ASSOCIATIONS BETWEEN MIGRATION AND COMMUTING TO WORK IN THE METROPOLITAN REGION OF SÃO PAULO1
Verônica de Castro Lameira2 André Braz Golgher3
INTRODUCTION
Commuting and migration are important factors in the population´s redistribution and the occupation of Brazilian territory. Both types of mobility are historically linked to structural transformations that occurred in Brazilian society in recent decades, such as the remarkable increase in population size, modernization, industrialization and urbanization (MATOS; BAENINGER, 2004).
The determinants of both types of mobilities resemble each other in many aspects, as there are many factors that affect both migration and commuting, such as personal attributes (sex, age, race, civil status, schooling level, labor market participation, etc.), household characteristics (income, household arrangement, etc.), and regional features (accessibility, local amenities, unemployment rates, etc.) (BORJAS; BRONARS; TREJO, 1992; CHISWICK, 1978; CLARK; HUANG; WITHERS, 2003; CRANE, 2007; CUNHA, 2012; EVERS; VAN DER VEEN, 1985; LEE; MCDONALD, 2003; MATOS; BAENINGER, 2004; STARK; BLOOM, 1985; SANDOW; WESTIN, 2010).
Moreover, migration and commuting can cause or be caused by one another. For instance, commuters can migrate to reduce displacement costs, in particular psychological costs due to traffic jams (SHUAI, 2012). On the other hand, migrants can turn into commuters due to changing their places of residence while not changing jobs.
In fact, there are many connections between the two types of spatial mobility, a topic investigated by different authors (AXISA; SCOTT; NEWBOLD, 2012; CHAMPION; COOMBES; BROWN, 2009; FINDLAY et al., 2001; GREEN; HOGARTH; SHACKLETON, 1999; LUKIC, 2009; RAMALHO; BRITO, 2016; REITSMÃ; VERGOOSSEN, 1988; RENKOW; HOOVER, 2000; ROMANÍ; SURIÑACH; ARTÍS, 2003). Intrametropolitan migration is particularly connected to
1
Trabalho apresentado no XI Encontro Nacional sobre Migrações, realizado no Museu da Imigração do Estado de São Paulo, em São Paulo, SP, entre os dias 9 e 10 de outubro de 2019.
2
Cedeplar/UFMG.
3
commuting (BAENINGER, 2002; CUNHA, 1994); as residences and jobs differ in terms of spatial distribution in urban centers, either commuting or intrametropolitan migration becomes a necessity (FROST; LINNEKER; SPENCE, 1998). Moreover, due to the trade-off between land prices and commuting costs, individuals rationally choose to commute (WHITE, 1988). In addition, longer-distance migration may also be linked to commuting. For instance, Lundholm (2010) observed for Swedish data that migration and commuting were substitutes, as individuals sought regions with greater availability of jobs either by commuting or by migrating.
Recent results from the Brazilian Demographic Census of 2010 (henceforth, Census of 2010) showed that the magnitude of migratory flows had changed when compared to relevant results of the previous Census. The increase in short-distance migration and the decrease of interregional migration are two of the observed differences (BAENINGER, 2005; CUNHA, 2012). The census results also verified an increase in the number of commuters. For instance, studies that analyzed the Metropolitan Region of São Paulo (RMSP), the largest and most complex urban center in Brazil, showed an increase in the number of commuters, which increased from 1,108,691 individuals in 2000 to 1,942,001 in 2010 (CUNHA et al., 2013).
The main objective of this study is to investigate associations between migration and commuting to work in the RMSP. The study pays special attention to migrations of different distances, as migration and commuting may have different associations depending on the distance of the migration.
In order to fulfill this objective, we used the Census of 2010 as our database. Our empirical strategy included univariate and bivariate probit models and structural equations models. The main purpose of using different techniques is to evaluate the robustness of the empirical findings. Unlike the cited studies, this paper addresses the relationship between migration and commuting while considering different types of migration that represent a gradient of distances: intrametropolitan, intrastate, intraregional and interregional. By doing so, we could propose different hypotheses concerning the complementary or substitutive relationship between migration and commuting, depending on the distance of migration.
The rest of this paper is further structured in five sections. The second section presents a literature review where the theoretical foundations of the paper are described. Section three details the methodology, including the database, the variables used in the empirical analysis, and the empirical strategy. The fourth
section presents descriptive statistics concerning the relationship between migration and commuting in the RMSP. Section five presents the results of the econometric models. The last section concludes the paper.
ASSOCIATIONS BETWEEN MIGRATION AND COMMUTING
Many authors analyzed points related to migration (For instance, BORJAS; BRONARS; TREJO, 1992; CHISWICK, 1978; CUNHA, 2012; MATOS; BAENINGER, 2004; SJAASTAD, 1962; STARK; BLOOM, 1985), while others addressed features associated with commuting (CLARK; HUANG; WITHERS, 2003; LEE; MCDONALD, 2003; CRANE, 2007; SANDOW; WESTIN, 2010). However, as proposed by Reitsma and Vergoossen (1988), in some settings it is particularly interesting to address both phenomena conjointly, as migration may affect commuting and commuting may influence migration.
Many recent studies followed this advice. Evers and Van der Veen (1985) observed for data from the Netherlands that migration and commuting showed similar determinants. In a similar vein, Lundholm (2010) verified for Swedish data that socioeconomic variables, such as age, marriage and household arrangements, determined migration and commuting.
