Abstract

Comparative research on international migration has increasingly focused on immigrant integration rather than the process of emigration. By investigating the different streams of Chinese migration to the United States and Europe, as well as the different stages of Chinese migration to the United States, this study examines the way in which both receiving and sending contexts combine to shape the process of emigration. Using data from a 2002–2003 survey of emigration from China's Fujian Province, we demonstrate that under restrictive exit and entry policies and high barriers to migration (i.e., clandestine migration from Fuzhou to the United States), resources such as migrant social capital, political capital (cadre resources), and human capital all play a crucial role in the emigration process. However, the roles of these resources in the migration process are limited when migration barriers are sufficiently low and when local governments adopt proactive policies promoting emigration (i.e., legal migration from Mingxi to Europe). Comparisons over time suggest that the importance of migrant social capital, political capital, and human capital has strongly persisted for Fuzhou-US emigration, as a result of tightening exit and entry policies. Despite these marked differences between Fuzhou and Mingxi emigration, the results also point to two general processes that are highly consistent across settings and over time—the cumulative causation of migration and the advantage conferred by traditional positional power (cadre status).

Introduction

The past three decades of China's economic reform and opening have led to large-scale international migration. This wave of Chinese migration is marked not only by scale, but also by the diversifying source regions and destinations that comprise different contexts of exit and reception (Liang 2001; Cheng 2007). By investigating different streams of Chinese migration to the United States and to Europe, as well as the different stages of migration to the United States, the present study examines how the receiving context combines with the sending context to shape the process of emigration across streams and over time.

The main research question is examined by investigating the roles of three kinds of resources relevant to the migration process: migrant social capital, political capital, and human capital. We first assess the ways in which kinship migrant networks and village migrant networks moderate the costs and risks of emigration across contexts and over time. Second, we examine how political capital, as accumulated through cadre status, interacts with context to shape the formal and informal conduct of emigration. Of particular interest is the context characterized by different and changing local state policies in sending areas. Third, we investigate the extent to which the availability or restrictedness of migration opportunities conditions the role of human capital characteristics in emigration. In assessing these three questions, we conduct both cross-setting (cross-stream) comparisons and over-time comparisons. Previous research conducted in the setting of Mexico-US migration suggests that migrant streams tend to progressively broaden to include less selective individuals over time (Massey, Goldring, and Durand 1994). We offer an alternative scenario, in which the specific contexts render emigration continuously reliant on various social resources over time.

The present study joins a growing body of comparative research on international migration (Massey 1999; Model, Fisher, and Silberman 1999; Portes 1999; Van Tubergen, Maas, and Flap 2004; Foner 2005; Alba and Waters 2011; Donato et al. 2010), which has focused largely on immigrant integration rather than the process of emigration. This study provides the first effort to compare different flows of Chinese international migration as ingrained in disparate contexts of exit and reception. To the extent that certain factors play out similarly across contexts and over time, comparative analyses facilitate an understanding of fundamental processes underpinning migration that have broader theoretical and practical significance. Comparative analyses also allow for identification of differences, and therefore help us identify circumstances under which migration processes operate differently. In general, the adoption of a comparative framework helps migration scholars test innovative hypotheses on the migration and integration processes that link the socio-political contexts of migrant-sending and migrant-receiving societies.

This study uses systematic data collected in 2002–2003 from two regions in Fujian Province. Fujian Province is chosen because it has become one of the most prominent migrant-sending regions in China. The province also sends divergent flows of emigrants with respect to backgrounds, destinations, and local structures: one stream is characterized by long-distance illegal immigration to the United States, and the other by shorter-distance legal immigration to Europe. The data-collection design adopted the ethnosurvey approach used in the Mexican Migration Project (MMP), and was implemented similarly in the two sending regions in Fujian. This enables useful comparisons between Chinese immigration flows and with non-Chinese immigration. We employ event history analysis with time-varying covariates to capture the dynamic migration process.

Contexts of Chinese Migration to the United States and to Europe

We first introduce the two streams of Chinese emigration and the respective destination and origin contexts. The history of Chinese immigration dates back over a century, to the gold rush in the United States (Poston and Luo 2007) and to European colonial expansion (Alexander 1973). These immigrants came predominately from the Guangdong and Zhejiang provinces. But the level of emigration from China dropped to extremely (and artificially) low levels following the 1949 Revolution and throughout China's collective period. Since the onset of economic reforms in the late 1970s, Chinese emigration increased again, this time to an unprecedented level. Fujian Province, located in the southeastern coastal region of China (figure 1), has become one of the largest immigrant-sending provinces, accounting for nearly 18 percent of Chinese international migrants by the 2000s (Liang 2001).

