1. bookVolume 12 (2021): Issue 1 (January 2021)
Journal Details
License
Format
Journal
First Published
30 Apr 2019
Publication timeframe
1 time per year
Languages
English
access type Open Access

Migrating out of mega-cities: Evidence from Brazil

Published Online: 26 Mar 2021
Page range: -
Accepted: 17 May 2020
Journal Details
License
Format
Journal
First Published
30 Apr 2019
Publication timeframe
1 time per year
Languages
English
Abstract

Traditional economic models predict rural to urban migration during the structural transformation of an economy. In middle-income countries, it is less clear which direction of migration to expect. In this article, the author shows that in Brazil as many people move out as into metropolitan cities and they mostly move to mid-sized towns. The author estimates the determinants of out-migrants’ destination choice accounting for differences in earnings, living costs, and amenities and tested whether the migrants gain economically by accepting lower wages but enjoying lower living costs. The findings suggest that in their destination choice, out-migrants aim to minimize costs of moving. On average, city-leavers realize higher real wages, including low-skilled migrants who would lose in nominal terms. The article thus provides new evidence on economic incentives to leave big cities in a middle-income country.

Keywords

JEL Classification

Introduction

The migration of workers from rural to urban areas forms an integral part of the structural change of a country's economy (Harris and Todaro, 1970). With higher income levels, this migration flow is expected to slow down or even reverse, as seen in the 70s and 80s in the U.S. and Europe as the so-called “population turnaround” (Cochrane and Vining, 1988). Recently, the growing literature on migration and labor misallocation in low- and middle-income countries finds evidence for migration patterns that seem somewhat surprising to standard theories of rural–urban migration (Morten and Oliveira, 2018; Bryan and Morten, 2019; Munshi and Rosenzweig, 2016; Hicks et al., 2017). These studies emphasize selectivity and migration costs as major drivers of such unexpected patterns and document implied overall productivity or welfare costs. One driver of such costs could be migrant sorting that is contrary to what classic models of rural–urban migration predict. At the individual level, the migration into urban areas is driven by earning differentials and by the fact that cities offer higher returns than rural areas (Sjaastad, 1962). In this article, the author thus investigates human capital-specific sorting of migrants in one of these unexpected migration patterns the move out of metropolitan cities to mid-sized towns.

Census survey data from Brazil show that around 20% of first-time internal migrants

These migrants leave their birth place and are not return migrants.

moved out of metropolitan cities between 2009 and 2010, which equals the share of migrants moving into the metropolises in the same period. The majority of out-migrants (around 78%) move to live and work in medium-sized destinations,

The median population size of the administrative unit, a microregião, is 173,453 inhabitants. The author classifies a medium-sized microregião as one that has between 170,000 and 1 million inhabitants and above 1 million as a metropolitan city.

not in small and rural locations. It appears that high- and low-educated out-migrants are equally likely to move, which gives rise to the questions: What drives these workers to move out of the cities, where do they choose to live, and whether their destination choice can still be explained with a gain in earnings.

Urban areas attract workers with job opportunities, high wages, and better services. Yet, with the urbanization waves in developing countries, large cities face many problems associated with overcrowding, such as informal housing, congested infrastructure, and unemployment. City growth increases the demand for housing and amenities, whose supply is rather inelastic. Higher living costs put pressure on workers whose budget share for these goods is relatively high (Giannetti, 2003). These factors could give rise to migration out of cities. While wages and amenities are expected to be higher in cities than in smaller towns, living costs are higher too, the migration choice, therefore, becomes a balancing act between these factors across a large set of possible destinations.

The author estimates how these factors affect the individual destination choice conditional on migration with a conditional logit model as in Fafchamps and Shilpi (2013) or Chernina (2020). The focus lies on the established determinants of migration: Expected wages based on individual characteristics, moving and living costs, as well as local amenities such as crime rates, and the quality of public education and health service provision. Coarsened exact matching (CEM) is applied to control the selection bias in the prediction of expected wages. Wages in neighboring locations are used as an instrument for living costs. Other papers investigate the sorting decision from an individual perspective. These studies found that workers sort themselves to destinations by balancing the highest return to their skills and the chance to find employment against local living costs and the presence of local amenities according to their individual preferences (Borjas, 1987; Borjas et al., 1992; Dahl, 2002; Moretti, 2011; Ham et al., 2011; Grogger and Hanson, 2011), but only a few studies use data from transitioning countries (Aroca Gonzalez and Maloney, 2005; Lokshin et al., 2007; Aguayo-Tellez et al., 2010; Fafchamps and Shilpi, 2013).

Following this analysis, the author further investigates how the realized wages instead of expected ones reflect a gain or loss because of migration from metropolitan cities. The author uses counterfactual wages of migrants to compute the return to out-migration in nominal and real terms. A few studies analyze the counterfactual situation of households that had their member not migrated, but not of the migrants themselves (Barham and Boucher, 1998; Rodriguez, 1998; Tunalı, 2000; Adams, 2006; Lokshin et al., 2007; Brown and Jimenez, 2008; Adams and Cuecuecha, 2013).

The author finds that migrants’ destination choices differ by education level. Low-educated metropolitan out-migrants prefer destinations where the cost of living is lower, whereas better-educated migrants do not evaluate prices in a significant way. The counterfactual analysis confirms that the return to metropolitan out-migration is positive in real wages. The difference in living costs between metropolitan origins and non-metropolitan destinations appears to exceed migration costs. This result is the strongest for the low-skilled workers who would normally experience a decline in nominal wages from leaving cities. Furthermore, out-migrants of all education levels prefer towns closer to their origin and within their own state of birth, which reduces the economic and social costs of moving. In terms of amenities, crime does not appear to matter. Less educated migrants seek better access to health care facilities, whereas highly educated migrants seem to be willing to accept lower quality in health service provision but prefer destinations where education quality is relatively better.

In this article, the author contributes to the related literature in several ways. To the best of the author's knowledge, it is the first work to empirically document the economic determinants of out-migration from metropolitan cities at the individual level in a middle-income country.

One exception is McCormick and Wahba (2005) who analyze migration in and out of big cities in Egypt. However, their sample of migrants moving out of the big cities is only 82 observations and their hypothesis focuses on the movement into compared to out of large cities and the concentration of specific skill and age groups in large cities.

The author exploits the detailed information on migration through a unique census survey. These data allow to calculate migration between local labor markets so that the author captures the largest share of labor mobility within Brazil, thereby improving on studies that investigate internal migration only at the regional level (Yap, 1976; Santos and Ferreira, 2007; dos Santos Junior et al., 2005; Lall et al., 2009; Aguayo-Tellez et al., 2010; Fally et al., 2010). This approach also allows testing the importance of secondary towns as potential destinations for migrants not only from rural areas, adding contribution to the recent literature (Christiaensen et al., 2017). Furthermore, by combining the destination choice and counterfactual analysis, the author assesses the ability of migrants to evaluate expected earnings across a large set of possible destinations. These insights into the location choice of workers are relevant for regional planning (Moretti, 2011).

The article is structured as follows. The data used for the empirical analysis are described in Section 2. Descriptive maps, graphs, and tables that explore the nature of migration from metropolitan to non-metropolitan cities are described in detail in Section 3. Thereafter, the conceptual framework of the destination choice model is discussed, and the results are presented in Section 4. The results of the counterfactual exercise are presented in Section 5. Finally, Section 6 concludes the article.

Data
Data source

Every 10 years, the Brazilian National Institute for Geography and Statistics (IBGE) conducts a 10% nationally representative household survey, the Census survey (Censo Demografico 2010, IBGE (2012)). The survey of 2010 comprises around 20 million individual observations in all municipalities of Brazil. It contains information on household composition, living conditions, labor market, education, geographic location, and migration.

Definition of migration

The Census survey from 2010 allows to identify migrants in the sample using the following questions: “Were you born in this municipality?” – to know whether people are living in their birthplace; “When did you move to this municipality?” – provides the year of migration; and “In which municipality (in which state) did you live before you moved to this municipality that you are currently living in?” – provides the exact origin of migrants. It further asks for the municipality of the current job and the previous job. Migrants are individuals who used to live and work in a different location than the one they are living in at the time of the survey.

Sample

The sample comprises working-age migrants and non-migrant residents. The legal working age in Brazil is 16 years, and the retirement age for men is 65 years. The age group for the sample has been restricted from 25 years to 65 years. This way it can be assumed that students are excluded. All individuals in the sample are currently not in school and are participating in the labor market, which means that they are either employed or unemployed but looking for work. The author restricts the sample of migrants to those who moved within the past year, between 2009 and 2010, to minimize recall bias.

Definition of origins and destinations

Migration is measured as a change in living and working location at the level of a microregião. Microregiões are geographic and administrative agglomerations of municipalities sharing labor market and economic activities, a bit larger than counties in the US. The author defines 22 of these microregiões as metropolitan based on their population size of 1 million and above.

This definition follows that of the United Nations’ World Urbanization Prospects (UNWUP) (Christiaensen et al., 2013).

There are 558 microregiões, 29 metropolitan, and 529 non-metropolitan microregiões. Information on the local characteristics is aggregated to the microregião level using individual-level data from the Census survey. The author uses survey weights to obtain local estimates of wages and housing prices measured with the amount of rent per room.

The average number of rooms across localities is six and its standard deviation 0.59 so that there is no concern of systematic differences in the number of rooms across locations.

At the level of the federal state, 43% of the metropolitan out-migrants leave their state of birth, the other 57% stay within the same state when they move and even more stay within their region. These observations confirm that the level of analysis at the microregião level captures also intra-regional population dynamics, the largest movements in the country.

Other variables

The author obtains average wages in 2009 from the National Employment Registry (RAIS). Other information on local characteristics is obtained from Ipeadata. This is an online data pool provided by Instituto de Pesquisa Economica Aplicada (Ipea), a Brazilian public research institute that collects data from several ministries and other public sources. It contains information at the microregião level on quality of education and health provision, and homicide rates as a measure of crime.

Quality of education and health is measured using an index, which is constructed and annually updated by the Industrial Federation of the federal state of Rio de Janeiro (FIRJAN). The index for education provision combines information about the subscription rate of pre-school children, dropout rate, rate of teachers with higher education, average daily teaching hours, and the results of a national education development score. The index for health provision quality comprises the number of pre-natal consultations, deaths due to badly defined causes, and child deaths due to evitable causes. These are measures commonly used in health economics to capture the quality of health care provision (Hanefeld et al., 2017; Donabedian, 1988). Access to health care provision is measured by the number of health care facilities per 100,000 inhabitants.

Health care facilities included are general hospitals, day hospitals, polyclinics, health point, general emergency, and pharmacies and basic health centers.

These data come from the National Health Establishments Registry (CNES).

All variables used and their source is specified in Table A10 in Appendix.

Descriptive Statistics
Patterns of internal migration in Brazil

Figure 1 shows a map of Brazil. The map shows 5 greater regions and the 22 metropolitan cities of Brazil, which are the focus of this analysis.

Some of the metropolitan cities comprise more than one microregião so that the number of cities is slightly lower than the number of metropolitan microregiões.

The metropolitan cities are located mainly along the coast except for the state capitals in the South-eastern region, Goiânia in the Central-West, Manaus in the Amazon, and the national capital Brasília.