Lukic (2009) and Romaní; Suriñach and Artís (2003) concluded that commuting can be either a cause or a consequence of migration. For instance, individuals who commute over long distances may choose to migrate to decrease mobility costs, or those who had previously migrated may face the necessity to commute due to differences in the spatial distributions of jobs and residences.
In this vein, Shuai (2012) observed for data from Virginia, USA, that commuting between two places was positively correlated with posterior migration between the same places. This result indicated that commuting caused migration.
Reitsma and Vergoossen (1988) studied data from Holland and verified that migrants tended to live closer to their jobs than they had before migration, also suggesting that commuting may have influenced migration. Conversely, recent migrants tended to commute for longer periods in Toronto (AXISA; SCOTT; NEWBOLD, 2012), suggesting a lack of spatial fine-tuning between jobs and residences for recently arrived individuals. Similarly; Renkow and Hoover (2000) studied associations between migration and commuting in North Carolina, USA. Individuals who had migrated to rural areas, places in general with a different set of
amenities than urban areas, tended to commute to places with larger labor markets. Thus, migration is the cause of commuting due to differences in the spatial distribution of rural residences and urban jobs.
These conflicting results indicate that associations between migration and commuting may depend on the context being analyzed and also on the type of migration being investigated (CHAMPION; COOMBES; BROWN, 2009). For instance, Zax (1994) emphasized that interregional migration implied/required/led to changes in the migrants’ places of residence and also in their places of work, and commuting between regions is not an option. On the other hand, short-distance migration does not necessary imply a change of jobs, and commuting from the new residence to the old job is an option. In this vein, some authors stated that the association between these types of mobility could be either complementary or substitutive, depending on the distance of the migratory process. Evers and Van der Veen (1985) concluded that these types of mobility could be considered substitutes, if work and residence were geographically distant, or complementary, if commuting was a consequence of intraurban migration.
Based on these concepts, associations between interregional migration and commuting were analyzed by Lundholm (2010) for Swedish data. She verified that these types of mobility could substitute for one another as individuals sought regions with greater availability of jobs either by commuting or by migrating. Similarly, associations between migration and commuting were recently analyzed with Brazilian data, but most of this analysis was only descriptive. Cunha (1994) and Silva and Rodrigues (2011) described the complementariness of intra-metropolitan migration and commuting, respectively for RMSP and for different metropolitan regions. One study (RAMALHO; BRITO, 2016) addressed this association between intrametropolitan migration and commuting with econometric models and also observed the complementariness of these types of mobility for the Metropolitan Region of Recife.
This paper, unlike the cited studies, addresses the relationship between migration and commuting across different types of migration: intrametropolitan, intrastate, intraregional and interregional. By doing so, we could propose different hypotheses for the association depending on the distance of migration. Long-distance migration (intraregional and interregional) may cause commuting, as recent migrants are not well-established in their new environment and commuting to their
place of origin is not an option. Intrametropolitan migration may cause commuting, as, for instance, individuals may seek cheaper residences, mostly in the outskirts of the metropolitan region, without changing jobs. Conversely, commuting might cause intrametropolitan migration, as an excess of time spent commuting may induce short-distance migration. Finally, intrastate migration may present intermediate aspects between these two extreme associations, as distances are also intermediate.
METHODOLOGY
This section is divided into three subsections: the first presents the database; the second describes the variables used in the empirical analysis; the third details the empirical strategy.
Database
The empirical analysis was done on the Census of 2010, which was the most recent at the time of this research. This database was compiled by the Brazilian Institute of Statistics and Geography (IBGE) uses an extremely large sample and covers many socioeconomic and demographic topics, including detailed information about migration and commuting.
Given that the objective of this paper was to analyze the relationship between migration and commuting to work in the RMSP, only working-age individuals, those aged between 25 and 65, who were working at the time of the Census, and who lived in one of the municipalities of this metropolitan region, were initially selected. The very few individuals who had personal income above 300,000 reais or household income above 800,000 reais were dropped from the analysis to homogenize the sample. Those who did not declare their race/ethnic group, schooling level or municipality of residence or work were also dropped from the sample, as were those who worked in another country. These dropped individuals represented close to 2% of the initial sample. The final size of the sample is 445,239 observations, that after weighting stand for 7,370,494 workers.
Variables
This section presents the dependent and independent variables, beginning with the former. Migrants are those who lived in different municipalities five years before the reference day of the Census than on this day itself. Non-migrants are
those who lived in the same municipality in both years. Notice, however, that individuals who lived in a municipality five years prior to the Census, migrated, and returned to the same municipality are classified as non-migrants, although they are actually short-term return migrants. Four types of migrants were defined depending on the distance of the migratory process: intrametropolitan, if migration occurred between municipalities in the RMSP; intrastate, if the origin of the migrant was one of the municipalities of São Paulo state; intraregional, if the migrant was from the São Paulo state macroregion; and interregional, if the migrant came from another macroregion.
The Census of 2010 also contains information on commuting defining commuters as those whose municipality of work and/or study is different from their municipality of residence. Thus, there are three possible types of commuters: those who commute only to work; those who commute only to study; and those who commute to work and to study. The great majority commuted only to work. Those who commute to work and study may show a more complicated pattern of commuting and are a small minority. In order to homogenize the sample, we selected those who worked and lived in different municipalities, commuted daily to work, and did not study.