Figure 1.

Map of China (and Fujian province) in relation to Europe and the US

Figure 1.

Map of China (and Fujian province) in relation to Europe and the US

Today's Fujianese emigrants originate from different sending areas and settle in different destinations. The largest (and best-known) group of emigrants comes from coastal Fujian to the United States, often through clandestine means. By the mid-1990s, roughly 200,000 illegal immigrants from Fujian had come to the United States (Kyle and Koslowski 2001). And as of 2007, more than 30,000 illegal Fujianese continued to arrive every year (Blatt 2007). A more recent and fast-growing group of Fujianese migrants originates from the interior of the province. Since the late 1980s, this group of emigrants has settled in Europe, especially Italy, Hungary, and Russia. By the early 2000s, the total estimated size of this group was over 200,000 (Laczko 2003). The large-scale Fujianese migration has drawn worldwide attention and scholarly interest. Most existing work focuses on migration to the United States (Kwong 1997; Chin 1999; Liang et al. 2008; Zhou 2009), although Pieke et al. (2004) and Li (2005) have studied the socioeconomic well-being of Fujianese in Europe. However, there has been no empirical study of the process of migration to Europe, nor any systematic comparison of these two streams of Chinese migration.

Geo-Political Contexts in Transit and Receiving Countries

What are the macro-level factors that shape these different streams of migration? We next highlight contrasts between Europe and the United States as destinations, as well as between the Fuzhou and Mingxi areas as sending regions. This discussion is summarized in appendix A (see the supplementary material online).

With respect to geopolitical context, the United States is geographically distant from China. The trip for Chinese clandestine emigrants often involves highly circuitous routes and irregular means, via transit regions in Southeast Asia, Latin America, and the Caribbean. Furthermore, clandestine migration from China to the United States requires highly professional organization via human smuggling networks. These networks have adopted several strategies. The most difficult is relying on forged documents to land directly in the United States. A more common strategy involves traveling to transit countries across the globe with legitimate visas, and then moving on to the United States by sea, by air, or overland (Chin 1999). Either way, such complex organization entails substantial migration expenses, which have increased steadily from $18,000 during the 1980s to $35,000 during the 1990s, to as high as $70,000 today (Keefe 2009). The rising costs are partly a result of tightening border controls, which have resulted in lower permeability of the US border.

Another important contextual factor is US immigration policy, which has prioritized family reunification, but set a national origin quota for other types of immigrants. Furthermore, the granting of amnesty is relatively rare, and has been offered only occasionally to specific groups since 1986. Despite these difficulties, a large number of Chinese have entered illegally, particularly since the 1980s. Some suggest that specific US asylum policies targeting Chinese nationals (i.e., political persecution, one-child policy) have actually encouraged illegal immigration (Chin 1999).1

In contrast to the US case, Fujianese going to Europe tend to emigrate legally. This glaring difference stems partly from the different geopolitical environment of Europe. China neighbors the former Soviet bloc and is relatively close to European countries (figure 1). This affects Chinese migration options in three distinct ways. First, geographic proximity means that the journey to Europe is often overland, or a short one by air. Second, the disintegration of the Soviet bloc and the fall of the Berlin Wall opened an easy entryway for migrants to cross over to many parts of Europe (Pieke et al. 2004). Third, the lack of natural barriers in Europe and the integration of the European Union effectively increased the permeability of the border and hence the mobility of immigrants (Naim 2005).

Equally important are the immigration policies across Europe (Pieke et al. 2004). For example, southern European countries (e.g., Italy) have systematically used immigrant regularization (i.e., regular amnesty and legalization) as a recruitment system. For this reason, Italy has become a main destination for Fujianese. The political transition in Hungary also opened doors for Chinese migrants. Between 1988 and 1992, a visa exemption agreement existed between China and Hungary, which meant that Chinese could freely enter Hungary and obtain legal residence in two to three years. In sum, the diverse immigration policies of European countries provide many Chinese with a convenient entryway into a large array of opportunities in Europe (Li 2005).

These geopolitical features also lead to unique patterns of Chinese migration to Europe (Li 2005). While most migrants arrive legally, some overstay their visas and become illegal aliens. Also, rather than being a onetime move, European migration is often an evolving process, with many migrants moving again (and sometimes clandestinely) in search of better opportunities across Europe. Although both the United States and Europe have embraced increasingly stringent measures to regulate immigration, the greater accessibility and flexibility associated with Chinese migration to Europe has resulted in much lower expenses and stakes compared to US-bound migration.

