metadata: authors: 0: author: Carletto,Calogero
1: author: Kilic,Talip
count: Albania
docna: 0: docna: Moving up the ladder ? the impact of
            migration experience on occupational mobility in Albania
docty: Policy Research Working Paper
theme: Poverty strategy, analysis and monitoring
majtheme: Social protection and risk management
subtopic: Labor Markets,Educational Sciences,Food Security,Employment and Unemployment,Rural Labor Markets
teratopic: Social Protections and Labor,Health, Nutrition and Population,Social Development,Finance and Financial Sector Development,Communities and Human Settlements
abstracts: cdata!: The contribution of return migrants to
            economic development in source countries can be significant.
            Overseas savings of returnees may lead to improvements in
            household welfare and provide liquidity for investments in
            the face of credit market failures. Labor market experience
            and skills acquired abroad may also lead migrants to find
            occupations higher in the skill and remuneration spectrum
            upon return. This study uses the 2005 Albanian Living
            Standards Measurement Study Survey and estimates the impact
            of international migration experience on the occupational
            mobility of return migrants vis a vis working-age Albanian
            residents that never migrated. Controlling for the
            non-random nature of international migration and return, the
            results show that past migration experience increases the
            likelihood of upward occupational mobility. Exploring the
            heterogeneity of impact by host country indicates that the
            positive effect of past migration experience on upward
            occupational mobility is driven by past migration experience
            in Italy and countries further a field, while past migration
            experience in Greece does not exert any significant impact
            on mobility outcomes. The results, which are consistent
            across different sample specifications and outcome variables
            measuring occupational mobility, hint at the link between
            migration and human/financial capital formation among
            migrants and foster optimism concerning the positive effect
            of return migration on economic development. This insight is
            particularly important since remittances from permanent
            migrants, which have fueled the impressive growth
            performance of the country in the recent era, may taper off
            in the medium to long term with the decline in out-migration
            and growing global economic woes.
geo_reg_key: 517208,517190,517191
repnme: repnme: Moving up the ladder ? the impact of
            migration experience on occupational mobility in Albania
display_title: Moving up the ladder the impact of
            migration experience on occupational mobility in Albania
listing_relative_url: /research/2009/04/10484831/moving-up-ladder-impact-migration-experience-occupational-mobility-albania
topicv3: Agriculture and Food Security m1326205 435,Labor Markets m1326488 855,Education m1327048 470,Jobs m1503931 2670,Self-Employment m1504000 2698
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Social Protections and Labor,Health, Nutrition and Population,Social Development,Finance and Financial Sector Development,Communities and Human Settlements
no_of_pages: 42
author_keys: 0: author_key: 550790
1: author_key: 886702
model_extractions: WPS4908 Policy ReseaRch WoRking PaPeR 4908

# Moving Up the Ladder?

The Impact of Migration Experience on Occupational Mobility in Albania _Calogero Carletto Talip Kilic_ The World Bank Development Research Group Poverty Team April 2009
Policy ReseaRch WoRking PaPeR 4908

# **Abstract**

The contribution of return migrants to economic development in source countries can be significant. Overseas savings of returnees may lead to improvements in household welfare and provide liquidity for investments in the face of credit market failures. Labor market experience and skills acquired abroad may also lead migrants to find occupations higher in the skill and remuneration spectrum upon return. This study uses the 2005 Albanian Living Standards Measurement Study Survey and estimates the impact of international migration experience on the occupational mobility of return migrants vis a vis working-age Albanian residents that never migrated. Controlling for the non-random nature of international migration and return, the results show that past migration experience increases the likelihood of upward occupational mobility. Exploring the heterogeneity of impact by host country indicates that the positive effect of past migration experience on upward occupational mobility is driven by past migration experience in Italy and countries further a field, while past migration experience in Greece does not exert any significant impact on mobility outcomes. The results, which are consistent across different sample specifications and outcome variables measuring occupational mobility, hint at the link between migration and human/financial capital formation among migrants and foster optimism concerning the positive effect of return migration on economic development. This insight is particularly important since remittances from permanent migrants, which have fueled the impressive growth performance of the country in the recent era, may taper off in the medium to long term with the decline in out-migration and growing global economic woes.

This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to study the impact of migration on poverty and other outcomes in source countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gcarletto@worldbank.org and tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Moving Up the Ladder? The Impact of Migration Experience on Occupational Mobility in Albania**

Calogero Carletto and Talip Kilic _Development Research Group, World Bank 1818 H Street NW, Washington DC, 20433, USA_ Keywords: Albania, Migration Experience, Occupational Mobility JEL Codes: C25, J62, O15, P20.
2

# **1. Introduction**

The relationship between international migration and human capital formation can be analyzed from a number of different angles. Researchers may consider a multitude of outcomes, such as skill acquisition, labor participation and mobility, and investments and achievements in education. Analyses may concern the migrants or household members that are left behind, or can differentiate the impact of different types of migration, such as permanent, circular or temporary movements, to different destinations. To date, much more emphasis has been given in the Albanian migration discourse to assessing the impact of current migration of former household members on outcomes concerning members of source households left behind. More limited attention has been paid to a different phenomenon that is becoming increasingly important in Albania as the migration process matures: return migration and impacts of migration on migrants upon return.

Following the dissolution of Albania’s communist regime in 1991 and the end of its ban on international migration that had lasted for 45 years, more than one-fifth of the Albanian population is thought to have moved abroad within the short span of a decade (Carletto et al., 2004; King and Vullnetari, 2003). Many Albanian households perceived international migration, whether temporary or permanent, as an effective strategy for improving their livelihoods in the face of widespread unemployment and persistent poverty at home. Out-migration, mostly to Greece and Italy, was also facilitated by Albania’s geographic location and the appeal of western affluence transmitted via media outlets.

In this context, much of the Albanian migration, particularly the flow to neighboring Greece, has been temporary in nature, whether seasonal or circular.<sup>1</sup> Empirical evidence indicates that migrants undertake multiple migration episodes prior to settling, either in the host or source country (Labrianidis and Hatziprokopiou, 2006). A large share of migrants tends to stay abroad long enough to save sufficient sums of money and accumulate enough knowledge to better their conditions once back at home. However, the decision to migrate (or to stay) is not irreversible. As noted in Labrianidis and Kazazi (2006), although 70 percent of the returnees interviewed reported to have returned for good, more than half also declared that they would

> 1Compared to Italy, the process of obtaining legal status in Greece is more difficult for Albanian migrants, as family reunification has been discouraged and migrant regularization has been slower (Baldwin-Edwards, 2002). In this respect, it should not be surprising that particularly the flow to Greece has been more temporary in nature. Geography, and the porous border between the two countries, has also certainly played a role in shaping up these flows.
3 migrate again if necessary. After an initial period of massive outmigration, emigration and return migration go hand in hand in today’s Albania. While a steady flow of individuals continue to emigrate towards increasingly more distant destinations, a growing number of returnees are establishing residence back home. A number of qualitative and case studies provide evidence that returnees use savings earned abroad to finance micro-enterprises and purchase productivityenhancing equipment in existing activities. Work abroad also appears to be providing a learning opportunity for migrants to enhance their skills so that, once back home, they can replicate businesses in which they worked abroad (Nicholson, 2001 and 2002; Labrianidis and Hatziprokopiou, 2006).

While the exodus of human capital via permanent migration has often been viewed as a curse for developing countries by the traditional brain drain literature, the potential contribution of return migrants to the economic development of sending countries may be significant. Through the financial and human capital that they may bring back, return migrants may play an important role in efforts to reduce poverty and foster growth upon successful re-integration into their home countries. In this context, financial capital encompasses overseas savings while human capital captures education, labor market experience and/or management/technical skills acquired abroad. While overseas savings of returnees may lead to improvements in household consumption levels and asset positions, they may also provide liquidity for productive household investments in the face of credit market failures. Human capital accumulated abroad may not only lead migrants to find occupations higher in the skill and remuneration spectrum upon return (with respect to the activity of choice in the pre-migration period), but may also have positive spillover effects on other non-migrants sharing the workplace with returnees. Overall, return migration’s impact on development in source countries is likely to be positive when “the socioeconomic conditions in countries of origin have improved or are strongly expected to do so[;] the return, whether on a temporary or permanent basis, has been voluntary and planned[;] and the returnees have gained skills and savings while abroad.” (Agunias and Newland, 2007: 7- 8) Given the empirical evidence from Albania on (i) the tendency of households to utilize migration as a pathway out of smallholder agriculture (McCarthy et al., forthcoming; Miluka, et al., 2007), and (ii) the positive impact of past migration experience on household non-farm business ownership (Kilic et al., forthcoming), it is plausible that migration fuels the process of
4 upward social and occupational mobility upon return, potentially translating into an important tool of economic development. Only recently, the importance of Albanian returnees and their potential contribution to growth at home have been recognized, and a number of civil society programs, such as MJAFT!, AlbStudent, and New Albanian Generation, have been initiated to encourage the return of high skilled Albanian Diaspora (Germenji and Gedeshi, 2008). However, unleashing the full potential of return migrants rests on a number of interventions that are part of a comprehensive package that not only creates sufficient incentives to encourage return but also recognizes the heterogeneity of returnees in terms of skill levels and occupations, and emphasizes the provision of opportunities in the domestic labor market as to match the needs and capabilities of return migrants. Unfortunately, these interventions remain vastly overlooked both in Albanian policymaking and research.