These already defined variables are the dependent variables in the models. Table 1 shows the other variables used as explanatory variables in the empirical analysis. They include individual attributes, labor market characteristics and household aspects that are commonly used in studies concerning the determinants of migration and/or commuting, such as sex (1 – Male; 0 – Female), age (age and age squared to account for non-linearities), race (1 – White/Asian; 0 – Black/Indigenous), civil status (1 – Married; 2 – Divorced/Separated/Widow(er); 3 – Single), schooling level (categorical variable taking into account the idiosyncrasies of the Brazilian system), whether the individual was a household head (1 – Yes; 0 – No) and occupation (formal workers, public servants, informal workers, workers producing goods for their own consumption, self-employed persons and employers) (AXISA; SCOTT; NEWBOLD, 2012; CRANE, 2007; ELIASSON; LINDGREN; WESTERLUND, 2003; GOLGHER; ROSA; ARAÚJO JR., 2008; GRUBER, 2006; LEE; MCDONALD, 2003; PAPANIKOLAOU, 2006; PRASHKER; SHIFTAN; HERSHKOVITCH-SARUSI, 2008; RAPINO; COOKE, 2011; SANDOW; WESTIN, 2010; SHEARMUR, 2006;
SJAASTAD, 1962; STARK; BLOOM, 1985; SILVEIRA NETO; MAGALHAES, 2015; TRENDLE; SIU, 2007).
Concerning the household, a continuous variable representing income was also included: the logarithm of household income. Individuals living in different household arrangements may have different likelihoods of of being commuters or migrants. In particular, the presence of young children in the household may change time allocation of males and females between labor market, commuting, leisure and domestic tasks (BRUSCHINI, 2006). Therefore, the models included three dummies: whether there were any children aged 0 to 4 years in the household, whether there were any children aged 5 to 9 years in the household, and whether there were any children aged 10 to 14 years in the household.
The amount of time dedicated to domestic chores by males and females differs. In general, women spend much more time on such chores than men, although there has been a recent convergence (BRUSCHINI, 2006; JUHN; POTTER, 2006). Therefore, the models included not only dummies indicating the presence of sons/daughters by age group, but also the interaction of these variables with the worker´s sex.
Contextual variables were also included in the models, such as a dummy for the nucleus of the metropolitan region, the municipality of São Paulo, as there might be some specificities of this area, such as a spatial concentration of jobs. The municipal unemployment rate is used as a proxy for job opportunities (IBGE, 2012), as this might be an important determinant for both types of mobility (LUNDHOLM, 2010; PIERRARD, 2008).
Finally, recent migrants may face longer commutes because they are still not well adapted to their new environment (AXISA; SCOTT; NEWBOLD, 2012; CHAMPION; COOMBES; BROWN, 2009; GREEN, 1999; FINDLAY et al., 2001). Therefore the time since migration is included in the models for commuting.
TABLE 1 – Independent variables
Variables Description Migration Commuting
Sex Dummy: 1 – Male; 0 – Female. x x
Age Age in years. x x
Age squared Age squared. x x
Race Dummy: 1 – White/Asian; 0 – Black/Indigenous. x x
Civil status Categorical variable: 1 – Married; 2 –
Divorced/separated/widow(er); 3 – Single. x x Schooling level Categorical variable: 0-3, 4-7, 8-10, 11, 12 or
more years of formal education.
x x
Household head Dummy: 1 – Household head; 0 – Otherwise. x x Occupation Categorical variables: 1 – Formal worker or public
servant; 2 – Informal worker; 3 – Self-employed/employer; 4 – No wage/own consumption.
x x
Logarithm of income Logarithm of per capita household income. x x
Sons/daughters aged 0 to 4 Dummy: 1 for yes; 0 for no. x x
Sons/daughters aged 5 to 9 Dummy: 1 for yes; 0 for no. x x
Sons/daughters aged 10 to 14 Dummy: 1 for yes; 0 for no. x x Metropolitan region nucleus Dummy: 1 – São Paulo municipality; 0 –
Otherwise
x x
Unemployment rate Municipal unemployment rate x x
Period in the destination Period of time living in the current municipality x
Empirical strategy
This section describes the empirical strategy of the paper. As mentioned in the previous sections, migration and commuting resemble each other in their determinants. Moreover, migration and commuting can cause or be caused by one other. The following equations exemplify these associations:
and , where is the probability of being a migrant, is the probability of commuting, is a set of determinants of both
mobilities, is a set of determinants of migration and is a set of determinants of commuting.
If decisions about migrating and commuting were independent, the equations above could be estimated using univariate probit models separately without incorporating the other dependent variable as an endogenous explanatory variable. However, as proposed in this paper, the decisions may be not independent. Thus, another empirical strategies should be pursued, as the errors in the univariate probit models might be correlated. Wald tests and likelihood ratio tests were applied to these models and indicated that the use of bivariate probit models was more adequate. In this model, decisions can be simultaneous or sequential (CAMERON; TRIVEDI, 2005) and the use of bivariate probit models could overcome some of the limitations of the univariate probit models. Nonetheless, in order to verify the
) , , ( 1 2 2 1 f x x Y Y Y2 h(x1,x3,Y1) Y1 2 Y x1 2 x x3
robustness of the findings, univariate probit models having the other dependent variable as endogenous explanatory variable were also used.