Local Policies and Environment in Sending Areas

Fujian Province's major sending areas, Fuzhou and Mingxi, bear important similarities and distinctions. In terms of similarities, emigration has been increasingly ingrained in local social and economic life in both regions, and has become a status symbol and a strategy for family advancement (Pieke et al. 2004; Liang et al. 2008). With hundreds of millions of dollars in remittances each year, emigration increases prosperity in sending communities.

Nevertheless, important differences also shape and are reshaped by migrant flows. Fuzhou is located in the coastal area of Fujian Province (figure 2). With respect to socioeconomic development, it is one of the most developed areas within the province (appendix B online). Fuzhou has generated the largest flow of US-bound migration from China and also has dominated overall international migration from Fujian Province (over 75 percent) (Liang 2001). At least half of Fuzhou emigrants are undocumented (Chin 1999). Within the local areas, there is an entire industry of emigration, from recruiting smugglers and shady visa services to pre-emigration English lessons and vocational training.

Figure 2.

Location of Fuzhou and Mingxi in Fujian

Figure 2.

Location of Fuzhou and Mingxi in Fujian

Based on its recent history, Fuzhou emigration to the United States can be categorized into three stages. The first stage began in the late 1970s and lasted till the mid-1980s, during which time China embraced an open-door policy that ended restrictions imposed on international migration. As a result, some Fujianese began to use their overseas family ties to seek better life circumstances (Lin 2009). During this first phase, Fuzhou migration to the United States was mostly legal, and linked to existing connections, although clandestine migration started to emerge. Even though overall costs of emigration were low, the process was difficult for ordinary villagers, and the rate of emigration remained low.

From the mid-1980s to the mid-1990s, Fuzhou migration entered a qualitatively and quantitatively different second stage, one that reflected a political environment that had been absent during the early stage. Enticed by the great fortune of earlier emigrants, co-villagers, many of whom had no foreign connections, were strongly motivated to undertake the adventure. As a result, irregular migration to the United States accelerated and culminated in the mid-1990s (Chin 1999). The cost of emigration also grew rapidly. This increase in irregular migration did not receive much international attention until the 1990s (in the wake of the Golden Venture tragedy in 1993—a ship bearing 286 illegal Fujianese ran aground in New York, leaving ten dead). Nor did government authorities in Fuzhou pay much attention. Because emigration fueled local economies, government sanctions remained relatively lenient even as illegal emigration was on the rise. Some local authorities tacitly permitted such movements; some were even peripherally involved in the highly profitable smuggling enterprises (Chin 1999).

The third stage of Fuzhou emigration to the United States began in the mid-1990s, when the Chinese government stepped up measures against human smuggling (Chin 2003). In 1997, the National People's Congress amended the Criminal Law by attaching articles detailing harsh penalties for human smuggling. Accordingly, Fujian authorities launched anti-smuggling campaigns to prosecute smugglers; they also imposed huge fines for individuals who were caught in illegal border crossings or deported back from the United States. The provincial government has made many efforts with regard to border patrol, and has periodically suspended the issuance of passports (although from time to time some of these provincial policies are stymied by pragmatic local officials for local economic interests). Illegal emigration has thus become increasingly difficult. However, this has done little to dampen the desire for migration, and the unintended consequences of these restrictive measures include increased demand for smuggling services and rising smuggling fees.

Mingxi county, which is located in the mountainous northwest of Fujian (figure 2), has sent the largest number of migrants from Fujian to Europe. Mingxi relies heavily on agriculture and has experienced lower socioeconomic development relative to the coastal Fuzhou area. Emigration from Mingxi emerged virtually from scratch since the late 1980s. By the early 2000s, about 10 percent of the total population in Mingxi had emigrated (Li 2005). These migrants spread across twenty-three countries, but the majority (over 70 percent) settled in Italy, Hungary, and Russia.

The large volume of emigration has had a major impact on the local economy. Annual remittances totaled over $10 million in 2002 (Pieke et al. 2004). Given the legality of emigration and the heavy reliance of local development on remittances, the local political context is favorable to emigration. The local government has embraced a much more proactive and open approach toward emigration than in Fuzhou (Pieke et al. 2004; Li 2005). Local authorities have set labor export as a top economic priority and have stipulated that all officials in emigration services must provide full support for prospective migrants by simplifying procedures and improving the quality and efficiency of their services (Li 2005).

