think that lower migration rates may be related to changes in
the labor market. First, as shown by Figure 1, job-related reasons are the most-common
explanation for inter-state moves, and they are less important for migration over shorter
3
If the decrease in migration were concentrated in particular groups, then we would have found larger contributions
from the change in the coefficients for those groups, and correspondingly a smaller contribution from the change in
the constant.
4
X percent of adults who moved across counties or states in the previous year also changed employers or
occupations; X percent of adults who changed employers or occupations also moved across counties or states.
7
distances. In addition, job-related migration has trended down from 2000 to 2010 (this question
was not asked in prior years).
Second, the decline in migration is similar to the downtrend in many measures of labor
market transitions, such as employer-to-employer flows and separation rates. For instance, in
Figure 2 we plot the fraction of the population (16 and older) that changed employers or changed
occupations in the previous year. Since labor market transitions are often associated with
geographic moves, it seems possible that these two phenomena may be related. To the extent that
trends in migration and job transitions are linked, better understanding of one may aid the
understanding of the other.
As additional evidence on the potential link between migration and job transitions, we
regress annual migration rates for a state on a variety of variables related to job transitions and
other proxies for structural change in the labor market, as well as state and year fixed effects,
state time trends, and other controls.5 The results are shown in Table 3. We find a statistically
significant, positive relationship between the fraction of a state’s population that changed firms
or occupations in the previous year and fraction that moved into the state or across counties
within the state (columns 1 and 2). Thus, states that experienced larger declines in employer-toemployer (E-to-E) transitions or in occupational switching also experienced larger declines in
long-distance migration. Specifically, columns 1 through 4 show that the declines in E-to-E and
occupational switching explain one-quarter to one-half the observed total decline in migration
rates of 0.018. These results are robust to the inclusion of state trends. However, the fraction that
transitioned from employment to non-employment is not related to migration rates (column 3).
5
Additional controls are: the fraction of the state unemployed, the log of average annual income for the state, and the
fraction of the state that is young (under 21) and of prime working age (21-64).
8
We can get a different perspective on the importance of labor-market transitions by
including a few other variables in the Oaxaca decompositions. The initial specification included
indicators for current employment status (specifically employed, unemployed, military or not in
the labor force). These variables did not have a large contribution to the change in migration, but
they do not directly reflect labor market transitions. The CPS does not report employment status
in the prior year, so we can only calculate these transitions for individuals who were privately
employed in the previous year. The drop in the average interstate migration rate for individuals
who were employed in the previous year was 1.2 percentage points, somewhat larger than the
aggregate decline. The transitions that we calculate are variables for whether an individual
changed 1-digit occupation, changed employers, or transitioned from employed to not working.6
Figure 2 shows that all three of these measures decreased from the 1980s to the 2000s. As
shown in Table 4, the downward trends in changing employers and changing occupation each
contributed 0.1 pp to the drop in interstate migration; the contribution of exiting employment is
relatively small because the probability of moving conditional on exiting employment is not as
high as it is for the other job transitions. Turning to the coefficients, the propensity to migrate of
people who change jobs fell relative to the average, reducing migration by 0.1 pp. All together, it
seems like declining labor market transitions can account for about one fifth of the drop in
migration of the employed population in the microdata that underpin these decompositions. This
is a smaller role for labor market transitions than suggested in the cross-state regressions of
Table 3. However, Moscarini and Thomsson (2007) show that typical measures of occupation
6
We cannot examine changes in occupation at a more detailed level or changes in industry because the detailed
occupation codes and the industry codes are not consistently defined over time. The measure of job changing is
calculated from the number of employers in the previous year. According to the CPS documentation this variable is
meant to reflect job changes across employers so multiple job holders are coded as having only one employer.
9
and job switching suffer from high degrees of measurement error. This error will tend to
attenuate their roles in the Oaxaca decompositions, which are based on individual-level data. The
cross-state regressions are based on comparisons across rates of switching and migration at the
state level, which we expect to improve the signal-to-noise ratio in observed job and occupation
changing. Of course, the simple correlation between labor market transitions and geographic
migration does not explain why these flows have been falling.
Another aspect of the labor market that might be related to falling migration rates is the
structural shift in the distribution of occupations. Specifically, the share of adults in lowerskill/lower-paying jobs (e.g. food service, personal care services, cleaning services) and higherskill/higher-paying jobs (e.g. professional, managerial, and design jobs) have both grown, while
the share of adults in middle-skill/middle-paying jobs (e.g. administrative, manufacturing, and
sales jobs) has fallen. This “hollowing out” or polarization of the occupational distribution is
thought to be due to the increasing use of computers, ease of automation, and ease of off-shoring,
which raises demand for higher-skill jobs, reduces demand for the middle-skill jobs, and
displaces some workers formerly employed in middle-skill jobs into lower-skilled ones.7 This
shift might have reduced migration if , in the past, less-educated workers were likely to migrate
for middle-skill jobs. The elimination of large shares of these jobs could then lower migration
rates by reducing the set of “migration worthy” jobs for middle skill workers. However, we find
no empirical support for this idea, as there is no significant relationship between a state’s
migration rate and the fraction employed in middle-skill or manufacturing jobs (an alternative
proxy for middle-skill jobs). In addition, when we include indicators for various middle-skill or
7
Autor, Katz, and Kearney (20XX). AEA Papers and Proceedings.
10
low-skill occupations in the Oaxaca decompositions, they do not make any notable contributions
to the drop in migration.
IV. Specific human capital as the link between labor market transitions and mobility?
The expanded Oaxaca decompositions described above suggest that the decline in labor
market transitions can explain a bit of the total decline in migration, but the additional
contribution is not that substantial. Nonetheless, we think it is valuable to spend additional effort
understanding the links between labor market flows and geographic mobility because of the
striking similarities in the trends for these two types of flows. But what might cause a secular
decline in both labor market and migration flows?
Kaplan and Schulhofer-Wohl (2011) propose that the decrease in migration can be
explained by a “flattening world.” Their idea is that job types have become less concentrated in
particular metropolitan areas, so that an individual wishing to change occupation or industry is
more likely to find their desired new job in the same metropolitan area where he or she already
resides. If this were the case, we would expect the propensity to move of people who change
occupation or industry to have fallen relative to others. While we found some evidence of this
effect in our Oaxaca decomposition, its contribution to the aggregate decline in migration over
our period of interest was relatively small. Moreover, this theory cannot explain why the rate of
job transitions has also fallen substantially.
An alternative possibility is that the returns to specific human capital (such as firmspecific human capital) may have risen over time. If so, workers would be less likely to change
jobs and, since long-distance migration frequently occurs alongside job changes, migration
across metropolitan areas would fall as well. For now, we use the term “specific human capital”
11
(SHC) broadly to encompass firm, industry, occupation, and location specific capital–any skills
that are not fully portable across firm, industry, occupation, or spatial boundaries. While our
Oaxaca decompositions quantified the contribution of some of these labor market transitions,
they were only for a selected sample and we could not robustly include changes in industry or
job.
The theory that returns to SHC have increased over time has two broad characteristics we
seek in a potential explanation for declining migration and labor market turnover. It is obviously
a labor market phenomenon, and one that could easily apply to many groups of labor market
participants. It also has the potential to lead to a general decline in job changing across a range of
categories as it raises the benefits of staying in one’s current job relative to those of switching to
a new job.
As this is preliminary work, we have simply begun by looking for evidence of whether
there have been changes in the returns to SHC over time. Some well-known work on trends in
wage inequality (Katz and Murphy, 1992) finds rising returns to general labor market experience
(typically measured as Mincerian approximated experience), but we have found no research
identifying how the returns to SHC have changed over time. We are ultimately interested in
testing for changes over time in returns to the several types of SHC. For this reason, we turned to
the National Longitudinal Surveys (NLS). The NLS are comprised of seven different surveys,
each named for a different set of cohorts that were followed longitudinally. Information varies
across the surveys, but a central focus of each NLS is to follow workers as they transition into
and out of employment, jobs, and training.