The analysis using the probit models was complemented by the use of generalized structural equations modeling (GSEM). We used non-recursive models using maximum likelihood techniques, as migration and commuting can affect each other, were measured cross-sectionally, and are binary variables (HOYLE, 2000 apud KLINE, 2011). Again, the objective was to observe the robustness of the results obtained using different estimation techniques.
Stata 13 was used in all estimations.
DESCRIPTIVE STATISTICS
This section presents an overview of the association between migration and commuting in the RMSP. Table 2 shows the distribution of migrants and commuters to work for workers aged between 25 and 65 years whose destination was in one of the municipalities of the RMSP. Notice that the weighted sample represented 7,370,494 individuals. Among those, 15.8% were commuters and the others 84.2% were non-commuters. Notice that among the 461,967 migrants, 32.9% were commuters, a much higher percentage than that observed for non-migrants, 14.7%. This suggests an overall positive association between migration and commuting. However, as anticipated in previous sections, this relationship might be dependent on the distance of migration.
TABLE 2 – Distributions of migrants, non-migrants, commuters and non-commuters Non-commuters Commuters Total
Non-migrants 5,893,777 1,014,750 6,908,527 85.3% 14.7% 100% Migrants 309,794 152,173 461,967 67.1% 32.9% 100% TOTAL 6,203,571 1,166,923 7,370,494 84.2% 15.8% 100%
A more detailed analysis by migration type is shown in Table 3. The proportions of commuters among intrametropolitan migrants, intrastate migrants, intraregional migrants and interregional migrants were respectively 54.1%, 15.2%, 14.0% and 13.2%. That is, both types of mobility seem to be positively correlated only for intrametropolitan migrants. Due to short-distance migrations, individuals change their municipality of residence, but many other features, such as jobs, do not
change. Indeed, among those who commute, more than half of intrametropolitan migrants continue to work in the same municipality as before migration.
TABLE 3 – Distribution of different type of migrants, commuters and non-commuters Migrants Non-commuters Commuters Total
Intrametropolitan 98,842 116,690 215,532 45.9% 54.1% 100% Intrastate 41,203 7,363 48,566 84.8% 15.2% 100% Intraregional 168,640 27,351 195,991 86.0% 14.0% 100% Interregional 116,346 17,664 134,010 86.8% 13.2% 100% TOTAL 6,203,571 1,166,923 7,370,494 84.2% 15.8% 100% ECONOMETRIC MODELS
This section presents the results in four tables. Table 4 shows the results for the bivariate probit models. The last line shows the results for the Wald tests for the correlations of the errors. These results show that bivariate rather than univariate probit models should be used in creating estimates for all types of migrants. Five models, each one with two equations, one for commuters and one for migrants, were estimated, respectively for all migrants, intrametropolitan migrants, intrastate migrants, intraregional migrants, and interregional migrants. Note that the results for commuters were very similar in the five models and they are shown only for all migrants. In order to make comparison more insightful, in the models analyzing commuters, commuters and non-commuters had the same place of residence. Similarly, migrants and non-migrants also had the same place of residence.
The correlations between the errors, ρ, were all significant. For all migrants and for intrametropolitan migrants they were positive, while for longer migrations they were negative. These results suggest that overall migration and commuting tend to be complementary. However, if the analysis is done without taking into account the heterogeneity of the migratory flows, the results may be limited and imprecise. For intrametropolitan migrations, ρ > 0, indicating that migration and commuting complement each other. Nonetheless, for longer-distance migration, ρ < 0, and the results indicate that long-distance migration and commuting tend to substitute for rather than complementing each other.
Concerning the socioeconomic and demographic variables, males had a larger likelihood of being commuters, as observed by Clark; Huang and Withers (2003). Males also showed a greater likelihood to be migrants of any type.
For commuters, age showed a positive sign and age squared showed a negative sign, both significant. That is, there is an initially increasing and concave relationship between age and the propensity of commutation. Older individuals had a greater propensity to commute. For migration, all the coefficients for age were negative and significant, and most coefficients for age squared were positive and significant. That is, this shows an initially decreasing and convex relationship between age and the propensity of migration. Young individuals showed a greater propensity to migrate, which was expected due to the greater period of time required to reap the benefits of migration (SJAASTAD, 1965; BORJA, 2012). Given that the coefficients for age squared are mostly positive, possibly much older individuals also may show a greater propensity to migration, which might be linked to retirement.
Regarding race, Whites and Asians were observed to have a greater likelihood of being commuters or short distance migrants. Moreover, this group also showed a greater propensity to migrate from another municipality of São Paulo state. Blacks and Indigenous people had a greater propensity to migrate between states. This is partially explained by the distribution of races in the places of origin of these individuals.
Married individuals had a greater propensity to be commuters, suggesting different dynamics for these individuals in the labor market. Conversely, this group showed a smaller propensity to be intrastate, intraregional or interregional migrants. That is, these results suggest that married individuals may face greater costs of migration, as expected due to decisions being made by groups of individuals (MINCER, 1978). However, for intrametropolitan migration this does not occur, as married individuals showed greater propensity to migrate when compared to singles. Perhaps for some of these married people marriage was part of the reason for migrating while continuing to work in the same job, thus turning into a commuter.