Local governments have also mounted concerted and structured efforts to assist emigrants. Across Mingxi, federations of overseas Chinese have been established, providing general information and assistance in areas such as passage and employment. Official emigration service centers have been built to help peasants apply for visas and purchase air tickets, as well as to provide consultation about job opportunities. Handbooks on European language and culture are available to aspiring migrants. Local authorities also set up free language and vocational training courses to teach aspiring migrants skills such as sewing, cooking, computers, and trading. Additionally, local governments have relaxed policies under which commercial banks and village credit cooperatives may provide loans to prospective migrants. In Mingxi, emigrants have also been given political recognition and are publicly regarded as model members of the community. This wide range of local institutional support substantially reduces the barriers to emigration and changes the cost-benefit calculation for aspiring migrants.

Three Types of Capital and International Migration Processes

Previous research has examined the roles of social, political, and human capital in shaping international migration, and in some cases, illustrated how macro contexts condition the roles of that capital.

Social Capital

A number of studies of the migration process have focused on the role of social capital, as formulated in social capital and cumulative causation theories (Massey 1990). The basic logic is that because migration is often a costly and risky enterprise, prospective migrants draw on interpersonal networks for information, resources, and assistance. These social resources from family and friends channel later immigrants to similar destinations, as well as facilitate their arrival and settlement (social capital theory). At the macro level, this process sets off structural and cultural changes in the origin communities in ways that expose people to greater diffusion of migration norms, lower barriers to migration, and the emergence of a culture of migration. The result is the perpetuation of migration (cumulative causation theory).

Previous research has also explored how the role of social capital and cumulative patterns varies depending on context. Fussell and Massey (2004) demonstrate different network effects in rural and urban origins in Mexico, with migrant networks operating less effectively in urban areas where social networks are more complex but less pervasive. Curran and Rivero-Fuentes (2003) contend that migrant networks are more important for international moves than for internal moves within Mexico, presumably because US migration entails greater barriers and thus requires more social resources. In addition, several studies have laid out the respective roles of different types of social networks, namely kinship ties and village networks. Family ties tend to provide more reliable information and greater assistance to migrants (Massey et al. 1987). Village migration networks, on the other hand, provide greater information and normative pressure (Winters, de Janvry, and Sadoulet 2001).

In the context of China-US migration, Liang et al. (2008) show that the role of migration networks is not uniformly positive. Migrant social capital acts not only to provide information about the destination and the passage, but more importantly to provide financial resources to cover the high costs incurred in clandestine migration. The high expenses involved in US migration imply that families are often unable to immediately provide for additional migrants until the debt for the previous migrant has been paid off. Therefore, having US migrants in the family tends to first decrease and then gradually increase the chances of further migration from China.

Political Capital

The role of political capital in immigration has been studied less extensively than that of social capital. Existing research has focused largely on transnational political networks, the implications of those networks for domestic and global politics, and the ways in which sending states implement policies to strengthen emigrants' homeland participation (Levitt, DeWind, and Vertovec 2003). Less is understood about the way that political capital interacts with local policies and interests to shape the emigration process. China is a suitable setting for addressing this inquiry, since political capital carries important resources and positional power in social and economic life. Despite some speculation that the economic transformation in China would lead to a decline of political power (Nee 1989), many studies demonstrate the persistent advantage of traditional distributive power (Parish and Michelson 1996; Zhou 2000; Walder 2002).

Liang et al. (2008) examine Fujianese emigration to the United States as it relates to one's political position in the sending community, measured by individual and household cadre status. Their study finds that cadres have much less incentive to emigrate, presumably because they often derive considerable socioeconomic advantage in local communities. Nevertheless, other individuals in cadre households seem more likely to emigrate, a finding that highlights the role of political capital in circumventing state policies and in reducing the costs of unauthorized migration. In China, cadres make many important decisions and control important resources in rural villages within their jurisdiction (O'Brien and Li 1999). Therefore, when it comes to clandestine emigration such as that from Fuzhou to the United States, cadres are able to facilitate family members' migration. They do so by providing crucial documents (i.e., birth certificate, passport); by offering firsthand information about migration opportunities and policies that would otherwise be difficult and costly to obtain; by offering favors to smugglers in exchange for reduced costs; and by ensuring the cooperation of local law-enforcement authorities (i.e., coast guards, border patrol agents).

