Previous research on migration and gendered career outcomes centers on couples and rarely examines the reason for the move. The implicit assumption is usually that households migrate in response to job opportunities. Based on a two-year panel from the Current Population Survey, this article uses stated reasons for geographic mobility to compare earnings outcomes among job migrants, family migrants, and quality-of-life migrants by gender and family status. We further assess the impact of migration on couples’ internal household economy. The effects of job-related moves that we find are reduced substantially in the fixed-effects models, indicating strong selection effects. Married women who moved for family reasons experience significant and substantial earnings declines. Consistent with conventional models of migration, we find that household earnings and income and gender specialization increase following job migration. Married women who are secondary earners have increased odds of reducing their labor supply following migration for job or family reasons. However, we also find that migrating women who contributed as equals to the household economy before the move are no more likely than nonmigrant women to exit work or to work part-time. Equal breadwinner status may protect women from becoming tied movers.
In popular and academic discourse, people are presumed to move in search of a better life, and a primary motivation is economic well-being. A recurrent theme in the large literature that examines the relationship between geographical mobility and economic well-being is the gender gap in labor market outcomes following household mobility. Conventional models of household migration center on the relative economic gains of moving from one labor market to another. All else equal, households are presumed to migrate if household income will be higher in the new locality than if no move took place. When the labor market advantages of mobility accrue primarily to one partner (most often the husband), the other partner (most often the wife) is considered a “tied mover.” For the tied mover, the household gains from migration may come at the expense of her own career.
One problem with the existing literature on gender differences in returns to mobility is that the motivation underlying the move is not generally known, so researchers are forced to make assumptions. In the prime working-age population, moves across labor markets are often presumed to be motivated by financial considerations with the goal of upward social mobility. Of course, migration decisions can also be based on family proximity, housing needs, or other factors, such as quality of life. Without taking actual mobility motivations into account, claims about differential investments in husbands’ and wives’ careers may be overstated. Moreover, migration studies that rely solely on evidence from married couples cannot distinguish between marital status effects and gender effects. By jointly considering mobility outcomes by gender and marital status, we are able to pinpoint more clearly the extent to which the impact of migration is gendered, depends on family status, or is shaped by both gender and family status.
In our article, we address three sets of questions: (1) Why do people move, and how does the reason for geographic mobility vary by gender and family status? (2) How do the outcomes of migration vary by reason for move, gender, and family status? (3) In married-couple households, to what extent do “tied-mover” effects vary depending on the reason for migration and women’s income contribution prior to the move? To address these questions, we use the Current Population Survey to investigate the immediate career impact of migration among working-age men and women in the United States. We distinguish between moves motivated by job changes, family reasons, and quality of life and other reasons.
Previous Research on Migration, Mobility Motivations, and the Role of Gender
The U.S. Census Bureau defines migration as a move across county borders, while moves within counties are referred to as residential mobility (Shachter et al. 2003). In this context, migration is viewed as an investment in future income streams (Sjaastad 1962), or more generally as the outcome of individual and household assessments of the costs and benefits of leaving one community for another in the pursuit of upward social mobility (Blau and Duncan 1967; Davis and Moore 1945) through moving. Broader theoretical models in the rational choice tradition allow for an expanded set of mobility motivations. For example, a strong preference for a specific area (because of climate, social networks, or other nonmonetary incentives) may trump the pecuniary costs of the move. However, because preferences are extremely difficult to measure, these considerations are rarely implemented in empirical research.
In contrast with the economic approach, the classic life-cycle model of residential mobility views moves as a response to life-course events (Clark and Withers 2002; Rossi 1955). Of course, these categories correspond imperfectly to actual distance moved, and even “local” moves may result in a different geographical labor market.
However, few U.S. studies relate mobility outcomes to actual reports of the reason for the move. Empirical research on migration generally relies on the assumption that long-distance household moves are motivated by economic gain. Nevertheless, evidence in the United States confirms that “most local moves, and not a trivial number of long-distance moves, occur for reasons other than labor market gains” (Clark and Withers 2002:943). Families might move in search of better housing or safer schools, renters may prefer to buy, or couples might prefer a location that provides career opportunities for both partners over a move that might enhance the husband’s career at the expense of the wife’s career. From the perspective of the neoclassical economic model, we would expect married couples to migrate when the change in the net present value of the sum of partner’s lifetime earnings exceeds the costs, both pecuniary and nonpecuniary. Moves for family reasons or quality-of-life reasons might be expected to result in lower household earnings gains than moves for job-related reasons. However, the question remains as to how the costs and benefits are distributed in the household.