The NLS offer the possibility of observing an individual’s wages for a given job
alongside information on how long an individual has held the job, how long he has worked in the
12
same industry and occupation as the current job, and how long he has worked in his local labor
market. This detailed work history potentially allows us to separately identify returns to firm
(job), industry, occupation, and location specific human capital. Moreover, we can obtain
estimates for workers of similar ages separated across time by using data from multiple NLS
cohorts. We use data from four cohorts: the NLS Young Women and Young Men, and the
National Longitudinal Surveys of Youth 1979 and 1997.8
We present preliminary results from this strategy in Table 5, where we have estimated a
simple Mincerian wage equation on young workers. The overlap in respondent ages across the
surveys limits the comparable samples that we can construct to young workers. The greatest
limitation comes from the NLSY97, where the oldest respondents in the most recent survey wave
are only 28. We therefore draw the sample of young workers from the NLSY97 ages 25 to 28
and construct a comparable sample in the NLSY79 using the same age restriction.
The top panel shows estimated returns to an additional month of tenure on an individual’s
current or most recent job (called the main job in most NLS) estimated from the full sample in
each NLS cohort. Estimates in the NLS-YM and NLS-YW samples are very similar, about 0.5
percent per additional month of job tenure, which corresponds to 6 percent higher wages for an
additional year of tenure. Questions about job tenure were not asked in every year of these
surveys, but they were asked of both male and female NLS respondents for several years over
the 1970s. Therefore these figures are likely reliable estimates of the return to job tenure during
that decade.
The years in which the NLSY79 cohort was aged 25-28 spanned the years 1982 to 1993,
or approximately ten years after then analogous years for the YM and YW cohorts. Returns in
8
We may also use the NLS Mature Women and the NLSY79-Child files. The Older Men sample does not contain
observations that overlap with any of the relevant age groups in the other surveys.
13
the NLSY79 sample are markedly higher than those for the earlier cohorts. For the 79 cohorts,
returns to an additional month on the job were .86 percent, or 10.3 percent for an additional year
on the job. The confidence intervals are sufficiently narrow to suggest that returns to job
experience in the decade of the late 1980s and early 1990s were statistically significantly higher
than in the 1970s.
The NLSY97 respondents entered the labor market 15 to 20 years after the 1979 cohort,
and therefore their estimated returns reflect conditions in the second half of the 2000s. Returns to
job specific tenure remain higher for this group than for the older NLS-YM and YW cohorts at
the same period in their lifecycle. Each additional month on the current job leads to 0.79 percent
higher wages, which equates to a 9.5% return to an additional year on the job. The bottom panel
repeats the entire analysis for a subsample of respondents who likely have higher labor force
attachment on average: white males not enrolled in school. The point estimates are quite similar
across the full and restricted samples.
Members of the 97 cohort saw returns that were lower (though not likely significantly so)
than those for the 79 cohort, but that were still well above returns for the older cohorts. This
result is suggestive that returns to job SHC have been higher in the late 1980s to late 2000s,
when migration was declining, than in earlier years. However, returns to job tenure for the most
recent cohort (97) seem at odds with the continued decline in migration during this period
because they were lower than for the 79 cohort. Moreover, average job tenure is lower in the 97
cohort, suggesting more labor market transitions by this measure. In future work, we will more
thoroughly assess whether and how these changes may have contributed to declining overall
migration rates. We will also explore in more detail how various components of job SHC have
changed over time.
14
Finally, it is worth noting that a limitation to all NLS is the high level of attrition that
typically occurs in longitudinal surveys. Since migration is a common reason for sample
attrition, the migrants who remain in the NLS may be a selected group. Also, workers with
higher rates of return to their specific human capital are less like to leave their jobs and locations.
Together, these patterns might result in overestimates of the returns to SHC within a given cohort
of the NLS since stayers are more likely to be observed as the survey progresses. However, if
anything, this bias should abate over subsequent surveys, as administrators improve retention
techniques and as electronic communication and the internet make finding respondents after a
move much easier. Consequently, we may be underestimating the change in returns to SHC
across NLS cohorts.
Another source of evidence on changing returns to SHC over time is the Displaced
Worker Survey (DWS). The DWS is a supplemental survey for the Current Population Survey
(CPS), and focuses on workers who were displaced from a job in the last year for no fault of their
own, e.g. due to a plant closing. For those who are identified as displaced workers, the survey
includes questions about current salary, occupation, and industry, as well as the same
information for the job that they were displaced from. Hence, using the DWS we can calculate
the change in the wage experienced by a displaced worker, and the relationship between the
wage differential and whether the worker changed occupation or industry. While changing
occupation or industry is surely not exogenous, in the absence of a convincing instrument we
rely on the argument that displacement due to an event such as a plant closing is likely
exogenous and so occupation/industry switching for this sample is likely to suffer from less bias
than for the population of all job switchers.
15
To assess the relationship between the change in wage that a displaced worker
experiences and whether the worker changed occupations or industries, and whether this has
changed over time, we pool the DWS sample from 1994, 1996, 1998, 2004, 2006 and 2008 (the
DWS is only administered every two years), and regress the difference in current log wage and
previous log wage on whether the individual changed industry/occupation, this
industry/occupation change dummy interacted with whether the observation is from 2004, 2006,
or 2008, state and year fixed effects, experience (age – years of education – 6) and experience
squared, years at previous employer and its square, and dummies for educational attainment,
race, sex, and marital status. We limit the sample to adults 21 years of age or older.
Starting with the industry change variable, changing industry is associated with a wage
decline of between 5 to 7 percent, depending on the sample (male, female, or pooled) and
whether the occupational change variable is also included (see Table 6). However, there is no
evidence that the wage penalty to changing industry changed much from the 1990s to the 2000s
(the coefficient on the interaction term is insignificant—negative when entered separately, and
positive when the occupational change variable is also included).
The story is different when considering the wage penalty associated with an occupational
change. There appears to be no wage penalty associated with changing an occupation in the
1990s sample, but a sizeable one (5 to 8 percent) in the 2000s (adding the coefficient on
occupational change and its interaction). This finding persists even after including the industry
change variable as well.
We tentatively view this evidence as suggesting that the penalty associated with changing
occupation has increased, consistent with our findings from the NLS that the returns to tenure at
a specific employer have also increased. To date, the literature on firm-specific, industry-
16
specific, or occupation-specific human capital has focused mainly on identifying and
understanding these forms of specific human capital. Neal (1995) and Parent (2000) both argue
that observed returns to job SHC are in fact driven by industry SHC. Recently, Kambourov and
Manovskii (2009) find an important role for occupation SHC, echoing earlier arguments in Shaw
(1984, 1987). Importantly, they find large returns to occupation SHC once the data have been
corrected for a high degree of measurement error. Their results are on the order of a 20% return
to 5 years of occupational experience. This estimate is still smaller than our estimate of returns to
job tenure in the NLSY97, but given that all respondents in our sample are young and therefore
likely to be on the steep portion of the returns curve, our large estimates may be reasonable.
To our knowledge, there is no research identifying how the returns to SHC have changed
over time. However, the literature illustrates the importance of carefully identifying the various
sources of SHC and examining their contributions to wages separately. Following this literature,
we plan to refine our results from the NLS and DWS by credibly documenting the evolution of
various forms of SHC since the 1980s.
To summarize, the striking confluence of trends in job transitions and migration,
combined with the fact that neither trend is explainable by demographic or socio-economic
shifts, is strongly suggestive of a link between the two. Even though including these job
transitions in an Oaxaca decomposition only explains an additional 20 percent of the decline in
migration, the CPS data on which the decompositions are based do not allow us to include all
types of labor market transitions (such as labor force entry). Moreover the available information
on industry and occupation transitions are not as detailed as we would like. Consequently, one
avenue that we plan to explore is to estimate similar Oaxaca decompositions in the NLS data.
17
The preliminary evidence that we have presented here suggests that rising returns to SHC may be
a promising avenue for further exploration.
Outline of further work to do:
Model/theory

Develop a (simple) model of how an increase in the return to firm/industry/occupation
specific human capital would reduce migration.

Think about why the return to specific human capital has risen. Could it be related to the
much-discussed rise in the return to education?