Human Capital

Some studies have examined the effect of personal characteristics such as human capital on likelihood of international migration as they are associated with the potential benefits of migration (e.g., expected wages). The findings are not clear cut because understanding the effect of human capital requires knowing the extent to which human capital is rewarded at places of origin and destination. For example, in Mexico, education is unrelated or negatively related to international migration, because human capital acquired in Mexico is not well remunerated in the United States (Borjas 1987; Massey and Espinosa 1997). By contrast, the effect of education turns positive in the context of Mexican internal migration (Bohra and Massey 2011). Adding to the complexity is the fact that many studies resort to a linear measure of education, which could conceal the potential nonlinear relationship between human capital and migration. For example, human capital may increase the likelihood of migration up to a certain threshold (neoclassical model). But after a certain level, human capital may be negatively related to migration as the economic return of additional schooling diminishes in the destination but remains high in the labor market at origin (new economics model).

Patterns of Immigrant Selectivity

Although migration often begins with selective socio-demographic groups in origin communities, the characteristics of migrants progressively broaden as migration becomes more ingrained in local life and as barriers for new migrants decrease (Massey, Goldring, and Durand 1994). Over time, therefore, personal characteristics such as human capital become relatively less important in determining migration behavior. This pattern, first observed in Mexico, was recently tested in the context of internal migration from rural Thailand (Garip and Curran 2010). The study finds that for individuals with less access to migratory social capital, their own characteristics remain important in the migration process. Previous studies, however, rarely examined over-time patterns in the role of resources other than human capital that underpin migration.

How Do Contexts Shape the Process of Chinese International Migration?

Our comparison of Fujianese migration to the United States and Europe suggests that distinct contexts of reception, combined with distinct local government practices in sending areas, produce different barriers and different cost-benefit analyses regarding emigration. The result is disparate processes of emigration. When barriers (costs and stakes) to migration are high, particularly when the receiving and local sending governments enact restrictive migration policies, resources embedded in migrant social capital and political capital can be crucial in overcoming these barriers and circumventing legal restrictions on exit (emigration from Fuzhou). Also, socio-demographic characteristics such as human capital play a salient role in migration-related cost-benefit analysis. In particular, when facing difficult migration circumstances, households may strategize to send individuals who are expected to garner higher economic returns to offset the high expenses and risks. By contrast, when immigration policy conditions are relatively lax, and when the local sending government proactively endorses emigration, migration opportunities are widely available (i.e., emigration from Mingxi). Under these circumstances, the roles of social and personal resources in facilitating migration are likely to be less pivotal to overcoming barriers. Thus, our main hypothesis is: US migration from Fuzhou is more heavily reliant on social and political resources and more selective of human capital than European migration from Mingxi (hypothesis 1).

Specifically, with respect to social capital, we expect family migration networks to matter more for US-bound migrants than for Europe-bound migrants (hypothesis 1.1). This is because family migration networks not only feed information about the process of irregular migration, but also offer tangible economic assistance for the trip. In a similar vein, we expect political capital (cadre resources) to have more importance for Fuzhou emigration to the United States than for Mingxi emigration to Europe (hypothesis 1.2). In Fuzhou, where the act of emigration conflicts with formal state institutions, political capital forms a pool of resources for circumventing state policies, as well as for reducing the costs and risks of unauthorized migration. Ordinary people lacking such resources would be much less capable of evading regulations and excessive expenses. Cadres' financial resources can also help facilitate irregular as well as legal emigration from Fuzhou, both of which tend to incur high costs. This is likely, given that local cadres are capable of accumulating more wealth than ordinary villagers. However, in Mingxi, where local governments openly promote and enable emigration, the privileges and resources conferred by access to political capital or financial resources accumulated through it are much less important for successful migration.

As for human capital, we expect that US migration from Fuzhou is much more selective on human capital than European migration from Mingxi (hypothesis 1.3). One explanation may be that under difficult migration circumstances (i.e., Fuzhou-US migration), the decision to migrate often involves a highly deliberate cost-benefit calculation. Households strategize to send individuals who are expected to garner higher economic returns in order to offset the high expenses and risks of migration. Such calculations are usually based on human capital considerations. It is also possible that, to the extent that migration is positively selected on human capital (e.g., innate ability, skills), such selectivity is more pronounced when the barriers to migration are high. By contrast, in situations where the costs are low and migration opportunities are readily available, especially when local authorities proactively promote emigration (i.e., Mingxi-Europe migration), selection based on human capital is likely to be low.

We further assess how the changing contexts underlying Fuzhou-US migration may shape its process over time. Whereas previous research suggests that migrant streams tend to progressively broaden to include less selective individuals, the case of Fuzhou emigration provides an unusual case study. There, barriers to migration remain high, especially as measures against illegal emigration become more stringent over time. Hence, we anticipate that rather than observing the diminishing importance of various resources, Fuzhou migration to the United States continues to rely heavily on migrant social capital and political capital, and continues to be selective on human capital over time (hypothesis 2).