Following Becker’s (1973, 1974) extension of human capital theory to the economic analysis of marriage, Mincer (1978) proposed a human capital model of family migration. In the Mincer model, married couples allocate household labor to paid or unpaid work in order to maximize the total benefit to the household, and each migration decision is based on potential economic opportunities and costs to the entire household, rather than the potential gain of any individual member. As a consequence, migration can result in one partner experiencing upward mobility while the other partner—a tied mover—experiences a career loss. Likewise, a “tied stayer” will forgo a move whenever the individual career gain from migration is fully offset by a career loss to the other partner, resulting in no overall financial gain for the household from a potential move (Clark and Withers 2002; Mincer 1978). Although either partner can be a follower in the migration decision, men are seen as likely to invest more heavily in their careers, while women are thought to have a comparative advantage in domestic labor. Consequently, when couples move, women are presumed to be the tied movers.
Empirical evidence to date is largely consistent with this gendered model of tied-mover theory. For married men, migration is generally associated with increased employment prospects, higher wage growth, and higher occupational status (Duncan and Perrucci 1976; Greenwood 1975). Evidence strongly suggests that migration increases household specialization and gender inequality by helping the career trajectories of men and hindering the careers of women. Studies have shown that among married women, migration results in stalled careers, slower wage growth, increased risk of employment exit, and underemployment (Boyle et al. 2003; Lichter 1982; Long 1974; Markham et al. 1983; Maxwell 1988; Morrison and Lichter 1988). For previous generations, the gender gap in migration outcomes is unsurprising, and women’s greater willingness to follow a partner who took a new job in another city has been well demonstrated (Baldridge et al. 2006; Bielby and Bielby 1992; Shihadeh 1991). Higher rates of overeducation among employed women in smaller labor markets (Büchel 2000; Büchel and van Ham 2003) are evidence that even today, women are much more likely than men to be tied movers. Cooke’s (2003) study of dual-earner family migrants found earnings growth among husbands and stagnant earnings for wives; even among wives with greater initial earnings potential than their husbands, their husband’s income increased and their own income remained stagnant.
In one recent study, however, Cooke and Speirs (2005) showed that men as well as women can be tied movers, and that being a tied mover has a negative impact on labor market status, both for men and women. While husbands’ occupations have a greater impact on migration decisions than wives’ occupations (McKinnish 2008), there is some evidence that a wife’s economic position does factor into the mobility decision (Bird and Bird 1985; McKinnish 2008). McKinnish (2008) found that highly educated women with less-educated partners have somewhat resilient careers: the negative effect of husbands’ mobility on wives’ earnings does not apply to couples in which the wife is college educated but her husband is not.
Much of the research on women’s mobility has focused on their status as tied movers within a couple. Theory predicts that in the absence of family ties and responsibilities, single women’s mobility behavior and mobility outcomes should be comparable to that of their male counterparts. However, previous research has not addressed gender differences among singles, comparing women’s outcomes within marriage with women’s outcomes before or after marriage. When comparing the income of single and married movers, the gains to married movers may be lower because they are thought to maximize the household gains, and not necessarily their own gains. When evaluating the overall gains, married individuals—even if they do not remain tied stayers but actually moved—may experience family migration as a process of compromise. Single parents are another group that has been undertheorized by tied-mover theory. They are “tied” not by a partner but by considerations of child well-being and by the support they may receive from a network of extended family and friends that cannot be easily replaced after a move.
The Present Study
In our study, we seek to address some of the limitations of the existing literature. We focus on the underlying reason for move as well as the variation of the outcomes of migration by reason for move, and we provide a more detailed analysis of women as tied movers. First, we examine individual mobility behavior, focusing on group differences in migration rates and motivations for moving by family status and gender.
Second, we test whether the returns of migration vary by type of move, and we specifically examine differences in the migration returns by family status and gender. Tied-mover theory assumes that the mobility decisions of single individuals can be seamlessly extended to the mobility decisions of households, yet previous research has not compared the mobility outcomes of singles with the mobility outcomes of men and women in couples. We can examine whether singles are more likely than couples to focus on labor market outcomes in mobility decisions and the extent to which singles benefit the most from moving.