Labor migration within Brazil is historically very common and is mainly attributed to socioeconomic differences between regions, underdeveloped rural areas, and several large urban centers (Yap, 1976). Recently, migration patterns in Brazil have been changing. Of all Brazilian internal migrants in the year before the Census of 2010, 47% moved between non-metropolitan areas (Table 1). The second-largest movement is into and out of metropolitan cities as well as from and to non-metropolitan microregiões comprising around 20% each of all recent migrants, a substantial share of migration in the country. The remaining 12% of migrants move between the metropolises.

Figure 1

Map of greater regions and metropolitan cities of Brazil.

Migrants between metropolitan and non-metropolitan microregiões between 2009 and 2010

Destination

Non-metropolitan Metropolitan
Origin N % N %
Non-metropolitan 380,627 46.9 167,781 20.7
Metropolitan 162,647 20.1 99,143 12.2

Total N = 810,196, using survey weights.

The rate of people leaving big cities has been increasing in the past decade as illustrated in Figure 2. The graph plots the out-migration rate from cities with over 1 million inhabitants in Brazil from 2004 to 2009.

Comparing origins and destinations

Figure 2

Out-migration rate from metropolitan cities from 2004 to 2009.

Table 2 compares metropolitan and non-metropolitan microregiões in terms of socioeconomic characteristics. In the second and fourth columns in Table 2, the author also includes the coefficient of variation for the metropolitan and non-metropolitan microregiões to illustrate how diverse non-metropolitan areas are.

Characteristics of metropolitan and non-metropolitan microregiões in 2010

Metropolitan Non-metropolitan


Mean Coeff. of variation Mean Coeff. of variation
Population 2,679,687 1.11 213,680 0.79
Room rent (R$, median) 72.47 0.23 45.22 0.42
Hourly wage (R$) 12.11 0.22 7.23 0.29

Share of
Unskilled workers 0.37 0.09 0.37 0.14
Skilled workers 0.31 0.11 0.40 0.14
High-skilled workers 0.24 0.17 0.16 0.23
Formally employed 0.58 0.11 0.40 0.36
Unemployed 0.06 0.29 0.05 0.41

Share of workers in
Agriculture 0.09 0.36 0.30 0.39
Industry 0.21 0.23 0.18 0.37
Services 0.53 0.08 0.35 0.23
Public services 0.11 0.25 0.12 0.24
People living in
Adequate living conditions 0.57 0.28 0.36 0.67

Other measures
GDP growth 2005–2010 0.16 0.31 0.18 0.79
Health facilities (per 100,000) 16.40 0.42 41.86 0.35
Health quality index (0–1) 0.82 0.09 0.79 0.11
Education quality index (0–1) 0.77 0.14 0.73 0.14
Homicide rate (per 100,000) 38.00 0.54 18.58 0.77

The skill level of workers is based on the occupation classification by the International Labor Organization (ILO). Industries include extractive industry, processing industry, electricity/gas, sanitation/sewage, and construction. Services include commerce, transport, housing/food, information/communication, financial services, real estate, professional consulting, science and technology, administrative services, arts/culture/sports, domestic services, and other services. Public services include public administration, security, education, health, and social services, international organizations/foreign institutions. Six of the microregiões had missing values for homicide rates. The author replaced them with 0 due to the way homicides are reported.

Metropolitan cities have on average around 10 times more inhabitants than non-metropolitan microregiões. In terms of prices, metropolitan residents face room rents that are >50% higher than in non-metropolitan areas. At the same time, they earn similarly higher wages. As expected, high-skilled occupations are concentrated in the metropolitan areas, and labor markets are much more formalized in these big cities. The employment share of various sectors is higher for services with 53% in the metropolitan areas and 35% in non-metropolitan microregiões. Yet, agriculture in the non-metropolitan areas employs around 30% compared to only 9% in metropolitan cities. While GDP is higher in metropolitan regions, it is growing faster in the non-metropolitan regions. In terms of living standards, almost 60% of metropolitan residents live with adequate sewage, water, and electricity provision compared to only 36% outside of these cities.

The definition of which type of sewage, water, and electricity provision is adequate comes from the report on sub-normal agglomerations in Brazil (IBGE, 2010).

This illustrates the stark spatial inequality not only in economic but also in social aspects.

This gap is similar for the indices for the quality of health and education provision in non-metropolitan areas in contrast to higher standards in the big cities. In contrast, crime is concentrated in big cities with a homicide rate of 38 homicides per 100,000 inhabitants compared to around 19 in non-metropolitan areas.

The variation in these characteristics among non-metropolitan microregiões is large. The second and fourth columns in Table 1 show the coefficients of variation for metropolitan and non-metropolitan microregiões, respectively. It is relatively larger for non-metropolitan areas in almost all categories, such as population, public service worker share, and education quality. This motivates the analysis of the metropolitan out-migrants’ destination choice. Labor mobility is expected to respond to this spatial variation of real wages and other socioeconomic characteristics.

Comparing migrants and residents

Metropolitan out-migrants are unlikely to be representative of the population of metropolitan cities. In Tables 3 and 4, the author compares the characteristics of non-metropolitan residents, metropolitan out-migrants, and metropolitan residents. The comparison of these residents with metropolitan out-migrants allows shedding light on the differences between migrants and residents at the origin and destination.

For simplification, in the descriptive analysis, this comparison does not account for the fact that migrants could all be concentrated in a specific sub-set of non-metropolitan microregiões so that residents in all non-metropolitan microregiões might not represent the exact comparison group of that specific subset.

Characteristics of migrants and non-migrants 2010

Non-metropolitan residents Metropolitan out-migrants Metropolitan residents
Number of observations 4,184,904 19,318 1,598,869
Age 40.25 36.85 40.22
Female 0.41 0.37 0.45
White 0.51 0.51 0.51

Education level
None, primary incomplete 0.47 0.29 0.29
Primary, secondary incomplete 0.16 0.16 0.17
Secondary, higher incomplete 0.26 0.33 0.36
Higher complete 0.11 0.21 0.19

Proportions and means are computed using survey weights.

Labor market characteristics of migrants and non-migrants 2010

Non-metropolitan residents Metropolitan out-migrants Metropolitan residents
Unemployed 0.05 0.12 0.06
Log (monthly wages) 6.59 6.95 6.98

Sector
Formal private 0.40 0.43 0.56
Formal public 0.06 0.08 0.06
Informal 0.26 0.23 0.21
Self-employed 0.02 0.02 0.01
Small business 0.26 0.24 0.15

Industry, ISIC
Agriculture 0.26 0.14 0.09
Industry 0.22 0.27 0.21
Services 0.38 0.44 0.54
Public services 0.15 0.17 0.17

Proportions and means are computed using survey weights.

Industries include extractive industry processing industry, electricity/gas, sanitation/sewage, construction.

Services include commerce, transport, housing/food, information/communication, financial services, real estate, professional consulting, science and technology, administrative services, Arts/culture/sports, domestic services, and other services.

Public services include public administration, security, education, health and social services, and international organizations/foreign institutions.

On average, migrants are slightly younger than residents and relatively more of them are male. Overall, they are much better educated than the average resident at the non-metropolitan destination and their education is very similar to that of metropolitan residents. A slightly larger share of migrants has a tertiary education compared with metropolitan residents. From this comparison, it does not seem that low- or high-educated workers are more likely to leave metropolitan cities than the respective other groups.

Table 4 documents the labor market characteristics of out-migrants and residents. Around 12% of workers who left the metropolitan cities for non-metropolitan destinations are unemployed in contrast to an unemployment rate of only 5% among non-metropolitan residents. This indicates that metropolitan out-migrants are a heterogeneous group and some lose out at their new destination. However, the high unemployment rate might just capture a period of adjustment for very recent migrants who have not found a job yet at their new destination.

In terms of wages, migrants earn on average more than their non-migrant counterparts at non-metropolitan destinations, and they earn almost as much as residents in metropolitan areas. This might only reflect differences in the productivity of locations where migrants live as well as different observable and unobservable characteristics of migrants in contrast to residents. The regression analysis in this article aims to disentangle these factors.

More than 60% of non-migrants in metropolitan cities are employed either in the public formal or in the private formal sector, whereas only around 46% of non-migrants in non-metropolitan towns work in the formal sector. Migrants appear to find relatively more formal employment at the destinations outside of the big cities compared with the residents there.

Most migrants work in service sectors. Only a few work in agriculture at the non-metropolitan destinations even though it is the second-largest sector after services in these locations. In metropolitan cities, services are the main sector of employment. This suggests that most migrants are unlikely to change their sector of activity when they move out of metropolitan areas.

These observations highlight three findings: first, there is a significant difference in economic and social characteristics between metropolitan and non-metropolitan microregiões that are likely to determine migration between these. Prices of housing, non-tradable living costs, are much higher in the metropolitan cities and the non-metropolitan areas are catching up economically. Second, there is a large spatial variation in the characteristics of non-metropolitan microregiões across the country. Hence, metropolitan out-migrants are unlikely to be indifferent between destinations in their choice of where to move. Third, migrants are not a random draw of the population and they are a heterogeneous group. It is important to account for migrants’ characteristics and underlying selection in the econometric analysis of this article.

Destination Choice of Migrants
Empirical methodology

The empirical analysis focuses on the estimation of the effect of various local attributes on the destination choice of migrants. The analysis is based on a multiple-choice setting presented by McFadden (1974). The empirical application is restricted to those who migrated.

The model allows to include also residents in the analysis and assume that they chose not to move. In the empirical application, this would result in a sample so large that it is not feasible to handle. The decision to migrate itself yields a selection bias distinct from the location choice. Costs of moving are heterogeneous for workers so that some of those who did not move might have done so due to high costs or risk which gives rise to a selection bias in the decision to migrate. By excluding the choice to stay at one's origin, and estimate the destination choice model with migrants only, this specific selection bias does not arise.

As in Fafchamps and Shilpi (2013), the author models destination choice conditional on the individual being a migrant.