Overall, these cross-context and over-time patterns generate variations in the aggregated migration streams. These variations can help explain the rapid expansion of immigration to Europe in a relatively short period of time (as compared to the stabilizing stream of Fuzhou-US migration).

Data, Measures, and Methods

The data used for this study were collected in identical ways in two regions in Fujian Province, the coastal Fuzhou prefecture and the inland Mingxi county. Probability samples were drawn, and standardized questionnaires were used to facilitate comparisons across settings. The study largely followed the ethnosurvey design for the MMP. A detailed description of the data is provided in appendix C (see the supplementary material online). In both surveys, the questionnaires for the MMP were used as models but were modified to fit into the Chinese context. The household questionnaire contains basic information on the socio-demographic characteristics of each household member (including those who went abroad), the internal and international migration history for all household members (including those who went abroad, such as the year and month of emigration, destination of the trip, costs for that trip, and type of departure documents used), and the household socioeconomic circumstances. Specific to the Chinese setting, questions on cadre status were also included. Because undocumented migration is a sensitive topic in Fujian, to improve response rates, detailed questions on the actual migration trip or costs were not asked. Instead, the questionnaire included a set of categories indicating the approximate expenses incurred in emigration.

The main models estimated the probability of making the first emigration trip using event history analysis. We study the first trip, because a very small fraction of the sample made multiple international trips. We restricted the analysis to individuals who are age fifteen or older, because emigration of younger people tended to be tied with parents' migration. For each individual in each year since age fifteen, we recorded migration events along with individual, household, and community characteristics in that year. This was accomplished by converting the data into person-year form and estimating discrete-time event history models via logistic regression.

Migrant social capital was measured by household and village migration networks. At the household level, we created a measure indicating in a given year whether a family member had migrated before, and if so, the number of years that had elapsed since the first emigration. We distinguished four categories: households with no prior emigrant, households with emigrants who had left within the past two years, between two and five years, and over five years. Such a measure is useful in capturing the dynamics of the effect. For village migration networks, we followed Massey, Goldring, and Durand (1994) to calculate the village emigration prevalence ratio, using each person's year of birth and the date of emigration. It was calculated for every community in every year and provided a simple indicator of how widespread migratory experience had become at any point in time in a given community. The denominator was the number of people fifteen years and over in a given year, and the numerator was the number of such people who had ever emigrated up to that year.

Other predictors included age, gender, marital status, education, cadre status at both individual and household levels, whether the household owned a family business, and place of residence. Family business was used to proxy family wealth (physical capital), following earlier research. Marital status, family business, and cadre status were designed to be time-varying predictors, with information on the timing of events such as the year and month of marriage, of starting and ending a family business, and of becoming a cadre (as discussed above, family- and village-level migrant social capital measures are also time varying). This allowed us to better ascertain the temporal order of events. As described in detail below, we conducted additional analysis to model emigration costs and temporal patterns of migration.

Descriptive Results

The results confirm that the emigration costs were much lower in Mingxi than in Fuzhou (table 1). In Mingxi, over 91 percent of all emigrants spent less than 10,000 US dollars, compared to only 24 percent of emigrants from Fuzhou. The median cost for Fuzhou emigrants was over $30,000, and 24 percent of migrants spent over $50,000. Also consistent with previous studies, different migration streams have stemmed from the two sending regions (results not shown). The vast majority of emigrants from Fuzhou went to the United States. In contrast, the vast majority of migrants from Mingxi headed for Europe, especially eastern and southern Europe. The top destinations were Italy, Hungary, and Austria, which drew over 87 percent of all migrants from Mingxi. Italy alone drew 70 percent.

Table 1.

Costs of Emigration from Fuzhou and Mingxi

US dollars Fuzhou (%) Mingxi (%) 
<10,000 24.3 91.1 
10,000–19,999 8.7 7.2 
20,000–29,999 11.3 0.9 
30,000–39,999 19.8 0.9 
40,000–49,999 11.7 0.0 
>49,999 24.3 0.0 
US dollars Fuzhou (%) Mingxi (%) 
<10,000 24.3 91.1 
10,000–19,999 8.7 7.2 
20,000–29,999 11.3 0.9 
30,000–39,999 19.8 0.9 
40,000–49,999 11.7 0.0 
>49,999 24.3 0.0 