Better estimates of the link between migration and labor market transitions

Estimate Oaxaca decompositions of interstate migration using the NLS/NLSY, which can
include better measures of labor market transitions

Replicate the state-level regressions with IRS migration data, since the CPS appears to
overstate the downward trend in migration.
Improve our understanding of the downward trend in labor market transitions

Estimate Oaxaca decompositions of labor market transitions using the CPS and/or
NLS/NLSY

Investigate the role of employer-provided health insurance in preventing people from
changing jobs and therefore moving long distances.
Better estimates of the return to specific human capital
18

Separate the returns to job tenure into returns to industry, occupation and job (also
possibly location)

Estimate the return to job tenure for NLSY79-Child, which would give us another cohort
of young workers for the 1990s.
19
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Kambourov, G. and Manovskii, I. 2009. “Occupational Specificity of Human Capital.”
International Economic Review. 50(1): 63-115.
Kaplan, G. and Schulhofer-Wohl, S. 2011. “Migration in a Flattening World.” SLIDES only.
Presented at SED Conference July 2011.
Katz, L. and Murphy, K. 1992. “Changes in Relative Wages, 1963-1987: Supply and Demand
Factors.” The Quarterly Journal of Economics. 107(1): 35-78.
Molly, R.; Smith, C. L.; and Wozniak, A. 2011. “Internal Migration in the US.” Journal of
Economic Perspectives. 25(3): 173-196.
Moscarini, G. and Thomsson, K. 2007. “Occupational and Job Mobility in the US.”
Scandinavian Journal of Economics. 109(4): 807-836.
Neal, D. 1995. “Industry-Specific Human Capital: Evidence from Displaced Workers.” Journal
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Parent, D. 2000. “Industry-Specific Capital and the Wage Profile: Evidence from the National
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Shaw, K. 1984. “A Formulation of the Earnings Function Using the Concept of Occupational
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Shaw, K. 1987. “Occupational Change, Employer Change, and the Transferability of Skills.”
Southern Economic Journal. 53(3): 702-719.
20
Table 1: Oaxaca decomposition of intra‐county migration 1981‐1989 Age distribution Age 25‐34 Age 35‐44 Age 45‐64 Age 65+ Education No high school degree High school degree, no college Some college College degree Racial composition White Black Hispanic Asian Marital status Married Separated/Divorced Single Kids in household Kids No Kids Live in dual‐earner household Dual earners No dual earners Homeownership status Renter Owner Income quintile 1st 2nd 3rd 4th 5th Employment status Employed Unemployed Military Not in labor force Geography (Census divisions) New England Middle Atlantic East North Central West North Central South Atlantic East South Central West South Central Avg. move rate Pop. share
0.17 0.09 0.05 0.03 0.08 0.10 0.10 0.09 0.08 0.11 0.14 0.12 0.07 0.12 0.14 0.10 0.08 0.08 0.09 0.19 0.05 0.08 0.08 0.09 0.10 0.08 0.10 0.16 0.13 0.06 0.08 0.09 0.07 0.06 0.08 0.08 0.08 0.28 0.22 0.31 0.19 0.26 0.02 0.16 0.11 0.80 0.10 0.06 0.03 0.68 0.20 0.12 0.38 0.62 0.31 0.69 0.28 0.72 0.13 0.18 0.20 0.23 0.25 0.60 0.04 0.00 0.36 0.07 0.93 0.05 0.16 0.18 0.07 0.17 Contribution of changes in: Quantities Coefficients 2002‐2010 Avg. move rate
Pop. share
0.15 0.08 0.04 0.02 0.09 0.07 0.07 0.07 0.06 0.10 0.11 0.09 0.05 0.09 0.13 0.09 0.06 0.05 0.08 0.18 0.04 0.08 0.08 0.08 0.07 0.05 0.08 0.13 0.10 0.05 0.06 0.07 0.06 0.05 0.07 0.06 0.07 0.21 0.22 0.38 0.19 0.14 0.31 0.26 0.19 0.71 0.11 0.12 0.05 0.62 0.21 0.16 0.33 0.67 0.32 0.68 0.26 0.74 0.12 0.20 0.22 0.23 0.24 0.63 0.04 0.00 0.33 0.08 0.91 0.05 0.14 0.16 0.07 0.19 21
‐0.45 ‐0.47 0.03 ‐0.21 0.19 0.07 0.05 0.01 0.00 0.01 0.05 0.00 0.01 0.04 0.01 ‐0.03 0.03 ‐0.01 ‐0.06 0.03 0.01 0.01 ‐0.02 ‐0.01 ‐0.01 ‐0.38 ‐0.19 ‐0.19 0.03 0.01 0.00 0.01 0.01 0.01 ‐0.36 ‐0.26 ‐0.10 0.00 0.00 0.14 0.01 0.08 0.00 0.00 0.00 0.00 ‐0.01 ‐0.01 ‐0.40 ‐0.11 0.15 0.34 ‐0.49 ‐0.18 ‐0.04 0.01 ‐0.28 0.23 0.16 0.17 ‐0.09 ‐0.01 ‐0.10 ‐0.04 ‐0.13 0.08 ‐0.17 0.21 ‐0.39 0.13 ‐0.08 0.21 0.03 ‐0.02 0.04 ‐0.07 0.11 0.06 ‐0.08 ‐0.14 ‐0.01 ‐0.21 ‐0.24 0.02 ‐0.01 0.03 ‐0.04 0.04 0.13 0.03 ‐0.02 0.02 0.03 ‐0.11 Mountain Pacific Metropolitan area status Lives in metro area Doesn't live in metro area Constant Total 0.08 0.11 0.12 0.11 0.06 0.10 0.05 0.15 0.07 0.09 0.10 0.09 0.06 0.11 0.07 0.16 0.06 0.01 0.05 0.03 0.03 ‐0.88 Note: Oaxaca decompositions also include year fixed effects. The contribution of the interactions is not listed. 22
0.00 ‐0.15 0.04 0.06 ‐0.02 ‐0.27 ‐0.99 Table 2: Oaxaca decomposition of inter‐state migration 1981‐1989 Age distribution Avg. move rate Contribution of changes in: Quantities Coefficients 2002‐2010 Pop. share
Avg. move rate
Pop. share
Age 25‐34 0.04 0.28 0.03 0.21 ‐0.09 ‐0.11 ‐0.01 ‐0.14 Age 35‐44 0.03 0.22 0.02 0.22 0.01 ‐0.04 Age 45‐64 0.01 0.31 0.01 0.38 ‐0.04 0.04 Age 65+ 0.01 0.19 0.01 0.19 0.05 0.14 ‐0.03 0.64 Education No high school degree 0.01 0.26 0.01 0.14 ‐0.20 0.37 High school degree, no college 0.03 0.02 0.01 0.31 0.00 0.02 ‐0.01 Some college 0.03 0.16 0.02 0.26 ‐0.01 College degree 0.04 0.11 0.02 0.19 0.18 0.26 ‐0.09 ‐0.14 ‐0.22 Racial composition White 0.03 0.80 0.02 0.71 ‐0.05 Black 0.02 0.10 0.02 0.11 0.01 0.05 Hispanic 0.02 0.06 0.01 0.12 ‐0.06 0.05 Asian 0.03 0.03 0.02 0.05 Marital status 0.02 ‐0.02 ‐0.05 ‐0.16 Married 0.02 0.68 0.01 0.62 ‐0.02 ‐0.19 Separated/Divorced 0.02 0.20 0.02 0.21 0.00 ‐0.01 Single 0.03 0.12 0.02 0.16 Kids in household ‐0.03 0.04 0.01 ‐0.02 Kids 0.03 0.38 0.02 0.33 0.00 0.02 No Kids 0.02 0.62 0.02 0.67 0.00 ‐0.03 ‐0.02 ‐0.10 Live in dual‐earner household Dual earners 0.02 0.31 0.01 0.32 ‐0.01 0.06 No dual earners 0.03 0.69 0.02 0.68 ‐0.01 ‐0.16 Homeownership status Renter 0.05 0.28 Owner 0.01 0.72 Income quintile 0.04 0.26 0.01 0.74 ‐0.11 0.19 ‐0.05 ‐0.14 ‐0.05 0.34 0.00 0.00 1st 0.03 0.13 0.02 0.12 0.00 0.01 2nd 0.02 0.18 0.02 0.20 0.00 ‐0.04 ‐0.04 3rd 0.02 0.20 0.02 0.22 0.00 4th 0.02 0.23 0.01 0.23 ‐0.01 0.02 5th 0.02 0.25 0.02 0.24 0.01 0.06 0.29 0.12 Employment status Employed 0.02 0.60 0.02 0.63 0.29 0.13 Unemployed 0.05 0.04 0.04 0.04 0.00 ‐0.01 Military 0.17 0.00 0.12 0.00 0.00 0.03 Not in labor force 0.02 0.36 0.01 0.33 0.01 ‐0.04 0.10 0.07 Geography (Census divisions) New England 0.02 0.05 0.01 0.05 0.00 0.01 Middle Atlantic 0.01 0.16 0.01 0.14 0.03 0.10 East North Central 0.02 0.18 0.01 0.16 0.01 0.05 West North Central 0.03 0.07 0.02 0.07 0.00 0.00 ‐0.07 South Atlantic 0.03 0.17 0.02 0.19 0.02 East South Central 0.02 0.06 0.02 0.06 0.00 0.02 West South Central 0.03 0.10 0.02 0.11 0.00 ‐0.05 23