Third, tied-mover theory implies that the household benefits of the move come at the expense of increasing gender specialization in the household, and previous research has focused on the career gains for men and career losses for women. Within marriage, the presumption of traditionally gendered labor market specialization may often be misplaced: women contribute substantially to the household income of married couples. In our sample of U.S. dual wage earners during the first five years of this century, married women contributed, on average, more than one-third of the household labor income. Families may be less willing to move for one partner’s career than was previously thought, and contemporary families may be more likely than ever before to move for the advancement of the wife’s career. Given these changes, it is crucial to investigate not only the average outcomes for individuals and couples who move but also the extent to which families exhibit diversity in these outcomes. By examining household income streams and labor supply prior to and after the move, we are able to shed light on the impact of different types of moves on the internal household economy.
We use data from the 1999–2005 March Basic Files and the Annual Social and Economic Supplement (referred to as ASEC, and formerly known as the Annual Demographic Survey/March Supplement) of the Current Population Survey, a key source of information about residential mobility and migration in the United States. These data are merged with harmonized variables taken from the Integrated Public Use Microdata Series: CPS (IPUMS-CPS) data available from the Minnesota Population Center (King et al. 2004). The CPS is a monthly survey of residential addresses in the United States designed around a 4-8-4 sampling rotation, with eight rotation groups designated by their “month in sample.” Each sample residence is in the survey for four consecutive months, out for eight months, and finally back in for four consecutive months. We select observations from the first four rotation groups to produce a pooled cross-sectional file for 1999–2004. We then created a two-year panel data set by matching households and individuals to ASEC data collected in March of the following year: that is, 2000–2005.1
Cross-Sectional and Longitudinal Matched Sample
Because our primary interest is in the social mobility consequences of moving for working-age individuals and families, we limit the analysis to householders and their partners aged 20–55 at the time of the move. We exclude individuals who moved from outside the United States in the previous year; individuals who were retired, disabled, or in the military; or those younger than 25 who were in school full-time. We also exclude the partners of these individuals, and any individuals who moved to attend or leave college. We include only the first four March rotation groups in each survey year, which eliminates overlap in our pooled cross-section file. The resulting cross-sectional geographical mobility sample includes observations for more than 110,000 households.2
To gain a wider window on the social mobility consequences of residential mobility, we produced a two-year panel data set of individuals and married couples with labor income in both years. The panel file was created using the subset of respondents in the pooled cross-section file who could be matched by linking information from their first March interview at time t to their second March interview a year later (at time t + 1).3 Matching individuals across survey years in the CPS is difficult for several reasons. First, the CPS is a survey or residences, not a survey of individuals; individuals who move from the residence are lost to the survey. Second, the household identifiers available in the CPS public-release files throughout much of the period covered by the data are not unique, even when combined with geographic identifiers, so care must be taken to avoid duplicate or mismatched records. Effective match techniques must combine household-level information with identifiers and survey responses at the person level. The response variables, including gender, ethnicity, age, and education, are less reliable and more prone to measurement error than other identifiers. Finally, as in all longitudinal panel surveys, respondents may exit the sample because of death, illness, disability, or refusal to participate in subsequent rounds of interviews. We devised a more effective variant of the matching procedure proposed by Madrian and Lefgren (2000) for the 1999–2002 base years. Beginning with the 2003 survey year, the March ASEC files include an individual-level identifier that, although not unique, greatly facilitated the match process. We used a restrictive decision-rule that accepted the match only if (a) there was a perfect fit for all survey and geographic identifiers, sex, and race; (b) immigrant status (but not necessarily year of arrival) matched in both periods; (c) education was the same or one level higher in the second period as compared with the first period; and (d) age in the second period was the same or up to two years older compared with age in the first period. The match algorithm resulted in person-match rates of 75% in 1999 and 2000, and 55%–60% in the expanded samples for 2001–2004. The first set of numbers is high relative to previously reported match rates, reflecting the relatively narrow sample that is being matched; we have not seen any reports of match rates using the expanded samples. Our underlying population in the panel analyses includes both nonmovers and individuals who remain in the same residence for 12–24 months following a move.
Measures of Geographic Mobility
Mobility is measured at the time of the initial survey at time t using reports of household residents who moved to the sample address during the 12-month period prior to the survey. All respondents are asked whether any household member (over the age of 1) lived in a different house or apartment in March of the calendar year preceding the survey. If a move took place, the respondent is probed for the reason why the move took place. Each household member who moved is then coded with one reason for moving, beginning with the householder. Other members of the household who moved with the householder are assigned the same reason for moving as that reported by the householder. Although there may be some problems with the fact that reason for the move is reported after the fact, we do not suspect that post-move outcomes motivate respondents to misstate their original reason for move. Respondents are asked to choose from 17 response categories. We collapsed these categories into three groups: job-change reasons can be motivated by “new job or job transfer” and “to look for work, or lost job.” Although both reasons represent labor market responses, the first corresponds to positive opportunities in the destination labor market (“job pull”), and the second corresponds to poor opportunities in the origin labor market (“job push”). Family reasons include “change in marital status” and “other family reason.” We categorized the remaining reasons as quality-of-life and other reasons: “to be closer to work/easier commute,” “wanted new or better house/apartment,” “wanted better neighborhood/less crime,” “wanted cheaper housing,” “other housing reason,” and “change of climate,” “to establish own home,” “retired,” “other job-related reason,” “health reasons,” and “other reasons.” Our definition of migration follows the established convention of including moves across county lines.