Figure 3 depicts the trend of emigration over time. We plotted the percentage of people who left Fuzhou and Mingxi in each year since the early 1980s. We see that the US migration from Fuzhou began at the time of China's economic reforms, and accelerated between the mid-1980s and mid-1990s. Migration experienced a slight decline shortly after the 1993 Golden Venture fiasco, and growth gradually leveled off in the mid-1990s due to the tightening measures against irregular migration. The drop in the early 2000s may be due largely to the September 11 attacks, which had a deterrent effect on migration to the United States. By contrast, European emigration from Mingxi lagged the development of Fuzhou emigration and has a shorter history. It began in the late 1980s and has become increasingly salient ever since. Over time there has been a rapid growth in Mingxi emigration, which by the 2000s reached levels similar to Fuzhou emigration (despite some ups and downs in the early 2000s associated with a few incidents involving Chinese immigrants in Europe, e.g., the Dover accident in 2000).

Figure 3.

Trend (percentage) of emigration from Fuzhou to the US and from Mingxi to Europe by year

Figure 3.

Trend (percentage) of emigration from Fuzhou to the US and from Mingxi to Europe by year

The cost of migration from Fuzhou steadily escalated as barriers continued to rise and the smuggling process became more sophisticated (figure 4). We took the midpoints of the migration cost categories and converted them into the 2003 constant US dollars. The graph is generally consistent with ethnographic work suggesting that the standard smuggling fee for Fujian-US migration rose from $18,000 to over $35,000 in the 1990s, reaching as high as $70,000 nowadays. By contrast, the cost of emigration from Mingxi to Europe was much lower and rather stagnant over time.

Figure 4.

Emigration cost for Fuzhou and Mingxi emigrants by year

Figure 4.

Emigration cost for Fuzhou and Mingxi emigrants by year

Table 2 presents sample descriptive statistics for Fuzhou and Mingxi with tests of the differences between emigrants and non-emigrants. Given the longer history of emigration from Fuzhou, emigrants represented 40 percent of the sample in Fuzhou, compared to 23 percent in Mingxi. With respect to socio-demographic characteristics, in both regions emigrants showed a classic profile. They were more likely to be young, single, and male. There was also some evidence that emigrants tended to be drawn from the middle of the educational distribution. The small fraction of people with college degrees were less likely to migrate, as they may be better positioned to take advantage of local opportunities. In Fuzhou, people from households owning family businesses were less likely to emigrate. We also see that the emigrants from Fuzhou were less likely to be cadres themselves but more likely to come from cadre households. Having a prior family emigrant seemed to deter further migration. Overall, these bivariate comparisons should be interpreted with caution because differences in basic characteristics between emigrants and non-emigrants were not adjusted.

Table 2.

Characteristics (Percentages) of Emigrants and Non-emigrants in Fuzhou and Mingxi, 2003

 Fuzhou
 
Mingxi
 
 Emigrants Non-emigrants Emigrants Non-emigrants 
Age     
 15–19 53.4* 46.6 22.7* 77.3 
 20–24 72.0 28.0 43.1 56.8 
 25–29 56.2 43.8 35.2 64.8 
 30–34 45.0 55.0 36.5 63.5 
 35–39 41.3 58.7 26.5 73.5 
 40–44 34.7 65.3 17.7 82.3 
 45–49 9.9 90.1 1.0 99.0 
 50–54 4.4 95.6 1.6 98.4 
 55–59 7.8 92.2 0.0 100.0 
 60 +  3.5 96.5 0.9 99.1 
Sex     
 Male 51.2* 48.8 31.0* 69.0 
 Female 28.7 71.3 16.4 83.6 
Marital status     
 Ever married 31.4* 68.6 21.4* 78.6 
 Never married 61.7 38.3 31.8 68.2 
Education     
 No formal education 33.7* 66.3 5.4* 94.6 
 Elementary school 49.6 50.4 15.3 84.7 
 Junior high school 48.5 51.5 34.1 65.9 
 Senior high school 31.4 68.6 28.6 71.4 
 Vocational high school 26.9 73.1 15.7 84.3 
 College or above 13.5 86.5 12.5 87.5 
Own a family business 
 Yes 34.5* 65.5 22.1 77.9 
 No 40.9 59.1 24.1 75.9 
Cadre     
 Yes 9.0* 91.0 4.6* 95.4 
 No 42.4 57.8 24.9 75.1 
Cadre in household     
 Yes 49.2* 50.8 20.6 79.3 
 No 39.7 60.3 24.8 75.2 
Ever emigrant in household 
 Yes 29.4* 70.6 16.0* 84.0 
 No 73.5 26.5 31.5 68.5 
Place of origin 
 Rural 40.2 59.8 23.7 76.3 
 Urban 42.5 57.5 22.0 77.9 
N 2,657 3,975 301 995 
 Fuzhou
 