Migrants are assumed to choose their location to maximize their utility. Motivated by a random utility model, a migrant i residing in the current location chooses among all possible destinations. Let zij be a vector of destination attributes that vary across alternatives and can vary by migrant i and let cj be the cost of moving to destination j from the current location o. Therefore, the author defines cj = 0 if j = o. The utility of moving to destination j is assumed to have the following form: Uij=βzijcj+ɛij {U_{ij}} = \beta '{z_{ij}} - {c_j} + { \epsilon _{ij}}

The utility of migrant i from moving to destination j depends on the destination attributes, moving costs, and an idiosyncratic random component ij. The observed choice by the migrant is assumed to reflect the maximum utility of all J utilities. The probability that migrant i chooses destination j is therefore Prob(Uij>Uik)forallotherkj \matrix{ {\Pr {\rm{ob}}\left( {{U_{ij}} > {U_{ik}}} \right)} & {{\rm{for}}\,{\rm{all}}\,{\rm{other}}} & {k \ne j} \cr }

It is assumed that the error terms are distributed independently and identically with Weibull distribution as in McFadden (1974): F(ɛij)=exp(eɛij) F\left( {{ \epsilon _{ij}}} \right) = {exp}\left( { - {e^{ - \epsilon_{ij}}}} \right)

The probability of moving to destination j is now modeled conditional on migration (i.e., leaving location o). If Yi represents a random variable indicating the destination choice of migrant i, the probability that this choice is destination j conditional on migration can then be expressed as: Prob(Yi=j|Yio)=eβzijcj[j=1Jeβzijcj]eβzioco \Pr {\rm{ob}}\left( {{Y_i} = j|{Y_i} \ne o} \right) = {{{e^{\beta '{z_{ij}} - {c_j}}}} \over {\left[ {\sum\nolimits_{j = 1}^J {{e^{\beta '{z_{ij}} - {c_j}}}} } \right] - {e^{\beta '{z_{io}} - {c_o}}}}}

This is equal to: Prob(Yi=j|Yio)=eβzijcjjoJeβzijcj \Pr {\rm{ob}}\left( {{Y_i} = j|{Y_i} \ne o} \right) = {{{e^{\beta '{z_{ij}} - {c_j}}}} \over { {\sum\nolimits_{j \ne o}^J {{e^{\beta '{z_{ij}} - {c_j}}}} }}}

Eq. (5) represents a conditional logit model. The vector zij may comprise individual-specific but destination-invariant characteristics wi and the attributes of each destination xij can vary across destinations and also vary across individuals: zij=g(wi,xij) {z_{ij}} = g\left( {{w_i},{x_{ij}}} \right)

In this analysis, the interest lies in the attributes of destinations and not on migrants’ characteristics. Greene (2000) shows how wi drops out of the probability in Eq. (5) so that this model automatically controls for any individual-specific factors in the destination choice.

In some applications, e.g., Fafchamps and Shilpi (2013), this is called individual fixed effect alternative specific conditional logit. It is, however, not to be confused with the inclusion of fixed effects as in a panel model.

However, this also implies that the author cannot estimate the effect of such factors, such as the age of the migrant, etc. Hence, the alternative specific conditional logit model takes the following form: Prob(Yi=j|Yio)=eβxijcjjoJeβxijcj \Pr {\rm{ob}}\left( {{Y_i} = j|{Y_i} \ne o} \right) = {{{e^{\beta '{x_{ij}} - {c_j}}}} \over {{\sum\nolimits_{j \ne o}^J {{e^{\beta '{x_{ij}} - {c_j}}}} } }}

This model can be estimated by the method of maximum likelihood. Let dij = 1 if Yi = j and 0 otherwise. Then the log-Likelihood function is: logL=i=1Nj=1JdijlogProb(Yi=j|Yio) {log}\,L = \sum\limits_{i = 1}^N {\sum\limits_{j = 1}^J {{d_{ij}}\log \Pr {\rm{ob}}\left( {{Y_i} = j|{Y_i} \ne o} \right)} }

For the main estimation, there are N observations and regressors for each of the 14,509 metropolitan out-migrants.

The sample of metropolitan out-migrants is slightly reduced as the author only includes those in the analysis who are matched so that the results are comparable. Those dropped were not matched.

They choose from J = 514 possible non-metropolitan microregiões as destinations. Only one of the destinations will have a positive outcome as a chosen destination, i.e., the one observed in the data. This results in 5,730,782 individual-destination observations for the multivariate analysis.

Based on the human capital migration model (Sjaastad, 1962), the destination attributes of interest in this analysis are wages and prices. Observed average wages and prices at microregiões level in the 2010 data are equilibrium results from local economic dynamics, among other factors people's location choices. The simultaneous inclusion of these variables in the model, therefore, raises endogeneity concerns.

To address this issue, the author computes expected wages instead of applying the observed wages realized by migrants at their destination. Average wages in a location need not reflect the wages a migrant can expect to earn. The author, therefore, predicts expected wages for migrants based on their characteristics and the coefficients from a wage estimation of residents at each location.

First, the author estimates a wage regression separately for all 6.9 million resident observations in each microregião. The wage regression takes the following form: Wij=αj(aija¯j)+βj(EijE¯j)+γj(SijS¯j)+χHij+δj+νij W_i^j = {\alpha _j}\left( {a_i^j - {{\bar a}_j}} \right) + {\beta _j}\left( {E_i^j - {{\bar E}_j}} \right) + {\gamma _j}\left( {S_i^j - {{\bar S}_j}} \right) + \chi H_i^j + {\delta _j} + \nu _i^j

Log hourly wages of individual i in location j are determined by the individual characteristics aij a_i^j , Eij E_i^j , and Sij S_i^j ; household characteristics Hij H_i^j ; a dummy for the microregião δj; and an idiosyncratic error term νij \nu _i^j .

The variable aij a_i^j summarizes age and age-squared, Eij E_i^j the education level, and Sij S_i^j measures gender and race (white vs. non-white). Each of these variables is demeaned at the level of the microregião, so that the coefficients αj, βj, and γj capture the return to these characteristics specific to each location. Additionally, this implies that δj measures the unconditional microregião-specific average wages. Household characteristics, Hij H_i^j , include the proportion of children and a dummy for whether the partner works, as these might vary by region, e.g., in more rural areas, households tend to be larger and female labor force participation lower so that wages would be overestimated in these areas if this was not controlled for. The author uses the survey weights in these regressions to make the estimates representative of the population.

For each microregião, the coefficients αj^ \widehat {{\alpha _j}} , βj^ \widehat {{\beta _j}} , γj^ \widehat {{\gamma _j}} from this regression are then used to predict a measure of expected wages for each migrant. This predicted wage reflects what each migrant can expect to earn in each microregião conditional on her characteristics ai, Ei and Si, and the unconditional local wage level δj^ \widehat {{\delta _j}} : E[Wij|^Xi]=δj^+αj^(aia¯j)+βj^(EiE¯j)+γj^(SiS¯j) E[\widehat {W_i^j|}{X_i}] = \widehat {{\delta _j}} + \widehat {{\alpha _j}}\left( {{a_i} - {{\bar a}_j}} \right) + \widehat {{\beta _j}}\left( {{E_i} - {{\bar E}_j}} \right) + \widehat {{\gamma _j}}\left( {{S_i} - {{\bar S}_j}} \right)

The coefficients of the wage predictions corresponding to Eq. (10) (see Table A8 in Appendix) confirm the relationships documented in the literature: Age has a positive, but diminishing effect on expected wages, women earn less than men, white Brazilians more than non-whites, and wages increase with the level of education.

This approach assumes that migrants are a random draw from the resident population so that the returns to individual characteristics should be the same for migrants and residents. In the descriptive statistics, the author showed that migrants differ from the resident population in several observable characteristics. This implies that the expected wage measures used in the analysis so far could be biased by unobservable characteristics. The author thus estimates another measure for expected wages that should reduce the selection bias. The author predicts expected wages in non-metropolitan destinations from a sample of previous migrants from the same origin as the migrant. These migrants have moved more than a year ago to the destinations. They are assumed to be more comparable to migrants than residents in terms of unobservable characteristics specific to migrants, for example, risk-taking preferences.

For the metropolitan origins, the author predicts expected wages based on a matched sample of residents at the origin. The author applies CEM to use only those residents that look most similar to the migrants. CEM bounds the degree of model dependence in the main analysis and the data are automatically restricted to common support. The large dataset of the Census at hand is very suitable for this matching method, without facing the trade-off of conventional matching methods between bias and variance. Migrants and non-migrants are matched on sex, age (for migrants the year at migration), race, education level, marital status, sector of activity, and city of origin. Balance statistics are presented in Tables A1–A3 in Appendix.

With this approach, expected wages are allowed to vary across individuals and locations in the individual choice model. In this setting, the endogeneity of wages would only arise if we believed that the destination choice of an individual systematically determines the expected wage level at all possible destinations also for other individuals. This is not the case as the model controls for individual-specific effects and employs individual-specific expected wages.

This approach does not apply to prices. Living costs vary by destination only and thus could be driven by location choice. The use of an instrumental variable is required that would predict differences in living costs across locations, but not destination choices. The alternative specific conditional logit model does not allow for the application of an instrumental variable approach as it does not have normally distributed error terms. The author thus proposes a reduced form regression using the mean of average monthly municipality wages in the contingent microregiões surrounding a given microregião as an instrument for local living costs. This approach follows Morten and Oliveira (2018) who used trade-cost weighted Bartik shocks in neighboring locations as an instrument for local prices. The rationale is simply that if productivity in neighboring locations increases, prices for traded goods increase, too. While the authors employed the dynamic approach of Bartik-shocks in wage growth, the author uses simply the average of monthly mean wages of the year before migration (2009). Figure A1 in Appendix shows a strong and positive correlation between these indicators (coeff.: 1.59; SE = 0.06) confirming that this is a valid approach in this setting.

Migration costs are measured by the Euclidean distance between origin and destination in kilometers.

The author also used road travel time based on OpenStreetMap. This measure is positively correlated with Euclidean distance (0.87, significant at 1% level) and results do not change in their sign nor significance. Results are available upon request.

Distances are first computed between municipality pairs and then collapsed to the level of microregiões weighting for the inter-municipal migration flow. In cases with zero migration flows between municipality pairs, the author assigned a very small positive value (0.1e–12) as migration flow to be used for the weighting to avoid losing municipality pairs.

771 migrant observations are dropped as the coordinates of their municipalities of origin or destination were imprecise. These locations are not systematically located in a specific region or remote location and can thus be assumed random.

Additionally, the author includes a dummy of whether a migrant moved out of her state of birth to a different state. This captures the social proximity of a destination to the migrant's origin as in Brazil people have a strong identity with their birth state. Both of these variables also imply the social cost of being farther away from one's family and friends. The author further controls for the local population to test for the different attractiveness of non-metropolitan microregiões due to varying population sizes.

Results

In the specific application of this paper, metropolitan out-migrants choose their destination not only based on destination attributes but also these attributes relative to the attributes of migrants’ metropolitan origins. For each location attribute, the author thus computes the difference between the destination and origin, e.g., the expected wages in destination j minus expected wages in origin o. Table 5 gives an overview of the differences between destinations and origins of all variables of interest and how these differences vary between the destinations that migrants chose to those that they did not choose.

Table 5 already indicates some patterns of destination choice. Similar to what we observed earlier in the descriptive part, nominal wages are on average always lower in non-metropolitan areas. Migrants tend to choose locations, where this gap is relatively smaller, −0.53 compared to −0.61. This also applies for selection-corrected expected wages (Matched wages). Similarly, housing prices, measured in rent per room, are on average higher in the big cities. Migrants settle in locations where this price gap is not as big as in other possible destinations. This could indicate a trade-off between higher wages and lower prices at destinations. The proposed instrument for prices, wages in neighboring microregiões, reflects the pattern of actual living costs (rent per room).

Difference between non-metropolitan destination and metropolitan origin comparing chosen destination to alternative destinations

Difference between destination and origin in Chosen destination Alternative destinations t-statistic, difference in mean
Expected hourly wages (log) −0.53 −0.61 −24.7
Matched expected wages (log) −2.73 −2.79 −16.2
Rent per room (log) −0.55 −0.64 −21.9
IV (wages in neighboring MRs, log) −0.10 −0.14 −23.5
Population in thousands −5,605 −6,326 −17.1
Homicide rate −17.66 −14.11 18.2
Health facilities (per 100,000) 25.49 26.46 8.1
Health provision quality index (0–1) −0.03 −0.05 −24.0
Education provision quality index (0–1) −0.00 −0.04 −32.8
Distance to origin (km) 573 1,295 108.6
Other state than origin 0.45 0.92 202.4

Chosen destinations are on average larger in population size than their alternatives. Based on their average size, they are however not the smallest locations, but still medium-sized microregiões. In terms of amenities, chosen destinations have relatively lower levels of homicide rates in contrast to alternative destinations and metropolitan origins. There are also relatively more health facilities in chosen destinations and health care provision is of better quality. Education service provision is also on average better in the chosen microregião than in alternative destinations. These averages are all statistically different between chosen and alternative destinations.