This study uses the 2005 Albanian Living Standards Measurement Study Survey (ALSMS05) and estimates the impact of international migration experience on occupational mobility of return migrants _vis a vis_ working-age Albanian residents that never migrated. Since such impact is likely to depend on earning potentials and opportunities for skill acquisition in different destination countries, we explore the heterogeneity of impact by host countries, namely Greece _vis a vis_ Italy and beyond (ITB)<sup>2</sup> . The topic of interest has never been explored in the context of Albania and has been given fairly limited attention in other transition economies. Our study is also a more recent contribution to the scant literature that has attempted to analyze labor market returns to past migration experience by comparing return migrants with non-migrants.

One unique aspect of the ALSMS05 was its unusually rich module on migration, which collected detailed information on international migration histories of all current household members, as well as information concerning household migration networks abroad. The ALSMS05 also collected data on _initial_<sup>3</sup> (prior to first migration episode for return migrants) and _present_ occupations of return migrants and non-migrants. The individual level information on international migration experience and employment outcomes over time, and a methodology for ranking occupational groups with respect to their human capital requirements, allow us to test the

> 2 While an increasing number of Albanians are migrating to further destinations, such as Germany, the United Kingdom, the United States and Canada, migrant flows to these destinations are still quite small. Hence, we classify the migrants to Italy and countries other than Greece under the “Italy and beyond” category.

> 3 Initial employment status is measured at January 1, 1990 for individuals that were 15 years of age or older at that date. For individuals below 15 years of age on January 1, 1990, initial employment status pertains to the date that they turned 15.
5 hypothesis of upward occupational mobility induced by past migration. Given the non-random nature of international migration and return, we use an instrumental variable (IV) approach to measure the impact of interest. The results show that past migration experience increases the likelihood of upward occupational mobility. Differentiating the impact by host country indicates that the positive effect of past migration experience on upward occupational mobility is driven by past migration experience in ITB, as past migration experience in Greece does not exert any significant impact on any mobility outcome.

The paper is organized as follows. Section 2 provides an empirical literature review on return migration. Section 3 describes the data. Section 4 explains our empirical approach to modeling occupational mobility. Section 5 reports the regression results and Section 6 concludes.

# **2. Literature Review**

The first strand of literature on return migration is composed of numerous studies from a number of international settings that use data _only on return migrants_ and analyze the determinants of occupational choice, particularly participation in self-employment, upon their return to the home countries. These studies emphasize the importance of human and financial capital accumulated abroad to start and successfully manage micro-enterprises back at home. Murillo Castaño (1988) highlights how in the case of Colombian return migrants from Venezuela once basic needs had been satisfied, savings were used to establish or expand self-employment activities. Arif and Irfan (1997) utilize survey data on Pakistani return migrants and study the factors affecting their occupational composition upon return from the Middle East. They estimate that 44 percent of those employed changed their pre-migration occupations upon return from employment in manufacturing or service jobs to self-employment in small family businesses. The highest level of occupational change was observed in non-irrigated areas. They add that occupational change was strongly related to duration of stay in the Middle East, age upon return, and the level of educational attainment.

Likewise, Ilahi (1999) uses cross-sectional data from Pakistan and finds that upon return, overseas savings become a critical determinant of occupational choice. Migrants with high savings choose self-employment while others opt for wage employment. McCormick and Wahba (2001) employ cross-sectional data from Egypt and explore the incidence of entrepreneurship among return migrants, and the effects of overseas savings, overseas work experience, and pre-
6 migration formal education on self-employment upon return. The authors find that both overseas savings and the duration of stay overseas increase the probability of being self-employed among literate returnees to Egypt. Among illiterate returnees, overseas savings alone exerts a positive impact on the likelihood of becoming an entrepreneur.

Dustmann and Kirschkamp (2002) develop a model where migrants decide simultaneously on the optimal migration duration and their activities after return. They find that Turkish migrants in Germany are more likely to engage in self-employment upon their return to Turkey, relying on savings from abroad to finance their businesses. McCormick and Wahba (2004) also suggest that financial and human capital accumulated abroad promote investments in small non-farm household enterprises among Egyptian return migrants, with the largest share of investments going towards urban areas. Mesnard (2004) models migration as a way to overcome credit constraints in the presence of capital market failures, accounts for the potential endogeneity of financial capital from abroad, and demonstrates that the majority of entrepreneurial projects started by Tunisian returnees were financed through overseas savings. Finally, Gubert and Nordham (2008) investigate the determinants of entrepreneurship among return migrants by using survey data from Morocco, Tunisia, and Algeria. Without accounting for the potential endogeneity of return migration, the authors show that vocational training overseas is positively and significantly associated with entrepreneurship for Moroccan and Tunisian returnees. Their results indicate that time spent abroad exerts a positive impact on the outcome of interest only in the Tunisian case.

In comparison to the first strand of literature on occupational choice among return migrants, the number of studies that _compare return migrants with non-migrants_ and estimate the impact of past international migration experience on productive investments and labor market outcomes in source countries is fairly limited. In terms of analyzing the impact of past migration experience on productive investments, Zhao (2002) use survey data from China and find that in comparison to non-migrants, return migrants invest significantly more in productive farm assets, but are no more likely to undertake non-farm investments. Similar to Zhao’s approach, Mansuri (2007) uses cross sectional data from Pakistan, attempts to deal with the endogeneity of return migration, and recovers evidence for the positive relationship between household return migration status and agricultural land purchases as well as investments in non-farm enterprises and farm assets. For Albania, Kilic et al. (forthcoming) use the same data of this paper, and find
7 that household past migration experience exerts a positive impact on the probability of owning a non-farm business.<sup>4</sup> While an additional year in Greece increases the probability of household ownership by roughly 7 percent, a similar experience in Italy or further destinations raises the probability by over 30 percent.

In regards to estimating the effect of past migration experience on labor market outcomes upon return, which is the main focus of our study, the number of available empirical studies is equally scarce, and the evidence is mixed. Co et al. (2000) use panel survey data from Hungary, and estimate the impact of past migration experience on wages. The authors take into account the selection into migration and labor force participation separately. They show that while there is a considerable premium (40 percent) to work experience abroad among women, there is no statistically significant impact of past migration experience on earnings within the male population. In contrast, without accommodating the endogeneity of return migrant status, Barrett and O’Connell (2000) use non-representative data on Irish return migrants and stayers, and report that on average, male returnees earn 10 percent more than their non-migrant counterparts, whereas there is no wage premium for migration experience among females. Lastly, Wahba (2007) utilizes cross-sectional data from Egypt, attempts to control for selection into wageemployment as well as migration, and shows that on average, the wage gap between return migrants and non-migrants is 38 percent in favor of returnees.

# **3. Data**

The data come from the ALSMS05, conducted by the Albanian Institute of Statistics (INSTAT), with technical assistance from the World Bank, between April and November 2005. The sampling frame for the survey was stratified into four regions –coastal, central, mountain and Tirana – and a total sample of 3,640 households from 455 census enumeration areas (EAs) was drawn based on a multi-stage cluster design. The ALSMS05 includes a typical household questionnaire covering general household demographics, education levels, asset ownership, expenditures and labor market participation. The questionnaire also contains an unusually rich module on migration, both internal and international, of current and former household members.

> 4 Despite these positive results, return migration continues to be associated with failure among Albanians (Labrianidis and Kazazi, 2006). In fact, using independent survey data from Albania, de Coulon and Piracha (2005) actually document the negative selection of return migrants compared to the native non-migrant population. The authors show that had they chosen to migrate and return, non-migrants would have earned more than twice the wages of return migrants.
8 The module provides detailed information on migration histories of all current and former household members, as well as information on the household migration networks abroad. Lastly, the survey supplies community-level data, which include information on prices, access to services and infrastructure in the locality.

Our sample of interest is 9,565 individuals in the working age category of [15-64] in 2005, excluding return migrants that have been in Albania less than 12 months.<sup>5</sup> The ALSMS05 collected data on _initial_ and _2005_ employment outcomes according to the three digit codes from the 1988 International Standard Classification of Occupations (ISCO). The three digit codes can be classified under 10 major occupational groups<sup>6</sup> , which can be collapsed into 5 distinct categories. These categories are namely (i) agricultural (major group 6), (ii) low-skilled blue collar (major groups 8 and 9), (iii) high-skilled blue collar (major group 7), (iv) low-skilled white collar (major groups 4 and 5), and (v) high-skilled white collar (major groups 1, 2 and 3) occupations.<sup>7</sup> Although agricultural occupations are traditionally included as part of the high skilled blue collar category under the ISCO definition, we found it appropriate to create a unique category for them, as agriculture still remains the principal source of employment in Albania.

# _3.1. Occupational Ranking_

As our objective is to test the hypothesis of upward occupational mobility that is induced by past migration experience, it is essential to first rank occupational groups in a reasonable and systematic manner. Following Sicherman and Galor (1990), we rank occupational groups according to the average level of human capital necessary to be employed in each of them. The

> 5 In our sample, there were initially 1313 individuals with past migration experience. Table A1 in the appendix provides the distribution of return migrants by the year of return from the last migration episode indicates that close to 40 percent returned to Albania in 2004 and 2005. Since our objective is to assess the impact of international migration on labor mobility of return migrants _vis a vis_ those individuals that never migrated, we need to be confident that the sample includes return migrants that are likely to have returned to Albania permanently. In Table A2, classifying return migrants into two groups; those that have returned to Albania within the last year, and those that have been in Albania for at least a year, and computing descriptive statistics across these two groups yield considerable differences in observable characteristics, particularly in terms of present day employment and wealth outcomes, circumstances associated with the latest migration episode, and intentions to migrate in the future. To make sure that our sample captures “real” returnees, we exclude from our analysis the return migrants that have been in Albania less than a year. Enforcing this restriction as well as the requirement of being between 16 and 64 years of age in 2005 limits the return migrant sample to 853 individuals.

> 6 10 major occupational groups are namely (1) legislators, senior officials, and managers, (2) professionals, (3) technicians and associate professionals, (4) clerks, (5) service workers, and shop and market sales workers, (6) skilled agricultural and fishery workers, (7) craft and related trades workers, (8) plant and machine operators and assemblers, (9) elementary occupations, and (10) armed forces.

> 7 Following the common practice, we exclude the individuals in armed forces from analysis.
9 rank for a particular occupational category is constructed by averaging the individual sums of weighted levels of schooling, labor market experience prior to entry to the occupation and their squared terms, where the weights are estimated coefficients of these variables in a wage regression. This methodology, whose details are provided in the appendix, yields the following rankings:

**<u>Table 1: Occupational Category Rankings</u>**

|_Rank_|_Category Name_|IndexValue|
|---|---|---|
|1|Not Working|--|
|2|Agriculture|0.55|
|3|Low Skilled Blue Collar|0.71|
|4|High Skilled Blue Collar|0.73|
|5|Low Skilled White Collar|0.80|
|6|High Skilled White Collar|1.03|

where the higher rank-value corresponds to a higher-level of occupation. As wages are not observed for unemployed individuals, the lowest rank value of 1 is assigned to the not working category.<sup>8</sup>

# _3.2 Descriptive Findings_

In accordance with the rankings presented in Table 1, individuals that have experienced upward occupational mobility over time belong to the cells above the diagonals in Tables 2 and 3. Table 2 reports employment transition matrices for working-age non-migrants and return migrants using initial and 2005 employment outcomes. Over time, the share of unemployed nonmigrants declined marginally, while the reduction was close to 20 percentage points for the return migrant population. The majority of return migrants that have upgraded from their initial employment status have found either high skilled blue collar or low-skilled white collar occupations. Conversely, the majority of non-migrants that have experienced upwardoccupational mobility over time were those that were initially not working and are engaged in agriculture by 2005. Overall, over 40 percent of the return migrant population has experienced upward mobility, while the comparable figure was below 25 percent for non-migrants.

The trends among return migrants from Greece and ITB, shown in Panels 3 and 4, are in line with the findings for the return migrant population as a whole. Although we see sizeable inflows of upwardly mobile return migrants from both locations into high skilled blue collar and

> 8 Since the ranking of occupational categories is computed by using wages earned in 2005, the inherent assumption of our analysis is that the initial ranking of an occupational category is the same as its present ranking.
10 low-skilled white collar occupations, a significantly higher share of upwardly mobile return migrants from ITB have found occupations in the high skilled white collar category by 2005. Overall, the share of return migrants from ITB that have experienced upward mobility stood at 45 percent, while the same figure for return migrants from Greece was close to 43 percent.

Table 3 presents employment transition matrices only for return migrants, using initial, migration, and present-day employment outcomes. The figures in Panel 1 indicate that once abroad, the incidence of unemployment was significantly reduced in comparison to the premigration period. This led to marginal increases in agricultural and low skilled white collar employment but a considerable surge in high skilled blue collar employment. Overall, almost 40 percent of return migrants held high skilled blue collar jobs in their last migration episode. The majority of migrants with high skilled white collar employment in the pre-migration period experienced downward mobility while abroad. As shown in Panel 2, upon return to Albania, many individuals that were engaged in agriculture throughout migration upgraded to either high skilled blue collar or low skilled white collar work. The considerable gains in low skilled white collar employment among returnees were also due to the upward mobility of those that were employed in high skilled blue collar work during migration.

We present similar employment transitions separately for return migrants from Greece vs. ITB in Panels 3 through 6. The findings for the return migration population as a whole hold particularly for returnees from Greece. With respect to return migrants from ITB, unemployment in the pre-migration period was not as common among returnees from Greece. The difference, however, was mostly due to the pervasive nature of pre-migration agricultural employment, i.e. underemployment, among the return migrant population from Greece. The share of agricultural employment during the last migration episode was even higher than the pre-migration levels for returnees from Greece. The opposite was true for their counterparts from ITB. While both return migrant populations experienced a significant boost in high skilled blue collar employment once abroad, the incidence of low-skilled white collar work throughout the migration episode was much more common among the return migrants from ITB.

With respect to the trends during migration, the incidence of unemployment increased among those returning from Greece, while the opposite was the case for returnees from ITB. The majority of returnees from Greece that experienced upward mobility upon return were employed in agricultural and high skilled blue collar occupations while abroad. On the other hand, the
11 majority of individuals that have returned from ITB and experienced upward mobility upon return with respect to their activity of choice during migration were employed either in high skilled blue collar or low skilled white collar occupations in the host country. Low skilled white collar and high skilled white collar employment increased upon return within both populations in comparison to the trends in the last migration episode. In this context, while the increase in low skilled white collar employment was more extensive among the return migrant population in Greece, the surge in high skilled white collar employment was more considerable for the case of returnees from ITB.

Table 4 reports weighted averages of individual and household characteristics by return migrant status. On average, return migrants are slightly older, more educated, richer in terms of household assets and social capital and more likely to participate in the labor force or be employed with respect to their non-migrant counterparts. Comparisons across the employed return migrant and non-migrant populations indicate that a greater share of non-migrants hold agricultural occupations, while the rates of low- and high-skilled blue collar employment are significantly higher among return migrants. While agriculture is the principal source of employment in both groups, it is closely followed by high skilled blue collar labor within the return migrant population. While non-migrants are, on average, more likely to experience downward occupational mobility or immobility over time, the likelihood of upward mobility is significantly greater among return migrants.

# **4. Empirical Strategy**

# _4.1. Definition and Analysis of Occupational Mobility_

The literature on occupational choice hosts various definitions of occupational/labor mobility. Dolton and Kidd (1998) analyze occupational mobility in a multinomial logit framework, where the mutually exclusive categories are (i) staying in the same job without a promotion; (ii) staying in the same job with a promotion; (iii) moving to a different job in the same occupation; and (iv) moving to a different occupation. Similarly, Kossoudji and CobbClark (2000) explore the determinants of pre- and post-legalization occupational mobility for Mexican men by using a multinomial logit model, where the mutually exclusive categories are (i) staying in the same occupational group; (ii) moving to a higher-ranked occupational group; and (iii) moving to a lower-ranked occupational group. Korpi and Mertens (2003) consider the
12 duration from the entry into a firm, occupation or industry until the entry into the next firm, occupation or industry, or right censoring at the time of an interview, as a measure of labor mobility. Heitmueller’s (2004) proxy for job mobility is the number of job changes within the 12 months period.

Our outcome of interest is _the degree of occupational mobility_ which is equal to the occupational ranking in _2005_ minus the _initial_ occupational ranking, as presented in Table 1. This definition is similar to the one used by Leigh (1975) and Chiswick and Lee (2005). The variable ranges from -5 to 5, and is positive (negative) for those that have experienced _upward (downward) occupational mobility_ , or takes a value of 0 for individuals that have not changed their occupational group over time. As the _degree of occupational mobility_ is ordinal, it can be analyzed using an ordered probit model (Model 1) in which the actual values taken by the dependent variable is irrelevant, except that larger values are assumed to correspond to “higher” outcomes.

Formally, the ordered probit model for an outcome (y) conditional on explanatory variables (x) can be derived from a latent variable model (Wooldridge, 2002). Assume that a latent variable y<sup>*</sup> is determined by where _β_ is a matrix of slope coefficients with a dimension of Kx1, and x does not include a constant term. Let α1 < α2 < ... < αJ be unknown threshold parameters where y = 0 if _y_<sup>_*_</sup> _≤ α1_ y = 1 if _α1 < y_<sup>_*_</sup> _≤ α2_ y = 2 if _α2 < y_<sup>_*_</sup> _≤ α3_ .

When J = 1, the ordered probit model is equivalent to the binary probit model. The parameters _α_ and _β_ can be estimated routinely by many statistical packages using maximum likelihood. The covariate of interest in Model 1 is a dummy variable equal to 1 for surveyed individuals that have spent at least one month abroad for work purposes. Based on the available evidence and our descriptive findings, we hypothesize that past migration experience exerts a positive impact on
13 the likelihood of upward occupational mobility. In Model 2, we try to differentiate the impact of past migration experience by destination country, where the covariates of interest are dummy variables that separately account for past migration experience in Greece, and ITB. Qualitative evidence indicates that compared to their counterparts in Greece, Albanian migrants in ITB generally enjoy better job prospects and higher earnings, with the potential to accumulate higher levels of overseas savings and human capital. Thus, we expect the positive impact of past migration experience in ITB, _vis a vis_ Greece, on upward occupational mobility to be greater.

# _4.2. Endogeneity of Past Migration and Return Decision_

In order for us to provide unbiased estimates of the impact of past migration experience on occupational mobility, it is imperative to account for non-random selection into migration. Moreover, the covariate of interest captures not only the past migration decision but also the decision to return to Albania. If there are unobservable individual or household characteristics that jointly influence past migration/return decision and the outcome of interest, than the estimated impact of past migration experience on occupational mobility will be biased. To account for this possibility, we need to adopt an instrumental variable (IV) approach, where the idea is to find a set of instrumental variables that are believed to predict past migration and return decisions, without directly influencing occupational outcomes.

For the IV approach to work, each instrument must satisfy two conditions; instrumental relevance and instrumental exogeneity. If an instrument is relevant, then the variation in the instrument is related to the variation in the endogenous variable _._ The exogeneity requirement is that the instrument must be uncorrelated with the outcome variable of choice, conditional on the other covariates. The fulfillment of both requirements would indicate the validity of a particular instrumental variable candidate. Since formal IV ordered probit models do not exist, we implement our estimation strategy as follows. For Models 1 and 2, we first run a (first-stage) probit regression of each potentially endogenous covariate on a set of variables that include independent variables chosen for our occupational mobility analysis as well as instrumental variables that do not enter the estimation of ordered probit models. Following the estimation first-stage regressions, we compute the predicted values for each endogenous covariate and use them as independent variables in Models 1 and 2 to avert biased impact estimates. In both stages of the estimation, we compute robust standard errors, adjusted for intra-cluster correlation.
14 For Model 1, the instrumental variables used in the first-stage regression are (i) a dummy variable equal to 1 if any household member spoke Greek or Italian in 1990 ( _Language90_ ), and (ii) the number of household children in the age group of [0,5] at the time of the return from the latest migration episode ( _HouseholdKids_ ).<sup>9</sup> In Model 2, we run two first-stage regressions to predict past migration experience in Greece and Italy and beyond separately. _HouseholdKids_ is used as an instrumental variable in both first-stage regressions for Model 2. While we use a dummy variable equal to 1 if any household member spoke Greek in 1990 ( _Greek90)_ while computing the predicted value of being a return migrant from Greece, a dummy variable equal to 1 if any household member spoke Italian in 1990 ( _Italian90)_ is used as an instrumental variable in the first stage regression of being a return migrant from Italy and beyond. Below, we present the theoretical arguments in favor of the validity of our instruments.

The knowledge of Greek or Italian by any household member in 1990 would indicate the affinity in culture and mentality in two main destination countries. It would also lower the costs assimilation at the time of migration, rendering both locations attractive. The familiarity with the language spoken in the host country has certainly been noted to be an important factor in determining the direction of migration in Albania (de Zwager et al., 2005). Consequently, mostly Orthodox-Christian Albanians from the southern and southeastern regions of the country, where a sizeable Greek minority has historically resided, emigrate to Greece, while Italy serves as the most preferred destination for Albanians from the central and coastal regions, where the Italian TV channels and way of life are most popular (de Zwager et al., 2005). The variation in _Language90_ is, therefore, expected to predict past migration, fulfilling in principle the requirement of instrumental relevance.

Moreover, the exposure to these languages was almost uniformly dependent on location and cultural background, mainly induced by exogenous factors such as the presence of Greek minorities or exposure to Italian TV broadcasts, and not necessarily a reflection of differences in education, skills or unobserved characteristics, such as ability or entrepreneurial drive, that might influence occupational outcomes. For this reason, we believe that _Language90_ does not influence the outcome of interest beyond its impact on past migration.<sup>10</sup>

> 9 To compute this variable for non-migrant households, we use 1998; the mid-point between 1990 and 2005, as the reference year.

> 10 The ALSMS05 reveals that over 90 percent of the heads of households with at least one member that knew Greek in 1990 are also ethnic Greek. This fact is another indication that knowledge of Greek in 1990 is unlikely to reflect
15 Considering that _Language90_ may solely explain past migration and that return migrants make up only a selected sub-sample of the migrant population, we introduce an additional instrumental variable; _HouseholdKids,_ to specifically predict return migration without exerting any direct impact on the dependent variable. The utilization of _HouseholdKids_ is informed by the analysis of Carletto and Azzarri (2007), who use the ALSMS05 to model the determinants of out-migration (using the entire sample) and return (only among migrants) through single-spell hazard models. They find that while the number of household children in the age group of [0,14] is negatively associated with the likelihood of out-migration, the variable increases the probability of return among migrants. Hence, _HouseholdKids_ can be assumed to capture “return” if it assumes a negative coefficient in the first stage probit regressions that predicts past migration decision.

The results for all first-stage regressions are reported in Table A3, where instrumental variables take on statistically significant coefficients with expected signs. The joint tests of significance of our instruments following the first-stage regressions also yield F-statistics that are greater than 10; a common threshold employed in the literature (Staiger and Stock, 1997), to conclude that a particular set of instruments is sufficiently strong. Given the absence of formal instrumental variable ordered probit models, empirical tests for instrumental exogeneity also do not exist. To ensure that our instruments satisfy the requirement of exogeneity from an empirical standpoint, we first estimated Models 1 and 2 including the endogenous covariates and other independent variables of interest together with the instrumental variables that are excluded from the ordered probit regressions. In this context, we posit that successfully passing a basic test of instrumental exogeneity would require the instruments to assume statistically insignificant coefficients and fail to be jointly significant. In addition, we estimated Models 1 and 2 using the linear IV estimator which allows us to test the joint validity of our instruments under the assumption that the linear model of choice is correct. The null hypothesis of the overidentification tests is that the instruments are jointly valid, and that the excluded instruments are correctly excluded from the estimated equation. For instrumental validity and thereby the inability to reject the null hypothesis of the overidentification test, the resulting Hansen’s J test superior ability, and is rather indicative of innate cultural affinity. One may still argue that the exogeneity of the language instrument is questionable as individuals with exposure to foreign languages or of a specific ethnic background might have been members of wealthier households. We counteract this argument by specifically controlling for household economic status and dwelling conditions in 1990.
16 statistic should be statistically insignificant. The findings presented in Table A4 support our arguments concerning the fulfillment of the instrumental exogeneity requirement.

# _4.3. Control Variables_

Our models of occupational mobility/attainment control for differences in individual human capital through (i) a dummy variable equal to 1 if an individual is male; (ii) years of age and its squared term; and (iii) years of education and its squared term. Disparities in household human capital are captured by (i) the number of male household members in the age category of [15,64]; (ii) the number of female household members in the age category of [15,64]; (iii) the number of household members above the age of 64; (iv) a dummy variable equal to 1 if an individual’s household is female headed; and (v) years of age of household head and its squared term. We try to account for discrepancies in household asset position via (i) area of land owned in hectares and its squared term; (ii) a dummy variable equal to 1 if an individual’s dwelling is a brick home; (iii) a dummy variable equal to 1 if an individual’s dwelling has indoor toilet; (iv) an indicator for economic status in 1990, i.e. a household’s perceived “step” in 1990 concerning a 10-step ladder where the poorest people stood on the first step and the richest were located on the tenth step; and (v) a dummy variable equal to 1 if an individual’s dwelling was a single family home in 1990. As non-labor income has been shown to affect labor supply outcomes in Albania (Dabalen et al., 2008), we include (i) a dummy variable equal to 1 if an individual’s household has access to public transfers; and (ii) a dummy variable equal to 1 if an individual’s household has access to non-farm real estate rental income in our specifications.

Moreover, social capital may aid in transition from unemployment to employment through increased information concerning labor market opportunities. It can also facilitate access to better job opportunities for the employed population. To control for differences in social capital, we constructed an index by using principal component analysis and the following variables: (i) a dummy variable equal to 1 if at least one HH member is a member of an organized social group; (ii) a dummy variable equal to 1 if household head can definitely or probably find people to borrow money; (iii) the number of close friends of household head; (iv) the number of times that household head met with others to have food or drinks last month; and (v) dummy variables indicating whether household head met last month with others from different (i) religious background; (ii) social status; (iii) economic status; and (iv) ethnic
17 background. Finally, given the considerable spatial heterogeneity across Albania, we incorporate in our models indicator variables for household regional location.

# **5. Results**

Since ordered probit regression coefficients do not provide us with direct interpretations of the impact of regressors on the probability of choice, we report in Tables 5 and 6 the marginal effects on the likelihood of being in each category of occupational mobility for Models 1 and 2, respectively.<sup>11</sup> Both tables are divided into two panels where the top panel displays only the marginal effects of our migration variables prior to the employment of the IV approach. Table 5 indicates that the impact of an infinitesimal change in the probability of being a return migrant on the likelihood of exhibiting upward occupational mobility of any degree is positive and statistically significant. The opposite is true concerning the categories of downward mobility and immobility.<sup>12</sup> In Table 6, where we differentiate the impact based on country of destination, it seems that the positive effect of the covariate of interest on upward mobility is driven by past migration experience in ITB, as being a return migrant from Greece is not associated with any of the mobility outcomes. This finding supports the hypothesis that the earnings potential of Albanian migrants in Greece is relatively lower than in countries further afield, and that migrants may take advantage of fewer opportunities for skill acquisition.<sup>13</sup> 11 The coefficient estimates for Models 1 and 2 are available upon request.

12 To test the robustness of our results, we estimated both models with a dependent variable that is the collapsed version of the one employed here. The variable was equal to -1 for any individual that experienced any degree of downward mobility; 0 for those that did not experience any occupational mobility; and 1 for any individual that experienced any degree of upward occupational mobility. The results indicated the positive impact of past migration experience, particularly in Italy and beyond, on the likelihood of upward occupational mobility. Moreover, since our occupational categories are aggregations of major occupational groups and essentially three-digit occupation codes, the direction of mobility for a particular individual may have been different if we had conducted our analysis using a less aggregated occupational categories. In this respect, we ranked the 9 (single-digit) major occupational groups using the same methodology employed by Sicherman and Galor (1990), where individuals in the not working category received a ranking of 1. The rankings of the major occupational groups are presented in Table A5. Estimating Models 1 and 2 based on these rankings and with a dependent variable ranging from -9 to 9 yield results that are qualitatively similar to those reported here. Lastly, we estimated Models 1 and 2 after (i) excluding handicapped individuals and students from the estimation sample, (ii) marginally increasing the age floor to 16, 17 or 18, (iii) including only the individuals that were employed initially, (iv) excluding those that initially held high skilled white collar occupations (as downward mobility or lack of occupational mobility are the only options for them over time), and (v) simultaneously imposing all restrictions in (i)-(iv). Our conclusions concerning the impact of past migration experience on occupational mobility did not change following these alterations in the estimation sample. The results from all robustness checks are available upon request.

13 Through proxy respondents, the ALSMS05 also collected data on household members that are currently migrating abroad. The comparisons of employed migrants in the age group of [15,64] in both locations indicate that over 20 percent of employed Albanians residing in Greece are involved in agriculture while the comparable figure is just 8
18 In both tables, employing an IV approach leads the positive effect of past migration experience on any upward mobility category to increase in magnitude without any loss of statistical significance. Had the correlation between labor market outcomes and unobservable attributes behind the past migration/return decision been positive, the positive effect of past migration experience on upward occupational mobility would have decreased in magnitude after using the predicted values of the endogenous covariates. Under the assumption that our instrumental variables are valid, our findings support the view that return migrants are negatively selected with respect to the resident population (also shown in de Coulon, et al. (2005) and argued in Kilic et al., forthcoming).

Given the similarities between Tables 5 and 6 in terms of the marginal effects for other independent variables, we focus on discussing Table 5 for the remainder of this section. In this respect, being male increases the likelihood of upward occupational mobility of step 1 by 2.9 percent. The relatively higher incidence of upward mobility among men may be a source of concern regarding the labor market advancement of females who already made up 58 and 63 percent of those _initially_ in not working and agriculture categories (i.e. were initially employed at lower occupations in comparison to men, with the opportunity to score higher degrees of upward mobility over time, should they have been willing and able to do so). Educational attainment appears to bear a non-linear relationship to occupational mobility. In particular, the relationship between the covariate and each category of upward mobility is likely to be U-Shaped, where calculations point to around 7 years of education, i.e. near completion of primary education of 8 years, as a necessary threshold for the variable to exert a positive impact on the probability of upward mobility of any degree.

Land ownership positively influences upward mobility, though at a decreasing rate. This seems to indicate the importance of household asset position to exhibit mobility gains over time, with the implication that at the higher end of the land ownership spectrum, individuals are likely to stay in agriculture and exhibit immobility over time or downgrade to agriculture from higher occupational categories by 2005. Finally, household access to public transfers or non-farm real percent for those in ITB. Conversely, over 20 percent of employed Albanians in ITB hold white collar occupations whereas only 9 percent of their counterparts in Greece perform similar duties. The differences in shares of agricultural and white collar employment across migrant workers in ITB and Greece are statistically significant at the 1 percent level. We also see that on average, individuals currently migrating in ITB send more remittances on an annual basis in comparison to those in Greece (144, 398 vs. 103,019 Lek; 1$ = 98 Lek on 05/13/2005) and that the difference is statistically significant at the 1% level. These findings strengthen the view that the opportunities for skill acquisition and higher earnings may be more abundant in ITB than Greece.
19 estate earnings is negatively associated with the probability of being in any upward mobility category, while exerting a positive impact on the likelihood of downward mobility and immobility. The adverse effect of non-labor income on upward mobility is likely to be another manifestation of the overall negative labor supply response to non-labor income as documented by Dabalen et al. (2008).

# **6. Conclusion**

Using a unique dataset that contained information on individual migration and employment histories, this study estimates the impact of international migration experience on occupational mobility of return migrants _vis a vis_ non-migrants, and contributes to the scant literature that has attempted to analyze labor market returns to past migration experience. Our results are consistent across different sample specifications as well as outcome variables measuring occupational mobility. After controlling for the non-random nature of international migration and return, we find that past migration experience exerts a positive and statistically significant impact on the likelihood of upward occupational mobility. Our efforts to differentiate the impact by host country indicates that the positive impact is driven by past experience in Italy and other countries further a field, as past migration experience in Greece does not exert any significant impact on mobility outcomes. This finding is not surprising given the nature of Albanian migration to Greece, where a relatively higher share of migrants is involved in lowskilled, low-return activities.

Consistent with previous findings that demonstrate the positive effect of household past migration experience on non-farm business ownership (Kilic et al., forthcoming), the positive impact of international migration experience on upward occupational mobility does not only hint at the link between migration and human/financial capital formation among migrants, but also fosters optimism concerning the potential contribution of return migration to economic development in Albania. This insight is particularly important since remittances from permanent migrants, which have fueled the impressive growth performance of the country in the recent era, may taper off in the medium to long term with the decline in out-migration, growing global economic woes and the inevitable weakening of social ties between migrants and source communities. In today’s context of growing flows of Albanian return migrants, it is essential to formulate timely policies that attempt to harness the full potential of skills and savings brought
20 back home. Hence, the recent government and civil society initiatives that encourage the return migration of the highly-skilled Albanian diaspora certainly constitute an appropriate first step. However, unleashing the full potential of return migration depends on the recognition of the heterogeneity among return migrants in terms of human and financial capital, and the sustainability of efforts aimed at creating labor market opportunities and an enabling environment that match the needs and aspirations of returnees.
21

**<u>Table 2: Employment Transition Matrices</u>**

|||**Non-M**<br>|**igrant Populati**<br>_20_<br>|**on (Panel 1)**<br>_05 Employment S_<br>|_tatus_<br>|||
|---|---|---|---|---|---|---|---|
|_Initial Employment_<br>_Status_|Not<br>Working|Agriculture|Low Skilled<br>Blue Collar|High Skilled<br>Blue Collar|Low Skilled<br>White Collar|High Skilled<br>White Collar|Total|
|Not Working|30.84|9.02|1.65|2.00|2.45|2.91|48.86|
|Agriculture|7.64|16.01|1.23|1.05|1.12|0.29|27.34|
|Low Skilled Blue Collar|3.17|0.57|2.43|0.40|0.66|0.20|7.42|
|High Skilled Blue Collar|2.18|0.42|0.51|2.46|0.54|0.38|6.50|
|Low Skilled White Collar|0.77|0.17|0.12|0.10|0.66|0.25|2.07|
|High Skilled White Collar|1.87|0.40|0.21|0.16|0.53|4.64|7.80|
|_Total_|46.46|26.59|6.15|6.18|5.95|8.67|100.00|
|||**Return**<br>|**Migrant Popula**<br>_20_<br>|**tion (Panel 2)**<br>_05 Employment S_<br>|_tatus_<br>|||
|_Initial Employment_<br>_Status_|Not<br>Working|Agriculture|Low Skilled<br>Blue Collar|High Skilled<br>Blue Collar|Low Skilled<br>White Collar|High Skilled<br>White Collar|Total|
|Not Working|9.33|5.87|4.08|6.96|5.00|4.27|35.51|
|Agriculture|1.83|13.80|2.81|5.27|2.46|0.47|26.63|
|Low Skilled Blue Collar|1.65|1.46|3.46|1.20|0.39|0.65|8.81|
|High Skilled Blue Collar|2.57|1.19|1.43|9.16|1.83|1.72|17.91|
|Low Skilled White Collar|0.45|0.35|0.20|0.52|0.73|0.17|2.44|
|High Skilled White Collar|1.12|0.83|0.49|0.71|0.84|4.72|8.70|
|_Total_|16.96|23.49|12.47|23.83|11.24|12.01|100.00|

|_Total_<br>|16.96<br>|23.49<br>**Return Migra**<br>|12.47<br>**nt Population fr**<br>_20_<br>|23.83<br>**om Greece (Pan**<br>_05 Employment S_<br>|11.24<br>**el 3)**<br>_tatus_<br>|12.01<br>|100.00<br>|
|---|---|---|---|---|---|---|---|
|_Initial Employment_<br>_Status_|Not<br>Working|Agriculture|Low Skilled<br>Blue Collar|High Skilled<br>Blue Collar|Low Skilled<br>White Collar|High Skilled<br>White Collar|Total|
|Not Working|8.22|6.35|3.99|6.71|4.13|2.63|32.03|
|Agriculture|1.77|14.78|3.05|6.01|3.11|0.60|29.32|
|Low Skilled Blue Collar|1.39|1.85|3.46|1.49|0.47|0.53|9.19|
|High Skilled Blue Collar|2.70|1.11|1.51|9.98|1.62|1.69|18.61|
|Low Skilled White Collar|0.38|0.28|0.26|0.61|0.44|0.22|2.19|
|High Skilled White Collar|1.27|1.05|0.61|0.40|1.04|4.30|8.66|
|_Total_|15.73|25.42|12.88|25.19|10.81|9.98|100.00|
||**Ret**<br>|**urn Migrant P**<br>|**opulation from I**<br>_20_<br>|**taly & Beyond (**<br>_05 Employment S_<br>|**Panel 4)**<br>_tatus_<br>|||
|_Initial Employment_|Not|Agriculture|Low Skilled|High Skilled|Low Skilled|High Skilled|Total|
|_Status_|Working||Blue Collar|Blue Collar|White Collar|White Collar||
|Not Working|13.53|4.04|4.41|7.92|8.25|10.41|48.55|
|Agriculture|2.04|10.11|1.91|2.50|0.00|0.00|16.56|
|Low Skilled Blue Collar|2.64|0.00|3.46|0.12|0.10|1.10|7.41|
|High Skilled Blue Collar|2.09|1.51|1.16|6.05|2.62|1.83|15.27|
|Low Skilled White Collar|0.72|0.59|0.00|0.20|1.83|0.00|3.34|
|High Skilled White Collar|0.59|0.00|0.00|1.91|0.08|6.29|8.87|
|_Total_|21.61|16.25|10.93|18.70|12.88|19.63|100.00|
22

**<u>Table 3: Employment Transition Matrices for Return Migrants</u>**

|||_Emplo_|_ymentStatus in L_|_ast Migration Epis_|_ode_(Panel 1)|||
|---|---|---|---|---|---|---|---|
|_Initial Employment_<br>_Status_|Not<br>Working|Agriculture|Low Skilled<br>Blue Collar|High Skilled<br>Blue Collar|Low Skilled<br>White Collar|High Skilled<br>White Collar|Total|
|Not Working|7.09|8.93|4.06|12.06|2.54|0.65|35.33|
|Agriculture|3.37|14.30|0.77|7.90|0.37|0.00|26.71|
|<br>Low Skilled Blue Collar|0.49|2.82|1.22|4.09|0.22|0.00|8.84|
|High Skilled Blue Collar|1.76|3.30|0.93|11.37|0.65|0.00|18.01|
|Low Skilled White Collar|0.28|0.64|0.28|0.99|0.26|0.00|2.44|
|High Skilled White Collar|1.54|1.74|1.22|2.74|1.16|0.26|8.67|
|_Total_|14.53|31.72|8.48|39.15|5.21|0.91|100.00|
||||_2005 Employ _|_ment Status_(Panel|2)|||
|_Employment Status in_|Not|Agriculture|Low Skilled|High Skilled|Low Skilled|High Skilled|Total|
|_Last Migration Episode_|Working||Blue Collar|Blue Collar|White Collar|White Collar||
|Not Working|5.66|2.54|1.31|2.15|1.03|1.73|14.43|
|<br>Agriculture|3.03|14.63|4.11|5.76|3.02|1.33|31.88|
|Low Skilled Blue Collar|2.69|0.73|1.78|0.79|1.04|1.73|8.77|
|High Skilled Blue Collar|4.30|5.60|5.04|14.40|5.01|4.50|38.85|
|Low Skilled White Collar|1.22|0.00|0.21|0.63|1.04|2.08|5.17|
|High Skilled White Collar|0.11|0.13|0.00|0.00|0.00|0.67|0.91|
|<br>_Total_|17.00|23.64|12.45|23.74|11.14|12.03|100.00|
||**Employ**|**ment Transitio**|**n Matrices for R**|**eturn Migrants f**|**rom Greece**|||
|||_Emplo_|_ymentStatus in L_|_ast Migration Epis_|_ode_(Panel 3)|||
|_Initial Employment_|Not|Agriculture|Low Skilled|High Skilled|Low Skilled|High Skilled|Total|
|_Status_|Working||Blue Collar|<br>Blue Collar|White Collar|<br>White Collar||
|Not Working|6.22|9.72|2.85|11.01|1.58|0.47|31.86|
|<br>Agriculture|2.78|16.81|0.84|8.59|0.37|0.00|29.39|
|<br>Low Skilled Blue Collar|0.26|3.34|0.96|4.51|0.13|0.00|9.21|
|High Skilled Blue Collar|1.33|3.69|0.64|12.31|0.68|0.00|18.66|
|Low Skilled White Collar|0.00|0.62|0.36|1.02|0.21|0.00|2.20|
|High Skilled White Collar|1.17|2.15|1.55|2.62|0.92|0.27|8.68|
|_Total_|11.76|36.34|7.20|40.07|3.89|0.74|100.00|
||||_2005 Employ _|_ment Status_(Panel|4)|||
|_Employment Status in_|Not|Agriculture|Low Skilled|High Skilled|Low Skilled|High Skilled|Total|
|_Last Migration Episode_|Working||Blue Collar|<br>Blue Collar|White Collar|<br>White Collar||
|Not Working|5.05|1.84|1.14|2.24|0.56|0.88|11.71|
|<br>Agriculture|3.13|16.92|4.99|6.43|3.40|1.48|36.34|
|<br>Low Skilled Blue Collar|1.97|0.55|1.46|0.81|1.24|1.42|7.45|
|High Skilled Blue Collar|4.77|5.89|5.03|15.08|4.64|4.48|39.89|
|Low Skilled White Collar|0.88|0.00|0.26|0.59|0.87|1.27|3.87|
|High Skilled White Collar|0.13|0.17|0.00|0.00|0.00|0.44|0.74|
|_Total_|15.93|25.37|12.89|25.15|10.71|9.96|100.00|
||**Employmen**|**t Transition M**|**atrices for Retur**|**n Migrants from**|**Italy & Beyond**|||
|||<br>_Emplo_|<br>_ymentStatus in L_|<br>_ast Migration Epis_|<br>_ode_(Panel 5)|||
|_Initial Employment_|Not|Agriculture|Low Skilled|High Skilled|Low Skilled|High Skilled|Total|
|<br>_Status_|<br>Working||<br>Blue Collar|<br>Blue Collar|<br>White Collar|<br>White Collar||
|Not Working|10.38|5.94|8.61|16.02|6.14|1.31|48.39|
|<br>Agriculture|5.59|<br>4.85|0.50|5.30|0.39|0.00|<br>16.63|
|<br>Low Skilled Blue Collar|1.36|0.85|2.16|2.52|0.56|0.00|7.44|
|High Skilled Blue Collar|3.37|1.83|2.02|7.81|0.54|0.00|15.57|
|<br>Low Skilled White Collar|1.32|0.71|0.00|0.87|0.46|0.00|3.36|
|High Skilled White Collar|2.92|0.18|0.00|3.20|2.07|0.24|8.60|
|_Total_|24.93|14.35|13.29|35.73|10.15|1.55|100.00|
23

**<u>Table 3 (Cont’d)</u>**

||**Employmen**|**t Transition M**|**atrices for Retur**<br>_2005 Employ _|**n Migrants from**<br>_ment Status_(Panel|**Italy & Beyond**<br>6)|||
|---|---|---|---|---|---|---|---|
|_Employment Status in_<br>_Last Migration Episode_|Not<br>Working|Agriculture|Low Skilled<br>Blue Collar|High Skilled<br>Blue Collar|Low Skilled<br>White Collar|High Skilled<br>White Collar|Total|
|Not Working|7.97|5.17|1.95|1.84|2.76|4.88|24.57|
|Agriculture|2.65|6.10|0.84|3.27|1.62|0.78|15.27|
|Low Skilled Blue Collar|5.38|1.38|2.98|0.73|0.32|2.89|13.67|
|High Skilled Blue Collar|2.53|4.53|5.05|11.86|6.39|4.61|34.97|
|Low Skilled White Collar|2.49|0.00|0.00|0.80|1.65|5.06|10.00|
|High Skilled White Collar|0.00|0.00|0.00|0.00|0.00|1.52|1.52|
|_Total_|21.02|17.18|10.82|18.50|12.74|19.74|100.00|
24

|**Table 4: Averages by Return**|**Migrant Stat**|**us**|||
|---|---|---|---|---|
||_Return_|_Non-_|_Differ_|_ence_|
|_Variables_|_Migrant_|_Migrant_|||
|_Basic Individual Characteristics_|||||
|Male∆|0.89|0.41|0.48|***|
|Married∆|0.87|0.64|0.23|***|
|HH Head∆|0.61|0.24|0.36|***|
|Years of Age<br>|39.75<br>|36.91<br>|2.84<br>|***<br>|
|Years of Education|10.43|9.41|1.02|***|
|_Individual Employment in 2005_|||||
|Labor Force Participation∆|0.87|0.58|0.30|***|
|Employed∆|0.83|0.54|0.29|***|
|Sector of Employment: Agriculture ^∆|0.28|0.50|-0.21|***|
|<br>Sector of Employment: Low Skilled Blue Collar ^∆|0.15|0.12|0.04|**|
|Sector of Employment: High Skilled Blue Collar ^∆|0.28|0.12|0.17|***|
|Sector of Employment: Low Skilled White Collar ^∆|0.13|0.11|0.02||
|Sector of Employment: High Skilled White Collar ^∆|0.15|0.16|-0.01||
|_Individual Initial Employment_|||||
|Employed∆|0.65|0.51|0.13|***|
|Sector of Employment: Agriculture ^∆|0.41|0.54|-0.12|***|
|Sector of Employment: Low Skilled Blue Collar ^∆|0.14|0.14|-0.01||
|Sector of Employment: High Skilled Blue Collar ^∆|0.28|0.13|0.15|***|
|Sector of Employment: Low Skilled White Collar ^∆|0.04|0.04|0.00||
|Sector of Employment: High Skilled White Collar ^∆|0.13|0.15|-0.02||
|_Individual Occupational Mobility Over Time_|||||
|Degree of Mobility|0.74|0.16|0.58|***|
|Downward Mobility∆|0.16|0.19|-0.03|**|
|Immobility∆|0.41|0.57|-0.16|***|
|Upward Mobility∆|0.43|0.24|0.19|***|
|_Household Demographics_|||||
|# of Members Age [0,5]|0.53|0.31|0.22|***|
|# of Members Age [6,14]|0.75|0.73|0.02||
|<br># of Male Members Age [15,64]|1.55|1.69|-0.14|***|
|# of Female Members Age [15,64]|1.49|1.81|-0.32|***|
|# of Members Age [65+]|0.35|0.27|0.08|***|
|Female Headed HH∆|0.05|0.06|-0.02|**|
|HH Head Years of Age|49.43|50.87|-1.43|***|
|_Household Asset Position_|||||
|Wealth Index (PCA)|0.28|0.01|0.27|***|
|<br>Land Area (Hectares)|0.58|0.58|0.00||
|<br>Brick Home∆|0.62|0.62|0.00||
|Economic Status in 1990|3.64|3.53|0.11||
|Single Family Dwelling in 1990∆|0.79|0.78|0.00||
|_Household Access to Non-Labor Income_|||||
|Public Transfers**∆**|0.50|0.52|-0.02||
|Real Estate Earnings∆|0.27|0.21|0.06|***|
25

**<u>Table 4 (Cont’d)</u>**

|_Variables_|_Return_<br>_Migrant_|_Non-_<br>_Migrant_|_Differ_|_ence_|
|---|---|---|---|---|
|_Household Social Capital_|||||
|Social Capital Index (PCA)|0.14|-0.02|0.16|***|
|_Location_|||||
|Tirana∆|0.13|0.14|-0.01||
|Coastal Urban∆|0.19|0.13|0.06|***|
|Coastal Rural∆|0.17|0.17|0.00||
|Central Urban∆|0.15|0.16|-0.02||
|Central Rural∆|0.31|0.29|0.03||
|Mountain Urban∆|0.01|0.02|-0.01|***|
|Mountain Rural∆|0.04|0.09|-0.04|***|
|_Observations_|853|8712|||

**Notes:** */**/*** indicate significance at the 10/5/1 percent level, respectively; ∆ denotes a dummy variable; ^ indicates that averages were computed among the employed population.
26

**<u>Table 5: Marginal Effects for Model 1</u>**

||||||_M_|_obility Categ_|_ory_|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|_Regressors_|_(-5)_|_(-4)_|_(-3)_|_(-2)_|_(-1)_|_(0)_|_(+1)_|_(+2)_|_(+3)_|_(+4)_|_(+5)_|
|_Individual Characteristics_||||||||||||
|Return Migrant∆|-0.002**|-0.001**|-0.004**|-0.006**|-0.013**|-0.009*|0.012**|0.006**|0.006**|0.005**|0.006**|
||(0.001)|(0.001)|(0.002)|(0.002)|(0.005)|(0.005)|(0.005)|(0.003)|(0.003)|(0.002)|(0.003)|
|Predicted(Return Migrant)|-0.013***|-0.009***|-0.025***|-0.037***|-0.082***|-0.038***|0.072***|0.035***|0.034***|0.031***|0.032***|
||(0.003)|(0.002)|(0.006)|(0.009)|(0.021)|(0.010)|(0.019)|(0.009)|(0.009)|(0.008)|(0.008)|
|Male∆|-0.004***|-0.003***|-0.007***|-0.010***|-0.023***|-0.011***|0.020***|0.010***|0.010***|0.009***|0.009***|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.004)|(0.003)|(0.004)|(0.002)|(0.002)|(0.002)|(0.002)|
|Married∆|-0.000|-0.000|-0.000|-0.001|-0.002|-0.001|0.001|0.001|0.001|0.001|0.001|
||(0.001)|(0.001)|(0.002)|(0.002)|(0.005)|(0.002)|(0.004)|(0.002)|(0.002)|(0.002)|(0.002)|
|HH Head∆|-0.002***|-0.002***|-0.005***|-0.007***|-0.016***|-0.009**|0.014***|0.007***|0.007***|0.006***|0.007***|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.005)|(0.004)|(0.005)|(0.002)|(0.002)|(0.002)|(0.002)|
|Years of Age|-0.001***|-0.001***|-0.002***|-0.003***|-0.006***|-0.003***|0.005***|0.003***|0.003***|0.002***|0.002***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|
|Years of Age Squared (/100)|0.002***|0.002***|0.004***|0.006***|0.014***|0.006***|-0.012***|-0.006***|-0.006***|-0.005***|-0.005***|
||(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Years of Education|0.003***|0.002***|0.006***|0.009***|0.019***|0.009***|-0.017***|-0.008***|-0.008***|-0.007***|-0.008***|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.002)|(0.002)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|
|Years of Education Squared|-0.000***|-0.000***|-0.000***|-0.001***|-0.001***|-0.001***|0.001***|0.001***|0.000***|0.000***|0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|_Household Demographics_||||||||||||
|<br># of Male Members [15,64]|-0.000|-0.000|-0.000|-0.001|-0.001|-0.001|0.001|0.001|0.001|0.001|0.001|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.002)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|
|# of Female Members [15,64]|-0.001***|-0.001***|-0.002***|-0.003***|-0.006***|-0.003***|0.006***|0.003***|0.003***|0.002***|0.003***|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.002)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|
|# of Members [65+]|-0.000|-0.000|-0.000|-0.001|-0.001|-0.001|0.001|0.001|0.001|0.001|0.001|
||(0.001)|(0.000)|(0.001)|(0.001)|(0.003)|(0.001)|(0.003)|(0.001)|(0.001)|(0.001)|(0.001)|
|Female Headed∆|0.001|0.001|0.003|0.004|0.009|0.003**|-0.008|-0.004|-0.004|-0.003|-0.003|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.001)|(0.006)|(0.003)|(0.003)|(0.002)|(0.002)|
|HH Head Years of Age|0.001***|0.001***|0.002***|0.003***|0.006***|0.003***|-0.005***|-0.002***|-0.002***|-0.002***|-0.002***|
||(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|
|HH Head Years of Age Squared|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|0.000***|0.000***|0.000***|0.000***|0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|_Household Asset Position_||||||||||||
|Area of Land Owned (Ha)|-0.001**|-0.001**|-0.002**|-0.003**|-0.007**|-0.003**|0.006**|0.003**|0.003**|0.003**|0.003**|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.003)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|
|Area of Land Owned Squared|0.000***|0.000***|0.000***|0.000***|0.000***|0.000***|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Brick Home∆|-0.001*|-0.001*|-0.002*|-0.003*|-0.007*|-0.003*|0.006*|0.003*|0.003*|0.003*|0.003*|
||(0.001)|(0.000)|(0.001)|(0.002)|(0.004)|(0.002)|(0.003)|(0.002)|(0.002)|(0.001)|(0.001)|
27

**<u>Table 5 (Cont’d)</u>**

||||||_M_|_obility Categ_|_ory_|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|_Regressors_|_(-5)_|_(-4)_|_(-3)_|_(-2)_|_(-1)_|_(0)_|_(+1)_|_(+2)_|_(+3)_|_(+4)_|_(+5)_|
|_Household Asset Position_||||||||||||
|Economic Status in 1990|0.001***|0.000***|0.001***|0.002***|0.003***|0.002***|-0.003***|-0.001***|-0.001***|-0.001***|-0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.000)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|
|Single Family Dwelling in 1990∆|-0.001|-0.001|-0.002|-0.004|-0.008|-0.003*|0.007|0.003|0.003|0.003*|0.003*|
||(0.001)|(0.001)|(0.002)|(0.002)|(0.005)|(0.002)|(0.004)|(0.002)|(0.002)|(0.002)|(0.002)|
|_Household Access to Non-Labor Income_||||||||||||
|Public Transfers∆|0.003***|0.002***|0.006***|0.009***|0.020***|0.010***|-0.018***|-0.009***|-0.009***|-0.008***|-0.008***|
||(0.001)|(0.000)|(0.001)|(0.002)|(0.004)|(0.002)|(0.003)|(0.002)|(0.002)|(0.001)|(0.002)|
|Non-Farm Real Estate Earnings∆|0.004***|0.003***|0.007***|0.010***|0.022***|0.005***|-0.019***|-0.009***|-0.009***|-0.008***|-0.008***|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.004)|(0.001)|(0.003)|(0.002)|(0.001)|(0.001)|(0.001)|
|_Household Social Capital_||||||||||||
|Social Capital Index (PCA)|-0.000|-0.000|-0.001|-0.001|-0.002|-0.001|0.002|0.001|0.001|0.001|0.001|
||(0.000)|(0.000)|(0.000)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|_Location_||||||||||||
|Coastal Urban∆|-0.001|-0.001|-0.002|-0.003|-0.007|-0.004|0.006|0.003|0.003|0.003|0.003|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.004)|(0.006)|(0.003)|(0.003)|(0.003)|(0.003)|
|Coastal Rural∆|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|0.000|0.000|0.000|0.000|0.000|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.003)|(0.007)|(0.003)|(0.003)|(0.003)|(0.003)|
|Central Urban∆|0.002|0.001|0.004|0.005|0.012|0.004***|-0.010*|-0.005*|-0.005*|-0.004*|-0.004*|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.001)|(0.006)|(0.003)|(0.003)|(0.002)|(0.002)|
|Central Rural∆|-0.002**|-0.002**|-0.004**|-0.007**|-0.015**|-0.010*|0.014**|0.007**|0.007**|0.006**|0.007**|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.006)|(0.006)|(0.003)|(0.003)|(0.003)|(0.003)|
|Mountain Urban∆|0.001|0.001|0.002|0.003|0.007|0.003|-0.006|-0.003|-0.003|-0.003|-0.003|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.002)|(0.006)|(0.003)|(0.003)|(0.002)|(0.002)|
|Mountain Rural∆|-0.000|-0.000|-0.000|-0.000|-0.001|-0.000|0.001|0.000|0.000|0.000|0.000|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.003)|(0.006)|(0.003)|(0.003)|(0.003)|(0.003)|
|**Clusters**||||||455||||||
|**Observations**||||||9,396||||||
|**Log-Likelihood**||||||-13,995||||||
|**Adjusted R2**||||||0.088||||||

**Notes for Tables 5 and 6:** Marginal effects are computed at the multivariate point of means; Robust standard errors, adjusted for intra-cluster correlation, are in parentheses; */**/*** indicate significance at the 10/5/1 percent level, respectively; ∆ indicates a dummy variable; The default region is Tirana.
28

**<u>Table 6: Marginal Effects for Model 2</u>**

||||||_Mo_|_bility Categor_|_y_|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|_Regressors_|_(-5)_|_(-4)_|_(-3)_|_(-2)_|_(-1)_|_(0)_|_(+1)_|_(+2)_|_(+3)_|_(+4)_|_(+5)_|
|_Individual Characteristics_||||||||||||
|Return Migrant from Greece∆|-0.001|-0.001|-0.003|-0.004|-0.009|-0.005|0.008|0.004|0.004|0.004|0.004|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.006)|(0.005)|(0.005)|(0.003)|(0.003)|(0.003)|(0.003)|
|Return Migrant from Italy & Beyond∆|-0.003***|-0.002***|-0.007***|-0.011***|-0.026***|-0.025*|0.024***|0.012**|0.013**|0.012**|0.014**|
||(0.001)|(0.001)|(0.002)|(0.004)|(0.009)|(0.014)|(0.009)|(0.005)|(0.005)|(0.005)|(0.007)|
|Predicted(Return Migrant from Greece)|0.000|0.000|0.000|0.000|0.001|0.000|-0.001|-0.000|-0.000|-0.000|-0.000|
||(0.005)|(0.003)|(0.009)|(0.013)|(0.030)|(0.014)|(0.026)|(0.013)|(0.013)|(0.011)|(0.012)|
|Predicted (Return Migrant from Italy & Beyond)|-0.046***|-0.032***|-0.087***|-0.130***|-0.287***|-0.133***|0.252***|0.123***|0.121***|0.108***|0.112***|
||(0.013)|(0.009)|(0.024)|(0.035)|(0.078)|(0.039)|(0.070)|(0.033)|(0.033)|(0.029)|(0.030)|
|Male∆|-0.003***|-0.002***|-0.006***|-0.009***|-0.020***|-0.010***|0.018***|0.009***|0.009***|0.008***|0.008***|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.004)|(0.003)|(0.004)|(0.002)|(0.002)|(0.002)|(0.002)|
|Married∆|-0.000|-0.000|-0.001|-0.001|-0.002|-0.001|0.002|0.001|0.001|0.001|0.001|
||(0.001)|(0.001)|(0.002)|(0.002)|(0.005)|(0.002)|(0.004)|(0.002)|(0.002)|(0.002)|(0.002)|
|HH Head∆|-0.003***|-0.002***|-0.006***|-0.010***|-0.022***|-0.014***|0.020***|0.010***|0.010***|0.009***|0.010***|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.005)|(0.004)|(0.005)|(0.002)|(0.002)|(0.002)|(0.003)|
|Years of Age|-0.001***|-0.001***|-0.002***|-0.003***|-0.006***|-0.003***|0.005***|0.003***|0.003***|0.002***|0.002***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|
|Years of Age Squared (/100)|0.002***|0.002***|0.004***|0.006***|0.014***|0.006***|-0.012***|-0.006***|-0.006***|-0.005***|-0.005***|
||(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Years of Education|0.003***|0.002***|0.006***|0.008***|0.018***|0.009***|-0.016***|-0.008***|-0.008***|-0.007***|-0.007***|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.002)|(0.002)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|
|Years of Education Squared|-0.000***|-0.000***|-0.000***|-0.001***|-0.001***|-0.001***|0.001***|0.000***|0.000***|0.000***|0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|_Household Demographics_||||||||||||
|# of Male Members [15,64]|-0.000|-0.000|-0.001|-0.001|-0.002|-0.001|0.002|0.001|0.001|0.001|0.001|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.002)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|
|# of Female Members [15,64]|-0.001***|-0.001***|-0.002***|-0.003***|-0.007***|-0.003***|0.006***|0.003***|0.003***|0.003***|0.003***|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.002)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|
|# of Members [65+]|-0.000|-0.000|-0.001|-0.001|-0.002|-0.001|0.002|0.001|0.001|0.001|0.001|
||(0.001)|(0.000)|(0.001)|(0.001)|(0.003)|(0.001)|(0.003)|(0.001)|(0.001)|(0.001)|(0.001)|
|Female Headed∆|<br>0.002*|<br>0.002*|<br>0.004*|<br>0.006*|<br>0.013*|<br>0.003***|<br>-0.011*|<br>-0.005**|<br>-0.005**|<br>-0.004**|<br>-0.004**|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.001)|(0.006)|(0.003)|(0.002)|(0.002)|(0.002)|
|HH Head Years of Age|0.001***|0.001***|0.002***|0.003***|0.006***|0.003***|-0.005***|-0.002***|-0.002***|-0.002***|-0.002***|
||(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|
|HH Head Years of Age Squared|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|0.000***|0.000***|0.000***|0.000***|0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
29

**<u>Table 6 (Cont’d)</u>**

||||||_M_|_obility Categor_|_y_|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|_Regressors_|_(-5)_|_(-4)_|_(-3)_|_(-2)_|_(-1)_|_(0)_|_(+1)_|_(+2)_|_(+3)_|_(+4)_|_(+5)_|
|_Household Asset Position_||||||||||||
|Area of Land Owned (Ha)|-0.001***|-0.001***|-0.002***|-0.004***|-0.008***|-0.004***|0.007***|0.003***|0.003***|0.003***|0.003***|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.003)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|
|Area of Land Owned Squared|0.000***|0.000***|0.000***|0.000***|0.000***|0.000***|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Brick Home∆|-0.001|-0.001|-0.002|-0.003|-0.006|-0.003|0.005|0.003|0.002|0.002|0.002|
||(0.001)|(0.000)|(0.001)|(0.002)|(0.004)|(0.002)|(0.003)|(0.002)|(0.002)|(0.001)|(0.001)|
|Economic Status in 1990|0.001***|0.000***|0.001***|0.001***|0.003***|0.001***|-0.003***|-0.001***|-0.001***|-0.001***|-0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.000)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|
|Single Family Dwelling in 1990∆|-0.002**|-0.001**|-0.003**|-0.005**|-0.010**|-0.004**|0.009**|0.004**|0.004**|0.004**|0.004**|
||(0.001)|(0.001)|(0.002)|(0.002)|(0.005)|(0.002)|(0.004)|(0.002)|(0.002)|(0.002)|(0.002)|
|_Household Access to Non-Labor Income_||||||||||||
|Public Transfers∆|0.003***|0.002***|0.006***|0.009***|0.021***|0.010***|-0.019***|-0.009***|-0.009***|-0.008***|-0.008***|
||(0.001)|(0.000)|(0.001)|(0.002)|(0.004)|(0.002)|(0.003)|(0.002)|(0.002)|(0.001)|(0.002)|
|Non-Farm Real Estate Earnings∆|0.004***|0.003***|0.007***|0.010***|0.021***|0.005***|-0.018***|-0.009***|-0.008***|-0.007***|-0.007***|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.004)|(0.001)|(0.003)|(0.002)|(0.001)|(0.001)|(0.001)|
|_Household Social Capital_||||||||||||
|<br>Social Capital Index (PCA)|-0.000|-0.000|-0.001|-0.001|-0.002|-0.001|0.002|0.001|0.001|0.001|0.001|
||(0.000)|(0.000)|(0.000)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|_Location_||||||||||||
|Coastal Urban∆|-0.001|-0.001|-0.003|-0.004|-0.009|-0.005|0.008|0.004|0.004|0.004|0.004|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.005)|(0.006)|(0.003)|(0.003)|(0.003)|(0.003)|
|Coastal Rural∆|-0.000|-0.000|-0.001|-0.001|-0.002|-0.001|0.002|0.001|0.001|0.001|0.001|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.004)|(0.007)|(0.003)|(0.003)|(0.003)|(0.003)|
|Central Urban∆|0.001|0.001|0.002|0.003|0.006|0.002|-0.006|-0.003|-0.003|-0.002|-0.002|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.002)|(0.006)|(0.003)|(0.003)|(0.002)|(0.002)|
|Central Rural∆|-0.003***|-0.002***|-0.006***|-0.009***|-0.020***|-0.015**|0.018***|0.009***|0.009***|0.008***|0.009**|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.007)|(0.006)|(0.003)|(0.003)|(0.003)|(0.004)|
|Mountain Urban∆|0.001|0.001|0.002|0.002|0.005|0.002|-0.004|-0.002|-0.002|-0.002|-0.002|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.002)|(0.006)|(0.003)|(0.003)|(0.002)|(0.002)|
|Mountain Rural∆|-0.000|-0.000|-0.000|-0.000|-0.001|-0.000|0.001|0.000|0.000|0.000|0.000|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.003)|(0.006)|(0.003)|(0.003)|(0.003)|(0.003)|
|**Clusters**||||||455||||||
|**Observations**||||||9,396||||||
|**Log-Likelihood**||||||-13,987||||||
|**Adjusted R2**||||||0.089||||||