Mountain 0.05 0.05 0.03 0.07 0.05 Pacific 0.02 0.15 0.01 0.16 0.00 0.04 0.04 ‐2.24 ‐0.05 Metropolitan area status Lives in metro area 0.02 0.72 0.02 0.82 0.01 Doesn't live in metro area 0.02 0.25 0.01 0.17 0.01 Constant ‐0.03 0.02 ‐1.37 Total 0.03 Note: Oaxaca decompositions also include year fixed effects. The contribution of the interactions is not listed. 24
‐0.84 Table 3: Relationship between average state long‐distance move rate (interstate + cross‐county) and select variables Fract. changed firms in last year (1) (2) (3) (4) (5) (6) (7) (8) 0.00 (0.03)
0.07 (0.04)
0.02 (0.03)
0.03 (0.03)
(9) (10) 0.00 (0.02) 0.00 (0.02) 0.00 (0.01) ‐0.01 (0.01) (11) (12) 0.25 0.14 (0.03) (0.02)
Fract. changed occupations in last year (1 digit) 0.36 0.20 (0.05) (0.04)
Fract. employed last year but not currently emp. 0.14 0.01 (0.04) (0.03)
Fract. of employed in medium‐skill jobs Fract. of employed in high‐skill jobs Log(p10)‐log(p50) Log(p50)‐log(p90) Fraction in households with dual earners 0.04 0.00 (0.03) (0.02)
Contribution to change in fraction ‐0.008 ‐0.005 ‐0.009 ‐0.005 ‐0.004 0.000 0.005 0.000 0.000 0.001 0.000 0.000
moving (avg. 1981‐89 to 2002‐2010) State time trends NO YES NO YES NO YES NO YES NO YES NO YES Note: Coefficients are from state‐year level regressions of long‐distance annual move rates (across state or county) on the listed coefficients, state and year fixed effects (and state time trends where indicated), the fraction of the state unemployed, the log of average annual income for the state, and the fraction of the state that is young (under 21) and of prime working age (21‐64). Contribution to change in fraction moving is the coefficient listed in the table multiplied by the change in the average of the listed variable between 1981‐89 and 2002‐2010 (weighted by population size). Across states, the average fraction moving in 1981‐89 is 0.057 and the average fraction moving between 2002‐2010 is 0.039 (the change in the average fraction moving was ‐0.018). 25
Table 4: Oaxaca decomposition of inter‐state migration, including labor market transitions 1981‐1989 Age distribution Age 25‐34 Age 35‐44 Age 45‐64 Age 65+ Education No high school degree High school degree, no college Some college College degree Racial composition White Black Hispanic Asian Marital status Married Separated/Divorced Single Kids in household Kids No Kids Live in dual‐earner household Dual earners No dual earners Homeownership status Renter Owner Income quintile 1st 2nd 3rd 4th 5th Geography (Census divisions) New England Middle Atlantic East North Central West North Central South Atlantic East South Central West South Central Mountain Pacific Metropolitan area status Lives in metro area Doesn't live in metro area Employment transitions Avg. move rate
Pop. share
0.04 0.03 0.01 0.01 0.01 0.03 0.03 0.04 0.03 0.02 0.02 0.03 0.02 0.02 0.03 0.03 0.02 0.02 0.03 0.05 0.01 0.03 0.02 0.02 0.02 0.02 0.02 0.01 0.02 0.03 0.03 0.02 0.03 0.05 0.02 0.02 0.02 0.28 0.22 0.31 0.19 0.26 0.02 0.16 0.11 0.80 0.10 0.06 0.03 0.68 0.20 0.12 0.38 0.62 0.31 0.69 0.28 0.72 0.13 0.18 0.20 0.23 0.25 0.05 0.16 0.18 0.07 0.17 0.06 0.10 0.05 0.15 0.72 0.25 Contribution of changes in: Quantities Coefficients
2002‐2010 Avg. move rate
Pop. share
0.03 0.02 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.01 0.02 0.01 0.02 0.02 0.02 0.02 0.01 0.02 0.04 0.01 0.02 0.02 0.02 0.01 0.02 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.03 0.01 0.02 0.01 0.21 0.22 0.38 0.19 0.14 0.31 0.26 0.19 0.71 0.11 0.12 0.05 0.62 0.21 0.16 0.33 0.67 0.32 0.68 0.26 0.74 0.12 0.20 0.22 0.23 0.24 0.05 0.14 0.16 0.07 0.19 0.06 0.11 0.07 0.16 0.82 0.17 26
‐0.15 ‐0.12 0.00 ‐0.03 0.00 0.14 0.18 ‐0.02 0.00 ‐0.02 ‐0.09 ‐0.05 0.00 ‐0.06 0.01 ‐0.06 ‐0.03 0.00 ‐0.03 0.02 0.01 0.01 ‐0.01 0.00 0.00 ‐0.14 ‐0.07 ‐0.07 ‐0.03 ‐0.01 0.00 ‐0.01 ‐0.01 ‐0.01 0.09 0.00 0.02 0.01 0.00 0.01 0.00 0.00 0.04 0.01 0.02 0.01 0.01 ‐0.25 ‐0.11 ‐0.08 ‐0.06 0.00 0.02 0.03 0.08 0.00 0.00 ‐0.05 ‐0.21 ‐0.27 0.03 0.05 ‐0.02 ‐0.19 ‐0.24 ‐0.01 0.06 ‐0.01 0.04 ‐0.05 ‐0.02 0.11 ‐0.13 0.25 ‐0.19 0.45 0.01 0.00 ‐0.02 0.00 0.02 0.00 0.08 0.00 0.09 0.06 ‐0.01 ‐0.07 0.02 ‐0.04 ‐0.03 0.07 ‐0.07 ‐0.11 0.03 1.25 Didn’t change job in last year Changed job in last year Emp. in last year, not emp. now Not emp. in last year, or emp. now Didn't change occ. in last year Changed occ. In last year Constant Total 0.02 0.09 0.05 0.02 0.02 0.10 0.59 0.08 0.08 0.92 0.59 0.03 0.01 0.06 0.04 0.01 0.01 0.08 0.61 0.07 0.06 0.92 0.63 0.02 ‐0.06 ‐0.06 ‐0.01 ‐0.01 ‐0.06 ‐0.06 ‐0.46 Note: Oaxaca decompositions also include year fixed effects. The contribution of the interactions is not listed. 27
0.89 ‐0.13 ‐0.01 0.20 0.31 ‐0.02 ‐1.86 ‐0.88 Table 5: Estimated Returns to a Month of Tenure from National Longitudinal Surveys
i. Full samples, ages 25-28
Tenure coefficient
Tenure-squared
Observation years
NLS-YW
NLS-YM
NLSY79
NLSY97
.0050
(.0008)
-.00002
(8.12e-06)
.0054
(.0007)
-.00003
7.12e-06
.0086
(.0004)
-.00005
(3.76e-6)
.0079
(.0009)
-.00006
(.00001)
‘70-‘73,
‘78
‘70,’71,’73,
1982-1993
‘75,’76
N
2169
5227
Mean tenure
(months)
27.3
25.4
2005-2009
31770
7825
30.5
23.8
ii. Subsample of white males in the labor force, ages 25-28
NLS-YW
NLS-YM*
NLSY79
NLSY97
Tenure coefficient
-
Tenure-squared
-
.0049
(.0008)
-.00002
(8.13e-06)
.0090
(.0010)
-.00005
(.00001)
.0070
(.0019)
-.00005
.00003
Observation years
‘70,’71,’73,
1982-1993
‘75,’76
N
3768
Mean tenure
(months)
25.4
2005-2009
2677
1468
30.3
23.7
Notes: Data are from National Longitudinal Surveys of Youth 1979 and 1997 (NLSY79 and NLSY97) and from the
National Longitudinal Surveys of Young Men and Young Women (NLSYM and NLSYW). Dependent variable is
log of real hourly wages on current/MR job as computed by the BLS and released with public use files for each
survey. Observations within top and bottom 2.5% of wages each survey year are excluded. Tenure is measured in
months; top 2.5% of tenure durations are excluded. Standard errors clustered on person identifier in parentheses. All
Panel i specifications include controls for age, age-squared, black race, Hispanic ethnicity, sex, highest grade
completed, and a full set of survey year dummies. Controls for sex, race, and ethnicity are excluded from Panel ii
specifications. Panel ii sample limited to those in the labor force by CPS definition in the NLSY79 and those not
enrolled in school in the NLSY97. *Exception is that NLSYM could not be limited using direct measures of labor
force participation.