Another clear pattern is revealed with regards to the geographic and social distance of chosen destinations. About 45% of chosen destinations are in a different state than the origin contrasting 92% of the destination alternatives. This is also reflected in a lower average distance of chosen destinations to the migrants’ origin. Many of these factors are highly correlated with each other which makes it necessary to apply multivariate analysis to disentangle their influence on the metropolitan out-migrants’ destination choice.

Many of these factors are highly correlated with each other which makes it necessary to apply multivariate analysis to disentangle their influence on the metropolitan out-migrants’ destination choice.

Table 6 reports the results of the alternative specific conditional logit model that estimates the probability for destination choice conditional on migration as specified in Eq. (8). The interpretation of coefficients in the alternative specific conditional logit model is not straightforward. It is not possible to compare the coefficient size directly, but only in relative terms which the author will do later in Section 4.3.

The specifications in columns 1 and 2 of Table 6 are that of only wage and price differences, migration costs in form of distance and controlling for population size. In column 1 of Table 6, the author presents the naive estimators of expected wages and local living costs not accounting for selection bias in wages and endogeneity of prices. Both coefficients are insignificant. Only distance and whether a destination is in the same state significantly predict migrants’ destination choice. Migrants prefer destinations that are closer to their origin and within their state of birth. These results remain unchanged across specifications.

Column 2 of Table 6 introduces the selection-corrected expected wages and the instrument for prices, wages in neighboring locations. The coefficients remain insignificant, but the sign for wages switches. This indicates that observable and unobserved characteristics of migrants led to an upward bias in expected wage differences. In column 3 of Table 6, the author then further includes other local amenities. In terms of public service provision, it appears that migrants show preferences for locations with relatively more health facilities, but they accept a relatively lower quality of health service provision. They also significantly prefer locations with relatively higher levels of education provision quality.

Destination choice conditional on migration, alternative specific logit

(1) (2) (3)

Wage measure: Expected wages (log) Matched expected wages (log)

Price measure: Rent per room (log) Wages in neighboring locations (log)
Difference in:
Wages 0.054 (0.175) −0.041 (0.234) −0.069 (0.244)
Prices −0.173 (0.213) −0.805 (0.730) −0.822 (0.631)
Population (log) −0.019 (0.068) −0.040 (0.064) 0.011 (0.080)
Homicide rate 0.004 (0.004)
Health facilities 0.008*** (0.003)
Health quality index −1.292* (0.758)
Education quality index 1.569* (0.929)
Destination specific:
Distance to origin (log) −0.524*** (0.085) −0.523*** (0.087) −0.521*** (0.087)
Other state −1.800*** (0.265) −1.850*** (0.258) −1.853*** (0.250)

Observations 5730782 5730782 5730782
Wald chi2 742 1222 1367
Number of cases 14509 14509 14509
Number of alternatives 514 514 514

Standard errors are clustered at the metropolitan microregião of origin. Estimator is alternative specific conditional logit. In each column, the first set of regressors is the difference between destination and origin for each destination alternative. The second set, indicated as Destination specific, is measured at destination relative to the origin. Prices are measured with the rent per room. Columns 2 and 3 use expected wage differences based on past migrants at the destination and matched residents at origin as explained in Section 4.1. Significance levels

10%,

5%,

1%.

In summary, the results of the multivariate analysis did not confirm that wages and prices matter significantly for the destination choice of metropolitan out-migrants. However, physical and social moving costs appear to matter. Finally, the author documents that amenities in terms of public service provision are significantly correlated with the destination choice.

One reason why wages and living costs do not enter significantly could be that they matter only for specific groups of migrants. The author, therefore, has run the analysis for sub-samples of migrants with different levels of education. Results are presented in Table 7. In each column, the author used selection-corrected expected wages and the instrument for prices. Column 1 shows the results for migrants with no or only primary education, column 2 for those with lower secondary education, column 3 for upper secondary, and column 4 for those with higher education.

Destination choice conditional on migration by the education of migrant, alternative specific logit

(1) (2) (3) (4)

Level of education: None or primary Lower secondary Upper secondary Higher
Difference in:
Matched expected wages (log) −0.279 (0.255) −0.161 (0.328) −0.023 (0.238) 0.336 (0.238)
Prices (IV) −1.360* (0.700) −1.205* (0.691) −0.686 (0.638) 0.202 (0.563)
Population (log) −0.028 (0.093) 0.049 (0.112) 0.022 (0.076) 0.036 (0.069)
Homicide rate 0.004 (0.004) 0.003 (0.003) 0.006 (0.004) 0.000 (0.004)
Health facilities 0.009*** (0.004) 0.010** (0.004) 0.008** (0.003) 0.002 (0.003)
Health quality index −0.787 (0.950) −0.754 (0.953) −1.668* (0.886) −2.184** (0.852)
Education quality index 1.684 (1.128) 1.238 (1.114) 1.977* (1.019) 1.055 (0.693)

Destination specific:
Distance to origin (log) −0.496*** (0.086) −0.574*** (0.081) −0.515*** (0.102) −0.538*** (0.104)
Other state −1.963*** (0.253) −1.808*** (0.261) −1.881*** (0.281) −1.663*** (0.282)

Observations 1871193 954109 1840023 1065457
Wald chi2 765 1255 1658 2850
Number of cases 4835 2425 4598 2651
Number of alternatives 514 514 514 514

Standard errors are clustered at the metropolitan microregião of origin. Estimator is alternative specific conditional logit. Expected wage differences are based on past migrants at destination and matched residents at origin as explained in Section 4.1. Prices are measured with average municipality wages from neighboring mciroregiões. In each column, the first set of regressors is the difference between destination and origin for each destination alternative. The second set, indicated as Destination specific, is measured at destination relative to the origin.

Significance levels

10%,

5%,

1%.

Three results can be highlighted in Table 7. First, expected wages do not matter significantly for the destination choice of any migrant group, but living costs do for those migrants with lower skill levels. Low-educated migrants (columns 1 and 2) significantly prefer destinations with relatively lower living costs than alternative locations and their metropolitan origins. If the price difference between a destination option and the origin increases, implying that prices are closer to those of the expensive origins, then migrants are less likely to choose such a destination. This does not apply to migrants with secondary schooling or higher education. Second, lower educated migrants give significant importance to the availability of health facilities (access and quantity) but not to the quality of health service provision or education. More educated migrants show a significant preference for education quality (column 3) but are willing to accept lower health provision quality. Third, migration costs in terms of geographical and social distance enter the destination choice equally for all education groups.

Relative effect size

Marginal effects of the alternative-specific logit model can be computed for each possible location choice, but this is computationally burdensome and ineffective in presenting the results. To illustrate and compare the effect sizes across variables and sub-samples, we can look at one destination alternative, e.g., the one with a price difference very close to the average price difference to metropolitan origins. The author takes the significant regressors from the full specification with selectivity robust wage measures and the price instrument as in Table 7 by education level and computes their elasticities for this example location. Let the probability of choosing destination j be Pj, then the elasticity of Pj with respect to an attribute xij evaluated at the mean x¯ij {\bar x_{ij}} can be written as: δlog(Pj)δlogxij=x¯ij(1Pj)βx {{\delta \log \left( {{P_j}} \right)} \over {\delta \log {x_{ij}}}}={\bar x_{ij}}\left( {1 - {P_j}} \right){\beta _x} where βx is the coefficient of the destination attribute from the conditional logit estimation (Greene, 2000). The elasticities for significant covariates for each education group are presented in Table 8:

The elasticities reveal that by far the largest effect on migrants’ destination choice is that of distance and a migrant leaving her/his state of birth. This captures the physical and social costs of moving and confirms that migration costs in Brazil are still high and a significant factor in labor mobility (Morten and Oliveira, 2018). Table 7 further showed that lower educated migrants value the presence of health facilities, but the elasticity of this amenity is relatively small compared to those of distance. Of similar size is the elasticity of living costs, which was also only significant for the sample of less educated migrants.

A 10% increase in the price difference between the metropolitan origin of a migrant and this specific location makes it on average 2% more likely to be chosen as a destination by migrants with no or only primary education. The range of living costs differences is around 148% of its mean so that this is not a negligible effect. Effect sizes for education and health quality are very small.

Elasticities of significant covariates by sub-sample

Education No or primary Lower secondary Upper secondary Higher
Distance (log) −3.4 −4.0 −3.6 −3.7
Other state −1.8 −1.7 −1.7 −1.5
Health facilities 0.2 0.3 0.2 0.1
Prices (IV) 0.2 0.2 0.1 0.0
Education quality −0.1 −0.1 −0.1 0.0
Health quality 0.0 0.0 0.1 0.1
Counterfactual Earnings of Metropolitan Out-migrants

The previous results showed that prices play a sizeable and significant role in the destination choice of metropolitan out-migrants whereas expected wages do not appear significant once the author controls for self-selection of migrants. This may be due to incorrect expectations of migrants about their earnings. Thus, this section focuses on the actual observed earnings of migrants at their destination in contrast to expected wages. The actual earnings are compared with a prediction of what a migrant would have earned had she not moved out of the metropolitan city, her counterfactual wage. With this comparison, this section aims to see whether and how metropolitan out-migration is associated with a wage loss or gain and what role living costs play in this question.

Empirical methodology

The wage return to migration is defined as the difference between income at destination, yd, and at the origin, yo: r=ydyo r = {y_d} - {y_o}

In the empirical application, income is proxied by the log of hourly wages, W.

The author chooses to look at hourly wages earned in the main job instead of total income as hourly wages in the main job should mostly reflect the return to individual characteristics based on location whereas total income also depends on household composition and other factors.

The comparison of migrant wages between origin and destination can be interpreted as an evaluation problem. Let migration be the treatment with Mi = 1 if the individual moved, Mi = 0 if not. For each individual, two outcomes in terms of wage differences can be defined as Yi0=log(wi,0)log(wi,0)ifMi=0 \matrix{ {Y_i^0 = {log}\left( {{w_{i,0}}} \right) - {log}\left( {{w_{i,0}}} \right)} & {{\rm{if}}} & {{M_i} = 0} \cr } Yi1=log(wi,1)log(wi,0)ifMi=1 \matrix{ {Y_i^1 = {log}\left( {{w_{i,1}}} \right) - {log}\left( {{w_{i,0}}} \right)} & {{\rm{if}}} & {{M_i} = 1} \cr }

Thus, the wage difference due to migration can be identified for migrants as average treatment effect on the treated (ATT): ATTE(Y1Y0|M1)E(Y1|M1)E(Y0|M1) ATT - E\left( {{Y^1} - {Y^0}|M - 1} \right) - E\left( {{Y^1}|M - 1} \right) - E\left( {{Y^0}|M - 1} \right)

The first term on the right-hand side is observable in the data at hand, the wages of migrants at their destination. The second term represents the counterfactual outcome, what migrants would have earned had they not migrated which cannot be observed. The author only observes wages for migrants at their destination and non-migrants at the origin. If wages were estimated using OLS and then compared, the wage differences would be biased due to selection into migration arising from individual-specific unobservable characteristics.