Schooling level is highly correlated to mobility. Individuals with higher levels of formal education showed a greater propensity of being commuters or migrants of any type, with a few exceptions for longer migrations. Individuals with higher schooling levels tend to participate more effectively in the labor market and can bear the cost of migration more successfully (SABBADINI; AZZONI, 2006). Hence these
results corroborate the hypotheses that commuters and migrants are positively selected. The exceptions are individuals with low levels of schooling with origins in other states. Mean schooling levels in the states from which they migrate tend to be lower that that observed in São Paulo state, and this might be part of the answer for these findings.
Household heads showed greater propensities to be commuters, and to be intrametropolitan or intrastate migrants. The contrary was observed for longer-distance migrants. These results suggest that commuters and intrametropolitan migrants are overrepresented among those who more effectively participate in the labor market and who are considered household heads. Those who were not household heads showed a greater propensity to be from origin states other than São Paulo state, indicating a distinct qualitative feature of long distance migration (However, further commentaries will be given while discussing Table 7.)
Type of occupation is also associated with mobility. Those who were formal workers or public servants (these tend to be the best paid and most prestigious jobs) had a greater propensity to commute. However, for migration, the scenario is rather different. Those who worked in the informal market or for their own consumption (these tend to be least paid and less prestigious occupations), had a greater propensity to be migrants of any type, probably because migrants, especially those recently arrived, are still not well adjusted to their new environment. Those classified as self-employed or employers are a very heterogeneous group and showed a smaller propensity to be intermetropolitan migrants, probably due to the contrary effect to the one described above. Nonetheless, this category showed greater mobility as intrametropolitan migrants, possibly because their occupations are more mobile than those of formal workers and public servants.
Male individuals who lived in households with children had a greater propensity to commute, suggesting the pursuit of opportunities in a spatially ampler labor market. The contrary was observed for women, possibly because they bear most of the domestic tasks (BRUSCHINI, 2006; JUHN; POTTER, 2006), and cannot cope with the extra time demands of commuting. Concerning migration, most coefficients were negative and significant, suggesting that children increase the costs of migration (MINCER, 1978). The coefficients for the interactions were also negative and significant, indicating that the presence of children in the household is a particular deterrence to migration for women (However, these results were not
robust, as will be discussed in Table 7). Nonetheless, one exception was observed. Men and women with very young children showed a greater propensity to migrate inside the metropolitan region, possibly adjusting to a new way of life.
Concerning the contextual variables, those who lived in the municipality of São Paulo showed a lower propensity to be commuters, as expected, as this municipality concentrates jobs. Individuals who lived in this municipality also showed a lower propensity to be intrametropolitan migrants, as these flows are mostly guided to the outskirts of the metropolitan region. However, this municipality tends to be the first place of absorption of longer-distance migrants, as represented by the positive coefficients for intrastate, intraregional and interregional migrants.
Municipal unemployment rate was positively correlated with daily travel, as the lack of jobs in the locality promotes commuting. Nonetheless, municipalities with high rates of unemployment tend not to be attractive to migrants, as verified by the negative and significant coefficients for all type of migrations. This result is commonly observed when commuting is not an option. (Nonetheless, further commentaries are given while discussing Table 7).
Finally, individuals who lived for longer periods in their municipality had a smaller propensity to commute. They are probably better adapted to their environment and better adjusted to the local labor market.
TABLE 4 – Bivariate probit models for different type of migrants
Variables Commuters Migrant All Intra metro. Intra state Intra regional Inter regional Male 0.186*** 0.094*** 0.074*** 0.034*** 0.086*** 0.068*** (0.002) (0.002) (0.003) (0.005) (0.003) (0.003) Age 0.021*** -0.081*** -0.011*** -0.060*** -0.104*** -0.106*** (0.001) (0.001) (0.001) (0.002) (0.001) (0.001) Age squared -0.000*** 0.001*** -0.000*** 0.000*** 0.001*** 0.001*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) White/Asian 0.032*** 0.044*** 0.074*** 0.258*** -0.060*** -0.103*** (0.002) (0.002) (0.002) (0.004) (0.002) (0.003) Married Reference Reference Reference Reference Reference Reference Divorced/Separated/Widow(er) -0.026*** 0.198*** 0.134*** 0.247*** 0.174*** 0.157***
(0.003) (0.003) (0.004) (0.006) (0.004) (0.005) Single -0.063*** 0.030*** -0.067*** 0.068*** 0.091*** 0.116***
(0.002) (0.002) (0.003) (0.004) (0.003) (0.003) Less than elementary -0.508*** -0.314*** -0.450*** -0.547*** 0.056*** 0.244***
(0.002) (0.003) (0.003) (0.005) (0.003) (0.004) Less than secondary -0.403*** -0.353*** -0.376*** -0.493*** -0.080*** 0.071***