28
Table 6: Relationship between change in log wage from former to current job and select variables Changed industry (3 digit) Changed industry (3 digit) X (year is 2004 or 2006) (1) (2) (3) (4) (5) (6) (7) (8) (9) ‐0.055
‐0.073 ‐0.064
‐0.071 ‐0.052 ‐0.068
(0.031)
(0.033) (0.037)
(0.037) (0.023) (0.024)
‐0.018
0.027 0.000 0.048 ‐0.018 0.031 (0.043)
(0.047) (0.055)
(0.054) (0.033) (0.034)
Changed occ. (3 digit) Changed occ. (3 digit) X (year is 2004 or 2006) 0.002 0.038 ‐0.007 0.018 0.007 0.037 (0.031) (0.033)
(0.032) (0.032)
(0.023) (0.023)
‐0.078 ‐0.091
‐0.091 ‐0.107
‐0.091 ‐0.105
(0.040) (0.044)
(0.049) (0.048)
(0.031) (0.032)
Mean (changed variable), 1994 or 1996 0.664 0.613 0.721 0.655 0.688 0.630 Mean (changed variable), 2004 or 2006 0.625 0.629 0.686 0.672 0.651 0.647 N 1863 1537 3400 1862 1537 3399 Note: Microdata is from the Displaced Worker Survey. In addition to variables listed in the table, regressions also include labor market experience and experience squared, years of tenure in the former firm and years squared, years since displacement from previous job, year fixed effects, and dummy variables for gender, educational attainment, race, and marital status. Sample includes survey data from 1994, 1996, 1998, 2004, 2006, and 2008. Robust standard errors are in parentheses. 29
Figure 1
Fraction of Population that Move by Reason
Across State
Family related
Housing related
Job related
Other reasons
.02
.015
.01
.005
0
2000
2001
2002
2003
2004
2005 2006
Survey year
2007
2008
2009
2010
Within County
Family related
Housing related
Job related
Other reasons
.07
.06
.05
.04
.03
.02
.01
0
2000
2001
2002
2003
2004 2005 2006
Survey year
30
2007
2008
2009
2010
Figure 2
Labor Market Transitions in the CPS
Changed employers
Changed occupation
Exited employment
.12
.1
.08
.06
.04
.02
0
1980
1985
1990
31
1995
Survey year
2000
2005
2010
OLS Regressions Underlying Oaxaca Decompositions
Interstate - 1980s
Transformed regress coefficients
Number of obs
=
387453
-----------------------------------------------------------------------------smigrant |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------agegrpdum5 |
1.755747
.0505601
34.73
0.000
1.65665
1.854843
agegrpdum6 |
.3668085
.0523453
7.01
0.000
.2642133
.4694038
agegrpdum7 | -.5408929
.0429026
-12.61
0.000
-.6249807
-.4568051
agegrpdum8 | -1.581662
.0633515
-24.97
0.000
-1.705829
-1.457495
marstbdum1 |
.470283
.0425925
11.04
0.000
.3868029
.553763
marstbdum2 |
.1929498
.0456584
4.23
0.000
.1034607
.2824388
marstbdum3 | -.6632327
.0574437
-11.55
0.000
-.7758206
-.5506448
kids |
-.222798
.0319897
-6.96
0.000
-.2854969
-.1600991
nokids |
.222798
.0319897
6.96
0.000
.1600991
.2854969
race2dum1 |
.7693903
.054179
14.20
0.000
.6632012
.8755795
race2dum2 | -.6433308
.0710623
-9.05
0.000
-.7826108
-.5040508
race2dum3 | -1.066861
.0816315
-13.07
0.000
-1.226857
-.9068662
race2dum4 |
.9408019
.1149207
8.19
0.000
.7155607
1.166043
incdumdum1 |
.1772545
.0662686
2.67
0.007
.0473701
.3071389
incdumdum2 |
.2049154
.0528898
3.87
0.000
.1012529
.3085778
incdumdum3 |
.1428145
.0491738
2.90
0.004
.0464354
.2391936
incdumdum4 | -.3431243
.0506012
-6.78
0.000
-.4423012
-.2439474
incdumdum5 | -.1818602
.0546818
-3.33
0.001
-.2890349
-.0746855
empstatdum1 | -3.490791
.1086506
-32.13
0.000
-3.703743
-3.277839
empstatdum2 | -1.739714
.1376879
-12.64
0.000
-2.009579
-1.46985
empstatdum3 |
8.343179
.2980873
27.99
0.000
7.758937
8.927421
empstatdum4 | -3.112674
.1155854
-26.93
0.000
-3.339218
-2.88613
nohswork |
.4699019
.035177
13.36
0.000
.400956
.5388477
hswork | -.4699019
.035177
-13.36
0.000
-.5388477
-.400956
educdum1 | -.9295633
.0503899
-18.45
0.000
-1.028326
-.8308007
educdum2 | -.0028389
.0914836
-0.03
0.975
-.182144
.1764662
educdum3 | -.0304997
.0481844
-0.63
0.527
-.1249396
.0639402
educdum4 |
.9629019
.0545087
17.67
0.000
.8560666
1.069737
renter |
1.675605
.029183
57.42
0.000
1.618407
1.732802
owner | -1.675605
.029183
-57.42
0.000
-1.732802
-1.618407
metro2 |
.1037793
.0300036
3.46
0.001
.0449732
.1625854
nonmetro | -.1037793
.0300036
-3.46
0.001
-.1625854
-.0449732
regiondum1 | -.5229967
.1018536
-5.13
0.000
-.7226268
-.3233666
regiondum2 | -1.369008
.0611546
-22.39
0.000
-1.488869
-1.249147
regiondum3 | -.7440205
.0591694
-12.57
0.000
-.8599907
-.6280504
regiondum4 |
.0874385
.0891545
0.98
0.327
-.0873017
.2621787
regiondum5 |
.9015262
.0590242
15.27
0.000
.7858406
1.017212
regiondum6 | -.0054698
.0890075
-0.06
0.951
-.1799219
.1689823
regiondum7 |
.4598243
.0691708
6.65
0.000
.3242516
.5953969
regiondum8 |
2.00556
.112141
17.88
0.000
1.785767
2.225353
regiondum9 | -.8128539
.0642926
-12.64
0.000
-.9388655
-.6868424
y1 | -.0308425
.0668454
-0.46
0.645
-.1618575
.1001726
y2 | -.0240111
.0664989
-0.36
0.718
-.154347
.1063249
y3 | -.3132778
.0659824
-4.75
0.000
-.4426012
-.1839543
y4 | -.0971625
.0657153
-1.48
0.139
-.2259624
.0316375
y5 |
.0296164
.0630176
0.47
0.638
-.0938962
.1531291
y6 |
.1101981
.0627312
1.76
0.079
-.0127532
.2331495
y7 |
.1299948
.0624373
2.08
0.037
.0076195
.2523701
y8 |
.1954844
.061965
3.15
0.002
.074035
.3169339
_cons |
5.670906
.1232828
46.00
0.000
5.429275
5.912536
------------------------------------------------------------------------------
32
Interstate - 2000s
Transformed regress coefficients
Number of obs
=
799356
-----------------------------------------------------------------------------smigrant |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------agegrpdum5 |
1.242454
.0297727
41.73
0.000
1.1841