It is necessary to account for this potential bias in the empirical estimation of migrants’ counterfactual wages. This is especially important in a context where spatial wage differences have been found to reflect variation in labor force composition and industry concentration. In Brazil, the labor force is distributed unequally across space, concentrating better-educated workers in metropolitan areas and economically stronger regions. Thus, the returns to education based on observable characteristics explain around half of the spatial wage differences (Almeida dos Reis and Paes de Barros, 1991; Foguel et al., 2015; Ferreira et al., 2006). Furthermore, Brazilian workers have shown little mobility across industries so that it seems reasonable to focus on self-selection by location and not by sector (Menezes-Filho and Muendler, 2011; Hering and Paillacar, 2015).

The author, therefore, uses the predicted wages from the matched sample of residents in metropolitan origins of migrants as described in Section 4.1. The difference between the actual observed wages at the destination and the predicted counterfactual wages at the origin is the return to migration out of metropolitan cities. Real wages are computed using the local average rent per room as a denominator of actual and predicted nominal wages.

Results

This section presents the results of the counterfactual analysis. Table 9 presents the average return to migration as the difference between average actual and counterfactual wages for migrants moving out of metropolitan areas. These migrants earn significantly lower wages at their non-metropolitan destinations. Once the author accounts for the local living costs by using real wages, the difference becomes positive. This indicates that metropolitan out-migrants lose in nominal terms, but gain in real wages due to lower living costs in non-metropolitan destinations.

The results without matching for nominal wage differences are around 0.1 log points larger than when matching is applied (see Table A4 in Appendix). This indicates an overestimation of wages at origin when not accounting for selection and it suggests that out-migrants are negatively selected from the metropolitan working population.

Table 10 documents heterogeneity in wage returns along with the education level of migrants. The author defines high-educated workers as those who completed high school or any higher level of education. Low-educated workers are those who did not complete high school or any lower level of education. Results show that real wages are higher at the destination than origin for both groups. For highly educated individuals leaving the big cities, the real wage gains are larger because their loss in nominal wages is relatively small. In contrast, low-educated workers see a large loss in nominal terms and a relatively smaller gain in real wages. For both groups, the nominal and real wage differences are statistically significant.

Differences in actual and predicted wages for metropolitan out-migrants, after matching

Log (nominal hourly wages) N Mean

Observed 15,424 1.816
Predicted 15,424 2.069
Difference −0.253***

Log (real hourly wages) N Mean

Observed 15,424 −2.237
Predicted 15,424 −2.396
Difference 0.159***

Significance levels

10%,

5%,

1% for t-test of difference in means between observed and predicted wages. Predicted wages are based on a matched sample of metropolitan residents.

Differences in actual and predicted wages for metropolitan out-migrants, by education level

High-educated

Log (nominal hourly wages) N Mean

Observed 3,107 2.846
Predicted 3,107 2.930
Difference −0.084***

Log (real hourly wages) N Mean

Observed 3,107 −1.270
Predicted 3,107 −1.544
Difference 0.274***

Low-educated

Log (nominal hourly wages) N Mean

Observed 12,317 1.556
Predicted 12,317 1.851
Difference −0.295***

Log (real hourly wages) N Mean

Observed 12,317 −2.481
Predicted 12,317 −2.611
Difference 0.130***

Significance levels

10%,

5%,

1% for t-test of difference in means between observed and predicted wages. Predicted wages are based on a matched sample of metropolitan residents.

So far, the counterfactual wage comparison has focused on the average wage return. However, the distributional graphs of actual and counterfactual wages document the return to metropolitan out-migration along with the wage distribution. Figures 3 and 4 show the wage distributions of workers who have moved out of metropolitan areas. They compare the observed wages of migrants at their destination and the predicted counterfactual wages at the origin. As suggested from the results in Table 9, for nominal wages, the distribution of observed wages lies left of the predicted earnings in metropolitan origins. Wages are generally higher in origins and out-migration implies a loss in nominal terms.

For real wages (see Figure 4), the distribution of observed wages lies now a bit to the right of counterfactual wages in metropolitan origins reflecting the positive return in real terms to leaving expensive cities.

The distributions are tested to be significantly different with a Kolmogorov-Smirnov test for equality of distributions. Both, the nominal and real wage distributions are significantly different.

In the analysis of the destination choice of migrants, we learned that low-educated metropolitan out-migrants choose between destinations where their gain in lower living costs is the largest. This can explain why some individuals do not experience a positive return to migrating out of metropolitan areas across the income distribution. Some might fail to successfully evaluate their destination alternatives, some might lack the information about wages and prices at all destinations, and others might just not be successful in acquiring the wage employment they had expected or they migrate for other reasons such as family. In this way, some metropolitan out-migrants lose out, while on average they gain in real wage returns.

Figure 3

Kernel density plots of actual and predicted nominal wages of metropolitan out-migrants with matching.

Figure 4

Kernel density plots of actual and predicted real wages of metropolitan out-migrants with matching.

To understand better how migrants evaluate possible destinations, the author contrasts the previous results with those of people moving into metropolitan cities applying the same methodology. Table A9 in Appendix confirms that in nominal terms, metropolitan cities present a huge income gain, but especially low-skilled migrants experience significant and large losses in real terms. This points to the importance of experience and information. Metropolitan residents face high prices and this variable thus dominates their evaluation of possible destinations. Workers in non-metropolitan areas might have never considered living costs as a significant factor so that they fail to account for them in their destination choice.

Robustness: Price measures

In the Census data, households are asked to state the monthly rent they pay if they live in a rented apartment or house and the number of rooms of the unit. The author used these data to aggregate the average room rent at the microregião level. This measure ignores the possibility that the price differences might just reflect differences in housing quality. Similar to Li and Gibson (2014), the author constructs a hedonic housing index that measures the differences in housing costs based on location-specific amenities rather than housing-specific characteristics.

Households are asked to provide information on the quality of walls, floors, and the presence of toilets. Additional questions inform about the quality of sewage, wastewater, and electricity access. With these variables, the author can estimate a hedonic housing price for each location. The author regresses the rent per room on these characteristics weighted by the household survey weight and the author includes a dummy for each microregião. These regression results are presented in Table A7 in Appendix. The coefficients of the microregião dummies capture any location-specific amenities that contribute to spatial price differences. The author extracts these estimates to construct a location-specific hedonic living cost measure. This variable is independent of differences in housing quality.

The author, therefore, also computes the real wages using the hedonic price as a denominator. Results are presented in Table 11. The wage difference remains positive and statistically significant, but it is smaller by around a third than in the initial results.

Further concerns regarding the measure of living costs could arise from the fact, that low and high-educated workers probably face different housing markets with different average prices. The author, therefore, conducts the counterfactual analysis using education-group specific living costs as a denominator when computing real wages (see Tables A5 and A6 in Appendix). The author also uses the median prices in the microregião instead of the mean. The signs and significance of wage differences remain the same as in the initial results, but their size changes. For high-educated workers, the estimates yield larger positive wage differences when the author uses education-group specific living costs. The differences are 0.37 and 0.3 log points for average and median rents specific to high-educated workers. For low-educated workers, the education-specific wage differences are smaller than unadjusted ones. Applying the median prices faced by low-educated workers yields the smallest difference of 0.07 log points compared to 0.13 in the initial results. Rents are on average lower for low-educated workers so that their gain from leaving metropolitan cities becomes smaller than when the author did not account for the education-group specific rents. The opposite applies to high-educated workers.

Differences in actual and predicted real wages for metropolitan out-migrants using hedonic prices as a denominator, after matching

Log (real hourly wages) N Mean
Observed 15,424 −2.105
Predicted 15,424 −2.155
Difference 0.050***

Significance levels

10%

5%

1% for t-test of difference in means between observed and predicted wages. Predicted wages are based on a matched sample of metropolitan residents.

Conclusion

The economic literature on migration in developing countries focuses on rural to urban movements because this was the dominant observation in most countries, among them Brazil during its transition from low to middle-income country. In the decade of the 2000s, the movements of workers across Brazil have shown to lead equally out of metropolitan cities as into them. This paper uses the Brazilian Census data of 2010 to study this movement. High and low-skilled workers are equally likely to leave the big cities and out-migrants move to smaller towns, not to remote rural areas. These secondary cities have been rapidly growing economically also due to targeted government investment in previously lagging areas of the country (Mata et al., 2005; Lall et al., 2009; do Planejamento, 2010).

City-leavers are on average comparable to metropolitan residents in terms of education, but they differ in their age and sex composition. In their medium-sized destination towns, the out-migrants tend to work for slightly lower wages in “urban” sectors, such as services and manufacturing, and less in agriculture. The descriptive part documents that non-metropolitan areas in Brazil are significantly different from the big cities, but expose a large variation among them. Wages are lower than in cities, but so are amenities resulting in lower living costs. The out-migrants face the balancing act to reconcile lower earnings with lower living costs and worse amenities.

The author, therefore, estimates the importance of real wages in the destination choice of metropolitan out-migrants and finds that migrants maximize their utility by moving into smaller towns not far from their metropolitan origins. In these destinations, they face lower nominal wages, but also lower prices. The counterfactual analysis reveals that on average the migrants achieve a positive return in real wages to leaving the city. This finding is especially strong for low-educated workers who would lose from leaving the big cities if only nominal wages were considered and who significantly prefer cheaper locations in their destination choice. Non-metropolitan areas have on the average worse quality of public service provision. Better educated metropolitan out-migrants reveal a preference for education provision over health service emphasizing that preferences vary between amenities and by skill level.

The findings are in line with the literature on wage returns to migration. It is confirmed that the comparison of wages conditional on individual skills is important for the destination choice, but migrants seem to consider them only jointly with living costs (Tunalı, 2000; Dahl, 2002; Kennan and Walker, 2011; Moretti, 2011). Furthermore, selection corrected expected wages entered the model of destination choice insignificantly, which could indicate that workers have incorrect expectations about their wages and they could do even better in their destination choice. Rather, metropolitan out-migrants choose destinations that reduce their costs of moving as well as their living costs, which is why the author finds a positive return in real wages. These results suggest that high prices are pushing workers out of metropolitan cities.

Figure 1

Map of greater regions and metropolitan cities of Brazil.
Map of greater regions and metropolitan cities of Brazil.

Figure 2

Out-migration rate from metropolitan cities from 2004 to 2009.
Out-migration rate from metropolitan cities from 2004 to 2009.

Figure 3

Kernel density plots of actual and predicted nominal wages of metropolitan out-migrants with matching.
Kernel density plots of actual and predicted nominal wages of metropolitan out-migrants with matching.

Figure 4

Kernel density plots of actual and predicted real wages of metropolitan out-migrants with matching.
Kernel density plots of actual and predicted real wages of metropolitan out-migrants with matching.

Figure A1

Price instrument (average wages in contiguous microregiões) and prices (average municipality rents in microregião).
Price instrument (average wages in contiguous microregiões) and prices (average municipality rents in microregião).