(0.003) (0.003) (0.004) (0.006) (0.004) (0.005) Less than a higher educ. deg. -0.247*** -0.344*** -0.284*** -0.401*** -0.189*** -0.079***
(0.002) (0.003) (0.003) (0.005) (0.003) (0.004) Higher education degree Reference Reference Reference Reference Reference Reference
Household head 0.004*** 0.013*** 0.049*** 0.042*** -0.030*** -0.034*** (0.002) (0.002) (0.002) (0.004) (0.002) (0.003) Formal worker or public servant Reference Reference Reference Reference Reference Reference Informal -0.362*** 0.093*** 0.042*** 0.087*** 0.107*** 0.098*** (0.002) (0.002) (0.003) (0.005) (0.003) (0.004) Self-employed/Employer -0.630*** -0.004*** 0.056*** -0.016*** -0.064*** -0.068*** (0.002) (0.002) (0.003) (0.005) (0.003) (0.004) Own consumption -1.351*** 0.092*** 0.007 0.299*** 0.091*** 0.092*** (0.016) (0.010) (0.014) (0.020) (0.014) (0.016) Sons/daughters aged 0 to 4 years 0.009*** 0.049*** 0.140*** -0.087*** -0.031*** -0.016*** (0.003) (0.003) (0.004) (0.007) (0.004) (0.004) Sons/daughters aged 5 to 9 years 0.003 -0.087*** -0.043*** -0.087*** -0.094*** -0.084*** (0.003) (0.003) (0.004) (0.007) (0.004) (0.004) Sons/daughters aged 10 to 14 years 0.015*** -0.097*** -0.070*** -0.088*** -0.098*** -0.089*** (0.003) (0.003) (0.004) (0.007) (0.004) (0.005) Interaction of a female dummy
with: Sons/daughters aged 0 to 4 years -0.048*** -0.024*** 0.027*** -0.035*** -0.048*** -0.065*** (0.005) (0.005) (0.006) (0.011) (0.006) (0.007) Sons/daughters aged 5 to 9 years -0.054*** -0.019*** 0.009 -0.034*** -0.037*** -0.053*** (0.004) (0.004) (0.006) (0.010) (0.006) (0.007) Sons/daughters aged 10 to 14 years -0.063*** -0.018*** 0.008 -0.037*** -0.034*** -0.041*** (0.004) (0.004) (0.006) (0.011) (0.006) (0.007)
Dummy for São Paulo -1.450*** -0.359*** -0.995*** 0.065*** 0.196*** 0.199***
(0.003) (0.002) (0.004) (0.005) (0.003) (0.004) Unemployment rate 0.071*** -0.016*** -0.004*** -0.020*** -0.016*** -0.018***
(0.000) (0.001) (0.001) (0.001) (0.001) (0.001)
Time in the destination -0.008*** - - - - -
(0.000)
Constant -1.507*** 1.678*** -0.434*** -0.178*** 1.314*** 1.085*** (0.017) (0.018) (0.023) (0.041) (0.025) (0.028)
Ρ 0.073*** 0.226*** -0.139*** -0.161*** -0.154***
Wald test for ρ 2363*** 18665*** 1919*** 6479*** 4326***
Note: (***) Significant at 1%; (**)Significant at 5%; (*)Significant at 10%. Number of observations: 445,239
Tables 5 and 6 complement the previous analysis with univariate probit models. The same controls included in the models in Table 4 were incorporated in the models of these tables. However, as the results are very similar to those observed in this previous table, they are not shown. Besides, as mentioned, the Wald tests indicated that the use of bivariate probit models was a better option.
Nevertheless, we opted to show the results of the univariate probit models because the endogenous variables were included as explanatory. Hence, in the model with migration as the dependent variable, commuting entered as the explanatory variable and vice-versa.
The models in Table 5 had commuting as the dependent variable, and the dummies for the different types of migration are included as explanatory variables, one dummy in each model. The objective is to observe the correlations between these two variables, observing the robustness of the findings in Table 4. The results corroborate the findings of the last table. Overall migration is positively correlated to commuting; however, results differ, depending on the type of migration. For intrametropolitan migration, the coefficient was positive and significant, while coefficients were negative and significant for longer migrations. These results show in another perspective the complementariness of commuting and intraurban migration, as observed by other authors (AXISA; SCOTT; NEWBOLD, 2012; RAMALHO; BRITO, 2016; RENKOW; HOOVER, 2000; SHUAI, 2012). Besides, the results also show the substitutive quality of daily commuting and longer migrations.
TABLE 5 – Results for the univariate probit model with migration as endogenous variable Dependent variable
Endogenous explanatory variable – Migration
All Intrametro. Intrastate Intrareg. Interreg. Commuting 0.135*** 0.458*** -0.357*** -0.359*** -0.359*** (0.003) (0.003) (0.008) (0.004) (0.005)
Pseudo R2 0.273 0.278 0.273 0.274 0.273
Note: Controls of the previous table included in the models. Robust standards errors in parentheses. Note: (***) Significant at 1%; (**) Significant at 5%; (*) Significant at 10%.
Number of observations: 445,239.
Table 6 also shows univariate probit models, but, contrary to Table 5, the dependent variables are the different types of migration and commuting is an endogenous explanatory variable. The results also corroborated the findings of the previous two tables, indicating the robustness of our results: the correlations between commuting and overall and intrametropolitan migration were positive and significant, while other correlations between commuting and migration were negative and significant.
TABLE 6 – Results for the univariate probit model with commuting as endogenous variable Endogenous explanatory
variable
Dependent variable – Migration
All Intrametro. Intrastate Intrareg. Interreg. Commuting 0.329*** 0.511*** -0.082*** -0.069*** -0.072*** (0.002) (0.002) (0.005) (0.003) (0.004)
Pseudo R2 0.073 0.165 0.059 0.060 0.075
Note: Controls of the previous table included in the models. Robust standards errors in parentheses. Note: (***) Significant at 1%; (**) Significant at 5%; (*) Significant at 10%.