1.300807
agegrpdum6 |
.1607371
.0276017
5.82
0.000
.1066386
.2148355
agegrpdum7 | -.4221945
.0224343
-18.82
0.000
-.466165
-.378224
agegrpdum8 | -.9809962
.0359399
-27.30
0.000
-1.051437
-.9105551
marstbdum1 |
.1764719
.0230516
7.66
0.000
.1312915
.2216523
marstbdum2 |
.1448219
.0246811
5.87
0.000
.0964477
.1931961
marstbdum3 | -.3212938
.0288913
-11.12
0.000
-.3779198
-.2646677
kids | -.1692549
.017349
-9.76
0.000
-.2032583
-.1352515
nokids |
.1692549
.017349
9.76
0.000
.1352515
.2032583
race2dum1 |
.4918372
.0260735
18.86
0.000
.4407341
.5429404
race2dum2 | -.2588594
.037257
-6.95
0.000
-.331882
-.1858369
race2dum3 | -.4037409
.0360407
-11.20
0.000
-.4743796
-.3331023
race2dum4 |
.1707632
.0491714
3.47
0.001
.0743887
.2671376
incdumdum1 |
.2199234
.0385134
5.71
0.000
.1444383
.2954084
incdumdum2 |
.0276918
.0288085
0.96
0.336
-.0287719
.0841555
incdumdum3 | -.0519638
.0265557
-1.96
0.050
-.104012
.0000844
incdumdum4 | -.2556331
.0276719
-9.24
0.000
-.3098691
-.2013971
incdumdum5 |
.0599817
.031214
1.92
0.055
-.0011968
.1211602
empstatdum1 | -2.831854
.0675675
-41.91
0.000
-2.964284
-2.699424
empstatdum2 | -1.252151
.0813944
-15.38
0.000
-1.411681
-1.092621
empstatdum3 |
6.55183
.1896125
34.55
0.000
6.180195
6.923464
empstatdum4 | -2.467824
.0712202
-34.65
0.000
-2.607414
-2.328235
nohswork |
.2531883
.0196165
12.91
0.000
.2147406
.291636
hswork | -.2531883
.0196165
-12.91
0.000
-.291636
-.2147406
educdum1 |
-.641359
.0309332
-20.73
0.000
-.7019871
-.580731
educdum2 | -.2134906
.0218872
-9.75
0.000
-.2563888
-.1705925
educdum3 |
.0857768
.0233131
3.68
0.000
.0400838
.1314698
educdum4 |
.7690729
.0274358
28.03
0.000
.7152996
.8228461
renter |
1.196785
.017091
70.02
0.000
1.163287
1.230283
owner | -1.196785
.017091
-70.02
0.000
-1.230283
-1.163287
metro2 |
.0310198
.0184212
1.68
0.092
-.0050852
.0671248
nonmetro | -.0310198
.0184212
-1.68
0.092
-.0671248
.0050852
regiondum1 |
-.402386
.0580027
-6.94
0.000
-.5160695
-.2887026
regiondum2 | -.7470632
.0359654
-20.77
0.000
-.8175542
-.6765722
regiondum3 | -.4398173
.0336787
-13.06
0.000
-.5058263
-.3738082
regiondum4 |
.1068554
.0489681
2.18
0.029
.0108795
.2028313
regiondum5 |
.4801561
.0313555
15.31
0.000
.4187004
.5416118
regiondum6 |
.2863644
.0513639
5.58
0.000
.1856929
.3870359
regiondum7 |
.0260973
.0393195
0.66
0.507
-.0509676
.1031623
regiondum8 |
1.236444
.0497672
24.84
0.000
1.138902
1.333986
regiondum9 | -.5466505
.0350548
-15.59
0.000
-.6153567
-.4779442
y1 |
.3601869
.036376
9.90
0.000
.288891
.4314827
y2 |
.2466016
.0361794
6.82
0.000
.1756911
.317512
y3 |
.1135642
.0360889
3.15
0.002
.0428312
.1842972
y4 |
.2881626
.0361277
7.98
0.000
.2173536
.3589716
y5 | -.1231126
.0356125
-3.46
0.001
-.1929119
-.0533132
y6 | -.2019293
.0352699
-5.73
0.000
-.2710571
-.1328014
y7 | -.2459998
.0352285
-6.98
0.000
-.3150465
-.1769531
y8 | -.4374735
.0354147
-12.35
0.000
-.5068852
-.3680618
_cons |
4.303516
.0718184
59.92
0.000
4.162754
4.444278
------------------------------------------------------------------------------
33
Intracounty – 1980s
Transformed regress coefficients
Number of obs
=
387453
-----------------------------------------------------------------------------hmigrant2 |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------agegrpdum5 |
7.282278
.0917663
79.36
0.000
7.102419
7.462137
agegrpdum6 |
1.351045
.0950064
14.22
0.000
1.164835
1.537255
agegrpdum7 | -2.749867
.0778679
-35.31
0.000
-2.902485
-2.597248
agegrpdum8 | -5.883456
.1149825
-51.17
0.000
-6.108819
-5.658094
marstbdum1 | -.8108427
.0773051
-10.49
0.000
-.9623585
-.659327
marstbdum2 |
2.124562
.0828696
25.64
0.000
1.96214
2.286984
marstbdum3 | -1.313719
.1042599
-12.60
0.000
-1.518065
-1.109373
kids | -.6407727
.0580612
-11.04
0.000
-.7545709
-.5269745
nokids |
.6407727
.0580612
11.04
0.000
.5269745
.7545709
race2dum1 | -.0272443
.0983344
-0.28
0.782
-.2199768
.1654883
race2dum2 | -.8891558
.1289777
-6.89
0.000
-1.141948
-.6363635
race2dum3 |
.6666382
.1481606
4.50
0.000
.3762479
.9570285
race2dum4 |
.2497619
.2085803
1.20
0.231
-.1590494
.6585731
incdumdum1 | -.7403306
.120277
-6.16
0.000
-.97607
-.5045912
incdumdum2 |
.0228638
.0959947
0.24
0.812
-.1652828
.2110105
incdumdum3 |
.4914532
.0892501
5.51
0.000
.3165258
.6663806
incdumdum4 |
.505728
.0918409
5.51
0.000
.3257225
.6857334
incdumdum5 | -.2797144
.0992471
-2.82
0.005
-.4742358
-.085193
empstatdum1 |
.8307889
.1972001
4.21
0.000
.4442826
1.217295
empstatdum2 |
3.053206
.2499026
12.22
0.000
2.563404
3.543007
empstatdum3 | -3.647731
.5410264
-6.74
0.000
-4.708127
-2.587335
empstatdum4 | -.2362636
.2097868
-1.13
0.260
-.6474394
.1749121
nohswork |
.3180321
.063846
4.98
0.000
.1928958
.4431684
hswork | -.3180321
.063846
-4.98
0.000
-.4431684
-.1928958
educdum1 |
.8405141
.0914573
9.19
0.000
.6612605
1.019768
educdum2 | -.3242109
.1660421
-1.95
0.051
-.6496486
.0012267
educdum3 | -.1782543
.0874543
-2.04
0.042
-.3496621
-.0068465
educdum4 | -.3380488
.0989329
-3.42
0.001
-.5319543
-.1441433
renter |
5.858695
.0529669
110.61
0.000
5.754882
5.962509
owner | -5.858695
.0529669 -110.61
0.000
-5.962509
-5.754882
metro2 |
.3522971
.0544562
6.47
0.000
.2455645
.4590297
nonmetro | -.3522971
.0544562
-6.47
0.000
-.4590297
-.2455645
regiondum1 | -2.211334
.1848637
-11.96
0.000
-2.573662
-1.849007
regiondum2 | -3.429468
.1109951
-30.90
0.000
-3.647015
-3.211921
regiondum3 | -.1911698
.107392
-1.78
0.075
-.4016549
.0193153
regiondum4 |
.0656498
.1618149
0.41
0.685
-.2515025
.3828021
regiondum5 | -.0665187
.1071285
-0.62
