Internal Migration and Life Satisfaction: Well-Being Paths of Young Adult Migrants

Abstract

Internal migration is typically associated with higher income, but its relation with life satisfaction remains unclear. Is internal migration accompanied by an increase in life satisfaction and does this increase depend on the reason for moving? What are the aspects of life underlying overall life satisfaction that change following migration? These questions are addressed using longitudinal data from the Swedish Young Adult Panel Study. Migration is defined as a change in municipality of residence. Comparing migrants to non-migrants, it is found that internal migration is accompanied by a short to medium term increase in life satisfaction for those who move due to work (work migrants), as well as those who move for other reasons (non-work migrants). However, only work migrants display an improvement in life satisfaction that remains significant 6 or more years following the move. Work and non-work migrants also differ in the aspects of life that change following migration. For work migrants the move is accompanied by an improvement in occupational status positively associated with well-being 6–10 years after the move. For non-work migrants, a persisting increase in housing satisfaction follows migration, but this housing improvement is accompanied by only a short to medium term increase in overall well-being.

Appendices

Appendix 1: Attrition in the Young Adult Panel Study

Given its longitudinal nature, the YAPS survey faces the inevitable problem of attrition. Of the 2,820 individuals first interviewed in 1999, 1,575 were successfully re-interviewed in 2009. This generated an attrition rate of 44 % over the 10 year period, which is similar to the rates typically observed in longitudinal surveys from other developed countries (Becketti et al. 1988; Abraham et al. 2006). The high non-response in the YAPS gives rise to concerns about the existence of an attrition bias. In what follows, first, the main characteristics at baseline of the people who attrit (are not re-interviewed in 2009) and who do not attrit are compared. Then, two main problems related to attrition are discussed: selection on migration, and selection on unobserved time-varying characteristics related to the changes in the dependent variables of the study.

At baseline, attritors have generally lower income,²⁰ lower economic satisfaction, and less years of education, then the people who are interviewed in both 1999 and 2009. Attritors are also more likely to be male, young, and have Swedish background (Table 7). The first series of characteristics related to income and education, stands in opposition to what has been observed in previous studies in both developing (Thomas et al. 2001, 2012) and developed countries (Hausman and Wise 1979; Becketti et al. 1988), where attrition has been found to have a positive association with income and education levels. This difference is probably due to the specific design of the YAPS survey which targets young adults (ages 22–30 in 1999), and therefore has a high relative proportion of student respondents (characterized by low income) at the time of the first survey. Given that young people are more likely to leave the survey, a higher percentage of attritors would have not achieved their final levels of education in 1999, lowering the average education level of this group, as well as their income and economic satisfaction.

Table 7 Comparison of the characteristics at baseline (1999) of surveyed people who consequently attrit (not interviewed in 2009) and do not attrit (interviewed in 2009)

The relationship between the birth cohort and attrition is similar to that observed in previous literature, with younger cohorts being more likely to attrit in subsequent interviews. The difference in the attrition rates of people with Swedish and non-Swedish background may be related to previous findings that early life experience and parent characteristics are related to attrition (Thomas et al. 2012). Interestingly, higher levels of attrition are not associated with more hours worked per week, as could be expected if busy people were less likely to be re-interviewed. Previous studies conducted with surveys from the United States have found that non-contact is in fact associated with longer work times, though the same did not hold for refusals, with refusal rates showing no association with work time (Abraham et al. 2006).

Attrition in the YAPS survey could represent a major problem if it was selective on migration given that the main focus of the present study is on comparisons of migrants and non-migrants. Past research has found that attrition in longitudinal surveys may, in fact, be selective on migration. This problem arises especially in the case of surveys performed in developing countries (Thomas et al. 2001, 2012), as in developed countries non-response rates in surveys are mostly associated with refusals as opposed to failure to contact the respondents. Still, Abraham et al. (2006) find that non-contact rates may also be high in developed countries, as documented by their observations about the American Time Use Survey.

The problem of attrition due to migration should be lessened in the YAPS due to the access of the employees of Statistics Sweden, who were in charge of the data collection, to the Swedish Register records. The Register consists of data collected by the Swedish Tax Agency and includes specific information about current place of residence for all individuals. Access to this information should potentially make the task of following migrants considerably easier than in countries with less precise demographic information on their inhabitants.

A comparison of non-contact versus refusal rates in the YAPS could be informative, as non-response associated with non-contact may be more related to trouble finding a person who has moved. Unfortunately, the YAPS survey was performed by mail, and no information of non-contact versus refusal rates was collected. Still, because attrition is generally associated with similar demographic characteristics across different surveys (Zabel 1998), a comparison of the characteristics of attritors in the YAPS to the characteristics of attritors due to non-contact in other surveys could provide insight into this problem. In developed countries such as the United States, non-contact is typically associated with being single, working longer hours, and being a high school graduate (Abraham et al. 2006). In the YAPS, the proportion of people married and the hours worked at baseline are not statistically different for attritors and non-attritors. Moreover, attritors have significantly less years of education, which is the opposite of the association between education and non-contact found by Abraham and co-authors. If the same associations between non-contact and demographic characteristics hold for Sweden as for United States, this could imply that a big proportion of attrition in the YAPS is due to refusal. Still, it is not clear that Swedish attrition should follow the same patterns as those observed in studies from other countries, and so the previous implication may be considered inconclusive.