Other Key Measures
We investigate variation in reasons for moving, labor market outcomes, and material well-being following a move by gender and by family status. We ascertain individual family status at the time of the interview, and this status may be different than at the time of the move. Individuals are assigned one of the following family statuses: married with children, married without children, cohabiting without children (including cohabitants living with partner’s children), single parents (including cohabiting parents), or single. In our analysis of dual-career mobility, we limit the analysis to married couples who were living together (whether married or not) prior to the observation period.
Labor market outcomes following a move are measured using matched samples. This measure is calculated twice for each respondent in the match file, as are all economic measures. The initial measure, constructed from reports in March of year t, encompasses the pre-move period, and the measure is again constructed from reports in March of year t + 1, which encompasses the post-move period. The earnings measures are calculated from respondents’ reports of total annual wage and salary earnings in the previous calendar year. For self-employed workers, wage and salary income may be zero, or it may be a small component of their annual income from work, so we use the larger of these two as well as a second measure of the total income from the longest job held during the previous calendar year. The annual measure of job earnings is then divided by the number of weeks worked to produce a measure of average weekly earnings, with a floor set at $1 per week.
Our measures of labor market outcomes following a move are not ideal; because of the design of the ASEC, annual income is reported for the period beginning January and ending December of the calendar year prior to the March survey in which the income is reported. The 12-month reporting period for moves is slightly later because respondents report moves that occurred since March of the previous year through the March date of the survey. This implies that respondents will report between 3 and 12 months of pre-mobility income in year t, and between 9 and 12 months of post-mobility income in year t + 1. As a result, our measures of the impact of mobility on earnings are imperfect, but they will capture differences between pre-mobility and post-mobility earnings.
We use two measures of household-level material well-being in married-couple households. The first is a measure of total labor market earnings, produced by summing the annual earnings reported by each spouse. Our second measure is based on total household income from all sources, including wages and salaries; income from farms, business, and rent; government transfers; and private transfers, such as child support, alimony, and financial support from friends and relatives. We create a household equivalent income measure by deflating total household income by the square root of family size to capture household standard of living in each period (OECD 2008). This measure adjusts for household needs and economies of scale by implying that a two-person household requires approximately 1.4 times the income of a one-person household to enjoy an equivalent standard of living, and a four-person household would require twice the income of a single person household to enjoy that same standard of living.
We rely on two simple measures of household specialization to assess the impact of geographical mobility on the gender division of labor in married-couple families. The first measure, wife’s share of labor supply, captures the gender balance of the couple’s labor supply. It is calculated by using retrospective reports on weeks worked in the previous calendar year and usual work hours during the weeks worked, capped at 70 hours per week. The product of these variables yields a measure of annual labor supply in the previous year, and wife’s share is constructed as the ratio of wife’s annual hours to the sum of each partner’s total work hours. The second measure of household specialization, wife’s share of earnings, is an earnings specialization measure calculated analogously to the labor supply specialization measure. It is the ratio of wife’s total previous-year earnings and the sum of each partner’s earnings.
The specialization measures, like the household income measures, are calculated for each of two consecutive years for all couples in the match file. This allows us to compare the outcomes of mobility for couples with conventional levels of gender specialization in the initial period to outcomes for couples with a relatively equal division of paid work and earnings. We use 40% as the threshold level for wife’s share of hours and earnings in a conventional household division of labor, and we categorize couples as egalitarian if the wife’s share of hours and earnings is between 40% and 60% in time t.4
The individual earnings models use a human capital specification with covariates for years of schooling, a second-order polynomial for imputed potential labor market experience, and an indicator for the implied school-leaving age to capture nonlinearities in the effect of education. Additional controls include indicators for race, ethnicity, and immigration status, as well as indicators for the year of the initial survey.