The results of the three last tables indicate the robustness of the results concerning the associations between commuting and migration of different types. Commuting tends to be complementary to short-distance migrations and substitutive for long-distance ones.
Table 7 shows the results for the GSEM. The objective is to observe the robustness of the results, including of the controls. Notice in the last line of the table that the covariance between commuting and the different types of migration corroborates the previous findings concerning the associations between daily commuting and permanent migration.
This table also shows the results for the controls, which should be compared with similar results previously shown for the bivariate probit models in Table 4. Notice that the results for most variables, such as for sex, age, race, civil status, schooling, occupation, the presence of children in the household, the São Paulo dummy, and time in the destination, are identical or very similar, indicting the robustness of the findings,. However, some differences were noticed for the other variables and they deserve some further commentaries.
In Table 4 it was observed that household heads had a lower propensity to be longer-distance migrants. In Table 7, the coefficients showed the contrary, that household heads had a greater propensity to be long-distance migrants. Therefore, conclusions about this topic are sensitive to the applied methodology and further studies should be done.
It was observed in the bivariate probit models that women with children in the household had a lower propensity to commute. This was also observed in the GSEM models. Moreover, in both models, it was observed that individuals with children in the household, both male and female, had a smaller propensity to be long-distance migrants. However, one difference is noted between the models. In Table 4, most coefficients for the interactions for long distance-migration were negative and significant, and in Table 7 they were mostly non-significant. Nonetheless, combining the results for household head (mostly males) and these interactions, the picture is quite similar.
Another difference between the models that deserves further commentaries concerns the municipal unemployment rate. For commuters, the results were the same in Tables 4 and 7. However, this was not observed for migrants. For all types of migration the coefficients were negative in Table 4. A possible explanation is that
regions with higher unemployment rates tend to be less attractive to migrants (TODARO, 1969; HARRIS; TODARO, 1970). However, this might happen in areas where commuting is not an option. For the RMSP, individuals could migrate to a municipality with higher unemployment rates and commute to other places with more job opportunities. Besides, migration can cause unemployment, as recently arrived individuals may be not well adjusted to their destination´s labor market. The coefficients in Table 7 for intrametropolitan, intraregional and interregional migration were positive and significant or non-significant, indicating the sensitivity of the results to the estimation technique.
TABLE 7 – GSEM models for different type of migrants
Variables Commuters Migrants All Intra metro. Intra state Intra regional Inter regional Male 0.042*** 0.006*** 0.003*** 0.000 0.003*** 0.002*** (0.002) (0.000) (0.001) (0.000) (0.001) (0.001) Age 0.004*** -0.007*** -0.001 -0.001*** -0.006*** -0.005*** (0.001) (0.000) (0.000) (0.000) (0.000) (0.000) Age squared -0.000*** 0.000*** -0.000*** 0.000*** 0.000*** 0.000*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) White/Asian 0.001 -0.003*** 0.001** 0.003*** -0.007*** -0.006*** (0.002) (0.001) (0.001) (0.000) (0.001) (0.000) Married Reference Reference Reference Reference Reference Reference Divorced/Separated/Widow(er) -0.008*** 0.014*** 0.007*** 0.004*** 0.007*** 0.004***
(0.002) (0.001) (0.001) (0.000) (0.001) (0.001) Single -0.144*** 0.001*** -0.004*** 0.002*** 0.007*** 0.005***
(0.002) (0.001) (0.001) (0.000) (0.001) (0.001) Less than elementary -0.115*** -0.016*** -0.023*** -0.008*** 0.016*** 0.018***
(0.002) (0.001) (0.001) (0.000) (0.001) (0.001) Less than secondary -0.091*** -0.028*** -0.021*** -0.009*** 0.002** 0.006***
(0.003) (0.001) (0.001) (0.000) (0.001) (0.001) Less than a higher educ. deg. -0.050*** -0.034*** -0.018*** -0.009*** -0.007*** -0.002***
(0.002) (0.001) (0.001) (0.000) (0.001) (0.001) Higher education degree Reference Reference Reference Reference Reference Reference Household head 0.001 0.013*** 0.006*** 0.002*** 0.004*** 0.003*** (0.002) (0.001) (0.001) (0.000) (0.001) (0.000) Sons/daughters aged 0 to 4 years 0.007*** 0.019*** 0.018*** -0.001*** 0.001 0.001 (0.003) (0.001) (0.001) (0.000) (0.001) (0.001) Sons/daughters aged 5 to 9 years 0.006*** -0.004*** 0.000 -0.001*** -0.004*** -0.002*** (0.003) (0.001) (0.001) (0.000) (0.001) (0.001) Sons/daughters aged 10 to 14 years 0.009*** -0.012*** -0.005*** -0.001*** -0.006*** -0.004*** (0.002) (0.001) (0.001) (0.000) (0.001) (0.001) Interaction of a female dummy
with: Sons/daughters aged 0 to 4 years -0.016*** -0.002 0.002 -0.000 -0.004*** -0.003*** (0.004) (0.002) (0.002) (0.001) (0.001) (0.001) Sons/daughters aged 5 to 9 -0.017*** -0.002 -0.002 -0.000 -0.001 -0.002
years (0.004) (0.002) (0.001) (0.001) (0.001) (0.001) Sons/daughters aged 10 to 14 years -0.018*** -0.002 0.001 -0.000 -0.000 -0.000 (0.004) (0.002) (0.001) (0.001) (0.001) (0.001) Formal worker or public servant Reference Reference Reference Reference Reference Reference Informal -0.098*** 0.013*** 0.004*** 0.002*** 0.007*** 0.005*** (0.002) (0.001) (0.001) (0.000) (0.001) (0.001) Self-employed/Employer -0.164*** 0.004*** 0.006*** -0.000*** -0.002*** -0.002*** (0.002) (0.001) (0.001) (0.000) (0.001) (0.001) Own consumption -0.253*** 0.013*** 0.005 0.004*** 0.005* 0.004 (0.008) (0.004) (0.003) (0.001) (0.003) (0.002)