Balancing statistics after matching

Multivariate L1 distance: 0.78689126

Univariate imbalance: L1 Mean Min 25% 50% 75% Max
Age at migration 0.04019 −0.15014 0 0 0 −1 0
Sex 0.06926 −0.06926 0 0 0 0 0
Education level 0.03117 −0.03117 0 0 0 0 0
Race 0.00467 −0.00467 0 0 0 0 0
City of origin 0.00479 0.00479 0 0 0 0 0
Marital status 0.02571 −0.02571 0 0 0 0 0
Sector of activity 0.00355 0.00355 0 0 0 0 0

Migrants between metropolitan and non-metropolitan microregiões between 2009 and 2010

Destination

Non-metropolitan Metropolitan
Origin N % N %
Non-metropolitan 380,627 46.9 167,781 20.7
Metropolitan 162,647 20.1 99,143 12.2

Labor market characteristics of migrants and non-migrants 2010

Non-metropolitan residents Metropolitan out-migrants Metropolitan residents
Unemployed 0.05 0.12 0.06
Log (monthly wages) 6.59 6.95 6.98

Sector
Formal private 0.40 0.43 0.56
Formal public 0.06 0.08 0.06
Informal 0.26 0.23 0.21
Self-employed 0.02 0.02 0.01
Small business 0.26 0.24 0.15

Industry, ISIC
Agriculture 0.26 0.14 0.09
Industry 0.22 0.27 0.21
Services 0.38 0.44 0.54
Public services 0.15 0.17 0.17

Destination choice conditional on migration, alternative specific logit

(1) (2) (3)

Wage measure: Expected wages (log) Matched expected wages (log)

Price measure: Rent per room (log) Wages in neighboring locations (log)
Difference in:
Wages 0.054 (0.175) −0.041 (0.234) −0.069 (0.244)
Prices −0.173 (0.213) −0.805 (0.730) −0.822 (0.631)
Population (log) −0.019 (0.068) −0.040 (0.064) 0.011 (0.080)
Homicide rate 0.004 (0.004)
Health facilities 0.008*** (0.003)
Health quality index −1.292* (0.758)
Education quality index 1.569* (0.929)
Destination specific:
Distance to origin (log) −0.524*** (0.085) −0.523*** (0.087) −0.521*** (0.087)
Other state −1.800*** (0.265) −1.850*** (0.258) −1.853*** (0.250)

Observations 5730782 5730782 5730782
Wald chi2 742 1222 1367
Number of cases 14509 14509 14509
Number of alternatives 514 514 514

Differences in actual and predicted wages for metropolitan out-migrants, by education level

High-educated

Log (nominal hourly wages) N Mean

Observed 3,107 2.846
Predicted 3,107 2.930
Difference −0.084***

Log (real hourly wages) N Mean

Observed 3,107 −1.270
Predicted 3,107 −1.544
Difference 0.274***

Low-educated

Log (nominal hourly wages) N Mean

Observed 12,317 1.556
Predicted 12,317 1.851
Difference −0.295***

Log (real hourly wages) N Mean

Observed 12,317 −2.481
Predicted 12,317 −2.611
Difference 0.130***

Differences of actual and predicted wages for metropolitan out-migrants, before matching

Log (nominal hourly wages) N Mean

Observed 14,810 1.767
Predicted 14,810 1.874
Difference −0.107***

Log (real hourly wages) N Mean

Observed 14,810 −2.466
Predicted 14,810 0.303
Difference 0.303***

Log (real hourly wages) N Mean

Hedonic price as denominator

Observed 14,810 −2.353
Predicted 14,810 0.204
Difference 0.204***

Matching summary

Number of strata: 9,796

Number of matched strata: 3,785

Non-migrants Migrants
All 683,517 16,172
Matched 587,346 15,401
Unmatched 96,171 771

Differences in actual and predicted real wages for metropolitan out-migrants using hedonic prices as a denominator, after matching

Log (real hourly wages) N Mean
Observed 15,424 −2.105
Predicted 15,424 −2.155
Difference 0.050***

Observed and predicted real wage differences using different measures of living costs, metropolitan in-migrants

Log (real hourly wages)
High skilled

Skill-specific mean rents N Mean

Observed 1,068 −1.931
Predicted 1,068 −1.974
Difference 0.043*

Skill-specific median rents N Mean

Observed 1,068 −1.795
Predicted 1,068 −1.894
Difference 0.099***

Low skilled

Skill-specific mean rents N Mean

Observed 7,357 −2.775
Predicted 7,357 −2.501
Difference −0.274***

Skill-specific median rents N Mean

Observed 7,357 −2.680
Predicted 7,357 −2.394
Difference −0.286***

Difference between non-metropolitan destination and metropolitan origin comparing chosen destination to alternative destinations

Difference between destination and origin in Chosen destination Alternative destinations t-statistic, difference in mean
Expected hourly wages (log) −0.53 −0.61 −24.7
Matched expected wages (log) −2.73 −2.79 −16.2
Rent per room (log) −0.55 −0.64 −21.9
IV (wages in neighboring MRs, log) −0.10 −0.14 −23.5
Population in thousands −5,605 −6,326 −17.1
Homicide rate −17.66 −14.11 18.2
Health facilities (per 100,000) 25.49 26.46 8.1
Health provision quality index (0–1) −0.03 −0.05 −24.0
Education provision quality index (0–1) −0.00 −0.04 −32.8
Distance to origin (km) 573 1,295 108.6
Other state than origin 0.45 0.92 202.4

Regression of housing prices on housing characteristics, OLS estimates

log(rent per room)
Urban area 0.256*** (0.005)

Type of dwelling (Base = House)
Townhouse/condominion 0.146*** (0.003)
Flat 0.396*** (0.002)
Hut 0.196*** (0.006)
Wall material (Base = Bricks coated)
Bricks not coated −0.160*** (0.002)
Wood −0.265*** (0.003)
Plaster coated −0.461*** (0.015)
Plaster not coated −0.521*** (0.020)
Wood unprepared −0.344*** (0.010)
Straw −0.073 (0.155)

Others −0.146*** (0.015)
Bathroom (Base = none)
1 −0.213*** (0.006)
2 −0.095*** (0.006)
3 0.047*** (0.007)
4 0.220*** (0.012)
5 0.355*** (0.027)
6 0.517*** (0.054)
7 0.430*** (0.119)
8 1.046*** (0.237)
9 or more 0.356*** (0.083)

Sanitation (Base = General sanitation network)
Septic sump −0.089*** (0.002)
Rudimentary Sump −0.200*** (0.002)
Ditch −0.225*** (0.005)
River, lake or sea −0.152*** (0.004)
Other −0.212*** (0.009)
Waste water (Base = General distribution network)
Well on property 0.007** (0.003)
Well outside property −0.088*** (0.005)
Carro-pipa −0.072*** (0.014)
Rainwater cistern −0.074*** (0.028)
Rain water other −0.097 (0.068)
Rivers, lakes, etc. −0.081*** (0.023)
Other −0.155*** (0.010)
Well in village 0.165** (0.066)
Canalization access (Base = Yes, in min. 1 room)
Yes, only on the property −0.052*** (0.004)
No −0.148*** (0.006)

Garbage collection (Base = Collected directly)

Collected in collective −0.054*** (0.002)
Burnt −0.229*** (0.008)
Buried −0.017 (0.043)
Tossed in a public area −0.229*** (0.008)
Tossed in river, lake, or sea −0.195*** (0.036)

Other 0.005 (0.027)
Electricity provision (base = Yes by the company)
Yes, other −0.094*** (0.010)
No electricity −0.238*** (0.021)
Constant 4.235*** (0.016)

Microregion dummies Yes

Observations 927,192
R-squared 0.539

Characteristics of metropolitan and non-metropolitan microregiões in 2010

Metropolitan Non-metropolitan


Mean Coeff. of variation Mean Coeff. of variation
Population 2,679,687 1.11 213,680 0.79
Room rent (R$, median) 72.47 0.23 45.22 0.42
Hourly wage (R$) 12.11 0.22 7.23 0.29

Share of
Unskilled workers 0.37 0.09 0.37 0.14
Skilled workers 0.31 0.11 0.40 0.14
High-skilled workers 0.24 0.17 0.16 0.23
Formally employed 0.58 0.11 0.40 0.36
Unemployed 0.06 0.29 0.05 0.41

Share of workers in
Agriculture 0.09 0.36 0.30 0.39
Industry 0.21 0.23 0.18 0.37
Services 0.53 0.08 0.35 0.23
Public services 0.11 0.25 0.12 0.24
People living in
Adequate living conditions 0.57 0.28 0.36 0.67

Other measures
GDP growth 2005–2010 0.16 0.31 0.18 0.79
Health facilities (per 100,000) 16.40 0.42 41.86 0.35
Health quality index (0–1) 0.82 0.09 0.79 0.11
Education quality index (0–1) 0.77 0.14 0.73 0.14
Homicide rate (per 100,000) 38.00 0.54 18.58 0.77

Differences in actual and predicted wages for metropolitan out-migrants, after matching

Log (nominal hourly wages) N Mean

Observed 15,424 1.816
Predicted 15,424 2.069
Difference −0.253***

Log (real hourly wages) N Mean

Observed 15,424 −2.237
Predicted 15,424 −2.396
Difference 0.159***

Observed and predicted real wage differences using different measures of living costs

Log (real hourly wages)
Low skilled

Skill-specific mean rents N Mean

Observed 11,393 −2.456
Predicted 11,393 −2.691
Difference 0.235***

Skill-specific median rents N Mean

Observed 11,393 −2.365
Predicted 11,393 −2.589
Difference 0.224***

Median hedonic prices N Mean

Observed 11,393 −2.374
Predicted 11,393 −2.555
Difference 0.181***

Balancing statistics before matching

Multivariate L1 distance: 0.83541258

Univariate imbalance: L1 Mean Min 25% 50% 75% Max
Age at migration 0.10634 −2.2895 −1 −1 −3 −3 −1
Sex 0.0979 −0.0979 0 0 0 0 0
Education level 0.07352 −0.12922 0 −1 0 0 0
Race 0.00576 0.00576 0 0 0 0 0
City of origin 0.14503 0.72656 0 0 4 0 0
Marital status 0.01909 −0.0098 0 0 0 0 0
Sector of activity 0.15388 −0.96689 0 −3 −1 0 −1

Destination choice conditional on migration by the education of migrant, alternative specific logit

(1) (2) (3) (4)

Level of education: None or primary Lower secondary Upper secondary Higher
Difference in:
Matched expected wages (log) −0.279 (0.255) −0.161 (0.328) −0.023 (0.238) 0.336 (0.238)
Prices (IV) −1.360* (0.700) −1.205* (0.691) −0.686 (0.638) 0.202 (0.563)
Population (log) −0.028 (0.093) 0.049 (0.112) 0.022 (0.076) 0.036 (0.069)
Homicide rate 0.004 (0.004) 0.003 (0.003) 0.006 (0.004) 0.000 (0.004)
Health facilities 0.009*** (0.004) 0.010** (0.004) 0.008** (0.003) 0.002 (0.003)
Health quality index −0.787 (0.950) −0.754 (0.953) −1.668* (0.886) −2.184** (0.852)
Education quality index 1.684 (1.128) 1.238 (1.114) 1.977* (1.019) 1.055 (0.693)