An additional indirect test of selection on attrition used by previous literature consists of comparing characteristics of interest of the observed survey sample to those of a similar sample of the general population (Groves 2006). Using this method, a test of attrition selective on migration in the YAPS is performed comparing rates of mobility by cohort of survey respondents interviewed in both years to those of the general population of Sweden (Table 8). For every cohort, the mobility of the general population is slightly above that of the non-attritors from YAPS, with the difference between the two populations being highest for the 1976 cohort. For all cohorts combined, the difference in the migration proportions between the general population and the YAPS is 3 % (44 % for general population and 41 % for YAPS). This difference implies that, though selection on migration might have certainly taken place in the YAPS survey, the magnitude of this selection appears small.

Table 8 Proportion of mobility by cohort: general population versus YAPS non-attritors

The second way in which attrition could bias the results is through selection on time-varying characteristics associated with either changes in life satisfaction or any of the other dependent variables used. To analyze this issue of selective attrition, the panel structure of the data is used to implement a test described by Wooldridge (2010). Selective attrition may represent a source of bias if it is correlated with the error term conditional on the explanatory variables (including the variable of interest, in this case migration). That is, in the model Y_ic = β’x_i + η_i + ε_ic the condition E(ε_ic|x_i, a_i, η_i) = 0 (where a_i represents attrition) must be satisfied to assure consistency of the parameter estimates (Wooldridge 2010). To test for this Wooldridge suggests adding to the equation a lead attrition indicator (that turns to one in the period before attrition) and testing for its significance using a standard t test. If the attrition indicator turns out to be not significant in this regression, that indicates that attrition should not represent a source of bias.

To implement the above method in the present analysis requires using the intermediate 2003 survey. Since some of the respondents who attrited between 1999 and 2009 still participated in the 2003 survey, using this data allows to estimate the first-difference model with the 99–03 variables adding future attrition (in the period 03–09) into the model. To assure robustness to the possible correlation between attrition and the explanatory variables, this test should control for all explanatory variables included in the original model (including migration). This rises a practical issue, as Register data on municipality of residence (used to determine migration) and on some of the control variables (such as education) is only available in the YAPS survey for the respondents who participated in the 2009 round, and is therefore missing for all attritors. Due to the absence of the Register data, migration may not be directly included into the model when estimating the first-difference regression using the 99–03 variables. Still, in an attempt to control for the correlation between attrition and migration, 1999 variables expected to determine the probability of future migration are included into the model testing for attrition bias.²¹ Additionally, since education completion is also unavailable (due to absence of Register data on education), a first difference in the student dummy is used to proxy for education completion. Estimating this full model attrition proves not to be a significant determinant of any of the dependent variables used in the analysis (Table 9). This indicates that attrition is unlikely to be selective on the first difference variables used in the main analysis.

Table 9 Test for attrition bias: OLS regressions of variables of interest (in 99–03 changes) regressed on attrition in 2009 and control variables

Finally, as a last test for selective attrition, a comparison may be carried out between the changes in a clue variable for the selected sample of respondents interviewed in both 1999 and 2009, and the changes in the same variable for the general population. This comparison is carried out for income changes (Table 10). There are two main reasons to use income for this test. First, disposable income is readily available from the Statistics Sweden for both, the YAPS sample, and the general population. Second, attrition has been specifically found to be selective on changes in returns to human capital, such as education (Thomas et al. 2012), which could possibly be reflected in changes in disposable income.

Table 10 Mean disposable income (in hundreds of SEK) from Register, whole population (1968, 1972 and 1976 cohorts) and YAPS (non-attritors), by migration status, by year

For both migrants and non-migrants observed in the YAPS survey in 1999 and 2009, the changes in disposable income are slightly above those of the general population.²² Because the present study is based on the comparison of migrants versus non-migrants, one may be especially interested in comparing the difference in changes in income for these two groups for the YAPS sample and the general population. For the sample of non-attritors from YAPS, the difference between changes in income for migrants and non-migrants is 21,800 SEK; the difference between the migrant groups for the general population is 26,500 SEK (Table 10). The closeness between these two differences is reassuring.

Because of the high levels of attrition in the YAPS survey, concerns with possible bias may certainly arise. However, given the previous analysis selective attrition on migration, though possible, appears to be generally small in magnitude. The first-difference regression analysis used in the study allows to control for all time invariant unobserved characteristics that could be related to both attrition and the variables of interest. Though the possibility of time varying unobserved characteristics related to attrition remains, the tests performed (using first difference variables over 99–03 and a comparison of the changes in income for migrants and non-migrants for the YAPS sample and the general population) both provide results indicating that the first difference variables do not appear to be selective on attrition. In conclusion, the results of the analysis performed in this section provide reassurance that the possible attirition bias in the survey should not have a strong effect on the main results of the study.

Appendix 2: Description of Variables Used in the Study

See Tables 11, 12 and 13.