Dummy for São Paulo -0.281*** -0.065*** -0.064*** -0.001*** 0.001* 0.002***
(0.002) (0.001) (0.001) (0.000) (0.001) (0.001) Unemployment rate 0.024*** -0.001*** 0.000** -0.011** 0.000* 0.000
(0.000) (0.000) (0.000) (0.002) (0.000) (0.000)
Time in the destination -0.002*** - - - - -
(0.000)
Constant -0.016 0.329*** 0.101*** 0.040*** 0.178*** 0.136***
(0.016) (0.008) (0.006) (0.003) (0.005) (0.004) Covariance (mig. x commuting) - 0.003*** 0.006*** -0.001*** -0.003**** -0.002***
Note: (***) Significant at 1%; (**) Significant at 5%; (*)Significant at 10%. Number of observations: 445,239.
CONCLUSIONS
The main objective of this study is to investigate associations between migration and commuting to work in the RMSP. Special attention is given to migrations over different distances, as migration and commuting may have different associations depending on the distance of the migration.
The proportion of commuters among intrametropolitan migrants was much greater than that observed among intrastate migrants, intraregional migrants and interregional migrants. In addition, the econometric models showed that commuting and intrametropolitan migration were positively correlated. In short-distance migrations, individuals change their municipality of residence, but may not change jobs. For the other types of migration, correlations with commutation were negative. These results suggest that intrametropolitan migration and commuting complement each other, whereas longer-distance migration and commuting tend to be substitutes, as suggested by Zax (1994).
Concerning the determinants of migration and of commuting, similarly to Evers and Van der Veen (1985) and Lundholm (2010), we verified that socioeconomic, demographic and contextual variables determined both types of mobility, and results were robust when estimations were done by different techniques. Some of the main conclusions are described below.
Males had a larger propensity to be commuters. These results indicate that men participate more effectively in a wider labor market than women, as emphasized by Silveira Neto and Magalhaes, (2015).
Older individuals had a greater propensity to commute, while younger individuals showed a greater propensity to migrate. This last point is expected due to the greater period of time required to reap the benefits of migration (SJAASTAD, 1965; BORJA, 2012).
Married individuals had a greater propensity to be commuters, suggesting different dynamics for these individuals in the labor market. Moreover, married individuals showed a greater propensity to be intrametropolitan migrants, and marriage may provide part of the reason to migrate. Conversely, this group showed a smaller propensity to be longer-distance migrants, possibly due to the greater costs of migration associated with decisions made by groups of individuals instead of individually (MINCER, 1978).
Individuals with higher levels of formal education showed a greater propensity to be commuters or migrants of most types. Highly-educated individuals tend to participate more effectively in the labor market and can bear the cost of migration more successfully (SABBADINI; AZZONI, 2006).
Male individuals who lived in households with children had a greater propensity to commute, suggesting that they pursue opportunities in a larger labor market. The contrary was observed for women, possibly because they cannot cope with the extra time demands of commuting, as they bear most of the domestic tasks (BRUSCHINI, 2006; JUHN; POTTER, 2006). Concerning migration, most coefficients were negative and significant, suggesting that children increase the costs of migration (MINCER, 1978). Nonetheless, for intrametropolitan migration it was observed that those with very young children showed a greater propensity to migrate, possibly adjusting to a new way of life.
Those who lived in the municipality of São Paulo, which spatially concentrates jobs, showed a lower propensity to commute. In addition, individuals who lived in this municipality also showed lower propensities to be intrametropolitan migrants, as most migrants migrate to the periphery of the metropolitan region. Individuals who lived for longer periods in their municipalities had a smaller propensity to commute, as they are probably better adapted to their environment.
Concerning the implications of this study for public policies, migration is a demographic process that may have a remarkable impact on the distribution of the population, in particular of human capital, with outstanding consequences for growth and regional disparities (GOLGHER; FIGUEIREDO; SANTOLIN, 2011). The results discussed in this paper clarify some of the determinants of migration, which directly influences regional economic inequalities.
In metropolitan areas, and in particular in the RMSP, commuting times and distances are quite large. Thus, in such regions, urban mobility impacts decisively on the resident´s quality of life. Individuals who commute for long distances tend to have lower levels of health and well-being, less leisure time, and higher stress levels (GOTTHOLMSEDER et al., 2009; ROBERTS; HODGSON; DOLAN, 2011; STUTZER; FREY, 2008). Hence, the results described in this paper may be used to optimize commuting, in particular the commuting of recently arrived migrants.
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