0.535
-.2764872
.1434499
regiondum6 |
.2262406
.1615481
1.40
0.161
-.0903888
.5428701
regiondum7 |
2.163469
.1255445
17.23
0.000
1.917406
2.409533
regiondum8 |
2.422342
.2035351
11.90
0.000
2.023419
2.821264
regiondum9 |
1.02079
.1166906
8.75
0.000
.7920794
1.2495
y1 | -.4223826
.121324
-3.48
0.000
-.660174
-.1845912
y2 | -.0026889
.1206951
-0.02
0.982
-.2392478
.2338699
y3 | -.5571842
.1197575
-4.65
0.000
-.7919054
-.322463
y4 | -.4859197
.1192728
-4.07
0.000
-.7196908
-.2521486
y5 |
.2630349
.1143766
2.30
0.021
.0388602
.4872096
y6 |
.6828243
.1138567
6.00
0.000
.4596685
.9059801
y7 |
.3709518
.1133234
3.27
0.001
.1488414
.5930622
y8 |
.1513645
.112466
1.35
0.178
-.0690655
.3717945
_cons |
10.42918
.2237574
46.61
0.000
9.990625
10.86774
------------------------------------------------------------------------------
34
Intracounty – 2000s
Transformed regress coefficients
Number of obs
=
799356
-----------------------------------------------------------------------------hmigrant2 |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------agegrpdum5 |
5.829921
.0619008
94.18
0.000
5.708597
5.951244
agegrpdum6 |
.7893038
.0573872
13.75
0.000
.6768268
.9017807
agegrpdum7 | -2.246318
.0466435
-48.16
0.000
-2.337738
-2.154898
agegrpdum8 | -4.372906
.0747233
-58.52
0.000
-4.519362
-4.226451
marstbdum1 | -.8795736
.047927
-18.35
0.000
-.9735089
-.7856384
marstbdum2 |
1.552667
.0513149
30.26
0.000
1.452091
1.653242
marstbdum3 | -.6730929
.0600684
-11.21
0.000
-.7908249
-.5553608
kids | -.0395242
.0360705
-1.10
0.273
-.1102213
.0311728
nokids |
.0395242
.0360705
1.10
0.273
-.0311728
.1102213
race2dum1 |
.1781399
.0542098
3.29
0.001
.0718906
.2843893
race2dum2 |
.5575155
.0774617
7.20
0.000
.4056931
.7093379
race2dum3 | -.6388079
.0749329
-8.53
0.000
-.7856738
-.4919419
race2dum4 | -.0968476
.1022332
-0.95
0.343
-.2972212
.103526
incdumdum1 |
.1091192
.0800739
1.36
0.173
-.047823
.2660613
incdumdum2 |
.2898768
.0598962
4.84
0.000
.1724823
.4072714
incdumdum3 |
.0865063
.0552123
1.57
0.117
-.0217079
.1947206
incdumdum4 | -.1471901
.0575331
-2.56
0.011
-.2599531
-.0344272
incdumdum5 | -.3383122
.0648976
-5.21
0.000
-.4655093
-.211115
empstatdum1 |
.503827
.1404807
3.59
0.000
.2284895
.7791645
empstatdum2 |
1.911561
.1692285
11.30
0.000
1.579879
2.243243
empstatdum3 | -1.487454
.3942265
-3.77
0.000
-2.260125
-.7147835
empstatdum4 | -.9279335
.1480752
-6.27
0.000
-1.218156
-.6377111
nohswork |
.6095049
.040785
14.94
0.000
.5295678
.6894421
hswork | -.6095049
.040785
-14.94
0.000
-.6894421
-.5295678
educdum1 |
.3144924
.0643137
4.89
0.000
.1884397
.4405452
educdum2 |
.0977714
.045506
2.15
0.032
.0085812
.1869616
educdum3 | -.2259148
.0484707
-4.66
0.000
-.3209158
-.1309137
educdum4 | -.1863491
.0570422
-3.27
0.001
-.29815
-.0745482
renter |
5.795038
.0355342
163.08
0.000
5.725393
5.864684
owner | -5.795038
.0355342 -163.08
0.000
-5.864684
-5.725393
metro2 |
.4265644
.0382999
11.14
0.000
.3514979
.5016308
nonmetro | -.4265644
.0382999
-11.14
0.000
-.5016308
-.3514979
regiondum1 | -1.416465
.1205944
-11.75
0.000
-1.652826
-1.180104
regiondum2 | -2.644218
.0747762
-35.36
0.000
-2.790777
-2.497659
regiondum3 | -.0064942
.0700218
-0.09
0.926
-.1437347
.1307463
regiondum4 |
-.280705
.1018104
-2.76
0.006
-.4802501
-.0811599
regiondum5 |
.0664281
.0651917
1.02
0.308
-.0613455
.1942017
regiondum6 |
.6192335
.1067915
5.80
0.000
.4099258
.8285413
regiondum7 |
1.231535
.0817498
15.06
0.000
1.071308
1.391762
regiondum8 |
2.385455
.1034718
23.05
0.000
2.182654
2.588256
regiondum9 |
.0452313
.072883
0.62
0.535
-.0976171
.1880796
y1 |
.4806529
.07563
6.36
0.000
.3324206
.6288853
y2 |
.1926532
.0752212
2.56
0.010
.0452221
.3400842
y3 |
.1256103
.075033
1.67
0.094
-.0214519
.2726724
y4 |
.047253
.0751136
0.63
0.529
-.0999672
.1944732
y5 |
.1318353
.0740426
1.78
0.075
-.0132856
.2769563
y6 | -.5966207
.0733302
-8.14
0.000
-.7403455
-.452896
y7 | -.3457847
.0732441
-4.72
0.000
-.4893408
-.2022286
y8 | -.0355992
.0736313
-0.48
0.629
-.1799143
.1087158
_cons |
10.15613
.1493188
68.02
0.000
9.863473
10.44879
------------------------------------------------------------------------------
35
Interstate with labor market transitions – 1980s
Transformed regress coefficients
Number of obs
=
224434
-----------------------------------------------------------------------------smigrant |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------agegrpdum5 |
1.079132
.0702742
15.36
0.000
.9413968
1.216868
agegrpdum6 |
.3552695
.0715946
4.96
0.000
.2149459
.4955931
agegrpdum7 | -.3006167
.0662093
-4.54
0.000
-.4303852
-.1708482
agegrpdum8 | -1.133785
.1326043
-8.55
0.000
-1.393686
-.8738842
marstbdum1 |
.6824023
.0647923
10.53
0.000
.5554109
.8093936
marstbdum2 |
.0175789
.0674601
0.26
0.794
-.1146411
.1497989
marstbdum3 | -.6999812
.0748051
-9.36
0.000
-.8465973
-.553365
kids | -.2982149
.0398895
-7.48
0.000
-.3763972
-.2200326
nokids |
.2982149
.0398895
7.48
0.000
.2200326
.3763972
renter |
1.790816
.0400065
44.76
0.000
1.712405
1.869228
owner | -1.790816
.0400065
-44.76
0.000
-1.869228
-1.712405
race2dum1 |
.7253665
.0727679
9.97
0.000
.5827432
.8679898
race2dum2 | -.5209568
.0981123
-5.31
0.000
-.7132544
-.3286592
race2dum3 | -.9554947
.1105744
-8.64
0.000
-1.172218
-.7387718
race2dum4 |
.7510851
.1527178
4.92
0.000
.4517621
1.050408
incdumdum1 |
.484911
.1385471
3.50
0.000
.2133623
.7564597
incdumdum2 |
.1267308
.09387
1.35
0.177
-.0572521
.3107137
incdumdum3 | -.2968385
.0739664
-4.01
0.000
-.4418108
-.1518662
incdumdum4 | -.3952872