Destination specific:
Distance to origin (log) −0.496*** (0.086) −0.574*** (0.081) −0.515*** (0.102) −0.538*** (0.104)
Other state −1.963*** (0.253) −1.808*** (0.261) −1.881*** (0.281) −1.663*** (0.282)

Observations 1871193 954109 1840023 1065457
Wald chi2 765 1255 1658 2850
Number of cases 4835 2425 4598 2651
Number of alternatives 514 514 514 514

Elasticities of significant covariates by sub-sample

Education No or primary Lower secondary Upper secondary Higher
Distance (log) −3.4 −4.0 −3.6 −3.7
Other state −1.8 −1.7 −1.7 −1.5
Health facilities 0.2 0.3 0.2 0.1
Prices (IV) 0.2 0.2 0.1 0.0
Education quality −0.1 −0.1 −0.1 0.0
Health quality 0.0 0.0 0.1 0.1

Coefficients and t-statistics of prediction of wages for migrants based on past migrants at the destination, OLS

Log(hourly wage)

Coefficient t-statistic
Age 0.048 18.250
Age squared −0.045 −13.939
Female −0.368 −61.567
White 0.123 19.385
Education (Base = none)
Primary, secondary incomplete 0.240 28.118
Secondary, higher incomplete 0.530 71.485
Higher complete 1.441 154.831

Mean (Log (hourly wage)) = −0.754

Characteristics of migrants and non-migrants 2010

Non-metropolitan residents Metropolitan out-migrants Metropolitan residents
Number of observations 4,184,904 19,318 1,598,869
Age 40.25 36.85 40.22
Female 0.41 0.37 0.45
White 0.51 0.51 0.51

Education level
None, primary incomplete 0.47 0.29 0.29
Primary, secondary incomplete 0.16 0.16 0.17
Secondary, higher incomplete 0.26 0.33 0.36
Higher complete 0.11 0.21 0.19

Variables and data sources

Variable Description Source
Variables for descriptive statistics and destination choice model on microregião level
Wages (IV) Average monthly wages in neighboring microregião RAIS*
Housing prices Average rent on microregião level Census, IBGE
Education provision quality index Index from 0 to 1, computed based on: Subscription rate of pre-school children, dropout rate FIRJAN**
Rate of teachers with higher education, average daily teaching hours, results of the IDEB (Indicator of development of education in Brazil)
Health provision quality index Index from 0 to 1, computed based on: Number of pre-natal consultations, deaths due to mal-defined causes, child-deaths due to evitable causes FIRJAN**
Number of health care facilities Per 100,000 inhabitants; include general hospitals, day hospitals, polyclinics, health point, general emergency, pharmacy, basic health center. CNES***
Homicide rate Per 100,000 inhabitants in 2008 Ipeadata
Distance to the state capital Indicator for market access (Fally et al. 2010) Ipeadata
GDP Log of GDP in 2009 Ipeadata
Distance between origin and destination Geodesic distance as an indicator for fixed moving costs, author's calculation from coordinates Census, IBGE
Additional variables for wage regression, on an individual level
Partner participation Dummy whether the partner is working Census, IBGE
The proportion of children in the household Census, IBGE
Marital status Separated/divorced/widowed, single, married Census, IBGE
Sector Public, private, informal, self-employed Census, IBGE
Industry 21 industries according to International Standard Industrial Classification of all Economic Activities (ISIC) Census, IBGE
Federal state 27 states Census, IBGE
Variables for matching, on an individual level
Age At the time of migration, i.e., one year ago Census, IBGE
Race White and non-white Census, IBGE
Education level Primary, middle, high-school, college Census, IBGE
Micro-region of origin/residency City of origin for migrants and city of residency for comparison group of non-migrants Census, IBGE

Adams, R. H. (2006): Remittances and Poverty in Ghana. Policy Research Working Paper Series. Washington, D.C.: The World Bank. AdamsR. H. 2006 Remittances and Poverty in Ghana. Policy Research Working Paper Series Washington, D.C. The World Bank Search in Google Scholar

Adams, R. H.; A. Cuecuecha (2013): The Impact of Remittances on Investment and Poverty in Ghana. World Development 50, 24–40. doi:10.1016/j.worlddev.2013.04.009. AdamsR. H. CuecuechaA. 2013 The Impact of Remittances on Investment and Poverty in Ghana World Development 50 24 40 10.1016/j.worlddev.2013.04.009 Open DOISearch in Google Scholar

Aguayo-Tellez, E.; M. A. Muendler; J. P. Poole (2010): Globalization and Formal Sector Migration in Brazil. World Development 38, 840–856. doi:10.1016/j.worlddev.2010.02.018. Aguayo-TellezE. MuendlerM. A. PooleJ. P. 2010 Globalization and Formal Sector Migration in Brazil World Development 38 840 856 10.1016/j.worlddev.2010.02.018 Open DOISearch in Google Scholar

Almeida dos Reis, J. G.; R. Paes de Barros (1991): Wage Inequality and the Distribution of Education. Journal of Development Economics 36, 117–143. doi:10.1016/0304-3878(91)90007-I. Almeida dos ReisJ. G. Paes de BarrosR. 1991 Wage Inequality and the Distribution of Education Journal of Development Economics 36 117 143 10.1016/0304-3878(91)90007-I Open DOISearch in Google Scholar

Aroca Gonzalez, P.; W. F. Maloney (2005): Migration, Trade, and Foreign Direct Investment in Mexico. Policy Research Working Paper Series 3601. Washington, D.C.: The World Bank. Aroca GonzalezP. MaloneyW. F. 2005 Migration, Trade, and Foreign Direct Investment in Mexico Policy Research Working Paper Series 3601. Washington, D.C. The World Bank Search in Google Scholar

Barham, B.; S. Boucher (1998): Migration, Remittances, and Inequality: Estimating the Net Effects of Migration on Income Distribution. Journal of Development Economics 55, 307–331. doi:10.1016/S0304-3878(98)90038-4. BarhamB. BoucherS. 1998 Migration, Remittances, and Inequality: Estimating the Net Effects of Migration on Income Distribution Journal of Development Economics 55 307 331 10.1016/S0304-3878(98)90038-4 Open DOISearch in Google Scholar

Borjas, G. J. (1987): Self-Selection and the Earnings of Immigrants. The American Economic Review 77, 531–553. BorjasG. J. 1987 Self-Selection and the Earnings of Immigrants The American Economic Review 77 531 553 Search in Google Scholar

Borjas, G. J., S. G. Bronars; S. J. Trejo (1992): Self-Selection and Internal Migration in the United States. Journal of Urban Economics 32, 159–185. doi:10.1016/0094-1190(92)90003-4. BorjasG. J. BronarsS. G. TrejoS. J. 1992 Self-Selection and Internal Migration in the United States Journal of Urban Economics 32 159 185 10.1016/0094-1190(92)90003-4 Open DOISearch in Google Scholar

Brown, R. P.; E. Jimenez (2008): Estimating the Net Effects of Migration and Remittances on Poverty and Inequality: Comparison of Fiji and Tonga. Journal of International Development 20, 547–571. doi:10.1002/jid. BrownR. P. JimenezE. 2008 Estimating the Net Effects of Migration and Remittances on Poverty and Inequality: Comparison of Fiji and Tonga Journal of International Development 20 547 571 10.1002/jid Open DOISearch in Google Scholar

Bryan, G.; M. Morten (2019): The Aggregate Productivity Effects of Internal Migration: Evidence from Indonesia. Journal of Political Economy 127, 2229–2268. BryanG. MortenM. 2019 The Aggregate Productivity Effects of Internal Migration: Evidence from Indonesia Journal of Political Economy 127 2229 2268 Search in Google Scholar

Chernina, E. M. (2020): The Role of Migration Experience in Migrants’ Destination Choice. IZA Journal of Development and Migration 11, 1–29. doi:10.2478/izajodm-2020-0001 CherninaE. M. 2020 The Role of Migration Experience in Migrants’ Destination Choice IZA Journal of Development and Migration 11 1 29 10.2478/izajodm-2020-0001 Open DOISearch in Google Scholar

Christiaensen, L.; J. De Weerdt; R. Kanbur (2017): Cities, Towns, and Poverty: Migration Equilibrium and Income Distribution in a Todaro-type Model with Multiple Destinations. Discussion Paper Series 10692. Bonn: Institute for the Future of Labor (IZA). ChristiaensenL. De WeerdtJ. KanburR. 2017 Cities, Towns, and Poverty: Migration Equilibrium and Income Distribution in a Todaro-type Model with Multiple Destinations Discussion Paper Series 10692. Bonn Institute for the Future of Labor (IZA) Search in Google Scholar

Christiaensen, L.; J. De Weerdt; Y. Todo (2013): Urbanization and Poverty Reduction: The Role of Rural Diversification and Secondary Towns. Agricultural Economics 44, 435–447. doi:10.1111/agec.12028. ChristiaensenL. De WeerdtJ. TodoY. 2013 Urbanization and Poverty Reduction: The Role of Rural Diversification and Secondary Towns Agricultural Economics 44 435 447 10.1111/agec.12028 Open DOISearch in Google Scholar

Cochrane, S. G.; D. R. Vining (1988): Recent Trends in Migration between Core and Peripheral Regions in Developed and Advanced Developing Countries. International Regional Science Review 11, 215–243. doi:10.1177/016001768801100301. CochraneS. G. ViningD. R. 1988 Recent Trends in Migration between Core and Peripheral Regions in Developed and Advanced Developing Countries International Regional Science Review 11 215 243 10.1177/016001768801100301 Open DOISearch in Google Scholar

Dahl, G. B. (2002): Mobility and the Return to Education: Testing a Roy Model with Multiple Markets. Econometrica 70, 2367–2420. DahlG. B. 2002 Mobility and the Return to Education: Testing a Roy Model with Multiple Markets Econometrica 70 2367 2420 Search in Google Scholar

Donabedian, A. (1988): The Quality of Care. How Can It Be Assessed? JAMA 260, 1743–1748. doi:10.1001/jama.1988.03410120089033. DonabedianA. 1988 The Quality of Care. How Can It Be Assessed? JAMA 260 1743 1748 10.1001/jama.1988.03410120089033 Open DOISearch in Google Scholar

do Planejamento, M. (2010): Balanço 4 Anos 2007 – 2010. PAC Programa de Aceleracao do Crescimento. Report. Brasilia: Ministerio do Planejamento. do PlanejamentoM. 2010 Balanço 4 Anos 2007 – 2010. PAC Programa de Aceleracao do Crescimento. Report Brasilia Ministerio do Planejamento Search in Google Scholar

dos Santos Junior, E. D. R.; N. Menezes-Filho; P. C. Ferreira (2005): Migracao, Selecao e Diferencas Regionais de Renda no Brasil. Pesquisa e Planejamento Economico 35, 299–331. dos Santos JuniorE. D. R. Menezes-FilhoN. FerreiraP. C. 2005 Migracao, Selecao e Diferencas Regionais de Renda no Brasil Pesquisa e Planejamento Economico 35 299 331 Search in Google Scholar