Table 11 Number of people surveyed answering each question in both 99 and 09, by migration status and reason to move, by cohort

Table 12 Description of all variables used in the analysis (listed in alphabetical order)

Table 13 Description of original survey questions used in the analysis

Appendix 3: Additional Regression Results

See Tables 14, 15, 16, 17, 18, 19 and 20.

Table 14 OLS regressions, life satisfaction in 1999, 2003, and 2009 as dependent variable, socio-demographic characteristics as explanatory variables

Table 15 Results dividing respondents by occupational status trajectory and reason for moving (MI ICE with OLS)

Table 16 Results for work/non-work migrants, dividing moves into between-county and within-county

Table 17 Results using likewise deletion instead of MI ICE estimation (OLS)

Table 18 Results changing the reference group for relative income to municipality of origin (instead of residence)

Table 19 Results for work/non-work migrants clustering the standard errors at level of municipality of origin

Table 20 Results for the two sub-period 1999–2003, and 2003–2009 (restricted sample used)

Appendix 4: Specification Used to Control for Fixed Community Effects

The original specification used in the main part of the analysis is:

$$\Delta {\text{Y}}_{\text{ci}} =\uplambda_{0,1} +\uptheta_{\text{co}} +\upgamma{\text{M}}_{\text{i}} +\upbeta^{{\prime }}\Delta {\text{x}}_{\text{i}} + {\Delta \varepsilon }_{\text{ci}}$$

(3)

where ΔY_ci is the change in life satisfaction in between times 0 and 1, λ_0,1 captures the time trend between times 0 and 1, θ_co controls for the community of origin (i.e. the community of residence at time 0) of both migrants and non-migrants, γM_i captures the effects of migration, β’Δx_i is a vector that controls for changes in observable characteristics, and Δε_ci is the error term. Because specification (2) does not control for the change in the community fixed effects experienced by migrants (such as the different weather conditions between community of origin and destination), these effects are captured by both θ_co and γ. Conditional on being a migrant, θ_co captures the effect of both, the one-time shock to the community of origin, φ_co, and the fixed effect of the community of origin, ρ_co, lost following the move conditional on being a migrant. Therefore:

$$\uptheta_{\text{co}} = {\upvarphi }_{\text{co}} -\uprho_{\text{co}} *{\text{M}}_{\text{i}}$$

At the same time, γ captures the effect of both migration, α, and the fixed effect of the community of destination that is gained after the move conditional on being a migrant. Therefore:

$$\upgamma{\text{M}}_{\text{i}} = \left( {\upalpha +\uprho_{\text{cd}} } \right)*{\text{M}}_{\text{i}}$$

The previous implies that (2) may be re-written as:

$$\Delta {\text{Y}}_{\text{ci}} =\uplambda_{0,1} + {\upvarphi }_{\text{co}} + \left( {\uprho_{\text{cd}} -\uprho_{\text{co}} } \right)*{\text{M}}_{\text{i}} +\upalpha*{\text{M}}_{\text{i}} +\upbeta^{{\prime }}\Delta {\text{x}}_{\text{i}} + {\Delta \varepsilon }_{\text{ci}}$$

(3b)

To capture the true effect of the shock, φ_co, and of migration, α, it is therefore necessary to include additional control variables for the fixed effects of both community of origin and destination conditional on being a migrant. This could be implemented by including two additional vectors of dummy variables: community of origin interacted with migration (1 if migrant originally from community c, 0 otherwise), and community of destination interacted with migration (1 if migrant living in community c after the move, 0 otherwise). However, doing so not only implies a big loss in power for the estimation (due to the additional 42 dummy variables), but also introduces serious mutlicollinearity issues between the control variables and the main variable of interest (migration).

To reduce the concerns regarding power and multicollinearity, an additional assumption is introduced into the estimation. Assuming that the fixed effect lost by migrants moving out of community c is equal to the fixed effect gained by migrants moving into the same community c (that is, assuming that the fixed effects are symmetric for gains and losses), these could be captured using a vector of ordinal variables with values 1 (if a person moved into community c), −1 (if a person moved out of community c), and 0 (if a person neither moved in nor out of community c). Such ordinal variables for each community capture the symmetric effect of moving in, or out of the community. This implies the following specification:

$$\Delta {\text{Y}}_{\text{ci}} =\uplambda_{0,1} + {\upvarphi }_{\text{co}} + (\uprho_{{{\text{c}}\_{\text{change}}}} )*{\text{M}}_{\text{i}} +\upalpha*{\text{M}}_{\text{i}} +\upbeta^{{\prime }}\Delta {\text{x}}_{\text{i}} + {\Delta \varepsilon }_{\text{ci}}$$

(3c)

where ρ_{c_change} is a vector of the community-specific ordinal variables with values 1, -1, and 0. To avoid a big loss of power and to reduce the multicollinearity introduced by additional dummy variables, the assumption of symmetric community fixed-effects is imposed on the estimation, and specification (3c) is used in Table 2, Columns 4, 8, and 12 (Table 21).

Table 21 Change in life satisfaction as dependent variable