Our analysis proceeds in three stages. We begin with an analysis of the reasons why people move. We use the pooled cross-sectional data to investigate the relationship between family status and motivation for geographical mobility, and we test for gender differences in reasons for the move among single men and women. In a second step, we use regression models on a matched sample of workers with labor income in both years to analyze the impact of geographical mobility on earnings for individual movers. In a final step, we investigate the impact of mobility on household income and gender specialization within married couples.
Conventional techniques for estimating the impact of residential moves on labor force outcomes may be biased for several reasons. First, it is well known in the migration literature that individuals who migrate may possess unobserved characteristics that make them more likely to move. If these unobserved traits are also correlated with labor market outcomes, such as earnings, ordinary least squares (OLS) estimates of the relationship between moving and earnings will be biased. Couples may also have unobserved characteristics that make them more or less prone to gender specialization, and these preferences for specialization may influence the decision to move. Fixed-effects estimation using panel data provides one solution for the problem of unobserved heterogeneity. Assuming that the unobserved characteristics and their effect on the outcome of interest are time invariant, fixed-effects estimation will produce unbiased estimates of the effect of mobility on earnings. One concern with fixed-effects estimation is that the reduction in bias achieved by the fixed-effects estimator might be offset by an exacerbation of bias due to measurement error, but our mobility measure should be subject to measurement error only in the first period because respondents reside at the same address at both interviews. We produce fixed-effects estimates for all results using the matched panel data. A second source of bias can occur because of the high rates of household turnover and the unequal probability of sample retention in the matched data. We addressed sample attrition by constructing longitudinal weights, using a series of probit analyses for the probability of a person match, estimated separately for each gender-year combination in the data. These estimates were used to generate predicted probabilities of sample retention. We then adjusted the basic CPS weights assigned by the Census Bureau, multiplying each basic weight by the inverse of the predicted probability of sample retention. All pooled cross-sectional analyses are weighted by using basic CPS weights, and all analyses of the matched sample use longitudinal weights.
Geographic Mobility Behavior and Motivation by Family Status and Gender
Table 1 sets the groundwork for the multivariate analyses that follow by comparing geographical mobility behavior among women, men, and couples. The top panel shows that cohabitants have the highest mobility rates, a reflection of the volatility of cohabiting unions. (In a separate examination, we found that one in three cohabiting couples report different migration histories for each partner during the previous year.) Married individuals are the least geographically mobile, especially married couples with children. Single women and single men are about equally likely to have moved. Overall, the strongest evidence for gender differences in migration is that single parents (who are predominantly female) are significantly less likely than singles and cohabitants to have migrated.5
The middle panel of Table 1 classifies respondents who migrated during the previous year by their current family status and the primary motivation for the move. Quality-of-life factors account for about one-half of all moves regardless of family status, and job changes account for more moves than family reasons for every group except single parents. Single men and women are less likely than married individuals, cohabitants, or single parents to have moved for family reasons. Single parents and cohabitants are significantly more likely than others to have migrated for family reasons, and significantly less likely than others to be job migrants.
Single women are slightly less likely than single men to cite a job change as the primary reason for their move, but this difference is not statistically significant. On balance, single women, single men, and married migrants have comparable odds of having moved in response to a job change. This is especially true in the case of a new job offer or a transfer, as shown in the bottom panel of Table 1. In contrast, cohabitants and especially single parents are significantly less likely to have migrated for new employment opportunities during the past year.
Migration and Individual Labor Market Outcomes
Are the geographically mobile also upwardly mobile? Overall, the evidence is mixed. Table 2 presents estimates of the pay differential between migrants and nonmigrants in the year following the move, using human capital regressions on the natural logarithm of weekly earnings. These descriptive results from cross-sectional data do not reflect the causal effect of migration, but they can shed light on selection processes. Panel A suggests that overall, no clear earnings differentials are associated with migration. The only exception is among the small group of men who are single fathers, who earn 21.5% less after migrating than single fathers who did not migrate. Because the results presented in the tables are based on log earnings, the coefficient was exponentiated and transformed into a percentage to reflect changes in absolute earnings: e−0.242 = 0.785 ∼ 78.5%.