Fafchamps, M.; F. Shilpi (2013): Determinants of the Choice of Migration Destination. Oxford Bulletin of Economics and Statistics 75, 388–409. doi:10.1111/j.1468-0084.2012.00706.x. FafchampsM. ShilpiF. 2013 Determinants of the Choice of Migration Destination Oxford Bulletin of Economics and Statistics 75 388 409 10.1111/j.1468-0084.2012.00706.x Open DOISearch in Google Scholar

Fally, T.; R. Paillacar; C. Terra (2010): Economic Geography and Wages in Brazil: Evidence from Micro-Data. Journal of Development Economics 91, 155–168. doi:10.1016/j.jdeveco.2009.07.005. FallyT. PaillacarR. TerraC. 2010 Economic Geography and Wages in Brazil: Evidence from Micro-Data Journal of Development Economics 91 155 168 10.1016/j.jdeveco.2009.07.005 Open DOISearch in Google Scholar

Ferreira, F. H. G.; P. G. Leite; J. A. Litchfield (2006): The Rise and Fall of Brazilian Inequality: 1981–2004. Policy Research Working Paper Series 3867. Washington, D.C. FerreiraF. H. G. LeiteP. G. LitchfieldJ. A. 2006 The Rise and Fall of Brazilian Inequality: 1981–2004. Policy Research Working Paper Series 3867 Washington, D.C. Search in Google Scholar

Foguel, M. N.; I. Gill; A. R. Mendonc; R. Paes De Barros (2015): The Public-Private Wage Gap in Brazil. IPEA Discussion Paper 95. Brasília: IPEA. FoguelM. N. GillI. MendoncA. R. Paes De BarrosR. 2015 The Public-Private Wage Gap in Brazil IPEA Discussion Paper 95. Brasília IPEA Search in Google Scholar

Giannetti, M. (2003): On the Mechanics of Migration Decisions: Skill Complementarities and Endogenous Price Differentials. Journal of Development Economics 71, 329–349. GiannettiM. 2003 On the Mechanics of Migration Decisions: Skill Complementarities and Endogenous Price Differentials Journal of Development Economics 71 329 349 Search in Google Scholar

Greene, W. (2000): Econometric Analysis. 4th ed. London: Prentice-Hall International (UK). GreeneW. 2000 Econometric Analysis 4th ed. London Prentice-Hall International (UK) Search in Google Scholar

Grogger, J.; G. H. Hanson (2011): Income Maximization and the Selection and Sorting of International Migrants. Journal of Development Economics 95, 42–57. doi:10.1016/j.jdeveco.2010.06.003. GroggerJ. HansonG. H. 2011 Income Maximization and the Selection and Sorting of International Migrants Journal of Development Economics 95 42 57 10.1016/j.jdeveco.2010.06.003 Open DOISearch in Google Scholar

Ham, J. C.; X. Li; P. B. Reagan (2011): Matching and Semi-Parametric IV Estimation, A Distance-Based Measure of Migration, and the Wages of Young Men. Journal of Econometrics 161, 208–227. doi:10.1016/j.jeconom.2010.12.004. HamJ. C. LiX. ReaganP. B. 2011 Matching and Semi-Parametric IV Estimation, A Distance-Based Measure of Migration, and the Wages of Young Men Journal of Econometrics 161 208 227 10.1016/j.jeconom.2010.12.004 Open DOISearch in Google Scholar

Hanefeld, J.; T. Powell-Jackson; D. Balabanova (2017): Understanding and Measuring Quality of Care: Dealing with Complexity. Bulletin of the World Health Organization 95, 368–374. doi:10.2471/BLT.16.179309. HanefeldJ. Powell-JacksonT. BalabanovaD. 2017 Understanding and Measuring Quality of Care: Dealing with Complexity Bulletin of the World Health Organization 95 368 374 10.2471/BLT.16.179309 Open DOISearch in Google Scholar

Harris, J. R.; M. P. Todaro (1970): Migration, Unemployment and Development: A Two-Sector Analysis. American Economic Review 60, 126–142. HarrisJ. R. TodaroM. P. 1970 Migration, Unemployment and Development: A Two-Sector Analysis American Economic Review 60 126 142 Search in Google Scholar

Hering, L.; R. Paillacar (2015): Does Access to Foreign Markets Shape Internal Migration? Evidence from Brazil. World Bank Economic Review 30, 78–103. HeringL. PaillacarR. 2015 Does Access to Foreign Markets Shape Internal Migration? Evidence from Brazil World Bank Economic Review 30 78 103 Search in Google Scholar

Hicks, J. H.; M. Kleemans; N. Y. Li; E. Miguel (2017): Reevaluating Agricultural Productivity Gaps with Longitudinal Microdata. NBER Working Paper Series 23253. Cambridge, MA: National Bureau of Economic Research. HicksJ. H. KleemansM. LiN. Y. MiguelE. 2017 Reevaluating Agricultural Productivity Gaps with Longitudinal Microdata NBER Working Paper Series 23253. Cambridge, MA National Bureau of Economic Research Search in Google Scholar

IBGE (2010): Censo Demografico 2010: Caracteristicas urbanisticas do entorno dos domicilios. Technical Report. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística - IBGE. IBGE 2010 Censo Demografico 2010: Caracteristicas urbanisticas do entorno dos domicilios Technical Report. Rio de Janeiro Instituto Brasileiro de Geografia e Estatística - IBGE Search in Google Scholar

IBGE (2012): Censo Demografico 2010: Microdados. www.ibge.gov.br. IBGE 2012 Censo Demografico 2010: Microdados www.ibge.gov.br Search in Google Scholar

Kennan, J.; J. R. Walker (2011): The Effect of Expected Income on Individual Migration Decisions. Econometrica 79, 211–251. doi:10.3982/ECTA4657. KennanJ. WalkerJ. R. 2011 The Effect of Expected Income on Individual Migration Decisions Econometrica 79 211 251 10.3982/ECTA4657 Open DOISearch in Google Scholar

Lall, S. V.; C. Timmins; S. Yu (2009): Connecting Lagging and Leading Regions: The Role of Labor Mobility. Policy Research Working Paper Series 4843. The World Bank. LallS. V. TimminsC. YuS. 2009 Connecting Lagging and Leading Regions: The Role of Labor Mobility Policy Research Working Paper Series 4843. The World Bank Search in Google Scholar

Li, C.; J. Gibson (2014): Spatial Price Differences and Inequality in the People's Republic of China: Housing Market Evidence. Asian Development Review 31, 92–120. LiC. GibsonJ. 2014 Spatial Price Differences and Inequality in the People's Republic of China: Housing Market Evidence Asian Development Review 31 92 120 Search in Google Scholar

Lokshin, M.; M. Bontch-Osmolovski; E. Glinskaya (2007): Work-Related Migration and Poverty Reduction in Nepal. Policy Research Working Paper Series 4231. Washington, D.C.: The World Bank. LokshinM. Bontch-OsmolovskiM. GlinskayaE. 2007 Work-Related Migration and Poverty Reduction in Nepal Policy Research Working Paper Series 4231. Washington, D.C. The World Bank Search in Google Scholar

Mata, D. D.; U. Deichmann; J. V. Henderson; S. V. Lall; H. G. Wang (2005): Examining the Growth Patterns of Brazilian Cities. Technical Report 3724. Washington, D.C.: The World Bank. MataD. D. DeichmannU. HendersonJ. V. LallS. V. WangH. G. 2005 Examining the Growth Patterns of Brazilian Cities Technical Report 3724. Washington, D.C. The World Bank Search in Google Scholar

McCormick, B.; J. Wahba (2005): Why do the Young and Educated in LDCs Concentrate in Large Cities? Evidence from Migration Data. Economica 72, 39–67. doi:10.1111/j.0013-0427.2005.00401.x. McCormickB. WahbaJ. 2005 Why do the Young and Educated in LDCs Concentrate in Large Cities? Evidence from Migration Data Economica 72 39 67 10.1111/j.0013-0427.2005.00401.x Open DOISearch in Google Scholar

McFadden, D. (1974): The Measurement of Urban Travel Demand. Journal of Public Economics 3, 303–328. doi:10.1108/eb028592. McFaddenD. 1974 The Measurement of Urban Travel Demand Journal of Public Economics 3 303 328 10.1108/eb028592 Open DOISearch in Google Scholar

Menezes-Filho, N. A.; M. A. Muendler (2011): Labor Reallocation in Response to Trade Reform. NBER Working Paper Series 17372. Cambridge, MA: NBER. Menezes-FilhoN. A. MuendlerM. A. 2011 Labor Reallocation in Response to Trade Reform NBER Working Paper Series 17372. Cambridge, MA NBER Search in Google Scholar

Moretti, E. (2011): Local Labor Markets, in: Handbook of Labor Economics, Volume 4b, Chapter 14. Amsterdam: Elsevier. 1237–1313. doi:10.1016/S0169-7218(11)02412-9. MorettiE. 2011 Local Labor Markets in: Handbook of Labor Economics, Volume 4b, Chapter 14 Amsterdam Elsevier 1237 1313 10.1016/S0169-7218(11)02412-9 Open DOISearch in Google Scholar

Morten, M.; J. Oliveira (2018): The Effects of Roads on Trade and Migration: Evidence from a Planned 703 Capital City. NBER Working Paper Series 22158. Cambridge, MA: National Bureau of Economic Research. MortenM. OliveiraJ. 2018 The Effects of Roads on Trade and Migration: Evidence from a Planned 703 Capital City NBER Working Paper Series 22158. Cambridge, MA National Bureau of Economic Research Search in Google Scholar

Munshi, K.; M. Rosenzweig (2016): Networks and Misallocation: Insurance, Migration, and the Rural-Urban Wage Gap. American Economic Review 106, 46–98. doi:10.1257/aer.20131365. MunshiK. RosenzweigM. 2016 Networks and Misallocation: Insurance, Migration, and the Rural-Urban Wage Gap American Economic Review 106 46 98 10.1257/aer.20131365 Open DOISearch in Google Scholar

Rodriguez, E. R. (1998): International Migration and Income Distribution in the Philippines. Economic Development and Cultural Change 46, 329–350. RodriguezE. R. 1998 International Migration and Income Distribution in the Philippines Economic Development and Cultural Change 46 329 350 Search in Google Scholar

Santos, C.; P. C. Ferreira (2007): Migracao e distribuicao de renda no Brasil. Pesquisa e Planejamento Economico 37, 405–426. SantosC. FerreiraP. C. 2007 Migracao e distribuicao de renda no Brasil Pesquisa e Planejamento Economico 37 405 426 Search in Google Scholar

Sjaastad, L. A. (1962): The Costs and Returns of Human Migration. The Journal of Political Economy 70, 80–93. SjaastadL. A. 1962 The Costs and Returns of Human Migration The Journal of Political Economy 70 80 93 Search in Google Scholar

Tunalı, I. (2000): Rationality of Migration. International Economic Review 41, 893–920. doi:10.1111/1468-2354.00089. TunalıI. 2000 Rationality of Migration International Economic Review 41 893 920 10.1111/1468-2354.00089 Open DOISearch in Google Scholar

Yap, L. (1976): Internal Migration and Economic Development in Brazil. The Quarterly Journal of Economics 90, 119–137. doi:10.2307/1886089. YapL. 1976 Internal Migration and Economic Development in Brazil The Quarterly Journal of Economics 90 119 137 10.2307/1886089 Open DOISearch in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo