Abstract
This article examines the dynamics and causes of the shift in the gender composition of migration, and more particularly, in women’s access to migration opportunities and decision-making. Our analysis focuses on Albania, a natural laboratory for studying international migration where out-migration was essentially nonexistent from the end of World War II to the end of the 1980s. Interest in the Albanian case is heightened because of the complex layers of inequality existing at the time when migration began: relatively low levels of inequality within the labor market and educational system—a product of the Communist era—while household relations remained heavily steeped in tradition and patriarchy. We use micro-level data from the Albania 2005 Living Standards Measurement Study, including migration histories for family members since migration began. Based on discrete-time hazard models, the analysis shows a dramatic increase in male migration and a gradual and uneven expansion of the female proportion of this international migration. Female migration, which is shown to be strongly associated with education, wealth, and social capital, appears responsive to economic incentives and constraints. Using information on the dependency of female migration to the household demographic structure as well as the sensitivity of female migration to household-level shocks, we show how household-level constraints and incentives affect male and female migration differently. Throughout this period, however, women’s migration behavior appears more directly aligned with household-level factors, and there is little evidence to suggest that increased female migration signals rising behavioral independence among Albanian women.
A central feature of the complex relationship between gender and migration is the shifting sex composition of international migration (Castles and Miller 2003; Cerrutti and Massey 2001; Donato 1993). Although women compose roughly one-half of the world’s international migrant population (Zlotnik 1999), the proportion varies considerably by region; and in some countries (such as the Philippines, Sri Lanka, and Indonesia), the majority of recent emigrants are female (Martin 2007; United Nations 2006). In addition to this variability across societies, substantial heterogeneity also exists within societies over time. The most common trend is one in which migration is primarily male at the onset and then becomes gender-equalized over time (Andall 1992; Kofman 1999). This pattern is particularly associated with migration flows that are labor-motivated, as in the Mexican case—the focus of much of the migration literature—where women compose a steadily increasing share of the migration flow (Donato 1993, 1999; Durand and Massey 1992; Massey, Durand, and Malone 2002). Similarly, empirical evidence from the Philippines and Sri Lanka, where men composed the majority of migrants for much of the twentieth century, shows that women made up more than 60% of out-migrants from each country by the end of the century (Barber 2000). These latter cases exemplify the feminization of labor migration—a process evident across a large number of societies (Castles and Miller 2003). Yet, the literature remains mired in uncertainty regarding the actual mechanisms that promote or hinder both increased female migration and the shifting gender composition of migration over time.
We focus on gender and migration from Albania, which is aptly labeled a “unique migration laboratory” (King 2005). Following several decades of closure and within a few short years following the opening of its borders to international migration in 1990, Albania witnessed a remarkable out-migration, primarily to Greece and Italy, which led to the departure of about one-fifth of the country’s population (Carletto et al. 2006; King and Vullnetari 2003). Whereas most migration from Albania at the start of the 1990s was male-dominated, women made up 41% of Albanians one decade later in Italy and Greece (Vullnetari 2007). We use 2005 survey data from Albania to examine the pattern as well as mechanisms that underlie the gender and migration relationship. The retrospective nature of our data and the lack of migration prior to 1990 provide an opportunity to overcome numerous concerns regarding causal validity.
Our study includes both descriptive and analytical components. The descriptive focuses on whether and at what rate women are incorporated into a migration stream initially dominated by males. Current understanding builds heavily on two studies of the temporal dynamics of migration from Mexico (Donato 1993; Massey, Goldring, and Durand 1994). However, little, if any, empirical evidence describes the evolution of the gender composition of migration for a nation over time, from an initial state of no migration to one in which migration becomes a normative practice. Our data enable us to quantify both male and female migration trends from the onset of migration out of Albania from 1990 to 2004.
The analytic component of our study focuses on the causal mechanisms—particularly inequality at the public and private levels—that drive female participation in international migration and how these mechanisms may or may not change over time (Boyd 1989; Pedraza 1991; Pfeiffer et al. 2007). A compelling feature of the Albanian case is the distinct forms of inequality experienced by women and how they relate to migration. In contrast to many other settings, such as Mexico, women in Albania experienced relatively egalitarian labor markets and educational systems at the time the country was opening while remaining tightly constrained within the household by traditional and patriarchal power relations (Falkingham and Gjonca 2001; Lawson, McGregor, and Saltmarshe 2000). Although efforts to incorporate women fully in the economy and society did not fully succeed under Communism—and gave way to increasing discrimination in the post-Communist era—the private status of women within households made far less progress, and enduring norms continue to maintain women’s low status, with restricted agency and heavy dependence on male household members (UNDP 2005). This provides a fascinating backdrop against which to examine the role of inequalities at the broader, societal level as well as the more local, household level in affecting the gender and migration association. More specifically, at the macro level, we investigate the role of inequalities in the returns to education and how they may affect the association between gender and migration. At the micro level, we examine how male and female migration outcomes are determined both by a household’s sex composition and by household-level health and income shocks. Both forms of micro-level constraints may shed light on how inequalities reproduced in the household influence gender and migration.
A deeper question underlies the specific hypotheses tested in this article: does increasing female migration signal an emergent independence of female action? The questions of independence and agency remain core issues in the context of gender and migration (Hondagneu-Sotelo 1992; Morokvasic 1984), yet surprisingly little empirical work explicitly tests agency in this context. It remains unclear from the literature whether increased incorporation of women into the migration process, both as familial migrants and as economic migrants, signals strengthening independence and empowerment over the course of development, or instead female migration is primarily a response to household migration strategies. The circularity of causal factors connecting migration, gender status, and development is partly responsible for this lack of clarity (Massey et al. 1993; Palloni et al. 2001). However, the unique nature of the Albanian migration exodus and the richness of our data provide an opportunity to shed some light on these questions. While avoiding a direct entanglement with long-standing debates on agency (Hitlin and Elder 2007; Loyal and Barnes 2001), we aim to shed light on the growth or decline of independent female action as evident from the evolution of female participation in the international migration process.
GENDER AND MIGRATION
Theoretical Perspectives
The complexity of the international migration process as well as the interdisciplinary nature of scholarship in this field has led to a particularly broad range of theoretical approaches for the study of migration. The economic approach, begun in the neoclassical tradition and further developed by Todaro (1969; 1976), has emphasized the expected gains to potential migrants and implications of policy programs that aim to reduce rural out-migration. Later developments in this field, coming under the title of the new economics of labor migration, have focused on the context and boundaries of decision-making and have pushed both economists and noneconomists to consider the complex household-level strategies underlying migration (see Stark 1991). This has meant increasing attention to risk and credit constraints, for example, as primary motivations underlying migration strategies (Rosenzweig and Stark 1989; Taylor 1986). Sociological and demographic theory-building has paid keen attention to the contributions of economists, and the underlying rational-actor micro-level model has been adopted in many cases; but parallel theories and models have paid equally close attention to the role of social networks and underlying structures in determining migration patterns (Boyd 1989; Entwisle et al. 2007; Massey and Espinosa 1997). These, in turn, have altered the thinking of economists who in recent years have incorporated network mechanisms directly into their models (Munshi 2003; Winters, de Janvry, and Sadoulet 2001).
Yet, the economic and the sociological approaches have only recently begun to contribute insight into the role of women in migration (Cerrutti and Massey 2001). Compelling critiques levied against the field, including Pedraza (1991) and Hondagneu-Sotelo (1992), argued for looking at how women’s roles are defined and their access to resources and decision-making both as migrants and nonmigrants. As Cerrutti and Massey (2001:188) noted in their discussion of both the neoclassical economics and the new economics of labor migration approaches, “(I)n neither case are women assigned much agency, either as autonomous decision makers or as independent participants in household bargaining.” This lack of agency attributed to women also offers an explanation for why much of the previous research on migration has positioned women as “secondary” or “associational” migrants (Kanaiaupuni 2000:1315). Nonetheless, evidence from Mexico provides clear support for women’s increasing role in migration (Donato 1993, 1994). Similar evidence from other countries with strong patriarchal systems strengthens the argument that women eventually pursue more independent migration strategies. For example, in Thailand, where traditional roles had been assigned to women whereas young men were expected to be “freer from the household . . . or go adventuring,” compelling evidence exists for a dramatic increase in female migration as economic opportunities and urbanization transform society (Curran and Saguy 2001:63). Evidence from recent decades from the Philippines paints a similar picture (Barber 2000; Oishi 2005; Tyner 1999). The implication is that at some stage of the migration process, cultural taboos against female migration are translated into a “culture of migration” (Kandel and Massey 2002). Despite this empirical evidence, little is known about how and why female migration increases, and whether female agency plays a central role in this process or instead increased female migration is simply a product of women following evolved household strategies.
Generally, explanations of the predominance of males in migration streams—particularly at the onset, when men form the majority and women are mostly tied migrants—focus on the role of inequalities both at the macro, structural level (Lindstrom and Lauster 2001; Stark 2006; Stark and Taylor 1991) and at the micro, household level (Hondagneu-Sotelo 1994; Kanaiaupuni 2000; Parrado and Flippen 2005) in fostering discriminatory, male-dominated regimes and limiting female agency and power in migration. Cultural and structural supports help to maintain a regime of discrimination and restricted access to opportunity reinforces patriarchal power structures at the household level. This system creates a lack of independence and agency for women that is intimately tied to their ability to consider migration as anything other than tied migrants (Pedraza 1991). It also explains why single, female migrants to the United States typically emanate from weakly bounded families lacking strong patriarchal authority (Hondagneu-Sotelo 1994). Yet, little is known about whether equality within the public sphere itself might compensate or overcome private inequalities, providing mechanisms and opportunities for migration that are accessible for women as well as men.
Gender and Migration in the Albanian Context
Three stages of Albania’s unique history are particularly relevant for our analysis of gender and migration and the role of inequality. The first is the period prior to WWII, when Albania, which had the highest fertility rate in Europe, was a traditional society with strong patriarchal values and high levels of gender discrimination (Falkingham and Gjonca 2001). Much of traditional Albanian society was organized around family clans, and the clan heads (primarily male) exercised tremendous authority in the daily lives of all family members. Marriage patterns were largely exogamous, further reducing the value of daughters to their parents. In 1938, only about one-third of primary school students and just over one-fifth of secondary school students were female (Falkingham and Gjonca 2001).
The second stage marks the onset of the Communist era, which began at the end of WWII and which is highly relevant for two reasons. The first is that the emigration entirely stopped, and under the rule of Hoxha, Albania became the most isolated and closed of Communist countries (Carletto et al. 2006). The second reason comprises efforts toward egalitarian reform, enacted by the Communist state, which sought to integrate women in all aspects of economic life (UNDP 2005). The result was that in addition to increasing educational levels throughout Albania for both sexes, women succeeded in making gains relative to men (Meurs, Miluka, and Hertz 2008). By 1966, girls comprised 43% of students in secondary schools (Thomas 1969), and by 1989, female employment levels were among the highest in Europe (UNDP 2005). Despite these dramatic and broad improvements in status, women continued to bear primary responsibility for family duties, thus placing a heavy onus on women and imposing restrictions on their full labor force integration (Falkingham and Gjonca 2001). This continued anchoring of women through family responsibilities, including childbearing, maintained their dependence on male household decision-makers, despite progress in the public sphere.
The death of Hoxha and the collapse of the regime marked the onset of the third stage, in which the Socialist apparatus was dismantled and eventually replaced with a democratic government and free markets replaced central planning. During the chaotic process of transition, a series of massive waves of emigration began between 1990 and 1992 and then sporadically surged at different intervals, including the mid-1990s with the failure of the infamous pyramid schemes1 (Carletto et al. 2006). These emigration waves changed the face of Albanian society, and by 2003, some one-fifth of the country’s 1990 population had left to live abroad. Important shifts occurred within Albania at the macro, structural level that had important implications for gender. Labor market inequality deepened during a series of severe economic crises following the end of the Hoxha regime, and this effect was exacerbated by rapid shifts in employment opportunities (Cuka et al. 2003), similar to those witnessed across a number of post-Socialist states (Einhorn 1993; Occhipinti 1996). These increasing inequalities had a strong gender dimension as labor force participation reached 70.5% for men and 46.7% for women by 2003 (INSTAT 2004). In one survey of a peri-urban community in northern Albania, near equal employment during the Communist period was replaced by a situation in which 92% of full-time jobs were held by men (Lawson et al. 2000). Similarly, school enrollment levels for girls began to fall as poverty rates rose and parents chose to limit their daughters’ rather than sons’ education (Silova and Magno 2004). These shifts can be partly explained by the fact that traditional gender roles, which had never been tackled by the Socialist state, reemerged with the collapse of the social support system for women, leading to more gender discrimination in the labor markets (Miluka 2009; Silova and Magno 2004). Thus, despite having made tremendous progress in the public sphere, women remained entrenched in patriarchal traditions and systems of control in the private sphere (INSTAT 2004), factors that hindered their progress in the post-Communist state.
To summarize, three interrelated processes within Albania provide a fascinating context to study gender and migration. The first is the virtual elimination of migration prior to 1990, creating a unique opportunity to investigate how migration and gender are related from a stage of no migration to one in which migration becomes endemic. The second involves the relatively high levels of female education and labor force participation found in Albania when the country first opened to migration. Although discrimination existed, women’s status in the public sphere improved dramatically over the course of the Communist era. A subsequent process of de-development followed in the early years after the transition to a market economy and democracy, which eroded women’s status as traditional patriarchal norms, and gender discrimination replaced government-enforced egalitarianism. Signs of improvement are evident toward the end of the 1990s, but this process has produced shifting incentives for migration. Finally, inequalities within households, which have endured throughout the Communist era and have been reinforced in the post-Communist period, have further contributed to this loss of female status because female age at marriage declined along with female school attendance. A study of gender and migration from Albania necessarily examines the implications of women’s relatively high education and labor force participation in the public sphere but limited decision-making power and agency in the private sphere.
Research Hypotheses
Our work focuses on the relationship between gender and migration and how this relationship depends on larger structural inequalities in place in the labor market, as well as inequalities operating within the boundaries of the household. Despite disputes that remain regarding the relationship between origin-country inequality and migration (Borjas 1987, 1991; Chiswick 2000), most scholars argue that migrants are positively selected on various characteristics, including education (Chiquiar and Hanson 2005; Feliciano 2005; McKenzie and Rapoport 2007). What has not been sufficiently studied, however, is whether the educational selectivity in migration is different for men and women (Feliciano 2008). Thus, if women suffer relatively high levels of discrimination in one country (such as Mexico) and will experience relatively lower levels in another country (such as the United States), there are reasons to argue that women with higher education will seek to migrate to better opportunities. Limited evidence appears to suggest that women are more positively selected for education than men. For example, Kanaiaupuni (2000) showed that higher levels of education raise the odds of female migration just as they lower the odds of male migration. Similarly, Feliciano (2008) showed that female migrants from Mexico are more highly selected than male migrants between the years 1960 and 2000. Qualitative studies provide further support showing how social norms and employment constraints are perceived as particularly constraining for women with higher levels of education (Hondagneu-Sotelo 1994).
These arguments regarding educational selectivity resonate in Albania, where women have achieved near-equality in terms of human capital and labor force participation, yet have been constrained in terms of their household-level status (INSTAT 2004). When the formal policies supporting gender equality were removed in the 1990s, labor market opportunities declined for everyone but more so for women (UNDP 2005). The evidence indicates that gender inequality in Albania rose over time, thus limiting women’s access to higher status occupations and generating “new forms of marginalization” (Calloni 2002; UNDP 2005). Such a turn is likely to push educated women, who have more to lose and easier access to migration-related information and resources, to consider migration as an alternative. In contrast, male migration is far less selective, with a massive outflow of able-bodied males seeking jobs outside the country (Carletto et al. 2006). Thus, our first hypothesis builds on these arguments to predict that female migration from Albania is more strongly associated with education than is male migration.
A related question is whether and how the absolute effect of education, as well as its differential by gender, evolves as migration becomes increasingly normative. Powerful evidence from Mexico highlights the declining degree of selectivity of migrants in terms of socioeconomic levels over time (Massey et al. 1994). This pattern is understandable given the central role of cumulative causation in the migration process, which emphasizes the need to account for migration networks over the course of the migration process. Albania should be similar as early waves of migrants established themselves, mainly in Italy and Greece, and facilitated migration for subsequent Albanians (Carletto et al. 2006). Thus, the effect of education on migration should weaken over time for both sexes. This shift should be strengthened by expanded labor market opportunities following the mid-1990s, when the economy stabilized following the pyramid collapses and related civil disturbances and began a period of continued growth with increased demand for educated workers (UNDP 2005). The decline in the role of education is likely to be weaker among women who continue to confront greater inequalities through increased labor market discrimination. Our second hypothesis predicts that the increasing discrimination in the public sphere provides an important push-factor for female migration and increases the male-female educational differences of migrants over time.
Our third and fourth hypotheses focus on how household-level inequalities affect the relationship between gender and migration. These include long-term household structural factors that constrain families—in this case, the supply of sons—and short-term shocks associated with changes in household members’ health status and resource constraints. Economic and health crises at the household level may both alter incentives and constraints on migration (Davis and Winters 2001) as well as gender relations (Peteet 1991). In Albania, women’s actions are constrained by neo-traditional and neo-patriarchal forms of authority that operate primarily, though not exclusively, in their household (Calloni 2002). These systems, many of which predate the Communist period, have continued to discriminate against women within households both during the Communist era and in the transition period (Becker 1983; INSTAT 2004). Women suffered further declines in status in the transition era because of the closure of a variety of industries in which women were employed and the growth of an unregulated market economy (Cuka et al. 2003; King, Dalipaj, and Mai 2006). The decline in labor market opportunities affected women more than men, leading to increased unemployment and female dependency on husbands and male family members (King et al. 2006). The deterioration in the status of women within society, but more so within households, makes daughters more likely than sons to be called upon to meet the needs of households. The implication is that female migration will be more sensitive than male migration to variations in the constraints and incentives faced by households.
In societies such as Albania that rely heavily on remittances from migrant children, all households seek to establish some children abroad (Carletto et al. 2006; Cuka et al. 2003). However, relatively little attention has been paid to the constraining role of sex structure of siblings on the gender and migration relationship. Our third hypothesis predicts that households with a high proportion of sons have little need to allow daughters to migrate and will thus focus on sending sons. On the other hand, households with relatively few or no sons face a household constraint and are more likely to enable daughters to migrate. To the extent that this is true—that demographic constraints play a direct role in whether daughters migrate—a compelling indicator is provided for female migration being driven by household necessity. Although male migration may also respond to household needs, we anticipate that male migration behavior will be less tied to household circumstances and not as strongly affected by the presence or absence of female siblings.
Unlike the third hypothesis, which focuses on household responses to long-term demographic constraints, the fourth hypothesis concerns unanticipated crises, as in the case of health or income shocks. Our fourth hypothesis predicts that female migration behavior responds more strongly to income or health shocks than does male behavior. A greater degree of female elasticity of migration in response to household-level factors reflects the weaker bargaining power of women within the household and provides a clear expression of their lack of independent decision-making. In this case, increased female migration may not necessarily signal an expansion of female empowerment or independence in the household or community.
The research value of the Albanian case is greatly heightened because of the lack of migration in the decades before 1990. Prior research, particularly that from Mexico, has led to the formulation of the concept of cumulative causation, which describes how networks create self-sustaining migration processes, partly through the generation of migration-specific social capital—that is, migration capital (Fussell and Massey 2004; Massey 1990; Myrdal 1957). Understanding the link between gender and migration becomes more complicated when migration streams go back in time and migration capital is interwoven with other forms of human, financial, and social capital. This situation makes it more difficult to identify coefficients of migration capital variables as well as the effects of other variables (Munshi 2003; Palloni et al. 2001). Innovative empirical strategies have been used to capture variation across regions in their access to migration resources from the distant past—such as McKenzie and Rapoport’s (2007) use of railroad data from Mexico—but migration becomes highly endogenous over time. Albania has both a clear starting point—with data on migration capital existing prior to the onset of migration—and information on the rapid accumulation of migration capital over time, offering an ideal environment in which to evaluate the effect of changing forms of inequality on migration while controlling for migration capital.
DATA
The data for this study come from the 2005 Albania Living Standards Measurement Study (ALSMS05) conducted by the Albanian Institute of Statistics (INSTAT), with technical assistance from the World Bank. The sampling frame for the survey was stratified into four regions (Coastal; Central; Mountain; and Tirana, the capital), and a total sample of 3,640 households from 455 census enumeration areas was drawn based on a multistage cluster design. The ALSMS05 household questionnaire collected data on general household demographics, education levels, asset ownership, expenditures, and labor market participation. Migration’s central role in Albanian society led to the inclusion of a set of unique survey modules to obtain migration histories for current and past household members. For adult children no longer living in the household, parents were interviewed as proxies and asked to provide migration histories from 1990 to 2004 on the timing of moves, destinations, and current locations as well as the basic demographic and socioeconomic characteristics of the offspring. The use of retrospective data is not without problems (Smith and Thomas 2003). However, concern is attenuated because our focus is on gender differences in migration over time rather than absolute migration levels. Furthermore, the focus on first year of migration—a relatively salient event in peoples’ lives—likely reduces any potential influence of recall bias.
Our analysis uses the detailed migration histories collected for all sons and daughters of the household head and household head’s spouse, whether currently living in the household, elsewhere in the country, or abroad.2 The data from this module enable us to construct time-varying measures of past migration of sons and daughters (i.e., our left-hand-side variable), composed of an annual series of dichotomous variables indicating whether the individual had migrated for the first time in that particular year. Although we focus on the timing of first migration, supplementary analyses alluded to later in the article also differentiate the outcome variable by country-destination and by whether the migration was temporary or permanent.
Family migration networks are time-varying and are estimated for each son or daughter based on the sum of all family migrants including siblings at each point in time, excluding ego (the son and daughter themselves). Also, aggregation of the migration data at the community level, excluding ego’s household migration capital, provides a measure of time-varying community migration capital. All community migration data are also separated by gender. The aggregation of past migration of other sons and daughters is carried out without incorporating migration from the most recent year in order to avoid endogeneity bias.
A rare perspective on migration incentives and constraints were collected through a series of questions on severe household-level shocks occurring in each year since 1990. Shocks were categorized into one of four categories: a job loss, a major illness or death, a large loss of property, or an income shock relating to the collapse of the pyramid saving schemes. The shock variables are built upon the cumulative experience of households, and the data are coded by whether a household had ever suffered a given type of shock. The shocks were measured annually and lagged, providing a time-varying indicator of their influence on both male and female migration patterns.
Discrete shifts in the effect of several of our explanatory variables are captured by using an “epoch” dummy variable that takes a value of 1 for the period 1996–2004 and 0 for the period prior to 1996. Although we don’t expect that the dynamics of migration changed dramatically in 1996, dichotomizing the period into two epochs both facilitates the interpretation of results and is substantively grounded in the fact that the mid-1990s are seen as a turning point in the relationship between many of our covariates and migration. In particular, the pyramid scheme expansion and crisis that began toward the end of 1996 and the subsequent Greek and Italian decisions to legalize the status of illegal Albanian created a surge in the migration of family members still in Albania, and introduced new incentives on migration from Albania beginning in late 1996 by raising the anticipated benefits of immigration to these countries. Interestingly, there are indications that legalization in Greece did not dramatically alter irregular migration flows (Geddes 2003). At the same time, signs indicate that relative political stability and economic growth toward the end of the 1990s began to alter the incentives for migration while potentially improving female status (Meurs et al. 2008).
All of the preceding variables are time-varying, as is the age of the son or daughter. The remaining control variables are not time-varying. These variables include education, which is divided into three categories (completed up to 8 years of schooling, at least some high school education, or at least some university education). A wealth proxy, based on a principal components analysis of durable goods owned by the household in 1990, controls for household wealth prior to the beginning of migration, thereby avoiding endogeneity with subsequent migration. Finally, the region of residence of the respondent is divided into seven categories, including Tirana and then Mountain, Central, and Coastal regions, each disaggregated into urban and rural sectors.
METHODS
Our population at risk includes sons and daughters ages 15 and older as reported by respondents. The retrospective migration module enables us to identify the timing of migration between 1990 and 2004 for the children of respondents. Because individuals enter our eligible sample only at age 15, many sons and daughters will not have reached age 15 by 1990, thus reducing the total amount of time they are exposed to the risk of migration within our analysis. Children still younger than 15 years of age by 2005 are not exposed at all. Children older than 15 are exposed to the risk of migration for up to 15 years or until their first migration episode, at which point their exposure ends. Our empirical strategy is based on hazard analysis, which assigns exposure to the relevant time periods, enabling us to calculate first-migration hazard rates.
We employ discrete-time hazard models, using logistic regression to estimate the hazard of first-migration between the years 1990 and 2004. No out-migration occurs over the observed time span for many individuals. These cases are right-censored because they may well end up migrating after the observation period is over (post-2004). Those who are between ages 0 and 14 in 1990 will be subject to “delayed entry,” which is treated in the measurement of exposure in the discrete-time model (Guo 1993; Jenkins 1995). Our focus on ever-migrants or on the timing of first-migration episodes is driven by both data and substance. Our data are better suited for analyzing first-migration episodes because not every migration episode is captured for those who migrate repeatedly, nor do we have information on multiple moves in a single year. Furthermore, the first-migration episode is substantively appealing because our interest is in the ability of women to access migration opportunities upon which some formal or informal threshold is likely crossed.
We adopt a flexible specification requiring little structure on the year-to-year variation in the baseline hazards by introducing dummy variables for each year, and further allowing the baseline to vary between men and women. Thus, our dependent variable indicates whether an individual has migrated for the first time following one or more years of exposure. Then our focus is on how this baseline hazard is altered over time, from 1990 onward, by age and by other factors. Because individuals are repeatedly observed between 1 and 15 times, coefficients and standard errors are adjusted in all models to avoid downward-biased estimates.
We focus on four hypotheses described earlier, relying primarily on a set of pooled baseline specifications for which all our main variables are included, but male and female differences are all subsumed in a dummy variable for gender. A series of interactions between gender and the control variables are then introduced. Subsequent models introduce a set of variables directly associated with a particular hypothesis. A near-saturated model with a full set of variables is examined wherein the gender dummy variable interacts with practically all the covariates for the different hypotheses to test whether the various mechanisms are complementary or competing.
When we test a hypothesis regarding temporal shifts in the effect of education, we rely on a set of single-sex models in which we introduce an interaction between an epoch dummy variable and the education variables to test whether their impact shifts between the period 1990–1995 and the period 1996–2004. This admittedly simplistic interpretation of any shift is founded on both popular and academic conceptions of the mid-1990s as a turning point both in the social, political, and economic climate as well as in the migration process.3 We also briefly discuss the results of an alternative and less-restrictive specification in which a series of dummy variables for each year are interacted with the educational categories.
All coefficients discussed in the text are significant unless otherwise noted. Our estimated coefficients are presented as odds ratios and are interpreted as the proportional effect of a change in a given variable on the hazard odds of ever migrating. We present the exponentiated coefficients along with stars to indicate significance rather than standard errors or t statistics. Admittedly, some details are removed, but the clarity in the tables is essential given the large number of models and coefficients (full results are available upon request). Finally, given concerns about unobserved heterogeneity (or frailty), we retest our baseline model by using a random-effects logistic model for both sexes, and we find little cause for concern.4
RESULTS
Descriptive Analysis
Our working sample contains 3,888 sons and 4,183 daughters reported by 2,501 households. These figures translate into an average of 3.2 children ages 15 and older per household. This high figure is not surprising given Albania’s historically high levels of fertility that have only recently declined (Falkingham and Gjonca 2001). Exceptional levels of migration from Albania led to a situation in which 41% of sons and 18% of daughters in the sample had ever migrated by 2004.5 Thus, the chances of a son ever migrating are more than twice that of a daughter. Although a staggering proportion of children migrated, migrant children are not spread equally across households. Almost one-half of parents (46%) reported that their children have no international migration experience. The data also reveal the primacy of Greece and Italy as migration destinations, with 85% of male and 78% of male ever-migrants reported to have made one of these two countries their first destination.
The male and female migration hazards clearly demonstrate differences in their migration propensities. The hazards of ever-migration from Albania are displayed in Figure 1, based on a discrete-time hazard model for men and women separately and including only the dummy variables for each year. The estimated hazards range from extremely low values near 0 for women and near 1% for men to nearly 3% and 9%, respectively. From the perspective of migration stages, two distinct stages are apparent. The two stages appear separated by the dramatic turnarounds associated with the failed pyramid schemes of the mid-1990s. The increase in the migration hazard in the early 1990s is much steeper among women but also settles down more quickly. For both sexes, migration declined dramatically in the mid-1990s. The male migration pattern shows striking shifts over time, beginning with the dramatic surge in the early 1990s, followed by a slow-down toward the mid-1990s. A second surge began in the second half of the 1990s, followed by a leveling off and a slowing down after 2000. In contrast, the female temporal pattern is rather less dramatic and, aside from the decline in the mid-1990s, indicates gradual and increasing levels of migration for much of the period; only for the early 2000s is there any clear evidence of declining migration. Migration levels increased slowly and steadily until 2002, with 1996 appearing as more of an exceptional year. As expected, a shift occurred around 1996, with an apparent rise in the probability of migration in the second half of the 1990s. This rise is most likely due to the expansion and subsequent failure of the pyramid saving schemes that erupted in late 1996 as well as the legalization of Albanian migrants in Greece in 1998, followed by additional legalization programs in Greece and Italy. Finally, a very notable slowdown in both male and female migration occurred around 2000, and this downward trend persisted through to 2004, likely attributable to recent economic and political progress (World Bank 2007).
Figure 1.
Estimated Male and Female Hazards of Ever Migrating in 1990–2004: 2005 Albania Living Standards Measurement Study
Summary statistics are shown in Table 1. These data must be carefully interpreted in the case of discrete-time hazard models because different individuals appear in the data for different durations, thus providing different amounts of input into the summary statistics. We partition the data by gender and then by ever-migration status. We test mean differences, which are adjusted for clustering because of multiple observations per person, using t tests for continuous variables and chi-squared tests for dichotomous measures. The asterisks in the first column of Table 1 indicate the level of significance in the comparison of mean differences between men and women, and asterisks in the third column represent the test between ever-migrants and nonmigrants.
Table 1.
Summary Statistics for Men and Women, Ever-Migrants and Nonmigrants, With Testing of Differences in Means Corrected for Clustering: 2005 Albania Living Standards Measurement Study
| Variable | Male | Female | Nonmigrants | Migrants |
|---|---|---|---|---|
| Year | 1997.282*** | 1997.824 | 1998.199*** | 1995.819 |
| Age | 25.840 | 25.962 | 26.930*** | 22.948 |
| Tirana | 0.147 | 0.139 | 0.146 | 0.132 |
| Coastal Urban | 0.107 | 0.119 | 0.0993*** | 0.156 |
| Coastal Rural | 0.168** | 0.197 | 0.179* | 0.202 |
| Central Urban | 0.107 | 0.117 | 0.106** | 0.131 |
| Central Rural | 0.152 | 0.152 | 0.150 | 0.157 |
| Mountain Urban | 0.089 | 0.087 | 0.092* | 0.076 |
| Mountain Rural | 0.230*** | 0.189 | 0.227*** | 0.147 |
| Household Wealth Index | 0.099** | 0.131 | 0.078*** | 0.231 |
| Primary Education | 0.510 | 0.514 | 0.523*** | 0.480 |
| At Least Some High School | 0.386* | 0.358 | 0.354*** | 0.416 |
| At Least Some College | 0.104** | 0.128 | 0.122* | 0.104 |
| Job Shock | 0.147 | 0.151 | 0.155** | 0.131 |
| Illness Shock | 0.114** | 0.131 | 0.139*** | 0.079 |
| Property-Loss Shock | 0.026 | 0.026 | 0.028* | 0.020 |
| Pyramid Shock | 0.020 | 0.020 | 0.018*** | 0.026 |
| Number of Male Siblings > Age 14 | 1.562 | 1.537 | 1.582*** | 1.450 |
| Number of Female Siblings >Age 14 | 1.712* | 1.788 | 1.803*** | 1.616 |
| Family Friends in 1990 | 0.015 | 0.015 | 0.015 | 0.016 |
| Relatives in 1990 | 0.062 | 0.060 | 0.058† | 0.070 |
| Household Male Migrants | 0.216*** | 0.339 | 0.300*** | 0.244 |
| Household Female Migrants | 0.085*** | 0.126 | 0.114* | 0.093 |
| Share of Community Male Migrants | 12.143*** | 14.767 | 14.579*** | 10.870 |
| Share of Community Female Migrants | 3.397*** | 3.842 | 4.058*** | 2.462 |
| Number of Cases | 30,916 | 40,317 | 52,972 | 18,261 |
p < .10,
p < .05;
p < .01;
p < .001
Several notable differences emerge from Table 1. Unsurprisingly, given the attention to female education during the Communist regime, women are better represented at the upper education level (at least some university education). Daughters are more represented in households that report an illness shock and slightly larger number of female siblings. Also, the network measures at both the family and community level indicate substantial gender differences in migration capital. The differences in the last two columns of Table 1 between ever-migrants and nonmigrants are more stark. Migrants are observed, on average, slightly before nonmigrants both in terms of the mean year of observation and the mean age, reflecting the fact that migrants are censored earlier than nonmigrants. Migrants are more likely to be from the Coastal or Central urban regions and also much less likely to be from the Mountain regions. There is also a wide gap in the wealth index with migrants coming from households that were better off in 1990. Migrants also have lower education levels, although this is primarily due to male migrants having lower education than female migrants. Neither indicator of existing networks in 1990 is significant, whereas time-varying network measures are significant though not necessarily in the expected direction.
Multivariate Analyses
Our main results are presented in two separate models. Table 2 shows pooled, two-sex models, and Table 3 results are estimated separately for men and women. Single-sex estimates in Table 3 make it easier to highlight substantive similarities and contrasts in male and female migration patterns over time, while the interaction terms in the pooled two-sex models in Table 2 provide tests of whether the gender differences are significant. The baseline model in Table 2 (Model 1), which subsumes the entire gender difference within the shift of the baseline hazard captured by the gender (female) dummy variable, indicates that the hazard odds of annual migration for women remain nearly 74% lower than men’s when all the control variables are included. The large gap in the underlying migration hazard for men and women is consistent with the descriptive statistics as well as Figure 1.6 The consistency of the female coefficient, which captures the gender difference, is evident when one compares its magnitude with that in the full model (Model 5 of Table 2). In this case, the gender coefficient indicates that the hazards for women are 80% lower than for men, despite allowing the covariate effects to vary by gender. Although the precise value of the gender coefficient varies across the models, the large gender differential remains stable.
Table 2.
Main Hypotheses Tested, Discrete-Time Hazard Analysis of Ever-Migration Using Male and Female Pooled Model, All Coefficients Exponentiated: 2005 Albania Living Standards Measurement Study (n = 71,233)
| Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
|---|---|---|---|---|---|
| Female | 0.262*** | 0.253* | 0.163** | 0.309† | 0.196* |
| 1991 | 5.830*** | 5.836*** | 5.838*** | 5.853*** | 5.849*** |
| 1992 | 6.028*** | 5.947*** | 5.940*** | 5.980*** | 5.949*** |
| 1993 | 5.516*** | 5.269*** | 5.257*** | 5.310*** | 5.268*** |
| 1994 | 4.707*** | 4.202*** | 4.188*** | 4.233*** | 4.202*** |
| 1995 | 6.122*** | 5.064*** | 5.042*** | 5.105*** | 5.083*** |
| 1996 | 4.862*** | 4.711*** | 4.693*** | 4.790*** | 4.790*** |
| 1997 | 8.160*** | 6.693*** | 6.657*** | 7.064*** | 7.072*** |
| 1998 | 10.987*** | 9.732*** | 9.683*** | 9.823*** | 9.891*** |
| 1999 | 9.513*** | 8.799*** | 8.749*** | 8.880*** | 9.007*** |
| 2000 | 11.667*** | 10.659*** | 10.606*** | 10.783*** | 11.024*** |
| 2001 | 10.134*** | 8.856*** | 8.817*** | 8.981*** | 9.221*** |
| 2002 | 9.773*** | 8.318*** | 8.299*** | 8.452*** | 8.733*** |
| 2003 | 8.814*** | 8.888*** | 8.885*** | 9.055*** | 9.456*** |
| 2004 | 7.308*** | 7.343*** | 7.344*** | 7.509*** | 7.901*** |
| Ages 20–25 | 1.799*** | 1.689*** | 1.688*** | 1.656*** | 1.670*** |
| Ages 25–30 | 1.530*** | 1.473*** | 1.473*** | 1.424*** | 1.442*** |
| Ages 30–35 | 0.992 | 0.909 | 0.910 | 0.873 | 0.887 |
| Ages 35–40 | 0.719*** | 0.668*** | 0.672*** | 0.642*** | 0.658*** |
| Ages 40–45 | 0.532*** | 0.379*** | 0.383*** | 0.364*** | 0.374*** |
| Ages 45+ | 0.220*** | 0.164*** | 0.166*** | 0.155*** | 0.162*** |
| Coastal Urban | 1.570*** | 1.705*** | 1.637*** | 1.696*** | 1.649*** |
| Coastal Rural | 1.253** | 1.757*** | 1.653*** | 1.721*** | 1.579*** |
| Central Urban | 1.236* | 1.293* | 1.276* | 1.309* | 1.285* |
| Central Rural | 1.009 | 1.299** | 1.220* | 1.269* | 1.157 |
| Mountain Urban | 0.797* | 0.945 | 0.918 | 0.942 | 0.890 |
| Mountain Rural | 0.686*** | 1.002 | 0.939 | 0.966 | 0.876 |
| Household Wealth Index | 1.036** | 1.017 | 1.027† | 1.020 | 1.031* |
| At Least Some High School | 1.139** | 1.109* | 0.975 | 1.108* | 0.989 |
| At least Some College | 0.788** | 0.757*** | 0.569*** | 0.744*** | 0.588*** |
| Job Shock | 1.024 | 1.025 | 1.019 | 1.011 | 1.016 |
| Illness Shock | 0.869* | 0.874* | 0.878* | 0.936 | 0.926 |
| Property-Loss Shock | 1.214† | 1.193 | 1.192 | 1.009 | 1.008 |
| Pyramid Shock | 1.143 | 1.125 | 1.129 | 0.944 | 0.953 |
| Number of Male Siblings >Age 14 | 0.931*** | 0.924*** | 0.924*** | 0.965† | 0.957* |
| Number of Female Siblings >Age 14 | 0.967* | 0.971† | 0.972† | 0.981 | 0.974 |
| Family Friends in 1990 | 0.817 | 0.841 | 0.846 | 0.829 | 0.809 |
| Relatives in 1990 | 1.226* | 1.212* | 1.211* | 1.218* | 1.245* |
| Household Male Migrants | 1.284*** | 1.293*** | 1.298*** | 1.286*** | 1.359*** |
| Household Female Migrants | 1.323*** | 1.294*** | 1.297*** | 1.297*** | 1.193* |
| Community Male Migrants (%) | 1.005*** | 1.006*** | 1.006*** | 1.006*** | 1.006*** |
| Community Female Migrants (%) | 0.994† | 0.993† | 0.993† | 0.994† | 0.985** |
| Female × 1991 | 1.020 | 1.019 | 1.001 | 1.005 | |
| Female × 1992 | 1.104 | 1.101 | 1.074 | 1.093 | |
| Female × 1993 | 1.269 | 1.267 | 1.217 | 1.245 | |
| Female × 1994 | 1.648 | 1.651 | 1.582 | 1.621 | |
| Female × 1995 | 2.085 | 2.095 | 1.989 | 2.023 | |
| Female × 1996 | 1.212 | 1.217 | 1.125 | 1.132 | |
| Female × 1997 | 2.141 | 2.153 | 1.743 | 1.742 | |
| Female × 1998 | 1.673 | 1.684 | 1.574 | 1.551 | |
| Female × 1999 | 1.471 | 1.487 | 1.384 | 1.334 | |
| Female × 2000 | 1.520 | 1.537 | 1.419 | 1.345 | |
| Female × 2001 | 1.737 | 1.764 | 1.622 | 1.521 | |
| Female × 2002 | 1.861 | 1.889 | 1.732 | 1.589 | |
| Female × 2003 | 1.135 | 1.148 | 1.048 | 0.938 | |
| Female × 2004 | 1.162 | 1.178 | 1.067 | 0.942 | |
| Female × Ages 20–25 | 1.303* | 1.296* | 1.417** | 1.385** | |
| Female × Ages 25–30 | 1.212 | 1.199 | 1.385* | 1.332* | |
| Female × Ages 30–35 | 1.378* | 1.369* | 1.582** | 1.526* | |
| Female × Ages 35–40 | 1.314 | 1.320 | 1.511* | 1.442† | |
| Female × Ages 40–45 | 2.665*** | 2.680*** | 3.102*** | 2.941*** | |
| Female × Ages 45+ | 2.509* | 2.672* | 3.112** | 3.038** | |
| Female × Coastal Urban | 0.741† | 0.787 | 0.752† | 0.762† | |
| Female × Coastal Rural | 0.338*** | 0.419*** | 0.360*** | 0.478*** | |
| Female × Central Urban | 0.844 | 0.863 | 0.841 | 0.858 | |
| Female × Central Rural | 0.426*** | 0.556*** | 0.462*** | 0.629** | |
| Female × Mountain Urban | 0.631* | 0.656* | 0.664* | 0.735 | |
| Female × Mountain Rural | 0.176*** | 0.227*** | 0.203*** | 0.279*** | |
| Female × Household Wealth Index | 1.047† | 1.020 | 1.039 | 1.008 | |
| Female × At Least Some High School | 1.654*** | 1.554*** | |||
| Female × At Least Some College | 2.232*** | 1.966*** | |||
| Female × Job Shock | 1.041 | 0.998 | |||
| Female × Illness Shock | 0.795 | 0.817 | |||
| Female × Property Loss | 1.643* | 1.622* | |||
| Female × Pyramid Loss | 1.613 | 1.593 | |||
| Female × Number of Male | |||||
| Siblings >Age 14 | 0.833*** | 0.852*** | |||
| Female × Number of Females | |||||
| Siblings >Age 14 | 0.979 | 1.000 | |||
| Female × Family Friends in 1990 | 1.116 | ||||
| Female × Relatives in 1990 | 0.930 | ||||
| Female × Household Male Migrants | 0.875* | ||||
| Female × Household Female Migrants | 1.197† | ||||
| Female × Community Male Migrants (%) | 0.999 | ||||
| Female × Community Female Migrants (%) | 1.020** | ||||
| Constant | 0.007*** | 0.007*** | 0.007*** | 0.006*** | 0.007*** |
| Chi-Squared | 1,681.8 | 1,666.9 | 1,666.6 | 1,646.8 | 1,716.0 |
Notes: Reference categories are 1990, ages 15–20, 0–8 years education, and Tirana.
p < .10,
p < .05;
p < .01;
p < .001
Table 3.
Discrete-Time Hazard Analysis of Ever-Migration for Both Men and Women Separately Including All Main Variables, All Coeffcients Exponentiated: 2005 Albania Living Standards Measurement Study
| Variable | Male | Female | |
|---|---|---|---|
| 1991 | 5.849*** | 5.878** | |
| 1992 | 5.949*** | 6.504** | |
| 1993 | 5.268*** | 6.561** | |
| 1994 | 4.202*** | 6.812** | |
| 1995 | 5.083*** | 10.281*** | |
| 1996 | 4.790*** | 5.421** | |
| 1997 | 7.072*** | 12.321*** | |
| 1998 | 9.891*** | 15.343*** | |
| 1999 | 9.007*** | 12.018*** | |
| 2000 | 11.024*** | 14.825*** | |
| 2001 | 9.221*** | 14.028*** | |
| 2002 | 8.733*** | 13.875*** | |
| 2003 | 9.456*** | 8.868*** | |
| 2004 | 7.901*** | 7.443*** | |
| Ages 20–25 | 1.670*** | 2.313*** | |
| Ages 25–30 | 1.442*** | 1.921*** | |
| Ages 30–35 | 0.887 | 1.354* | |
| Ages 35–40 | 0.658*** | 0.949 | |
| Ages 40–45 | 0.374*** | 1.101 | |
| Ages 45+ | 0.162*** | 0.493* | |
| Coastal Urban | 1.649*** | 1.256† | |
| Coastal Rural | 1.579*** | 0.754* | |
| Central Urban | 1.285* | 1.102 | |
| Central Rural | 1.157 | 0.728* | |
| Mountain Urban | 0.890 | 0.654** | |
| Mountain Rural | 0.876 | 0.245*** | |
| Household Wealth Index | 1.031* | 1.039* | |
| At Least Some High School | 0.989 | 1.537*** | |
| At Least Some College | 0.588*** | 1.155 | |
| Job Shock | 1.016 | 1.014 | |
| Illness Shock | 0.926 | 0.756* | |
| Property-Loss Shock | 1.008 | 1.636** | |
| Pyramid Shock | 0.953 | 1.517† | |
| Number of Male Siblings >Age 14 | 0.957* | 0.815*** | |
| Number of Female Siblings >Age 14 | 0.974 | 0.974 | |
| Family Friends in 1990 | 0.809 | 0.903 | |
| Relatives in 1990 | 1.245* | 1.157 | |
| Household Male Migrants | 1.359*** | 1.189*** | |
| Household Female Migrants | 1.193* | 1.429*** | |
| Community Male Migrants (%) | 1.006*** | 1.005* | |
| Community Female Migrants (%) | 0.985** | 1.004 | |
| Constant | 0.007*** | 0.001*** | |
| Number of Cases | 30,916 | 40,317 | |
| Chi-Squared | 642.7 | 609.8 | |
Notes: Reference categories are 1990, ages 15–20, 0–8 years education, and Tirana.
p < .10,
p < .05;
p < .01;
p < .001
We focus on the first instance of international migration and make no effort to distinguish migration determinants by the duration of the migration episode. Nonetheless, part of the difference between the overall male and female migration odds is due to differences in the odds of permanent and temporary migration for men and women.7 The gender migration gap is larger for temporary migration (which is primarily driven by labor motives) than for permanent migration (driven by a combination of labor, marriage, and other factors). When Model 1 from Table 2 is replicated but only permanent migration is considered (not shown), the female odds are 56% smaller than the male odds of migration (p = .000). In contrast, when only temporary migration is included, which is primarily for labor motives and to Greece, the female odds of migration are 84% smaller than the male odds (p = .000). The remaining analysis focuses on the factors that affect “first migration” irrespective of the type.
The temporal dynamics of out-migration from Albania are apparent from the coefficients on the year dummy variables, which are consistent with Figure 1, and show both male and female migration peaking toward the end of the 1990s, with two distinct stages of migration. A more formal statistical test of the gap between male and female migration patterns relies on reestimating the pooled model while interacting the gender and year dummy variables (see Table 2, Model 2). Although none of the individual interactions are significant, their magnitudes suggest that relative female migration odds peaked between 1997 and 2002. A joint test of the gender and year interactions from Model 2 is significant (χ2(14) = 24.6; p = .039), indicating significant temporal differences; however, this same test loses significance in the full specification of Model 5 (χ2(14) = 23.5; p = .053). Furthermore, there is no indication of an overall strengthening in the odds of female migration relative to male migration over time. Instead, the relative hazards appear to rise following the legalization and the economic collapse and then eventually subside. The legalization increased female tied migration as women moved to join male family members that were legalized and offers no support for claiming an increase in independent female migration.
The baseline results in Model 1 (Table 2), with Tirana as the reference category, highlight the regional disparities in international migration, with important gender differences. Separate male and female models in Table 3 indicate large and significant increases in the hazards of male migration for the Coastal and urban Central zones relative to Tirana, whereas the strongest (most negative) effects on migration for women are seen in Central rural and Mountain regions. According to Model 2 (Table 2), the weaker effects of other regions relative to Tirana for women relative to men is demonstrated by several substantively large and significant interactions between gender and the regional variables. These interactions, which are also highly significant when tested jointly (χ2(6) = 111.2; p = .000), emphasize how differential gender empowerment is shaped by variations in economic conditions and cultural norms across Albania.
The age pattern of migration for both sexes show a similar inverted-U relationship, peaking at ages 20–25 (odds ratios of 67% and 131% increases relative to ages 15–20, respectively, in Table 3). For women relative to men, the peak is higher at ages 20–25, and the subsequent drop in migration risk is lower. After introducing the interaction term between gender and age in Model 2 (Table 2), the odds of a woman versus a man migrating increases with age, and these results are jointly significant (χ2(6) = 20.9; p = .002). The increased migration of older women, particularly those ages 40 and older, is consistent with the “orphaned granny” hypothesis suggesting grandparents migrate to provide childcare for the children of their own migrant children (King and Vullnetari 2006). Similar patterns have also been identified among older Mexican migrants (Kanaiaupuni 2000).8
After migration begins, it creates its own dynamic influence by altering the incentives and constraints for future potential migrants. The importance of cumulative causation leads us to incorporate migration networks into our models, something that is particularly useful in the Albanian case because networks were practically nonexistent in 1990. Nonetheless, perhaps family and friends who emigrated from Albania prior to the closing of the borders under Hoxha, or the very limited numbers of people who successfully eluded border controls, may conceivably provide additional migration network capital (Vullnetari 2007). Fortunately, our models include controls for the effects of both time-fixed measures of pre-1990 networks as well as post-1990 time-varying, lagged measures of family and community networks. We briefly explore these results across our models.
The results in Model 1 of Table 2 show that pre-1990 migration networks based on family friends abroad have no impact, but those based on relatives abroad have a positive and significant effect on current migration.9 The time-varying family migration network measures are highly influential with both male and female family network variables strongly associated with increasing migration. Furthermore, both male and female migration are more strongly affected by the availability of family networks of the same gender (see Table 3). Additional support for the gendered nature of networks comes from the interaction coefficients in Model 5 (Table 2), which are jointly significant (χ2(2) = 8.6; p = .014). Although family network effects are gendered, the results are less clear with respect to community networks. Male community networks strongly increase male migration, while female community networks reduce male migration (Table 3), and the difference in how male and female community networks affect male versus female migration in Model 5 (Table 2) is also significant (χ2(2) = 6.1; p = .014). Overall, these findings support previous work showing the gendered nature of migration networks (Curran and Rivero-Fuentes 2003; Davis and Winters 2001). Further study is needed to understand why female community networks are negatively associated with male migration (see Table 3). One explanation is that the community networks, more than family network measures, are themselves capturing a host of unobservable characteristics and thus need to be carefully interpreted.
Hypotheses 1 and 2: Gender and education. Our first two hypotheses explore differences in how education is associated with migration by gender and over time. According to the baseline model (Model 1) of Table 2, individuals with at least some high school education have migration odds that are 14% higher than those with no high school education, but those at the top level of education (at least some university) exhibit the lowest migration odds: 21% lower than those with no high school. Thus, migrants appear primarily from the low or middle education levels. Our first hypothesis can be evaluated by using both the single-sex models (Table 3) and the pooled model (Model 3 of Table 2). Male migration is lowest for those in the highest education category, and female migration is highest for those in the middle education category. From Model 3, the interaction coefficients indicate that the effect of education on female migration relative to male migration is stronger by 65% for those in the middle education level and by 123% for those in the top education category, relative to the male-female differences at the lowest level of schooling. A joint test of both gender-education interactions indicates that education-migration association differs for men and women (χ2(2) = 33.3; p = .000).
These findings support our prediction that increasing inequality and marginalization of women in the public sphere in Albania has generated particularly strong incentives for educational selectivity for female migration relative to male migration. This stronger degree of selection on education for female relative to male migration is consistent with earlier findings from Mexico (Feliciano 2008; Kanaiaupuni 2000). A series of separate analyses also tested whether this same selectivity depends on the migration destination (not shown) and indicates that higher levels of education are associated with greater odds of migration beyond Greece and Italy, particularly for women.10
Our second hypothesis focuses on temporal change in the effect of education and particularly on how it varies by gender. Predicting the trend in educational selectivity by gender is not trivial because patterns of inequality continue to evolve both within and outside the household. However, the evidence suggests that increasing levels of inequality in the public sphere, primarily through market and educational mechanisms, have likely increased differences in the male and female educational selectivity with respect to migration. In Table 4, we test for both change in the effect of education by epoch for each sex as well as for both sexes combined. The model for both sexes shows that the effect of education is weakened in the second epoch by roughly 26% and 36% when comparing the middle and upper educational categories to the lower category. Thus, the results indicate a general temporal decline for both sexes combined in the role of education on migration.
Table 4.
Discrete-Time Hazard Analysis of Ever-Migration for Both Sexes Combined and for Men and Women Separately, Including an Epoch (post-1995 dummy variable) and Single-Year Interactions: 2005 Albania Living Standards Measurement Study (control variables from Table 2, Model 1 not shown)
| Variable | Epoch Interaction |
Single-Year Dummy Variable Interactions |
||||
|---|---|---|---|---|---|---|
| Both | Male | Female | Both | Male | Female | |
| Epoch | 7.861*** | 10.238*** | 14.630*** | |||
| At Least Some High School | 1.416*** | 1.413*** | 1.725** | 1.672 | 1.930 | 1.416*** |
| At Least Some College | 0.983 | 0.884 | 1.799* | 1.694 | 1.829 | 0.983 |
| Epoch × At Least Some High School | 0.742** | 0.614*** | 0.874 | |||
| Epoch × At Least Some College | 0.641** | 0.580* | 0.577* | |||
| 1991 × At Least Some High School | 0.865 | 0.743 | 2.480 | |||
| 1991 × At Least Some College | 0.805 | 0.777 | 1.255 | |||
| 1992 × At Least Some High School | 0.904 | 0.787 | 2.923 | |||
| 1992 × At Least Some College | 0.701 | 0.544 | 1.692 | |||
| 1993 × At Least Some High School | 0.664 | 0.669 | 0.966 | |||
| 1993 × At Least Some College | 0.556 | 0.541 | 0.566 | |||
| 1994 × At Least Some High School | 1.072 | 0.929 | 2.718 | |||
| 1994 × At Least Some College | 0.691 | 0.472 | 1.130 | |||
| 1995 × At Least Some High School | 0.809 | 0.591 | 2.560 | |||
| 1995 × At Least Some College | 0.264† | 0.111* | 0.484 | |||
| 1996 × At Least Some High School | 0.764 | 0.686 | 1.671 | |||
| 1996 × At Least Some College | 0.475 | 0.518 | 0.423 | |||
| 1997 × At Least Some High School | 1.083 | 0.783 | 3.379 | |||
| 1997 × At Least Some College | 0.532 | 0.414 | 0.699 | |||
| 1998 × At Least Some High School | 0.786 | 0.594 | 2.201 | |||
| 1998 × At Least Some College | 0.498 | 0.565 | 0.415 | |||
| 1999 × At Least Some High School | 0.724 | 0.547 | 2.149 | |||
| 1999 × At Least Some College | 0.548 | 0.479 | 0.726 | |||
| 2000 × At Least Some High School | 0.689 | 0.512 | 2.040 | |||
| 2000 × At Least Some College | 0.382 | 0.200† | 0.689 | |||
| 2001 × At Least Some High School | 0.470 | 0.259* | 1.870 | |||
| 2001 × At Least Some College | 0.253† | 0.167† | 0.361 | |||
| 2002 × At Least Some High School | 0.529 | 0.355† | 1.524 | |||
| 2002 × At Least Some College | 0.333 | 0.120* | 0.547 | |||
| 2003 × At Least Some High School | 0.524 | 0.391 | 1.333 | |||
| 2003 × At Least Some College | 0.295 | 0.243 | 0.328 | |||
| 2004 × At Least Some High School | 0.386† | 0.252* | 1.194 | |||
| 2004 × At Least Some College | 0.192* | 0.123* | 0.267 | |||
| Number of Cases | 71,233 | 30,916 | 40,317 | 71,233 | 30,916 | 40,317 |
| Chi-Squared | 920.6 | 664.8 | 603.7 | 971.0 | 705.7 | 641.5 |
Note: The reference category is 0–8 years education.
p < .10,
p < .05;
p < .01;
p < .001
Our hypothesis, however, is more specific and argues that the decline is stronger for men. The results of this test are shown in the single-sex models of Table 4. There is some support for our second hypothesis because the decline over time is more pronounced for men, so the education-migration gradient strengthens over time for women, relative to men. Introduction of the epoch dummy variable and its interaction with education in a male-only model produces a large and significant decline of about 40% in the impact of each of the two upper education categories on the migration hazards (see Table 4). For women, in contrast, there is no apparent change in the effect of the high school education in the post-1995 period relative to pre-1996 although the higher educational category interaction with the epoch dummy variable is significant and of similar magnitude to the male coefficient. We also test a more flexible model in which we use individual dummy variables for each year and interact them with the educational category variables (Table 4). The year-education interaction coefficients from this model are insignificant over most of the years, but the male coefficients are occasionally significant and negative, while the female coefficients are never significant and larger. The results lend credence to the hypothesis that the role of education weakened more strongly for men than women in the later years.11
Hypotheses 3 and 4: Gender, household shocks and household demographics. We next explore the elasticity of male and female migration in response to household demographic, economic, and health-related circumstances. Both household demographic factors and household-level shocks generate incentives and constraints with respect to migration. To the extent that female relative to male migration is more closely tied to household-level incentives and constraints, we expect female migration to be more dependent on both types of factors.
We test two very different dimensions of household-level constraints and incentives. The demographic dimension provides a relatively stable indicator of whether a low supply of sons may influence a household’s decision to use daughters as migrants. This relative stability, although time-varying, is due to the fact that households develop foresight regarding their eventual number of children of each sex ages 15 and older. Variation in the demographic composition of households offers a very different perspective on incentives and constraints than is obtained by looking at the influence of household-level shocks on son and daughter migration. Household shocks are, by definition, unpredictable. Thus, we view household demographic factors as a relatively static gauge of the embeddedness of female migration in household decision-making, whereas household-level shocks are a more dynamic indicator of the extent to which female migration behavior is bounded by household strategies.
Our tests of the effect of demographic constraints show that both son and daughter migration are relatively insensitive to the total number of daughters in the household with both coefficients from the single-sex models in Table 3, indicating that an additional daughter reduces the hazard odds of migration by an insignificant 2.6%. An additional son, however, reduces the hazard odds of male migration by 4.3% (p < .05), and each additional son reduces the hazard odds of female migration by a full 18.5% (p < .001). The difference in the effect of number of male siblings on male and female migration is tested by using the pooled model (Table 2, Model 4). As expected, the number of sons reduces the hazard odds of female migration by 16.7% more than male migration (p < .001), while the effect of the number of female siblings does not differ for male and female migration. Because our controls include a series of indicators for household migration capital, we can be confident that our results are not simply capturing the effect of migration networks. Furthermore, this result is robust to whether we specify the sex composition of the siblings in proportional terms with controls for sibship size or in terms of the absolute number of siblings of each sex as shown here.
The finding thus supports our third hypothesis in that the migration of daughters is far more responsive than the migration of sons to the number of sons ages 15 and older in the household, implying that daughter migration may be substituted for son migration where households, wanting migration, have no alternative. In contrast, the number of female siblings has no substantive or statistical impact on migration for either sex. Thus, despite cultural scripts that generate strong preferences for son versus daughter migration, households lacking sons may find it necessary to adapt and enable daughter migration. Eventually, such mechanisms may be instrumental in redefining normative migration behavior and facilitate future female migration, although this may not necessarily translate into greater independence for potential female migrants.
Our last hypothesis focuses on whether household-level shocks affect male and female migration outcomes differently. We estimate separate models on men and women with four household shock variables (Table 3), as well as a joint model to test differences in gender responses (Model 4 in Table 2). The results readily support our hypothesis that certain household-level shocks affect female migration more than male migration. In fact, male migration appears wholly unaffected by the shocks. This is true from the coefficients on each of the shocks in Table 3 as well as from a joint test of all four combined (χ2(4) =1.1; p = .902). For women, two types of shocks provide enough of an incentive (property loss) or a deterrent (illness) to affect migration. Illness of a household member reduces the odds of first migration for women by 24% (p < .05), and property loss increases the odds by 64% (p < .01). A loss associated with the pyramid schemes also suggests a 52% increase in the hazard odds of female migration, although this last coefficient is only marginally significant. Both the property-loss and pyramid-scheme shocks indicate that households may, in times of financial need, turn to female migration to expand their support base. The effect of illness shocks supports the argument that women’s migration behavior is more constrained by their role as homemakers and their relative lack of agency within the household.
The stark contrast between the effect of shocks on female migration versus male migration highlights the reliance of women on household decision-making. A subsequent analysis (not shown) also tested and rejected the possibility that the effect of number of sons on female migration shifted and weakened in the second epoch (p = .905). We also tested whether the shock effects changed over time, using the epoch dummy variable. None of these interactions came out significant, either, although neither property loss nor pyramid loss could be estimated because both shocks primarily occurred in the second epoch (post-1995). What we could estimate was insignificant, indicating no decline in the effect of the shocks on female migration over time. Thus, despite finding that female migration outcomes depend heavily on household constraints and incentives, we see no evidence to suggest that this reliance is weakening over time.
Competing Explanations
Our analysis has focused on a series of tests examining how the determinants of female and male migration vary. Several distinct mechanisms have been explored in turn. Focusing on the main effects—that is, putting aside the preceding tests of whether the effects of education or household incentives and constraints change over time—a picture emerges that is broadly consistent with our main hypotheses. The results provide strong support for our hypotheses regarding human capital as well as the supply of sons and household shocks. However, it is unclear whether our main hypotheses actually operate in isolation or whether their effects are overlapping. We examine this question by estimating a model where both components are introduced simultaneously (Model 5 in Table 2) in addition to the migration network variables. The lack of change in the main coefficients of interest relating to our hypotheses indicates little overlap in the alternative interpretations in terms of how they explain the male-female migration differences. Thus, these three factors are primarily complementary rather than competing hypotheses for understanding male-female differences in migration from Albania. Because studies are rarely in a position to test these explanations simultaneously, our findings offer support in that results in such cases may not be entirely misspecified by omitted variable bias.
DISCUSSION AND CONCLUSION
Albania, perhaps more than any other nation, offers a unique perspective on the entire international migration process, from a point when migration was legally forbidden until a time when migration became a central demographic and social process with more than one-half of all households reporting family members with migration experience (Carletto et al. 2006). At the same time, massive flows of international migration introduced a range of new incentives and constraints on households in general, and on women in particular. The Albanian case provides an interesting lens for gender and migration when labor markets and educational systems are relatively egalitarian in comparison to household-level power relations, which remain traditional and male-dominated. This unique setting sheds light on how these different forms of inequality affect gender and migration.
Preliminary analyses document the evolution of migration for both men and women from the opening of Albania in 1990–2004. While the probability of first migration generally increased through the 1990s and peaked toward the end of the decade for both men and women, a closer look reveals distinctive gender-based patterns of migration. Over this 15-year period is a substantial shift in female migration patterns relative to male patterns. At least until 2001, there was a progressive if not monotonic trend toward more equality in migration risks. This equality may be associated with several distinct processes including a series of exogenous events that occurred in the mid-90s, including the failed pyramid saving schemes and the first Greek migrant legalization program, and also the dynamics of family reunification after the early male migrants at the start of the 1990s settled and were able to bring families. After 2002, when these effects appear to have subsided, migration odds weakened further for females relative to males.
We posed a series of hypotheses to help understand the gender and migration connection. First, we showed distinct gender-specific gradients linking education and migration, with female migration but not male migration positively selected for education. Further analysis revealed that the importance of education declines to a greater extent over time for men. This supports our claim that increasing labor market inequality within Albania heightens differentials in the returns to migration for educated women relative to educated men.
Demonstrating the differential impact of human capital on female and male migration patterns highlights the extent to which incentives and constraints on migration differ by gender. However, these findings offer almost no insight as to whether educated women take advantage of opportunities as independent, empowered agents or whether migration behavior remains firmly anchored in family and household strategies (Hondagneu-Sotelo 1992; Stark 1991). Our last two hypotheses, which aim at understanding how household-level inequality may be associated with incentives and constraints on male and female migration, employ unique data on household-level shocks and household demography. Both sets of findings prove valuable. We find that female migrants substitute for male migrants when households lack sons, suggesting that the demographic structure of households may be pivotal in household migration behavior. The flexibility on the part of households may be encouraging, yet it also highlights the secondary role that female migration may serve for households lacking alternatives. Our results also indicate that female migration is more responsive than male migration to household-level shocks. In particular, household health shocks provide a large deterrent for female migration, and household property loss and pyramid-related shocks provide a large incentive for female migration; however, none of these shocks have any impact on male migration. Thus, although female migration remains more tied to human capital factors, it remains simultaneously more tightly bounded by household-level incentives and constraints. Further analyses of the demographic and shocks variables also provide no indication of an increase over time in independence exhibited in the migration behavior of women.
In conclusion, our study reveals a complex and dynamic picture—one that emphasizes both the distinctiveness of female migration from Albania relative to male migration as well as the continued lack of independent female migration. Female migration increased over time—and rose relative to male migration over certain intervals—but there is no evidence of a secular rise in the proportion of female migrants during this 15-year period. Moreover, as absolute levels of female migration did generally rise, there is no evidence that this resulted from reduced female discrimination. In fact, gender discrimination at the public level was likely at its lowest right when migration began and when female migration was at its lowest. The lack of female participation in migration appears tied to the high levels of inequality existing within the households themselves. Apparently, public-level gender equality appears unable to compensate for low levels of female empowerment and agency within households.
Our findings also suggest that the households themselves appear to be the main decision-making agents behind the economic calculus of increasing female migration, and there is little to suggest an emergence of female agency. The embedment of female migration from Albania in the context of household-level strategies is demonstrated by both the dependency of daughter migration on the availability of sons as well as by the reaction of daughters to health or property-loss shocks at the household level. Here, it would nice to conclude with a note of optimism—signs that women are increasingly agents of their own destiny, at least in terms of migration. However, our analyses of changes over time appear to indicate that women’s migration remains solidly entrenched in other people’s decision-making.
Acknowledgments
We thank Aziza Khazzoom, Peter Lanjouw, Sylvie Lambert, Michael Shalev, and Jackie Wahba for helpful comments.
The authors would like to acknowledge INSTAT for giving access to the data and the World Bank for funding. The views expressed are those of the authors alone and do not reflect the position of the World Bank or UNICEF. All errors are the sole responsibility of the authors.
Footnotes
Pyramid schemes typically promise investors unrealistically high returns, which are initially funded by subsequent, new investors, but which eventually cannot be met as obligations grow.
As typical in other migration analyses based on samples from the country-of-origin (McKenzie and Rapoport 2007; Winters et al. 2001), we are unable to provide information on the migration of entire families.
We replicated our main tests using 1997 as the beginning of the second epoch rather than 1996 with no substantive difference in the findings.
Practically, failure to treat the annual changes as random effects may lead us to underestimate the increase in the hazard of ever migrating. However, the fact that our hazard is not declining over time reduces the potential bias. Furthermore, when we compare the discrete-time hazard model and the model with random effects on the same sample, we find that the time coefficients are higher with inclusion of the random parameter, although we observe a similar time pattern (available upon request). Also, the similarity of the coefficients across both models further alleviates our concern regarding unobserved heterogeneity.
Our estimates based on the 2005 survey using only daughters and sons are generally consistent with estimates based on the entire sample including spouses and siblings of the head and spouse, which indicates that 74% of males and 26% of females had ever migrated. Overall migration rates from the survey are lower than those estimated from other sources (Bonifazi and Sabatino 2003; Carletto et al. 2006; King 2003), which is not surprising because survey estimates do not capture migration of entire households. This is not likely to bias our estimates of male-female differences.
A model including only gender and no other controls indicates that the hazards odds for women are 69% lower than for men (not shown). The similarities across the two models reflect the limited ability of the control variables, when their effect does not vary by gender, to explain overall gender disparities.
We define permanent migrants in our sample as adult children now living abroad and temporary migrants as those individuals that migrated internationally at some point but have now returned home.
The effect of age appears to change post-1995, but this shift differs for men and women (available upon request). Single-sex models with interactions between the epoch dummy variable and age indicate that the dominant trends are an 80% increase over time in the hazard odds for men at ages 20–25 (p = .000) and a 78% decline in the hazard odds for female migration at ages 40–45 (p < .01).
Although this latter coefficient is neither very strong nor robust, it nonetheless suggests that having some relatives who migrated is associated with higher migration after 1990. However, only the effect of relatives abroad pre-1990 is significant and is qualitatively similar for male and female migration. Furthermore, the inclusion of the pre-1990 network variables has no impact on the coefficients of our time-varying network measures. This provides some confidence that controlling for the pre-1990 migration, itself a proxy for household-level characteristics, helps ensure that the measured effects of post-1989 networks are specified accurately and that 1990–1991 captures the onset of migration from Albania.
Using only ever-migrants, we tested how gender and education affect migration to Greece and Italy versus migration to countries beyond these two major destinations. In a very simple pooled model including only dummy variables for year and gender (available upon request), the hazard odds of migrating beyond Greece and Italy is shown to be 47% greater for women than for men (p = .000). When education is included, the gender effect disappears, but having at least a high school education raises the hazard odds of migration beyond Greece and Italy by a factor of almost 2 (p = .000) and that at least some university education increases the hazard odds by a factor of 8 (p = .000). When education and gender are interacted, the effect of education on migration beyond Greece and Italy is greater by some 25%–30% for women in the middle and upper educational category relative to men in those categories; these two coefficients are jointly significant (χ2(2) = 6.8; p = .037).
We also tested a third alternative in which time was divided into three periods (1990–1995–1996–1999, and 2000–2004) based on visual inspection of the data. This three-period specification with interactions with education clearly shows that the decline in the role of education—particularly for men—occurs from 2000 and afterward and is likely associated with the surge in family reunification toward the late 1990s.
REFERENCES
- Andall J. “Women Migrant Workers in Italy”. Women’s Studies International Forum. 1992;15(1):41–48. [Google Scholar]
- Barber PG. “Agency in Philippine Women’s Labour Migration and Provisional Diaspora”. Women’s Studies International Forum. 2000;23:399–411. [Google Scholar]
- Becker B. “Mother, Sister, Daughter, Wife: The Pillars of the Traditional Albanian Patriarchal Society.”. In: Utas B, editor. Women in Islamic Societies, Social Attitudes and Historical Perspectives. London: Cuzon Press; 1983. pp. 48–64. [Google Scholar]
- Bonifazi C, Sabatino D. “Albanian Migration to Italy: What Official Data and Survey Results Can Reveal”. Journal of Ethnic and Migration Studies. 2003;29:967–95. [Google Scholar]
- Borjas GJ. “Self-selection and the Earnings of Immigrants”. American Economic Review. 1987;77:531–53. [Google Scholar]
- Borjas GJ. “Immigration and Self-selection.”. In: Abowd JM, editor. Immigration, Trade, and the Labor Market. Chicago: University of Chicago Press; 1991. pp. 29–76. [Google Scholar]
- Boyd M. “Family and Personal Networks in International Migration: Recent Developments and New Agendas”. International Migration Review. 1989;23:638–70. [PubMed] [Google Scholar]
- Calloni M. “Albanian Women After Socialism and the Balkan War.”. In: Braidotti R, Lazaroms I, Vonk E, editors. The Making of European Women’s Studies. Utrecht: Utrecht University Press; 2002. pp. 49–60. [Google Scholar]
- Carletto C, Davis B, Stampini M, Zezza A. “A Country on the Move: International Migration in Post-Communist Albania”. International Migration Review. 2006;40:767–85. [Google Scholar]
- Castles S, Miller MJ. The Age of Migration: International Population Movements in the Modern World. New York: Guilford Press; 2003. [Google Scholar]
- Cerrutti M, Massey DS. “On the Auspices of Female Migration From Mexico to the United States”. Demography. 2001;38:187–200. doi: 10.1353/dem.2001.0013. [DOI] [PubMed] [Google Scholar]
- Chiquiar D, Hanson GH. “International Migration, Self-selection, and the Distribution of Wages: Evidence From Mexico and the United States”. Journal of Political Economy. 2005;113:239–81. [Google Scholar]
- Chiswick BR. “Are Immigrants Favorably Self-selected.”. In: Brettell CB, Hollifield JF, editors. Migration Theory: Talking Across Disciplines. New York: Routledge; 2000. pp. 61–76. [Google Scholar]
- Cuka E, Papapanagos H, Natasha P, Sanfey P. “Labor Market Developments in Albania: An Analytical Overview”. Review of Development Economics. 2003;7:217–27. [Google Scholar]
- Curran SR, Rivero-Fuentes E. “Engendering Migrant Networks: The Case of Mexican Migration”. Demography. 2003;40:289–307. doi: 10.1353/dem.2003.0011. [DOI] [PubMed] [Google Scholar]
- Curran SR, Saguy AC. “Migration and Cultural Change: A Role for Gender and Social Networks?”. Journal for International Women Studies. 2001;2:54–77. [Google Scholar]
- Davis B, Winters P. “Gender, Networks and Mexico-US Migration”. The Journal of Development Studies. 2001;38(2):1–26. [Google Scholar]
- Donato KM. “Current Trends and Patterns of Female Migration: Evidence From Mexico”. International Migration Review. 1993;27:748–71. [PubMed] [Google Scholar]
- Donato KM. “U.S. Policy and Mexican Migration to the United States, 1942–92”. Social Science Quarterly. 1994;75:705–29. [Google Scholar]
- Donato KM. “A Dynamic View of Mexican Migration to the United States”. Gender Issues. 1999;17:52–75. [Google Scholar]
- Durand J, Massey DS. “Mexican Migration to the United States: A Critical Review”. Latin American Research Review. 1992;27(2):3–42. [Google Scholar]
- Einhorn B. Cinderella Goes to Market: Citizenship, Gender and Women’s Movements in East Central Europe. New York: Verso; 1993. [Google Scholar]
- Entwisle B, Faust K, Rindfuss RR, Kaneda T. “Networks and Contexts: Variation in the Structure of Social Ties”. American Journal of Sociology. 2007;112:1495–533. [Google Scholar]
- Falkingham J, Gjonca A. “Fertility Transition in Communist Albania, 1950–90”. Population Studies. 2001;55:309–18. doi: 10.1080/00324720127699. [DOI] [PubMed] [Google Scholar]
- Feliciano C. “Education Selectivity in U.S. Immigration: How Do Immigrants Compare to Those Left Behind”. Demography. 2005;42:131–52. doi: 10.1353/dem.2005.0001. [DOI] [PubMed] [Google Scholar]
- Feliciano C. “Gendered Selectivity: U.S. Mexican Immigrants and Mexican Nonmigrants, 1960–2000”. Latin American Research Review. 2008;43:139–60. [Google Scholar]
- Fussell E, Massey DS. “The Limits to Cumulative Causation: International Migration From Mexican Urban Areas”. Demography. 2004;41:151–71. doi: 10.1353/dem.2004.0003. [DOI] [PubMed] [Google Scholar]
- Geddes A. The Politics of Migration and Immigration in Europe. London: Sage Publications; 2003. [Google Scholar]
- Guo G. “Event-History Analysis for Left-Truncated Data”. Sociological Methodology. 1993;23:217–43. [PubMed] [Google Scholar]
- Hitlin S, Elder GH. “Time, Self, and the Curiously Abstract Concept of Agency”. Sociological Theory. 2007;25:170–91. [Google Scholar]
- Hondagneu-Sotelo P. “Overcoming Patriarchal Constraints: The Reconstruction of Gender Relations Among Mexican Immigrant Women and Men”. Gender and Society. 1992;6:393–415. [Google Scholar]
- Hondagneu-Sotelo P. Gendered Transitions: Mexican Experiences of Immigration. Berkeley: University of California Press; 1994. [Google Scholar]
- INSTAT . “Gender Perspectives in Albania.” Population and Housing Census 2001. Tirana: Albania Statistics Institute; 2004. [Google Scholar]
- Jenkins SP. “Easy Ways to Estimate Discrete Time Duration Models”. Oxford Bulletin of Economics and Statistics. 1995;57:129–38. [Google Scholar]
- Kanaiaupuni SM. “Reframing the Migration Question: An Analysis of Men, Women, and Gender in Mexico”. Social Forces. 2000;78:1311–48. [Google Scholar]
- Kandel W, Massey DS. “The Culture of Mexican Migration: A Theoretical and Empirical Analysis”. Social Forces. 2002;80:981–1004. [Google Scholar]
- King R. “Across the Sea and Over the Mountains: Documenting Albanian Migration”. Scottish Geographical Journal. 2003;119:283–309. [Google Scholar]
- King R. “Albania as a Laboratory for the Study of Migration and Development”. Journal of Southern Europe and the Balkans. 2005;7:133–56. [Google Scholar]
- King R, Dalipaj M, Mai N. “Gendering Migration and Remittances: Evidence From London and Northern Albania”. Population Space and Place. 2006;12:409–34. [Google Scholar]
- King R, Vullnetari J. Development Research Centre on Migration, Globalisation and Poverty; Brighton, United Kingdom: 2003. “Migration and Development in Albania.”. Working Paper C5. [Google Scholar]
- King R, Vullnetari J. “Orphan Pensioners and Migrating Grandparents: The Impact of Mass Migration on Older People in Rural Albania”. Ageing & Society. 2006;26:783–816. [Google Scholar]
- Kofman E. “Female ‘Birds of Passage’ a Decade Later: Gender and Immigration in the European Union”. International Migration Review. 1999;33:269–99. [PubMed] [Google Scholar]
- Lawson CW, McGregor JA, Saltmarshe DK. “Surviving and Thriving: Differentiation in a Peri-Urban Community in Northern Albania”. World Development. 2000;28:1499–514. [Google Scholar]
- Lindstrom DP, Lauster N. “Local Economic Opportunity and the Competing Risks of Internal and U.S. Migration in Zacatecas, Mexico”. International Migration Review. 2001;35:1232–56. [Google Scholar]
- Loyal S, Barnes B. “‘Agency’ as a Red Herring in Social Theory”. Philosophy of the Social Sciences. 2001;31:507–24. [Google Scholar]
- Martin S. “Women, Migration, and Development”. 2007. Transatlantic Perspectives on Migration Policy Brief No 1 Institute for the Study of International Migration, Georgetown University, Washington, DC.
- Massey DS. “The Social and Economic Origins of Immigration”. Annals of the American Academy of Political and Social Science. 1990;510:60–72. [Google Scholar]
- Massey DS, Arango J, Hugo G, Kouaouci A, Pellegrino A, Taylor JE. “Theories of International Migration: A Review and Appraisal”. Population and Development Review. 1993;19:431–66. [Google Scholar]
- Massey DS, Durand J, Malone NJ. Beyond Smoke and Mirrors: Mexican Immigration in an Era of Economic Integration. New York: Russell Sage; 2002. [Google Scholar]
- Massey DS, Espinosa KE. “What’s Driving Mexico-U.S. Migration? A Theoretical, Empirical, and Policy Analysis”. American Journal of Sociology. 1997;102:939–99. [Google Scholar]
- Massey DS, Goldring L, Durand J. “Continuities in Transnational Migration: An Analysis of Nineteen Mexican Communities”. American Journal of Sociology. 1994;99:1492–533. [Google Scholar]
- McKenzie D, Rapoport H. “Network Effects and the Dynamics of Migration and Inequality: Theory and Evidence From Mexico”. Journal of Development Economics. 2007;84:1–24. [Google Scholar]
- Meurs M, Miluka J, Hertz T. “Declining Secondary Enrollment in Albania: What Drives Household Decisions?”. 2008. American University Department of Economics Working Paper No. 2008-13. American University, Washington, DC.
- Miluka J. “Education, Migration, and Labor Markets in Albania: A Gender Perspective”. 2009. PhD thesis Department of Economics, American University, Washington, DC.
- Morokvasic M. “Birds of Passage Are Also Women . . .”. International Migration Review. 1984;18:886–907. [PubMed] [Google Scholar]
- Munshi K. “Networks in the Modern Economy: Mexican Migrants in the U.S. Labor Market”. Quarterly Journal of Economics. 2003;118:549–99. [Google Scholar]
- Myrdal G. Rich Lands and Poor. New York: Harper and Row; 1957. [Google Scholar]
- Occhipinti L. “Two Steps Back? Anti-feminism in Eastern Europe”. Anthropology Today. 1996;12:13–18. [Google Scholar]
- Oishi N. Women in Motion: Globalization, State Policies, and Labor Migration in Asia. Stan-ford, CA: Stanford University Press; 2005. [Google Scholar]
- Palloni A, Massey DS, Ceballos M, Espinosa K, Spittel M. “Social Capital and International Migration: A Test Using Information on Family Networks”. American Journal of Sociology. 2001;106:1262–98. [Google Scholar]
- Parrado EA, Flippen CA. “Migration and Gender Among Mexican Women”. American Sociological Review. 2005;70:606–32. [Google Scholar]
- Pedraza S. “Women and Migration: The Social Consequences of Gender”. Annual Review of Sociology. 1991;17:303–25. doi: 10.1146/annurev.so.17.080191.001511. [DOI] [PubMed] [Google Scholar]
- Peteet JM. Gender in Crisis: Women and the Palestinian Resistance Movement. New York: Columbia University Press; 1991. [Google Scholar]
- Pfeiffer L, Richter S, Fletcher P, Taylor JE. “Gender in Economic Research on International Migration and Its Impacts: A Critical Review.”. In: Morrison AR, Schiff M, Sjöblom M, editors. Women in International Migration. New York: Palgrave Press; 2007. pp. 11–49. [Google Scholar]
- Rosenzweig MR, Stark O. “Consumption Smoothing, Migration, and Marriage: Evidence From Rural India”. Journal of Political Economy. 1989;97:905–26. [Google Scholar]
- Silova I, Magno C. “Gender Equity Unmasked: Democracy, Gender, and Education in Central/Southeastern Europe and the Former Soviet Union”. Comparative Education Review. 2004;48:417–42. [Google Scholar]
- Smith JP, Thomas D. “Remembrances of Things Past: Test-Retest Reliability of Retrospective Migration Histories”. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2003;166(1):23–49. [Google Scholar]
- Stark O. The Migration of Labor. Cambridge: Basil Blackwell; 1991. [Google Scholar]
- Stark O. “Inequality and Migration: A Behavioral Link”. Economics Letters. 2006;91:146–52. [Google Scholar]
- Stark O, Taylor JE. “Migration Incentives, Migration Types: The Role of Relative Deprivation”. Economic Journal. 1991;101:1163–78. [Google Scholar]
- Taylor JE. “Differential Migration, Networks, Information and Risk.”. In: Stark O, editor. Migration, Human Capital and Development. Greenwich: JAI Press; 1986. pp. 147–71. [Google Scholar]
- Thomas J. Education for Communism: School and State in the People’s Republic of Albania. Stanford, CA: Hoover Institution Press; 1969. [Google Scholar]
- Todaro MP. “Model of Labor Migration and Urban Unemployment in Less Developed Countries”. American Economic Review. 1969;59:138–48. [Google Scholar]
- Todaro MP. Internal Migration in Developing Countries. Geneva: International Labor Organisation; 1976. [Google Scholar]
- Tyner JA. “The Global Context of Gendered Labor Migration From the Philippines to the United States”. American Behavioral Scientist. 1999;42:671–89. [Google Scholar]
- United Nations Development Programme (UNDP) National Human Development Report. UNDP; Albania: 2005. “Pro-Poor and Pro-Women Policies and Development in Albania: Approaches to Operationalising the MDGs in Albania.”. [Google Scholar]
- United Nations . 2004 World Survey on the Role of Women in Development: Women and International Migration. New York: United Nations Publishing Section; 2006. [Google Scholar]
- Vullnetari J. International Migration, Integration and Social Cohesion; Amsterdam: 2007. “Albanian Migration and Development: State of the Art Review”. IMISCOE Working Paper No. 18. [Google Scholar]
- Winters P, de Janvry A, Sadoulet E. “Family and Community Networks in Mexico-U.S. Migration”. Journal of Human Resources. 2001;36:159–84. [Google Scholar]
- World Bank . Poverty Reduction and Economic Management Unit, Europe and Central Asia Region; Washington, DC: 2007. “Albania: Urban Growth, Migration and Poverty Reduction—A Poverty Assessment.”. Report No. 40071-AL. [Google Scholar]
- Zlotnik H. “Trends of International Migration Since 1965: What Existing Data Reveal”. International Migration. 1999;37:21–61. doi: 10.1111/1468-2435.00065. [DOI] [PubMed] [Google Scholar]