Panel B disaggregates migration by reason for move to show that post-migration earnings vary substantially depending on family status and the motivation for migration. In particular, migration related to job changes is associated with earnings advantages of 10.2% among married men and 12.5% among single women; the results for single men are also positive but do not reach significance. These findings are in sharp contrast with the 9.4% earnings disadvantage for married women following job migration. Among married women, those who moved for new job opportunities—or presumably, new job opportunities for their husbands—have significantly lower earnings than married women who did not migrate. In Panel C, we disaggregate the two types of labor market migrants: job-pull migrants who moved for a new job or transfer, and job-push migrants who moved to seek work. The earnings bonus is most clearly associated with job-pull migration (moving for new job or job transfer), with single women, single men, and married men all earning 11%–12% more than their peers following the move. Again, this is in striking contrast with married women and single parents, who show either significant disadvantages or no significant earnings difference following a job-related move.
The earnings differentials shown in Table 2 are important evidence of differences between migrants and nonmigrants, but they do not account for the causal impact of mobility on earnings. We next estimate fixed-effects models of the impact of moving on average weekly earnings, which allows us to isolate the average impact of mobility on individual earnings. In this two-period model, a comparison of fixed-effects estimates with uncorrected estimates from Table 2 also provides a window into the labor market performance of migrants prior to the move, which in turn sheds light on selection into migration.
The fixed-effects estimates are presented in Table 3. Overall, there are average earnings gains from migration among unmarried men, but not for other groups. The results in Panel B suggest that these earnings gains for geographically mobile singles are attributable to quality-of-life moves rather than other types of moves. The results also indicate that much of the earnings advantage or disadvantage associated with job migration shown in Table 2 is due to unobserved heterogeneity; although the pattern shown in the two tables is the same (earnings gain for men and unmarried women following job migration, earnings decline for single parents and married women), the results are sharply attenuated. This is a strong indicator of positive selection of married men, single men, and single women into job-pull migration.
Although the fixed-effects results are imprecise, single fathers (but not necessarily single mothers) are likely to experience earnings declines following a move. Among married women, the estimated impact of migration is also negative, but the earnings disadvantage for job-related moves is substantially smaller in the fixed-effects models than in the descriptive estimates and is no longer statistically significant. This suggests that wives in job-migrant households were investing less in their own careers than other married women prior to the move, so the move itself is not the cause of their depressed earnings. Rather, these women, most of whom likely moved for a job other than their own, have low earnings because of a series of household bargaining decisions over time that shaped their labor market outcomes. The immediate impact of job migration on married women’s earnings may be minimal, but the immediate impact of family-motivated migration is substantial. Married women who report moves for family reasons see an earnings decline of 13% following the move, and although not statistically significant, the results suggest that single women who move for family reasons also see a decline in earnings.
Migration and Couple’s Labor Market Outcomes
The previous analyses shed light on migration and earnings outcomes in the population of individuals who are employed in two consecutive years. The question remains how migration affects not only individual earnings but also household labor supply, material well-being, and the gender division of paid labor in married couple households. Figure 1 provides a first look at married couples’ labor market behavior in the calendar year the couple migrated (or not) and the calendar year following the (potential) migration event. Most couples in our sample have a male breadwinner in both years, and most wives do not change their overall employment status from year to year. Figure 1 shows that among migrants and nonmigrants alike, stable dual-earner couples are the largest group, and the second-largest group consists of couples with a male breadwinner and a wife not in paid work in either year.
However, household employment transitions are more common among movers than among stayers. In the year following a move, women who migrate are more likely to enter or exit employment compared with women in couples that did not migrate. The difference in women’s employment in migrant households is especially pronounced among married couples with children. Movers with children are most likely to have a single male breadwinner6 in both periods, and mothers who migrate are most likely to exit employment in the year following the move. The difference in gender specialization in families with children is noteworthy. In the year after a move, only 60% of mothers in migrant households are in the workforce, compared with three-quarters of mothers in nonmigrant households. Perhaps it is not surprising given the challenges of coordinating careers in the dual-earner household, but migration appears to promote gender specialization.
The next set of analyses addresses income and work outcomes for married couple–headed households. First we ask whether geographic mobility is associated with short-term material gains to couple-headed households. We then consider the effect of geographic mobility on the gender division of paid work hours and on earnings equality within the household.
Table 4 shows fixed-effects regression estimates for the change in couples’ material well-being following different types of moves. The first column shows the effect of migration on total annual job earnings from both wife and husband. The second column shows the effect on total household income, including income from government transfers, child support and alimony, dividends and interest, and job earnings.7 The third column shows total household income adjusted for household size.