.0664824
-5.95
0.000
-.5255911
-.2649834
incdumdum5 |
.0804839
.0686893
1.17
0.241
-.0541455
.2151133
nohswork |
.5163611
.0430181
12.00
0.000
.4320467
.6006755
hswork | -.5163611
.0430181
-12.00
0.000
-.6006755
-.4320467
educdum1 | -.8349753
.0681321
-12.26
0.000
-.9685125
-.7014381
educdum2 | -.0722525
.1123582
-0.64
0.520
-.2924717
.1479666
educdum3 | -.0080201
.0598212
-0.13
0.893
-.1252682
.109228
educdum4 |
.9152479
.066438
13.78
0.000
.7850311
1.045465
metro2 |
.1923696
.0424002
4.54
0.000
.1092662
.2754729
nonmetro | -.1923696
.0424002
-4.54
0.000
-.2754729
-.1092662
regiondum1 | -.2820066
.1352962
-2.08
0.037
-.5471836
-.0168295
regiondum2 | -1.197896
.0854656
-14.02
0.000
-1.365406
-1.030386
regiondum3 | -.8044721
.081019
-9.93
0.000
-.9632674
-.6456768
regiondum4 |
.2616558
.1201303
2.18
0.029
.0262034
.4971082
regiondum5 |
.8634863
.0810615
10.65
0.000
.7046079
1.022365
regiondum6 |
.0607074
.1267814
0.48
0.632
-.1877809
.3091957
regiondum7 |
.30158
.093941
3.21
0.001
.117458
.4857021
regiondum8 |
1.801642
.1479179
12.18
0.000
1.511727
2.091558
regiondum9 | -1.004697
.0853075
-11.78
0.000
-1.171898
-.8374968
y1 | -.0404956
.0923363
-0.44
0.661
-.2214725
.1404812
y2 | -.0859648
.091838
-0.94
0.349
-.265965
.0940353
y3 | -.2640156
.0914203
-2.89
0.004
-.4431971
-.0848341
y4 | -.0261901
.0908457
-0.29
0.773
-.2042453
.1518651
y5 |
.0005024
.0860202
0.01
0.995
-.168095
.1690998
y6 |
.1625761
.0852994
1.91
0.057
-.0046085
.3297606
y7 |
.1411897
.0843532
1.67
0.094
-.0241404
.3065199
y8 |
.112398
.0835233
1.35
0.178
-.0513056
.2761016
nodjob | -2.441847
.0533906
-45.74
0.000
-2.546492
-2.337203
djob |
2.441847
.0533906
45.74
0.000
2.337203
2.546492
w2nw |
.904806
.0711505
12.72
0.000
.7653529
1.044259
now2nw |
-.904806
.0711505
-12.72
0.000
-1.044259
-.7653529
nodocc1 | -2.619246
.0760064
-34.46
0.000
-2.768217
-2.470275
docc1 |
2.619246
.0760064
34.46
0.000
2.470275
2.768217
_cons |
7.289872
.1368835
53.26
0.000
7.021584
7.55816
------------------------------------------------------------------------------
36
Interstate with labor market transitions – 2000s
Transformed regress coefficients
Number of obs
=
525305
-----------------------------------------------------------------------------smigrant |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------agegrpdum5 |
.8709772
.0360719
24.15
0.000
.8002774
.9416771
agegrpdum6 |
.13945
.0336144
4.15
0.000
.0735667
.2053332
agegrpdum7 |
-.285133
.0301932
-9.44
0.000
-.3443108
-.2259552
agegrpdum8 | -.7252942
.0630835
-11.50
0.000
-.8489359
-.6016525
marstbdum1 |
.3325196
.0311062
10.69
0.000
.2715524
.3934867
marstbdum2 | -.0314052
.0318472
-0.99
0.324
-.0938248
.0310143
marstbdum3 | -.3011144
.033428
-9.01
0.000
-.3666321
-.2355966
kids | -.2114655
.0189408
-11.16
0.000
-.2485889
-.1743421
nokids |
.2114655
.0189408
11.16
0.000
.1743421
.2485889
renter |
1.151324
.0205312
56.08
0.000
1.111083
1.191564
owner | -1.151324
.0205312
-56.08
0.000
-1.191564
-1.111083
race2dum1 |
.3824756
.0308927
12.38
0.000
.3219269
.4430243
race2dum2 | -.2571665
.0449067
-5.73
0.000
-.3451822
-.1691507
race2dum3 | -.2741435
.0426768
-6.42
0.000
-.3577887
-.1904982
race2dum4 |
.1488343
.0581522
2.56
0.010
.0348579
.2628107
incdumdum1 |
.5595409
.084109
6.65
0.000
.3946898
.724392
incdumdum2 | -.0332035
.0442303
-0.75
0.453
-.1198935
.0534865
incdumdum3 | -.2787823
.0356554
-7.82
0.000
-.3486657
-.208899
incdumdum4 | -.3189805
.0338745
-9.42
0.000
-.3853735
-.2525874
incdumdum5 |
.0714254
.0366602
1.95
0.051
-.0004275
.1432784
nohswork |
.2856801
.0221618
12.89
0.000
.2422437
.3291166
hswork | -.2856801
.0221618
-12.89
0.000
-.3291166
-.2422437
educdum1 | -.5757076
.0435238
-13.23
0.000
-.6610129
-.4904022
educdum2 |
-.167988
.0272609
-6.16
0.000
-.2214185
-.1145575
educdum3 |
.0012867
.0277391
0.05
0.963
-.0530811
.0556546
educdum4 |
.7424088
.0316864
23.43
0.000
.6803044
.8045132
metro2 |
.0545053
.0224104
2.43
0.015
.0105815
.0984291
nonmetro | -.0545053
.0224104
-2.43
0.015
-.0984291
-.0105815
regiondum1 | -.3153183
.0684146
-4.61
0.000
-.4494087
-.1812279
regiondum2 | -.6209498
.04325
-14.36
0.000
-.7057184
-.5361811
regiondum3 | -.4538762
.039832
-11.39
0.000
-.5319457
-.3758068
regiondum4 |
.0389805
.0562697
0.69
0.488
-.0713063
.1492673
regiondum5 |
.4751482
.0376856
12.61
0.000
.4012855
.5490108
regiondum6 |
.4533946
.0627831
7.22
0.000
.3303418
.5764474
regiondum7 | -.0510392
.0467344
-1.09
0.275
-.1426371
.0405587
regiondum8 |
1.050938
.0583573
18.01
0.000
.9365596
1.165317
regiondum9 | -.5772778
.0417831
-13.82
0.000
-.6591714
-.4953842
y1 |
.2488027
.0432504
5.75
0.000
.1640334
.3335721
y2 |
.2162857
.0428988
5.04
0.000
.1322054
.300366
y3 |
.0527586
.0430047
1.23
0.220
-.0315293
.1370465
y4 |
.2306884
.0430926
5.35
0.000
.1462282
.3151486
y5 | -.0516676
.0422988
-1.22
0.222
-.134572
.0312368
y6 | -.1830599
.0419055
-4.37
0.000
-.2651935
-.1009264
y7 | -.1733759
.0420275
-4.13
0.000
-.2557485
-.0910033
y8 |
-.340432
.0426121
-7.99
0.000
-.4239503
-.2569137
nodjob | -1.420702
.0279076
-50.91
0.000
-1.4754
-1.366004
djob |
1.420702
.0279076
50.91
0.000
1.366004
1.4754
w2nw |
.6884952
.0372634
18.48
0.000
.6154601
.7615303
now2nw | -.6884952
.0372634
-18.48
0.000
-.7615303
-.6154601
nodocc1 | -2.288697
.0458368
-49.93
0.000
-2.378536
-2.198859
docc1 |
2.288697
.0458368
49.93
0.000
2.198859
2.378536
_cons |
5.425768
.0694396
78.14
0.000
5.289668
5.561867
------------------------------------------------------------------------------
37