These results show that job migration has a substantial effect on annual household earnings and income. Married couples who move for a job change realize an average gain of about 9% in labor income, with slightly smaller gains to household total income and adjusted household income. Labor income increased by a healthy 6.5% in households that moved in response to a job transfer or a new job offer. Even larger earnings gains accrued to those who migrated following job loss or to search for a job, presumably because household labor supply increased in the post-migration household. This would be the case, for example, if the move were motivated by a layoff or a plant closing that resulted in unusually low pre-migration earnings. Household income gains for job-pull and especially job-push migrants are somewhat attenuated after all sources of income and household size are taken into account. Couples who moved for family reasons seem to experience a reduction in adjusted household income, perhaps due to the combination of lowered labor supply and family growth, but the declines are not statistically significant in these analyses.
How does moving affect the household division of labor? Table 5 shows results from the fixed-effects analysis of gender specialization in the household. The first two columns indicate that for all couples, women’s share of married couples’ combined labor income and labor supply is modestly shaped by migratory moves, toward increasing household specialization. Family-related moves reduce women’s share of household labor income by 3 percentage points, and job-pull migration is associated with a 2 percentage point decline in wives’ share of household labor supply, but most moves—those that we classified as “quality-of-life moves—have only a negligible effect on specialization. Surprisingly, when we distinguish between secondary-earner and dual-breadwinner households, we find that quality-of-life moves reduce household specialization. These moves are associated with an increase in wives’ share of earnings and work hours if the wife was a secondary earner, but a reduction in women’s contributions to post-migration household income and labor supply in dual-breadwinner households.
We also find that job-related moves appear to have little impact on couples in which the wife is the secondary earner, but job-pull migration appears to increase specialization by reducing the share of women’s labor supply in dual-breadwinner households.
In columns 7 and 8, we crosslink between earnings and labor supply: women who are equal participants with their husbands in the workforce in terms of their labor supply do not see a decline in their earnings share following a job-related move. Instead, earnings specialization takes place primarily among wives who work fewer hours than their husbands. Among these women, who typically work a part-time schedule, post-migration earnings contributions decline by 2.9 percentage points.
To better understand the link between moving and household division of labor, we examine women’s pre-move contributions to the household as a predictor of household specialization after the move. Table 6 shows results from a fixed-effects logistic regression that estimates the probability that an employed married woman will reduce her labor supply, either from full-time to part-time or from part-time to workforce exit, in the year following a migratory move. For all households combined, we find that moves for a new job are linked to labor supply reductions, a result that is entirely consistent with tied-mover theory. However, we also find that moves due to a job loss are associated with lower odds of labor supply reduction. The remainder of the table shows disparities in women’s outcomes depending on the strength of their breadwinner role prior to the move. In secondary-earner households, women who move for new jobs have about twice the odds of reducing their labor supply, and secondary earners who move for family reasons are also significantly more likely than those who do not migrate to reduce their labor supply. In contrast, married women in equal-breadwinner households are not more likely to reduce their labor supply following a migratory move, regardless of reason.8 Again, these results affirm the importance of women’s breadwinner roles in mobility decisions and household outcomes.
Summary and Conclusions
Although research findings on the labor market outcomes of migration generally support a human capital model in which migration is motivated by a job search, our study shows that most people who move do so primarily for quality-of-life reasons, family reasons, and other reasons aside from job changes. This article makes several contributions to the literature.
First, our comparisons of post-migration earnings among migrants and nonmigrants based on cross-sectional and matched CPS data show that the impact of migration depends on the reason for move. Our findings are an important extension of theoretical models of mobility that infer the reason for move from the post-migration outcome. We find compelling evidence that gender as well as family status shape both the post-migration labor market outcomes and the processes leading to the migration decision. Overall, we find some evidence of upward mobility among single women, single men, and married men following job migration, but much of the observed earnings gains are due to positive selection into migration. Among married women and single parents, post-migration earnings are either stagnant or in decline.
Second, we illustrate how different types of migration are linked to the internal household economy. We find that quality-of-life moves reduce household specialization, as wives’ share of earnings and work hours increase for secondary earners, but decrease in dual-breadwinner households. At the household level, labor market migration is associated with gains to total earnings and income, as predicted by human capital theories of migration. Wives who work fewer hours than their spouses see their post-migration earnings contributions decline significantly.
Our results indicate that women who were secondary earners were more likely to reduce their labor supply following a job- or family-related move, whereas wives who shared equally in the internal economy are no different than nonmovers in their commitment to the labor force following a move. This clearly suggests that women whose participation is secondary to their husbands’ are at risk of becoming tied movers and may see their breadwinner role further eroded by moves motivated by husbands’ job opportunities.
This article underlines that knowledge about migration motivation is helpful in understanding post-migration outcomes. Our finding that migration for quality-of-life reasons may reduce household specialization calls for further research on this type of type of geographic mobility. We also show that while gender may not matter in the impact of mobility among singles, there are clear differences between married men and women. Job-centered mobility (as well as family migration) may be polarizing for married women’s economic standing. Those who are not equal participants in the household economy face the risk of being pushed out of the labor market following a move, while women who are relative equals seem to maintain their participation and avoid being tied movers and the negative consequences associated with this status. At the same time, we see no evidence that men become tied movers in equal-breadwinner households.
Our results provide both support and new challenges for existing theories of migration. Our findings on job-motivated migration are largely in line with human capital perspectives on migration. Tellingly, the evidence suggests that in couple-headed households, job-motivated migration is oriented around the husband’s career. A limitation of the data used in this study is that couples who migrate together are assumed to share the same primary motivation; and if couples move for a job-related reason, we do not have information on whose job motivated the move. Although information about reason for move was collected after a move took place, our findings do not suggest that respondents state their motivation contingent on the post-move outcomes. If that were the case, we would expect much stronger positive effects of employment-motivated moves. Nevertheless, data on mobility intentions would be extremely useful, especially because it would allow more extensive testing of tied-mover theory by identifying tied stayers. In general, an ideal measure of relocation motivation would allow each partner to specify a primary reason for the move; it would include detailed information for job-related moves on whether the coupled moved for his job, her job, or the jobs of both partners; and it would also capture information on secondary reason(s) for the move. Additional information on possible secondary motivations for relocation would also be very helpful in understanding the complexity of these decisions. Detailed information such as this would advance research on tied-mover effects in migration. It would also contribute more generally to research on gender negotiations in couple-headed households. Because these ideal measures are not available, we argue that capturing the main reason for move is a substantial improvement over inferring migration motivation based on distance moved and post-relocation outcomes.
We demonstrate that only a proportion of long-distance moves are primarily motivated by new jobs or job searches; these moves occur for a variety of reasons that have primarily been associated with residential mobility. Although rational choice–based theories may allow for a vague “utility” term in addition to earnings in the modeling of mobility decision, our results point to a feasible improvement in more specific models of mobility and migration. Integrating a life-course perspective will allow researchers to achieve a better understanding of a broader range of moves, as integrating preference sets with respect to climate and other factors into empirical analyses is nearly impossible because of data limitations.
Our finding that job-motivated moves are associated with earnings losses for single fathers and that family-related moves have a negative impact on married women’s earnings implies that a broader notion of what constitutes a “tied mover” is necessary. Needs of children as well as families constrain moves and should be addressed more explicitly in future research. The outcomes of migration remain very different for men and women, but extending the scope of what factors could constrain households in their mobility decisions is very important to understand the mobility behavior of family forms other than married-couple households.
Our study further underlines the importance of taking into account the internal household economy. Our finding that migration increases the specialization of households in which the female partner is the secondary breadwinner, but does not affect equal-breadwinner households, suggests a threshold model of tied moving. Women who are secondary earners before a move are likely to become tied movers. However, as women’s economic contributions become increasingly central to household economies, we expect to see fewer negative consequences of migration for women.
Previous versions of the article were presented at the annual meeting of the Population Association of America, the Workshop in Politics, Economy and Culture at Indiana University, and the MZES Colloquium at the University of Mannheim.
A “month-in-sample” variable ranging from 1 to 8 identifies the household’s rotation in the sample. Households participating in the March survey during their first four months in sample are also scheduled for participation in the following March survey.
Expansion of the sampling frame in 2001 increased our sample sizes from roughly 15,000 in 1999–2001 to roughly 22,000 households in 2002, 2003, and 2004.
We are indebted to Donna E. Leicach of the Minnesota Population Center for her generous assistance with the IPUMS data and for providing supplemental information to facilitate the matching process.
Married couples with wives who contribute 60% or more to household earnings and paid work hours are a small and heterogeneous group. Most of these households include a husband who is long-term unemployed or temporarily or permanently out of the workforce, many are self-employed, some are households with high nonlabor income, and some are households with two full-time wage and salary earners.
Because men make up a fairly small proportion of single parents and often experience single parenthood differently than single women, the real-life implications of single parenthood are mostly experienced by women.
For this figure, we focus on the employment of wives with a breadwinner husband who is employed full-time in both years; the “other” category includes couples in which the husband is not employed or is working part-time in either or both years.
The sample size decreases because we dropped a small number of households with positive labor income but negative household income in either period.
There were not enough job loss–related moves for dual-earner households to allow us to estimate an effect.