Mingxi
 
 Emigrants Non-emigrants Emigrants Non-emigrants 
Age     
 15–19 53.4* 46.6 22.7* 77.3 
 20–24 72.0 28.0 43.1 56.8 
 25–29 56.2 43.8 35.2 64.8 
 30–34 45.0 55.0 36.5 63.5 
 35–39 41.3 58.7 26.5 73.5 
 40–44 34.7 65.3 17.7 82.3 
 45–49 9.9 90.1 1.0 99.0 
 50–54 4.4 95.6 1.6 98.4 
 55–59 7.8 92.2 0.0 100.0 
 60 +  3.5 96.5 0.9 99.1 
Sex     
 Male 51.2* 48.8 31.0* 69.0 
 Female 28.7 71.3 16.4 83.6 
Marital status     
 Ever married 31.4* 68.6 21.4* 78.6 
 Never married 61.7 38.3 31.8 68.2 
Education     
 No formal education 33.7* 66.3 5.4* 94.6 
 Elementary school 49.6 50.4 15.3 84.7 
 Junior high school 48.5 51.5 34.1 65.9 
 Senior high school 31.4 68.6 28.6 71.4 
 Vocational high school 26.9 73.1 15.7 84.3 
 College or above 13.5 86.5 12.5 87.5 
Own a family business 
 Yes 34.5* 65.5 22.1 77.9 
 No 40.9 59.1 24.1 75.9 
Cadre     
 Yes 9.0* 91.0 4.6* 95.4 
 No 42.4 57.8 24.9 75.1 
Cadre in household     
 Yes 49.2* 50.8 20.6 79.3 
 No 39.7 60.3 24.8 75.2 
Ever emigrant in household 
 Yes 29.4* 70.6 16.0* 84.0 
 No 73.5 26.5 31.5 68.5 
Place of origin 
 Rural 40.2 59.8 23.7 76.3 
 Urban 42.5 57.5 22.0 77.9 
N 2,657 3,975 301 995 

* p<0.05 (chi-square tests)

Regression Results

Cross-Setting Comparison

Results from event history regressions predicting the first overseas trip are shown in table 3. Findings generally support our hypothesis about the differential roles of various resources in migration to the United States and Europe. In Fuzhou, having a family member who previously migrated to the United States had a negative impact on the individual's propensity to emigrate in the short run. But the effect became positive over time (after about five years). This is because clandestine migration to the United States incurred very high expenses and thus required considerable economic assistance from close kin; it would be very difficult for the family to support another emigrant shortly after the previous migrant departed (Liang et al. 2008). The ability for financing more migrants improved over time as prior emigrants paid off their debt and sent back regular remittances. With regard to Mingxi, however, the geopolitical environment in Europe and the active role of the local governments substantially lowered the barriers to emigration. Immigration to Europe thus became widely accessible to average villagers, both in informational and monetary terms, which reduced the importance of family migration networks. We performed joint tests of family migration network categories using likelihood ratio tests. The test was significant in Fuzhou but not in Mingxi, confirming the different roles of family migration networks across settings (hypothesis 1.1).

Table 3.

Coefficients of Discrete-Time Event History Analysis Predicting First Overseas Trip (Standard Errors in Parentheses)

 Fuzhou-US Mingxi-Europe 
Prior emigrant in household (ref. no prior emigrant) 
 Prior emigrant within 2 years −0.43**,a 0.06b 
(0.08) (0.23) 
 Prior emigrant 2–5 years 0.03 0.35 
(0.07) (0.22) 
 Prior emigrant over 5 years 0.19* −0.05 
(0.09) (0.28) 
Village emigration prevalence ratio 4.17** 4.48** 
(0.32) (0.64) 
Cadre −1.19** −1.68** 
(0.17) (0.64) 
Cadre in household 0.29** 0.16 
(0.06) (0.19) 
Education (ref. no formal education)   
 Elementary school 0.44**,a 0.34b 
(0.13) (0.35) 
 Junior high school 0.67** 0.72 +  
(0.13) (0.37) 
 Senior high school 0.74** 0.60 
(0.14) (0.39) 
 Vocational high school 0.43* −0.26 
(0.20) (0.47) 
 College or above −0.41 +  −0.78 
(0.23) (0.56) 
Age (ref. 60+)   
 15–19 1.76** 2.64* 
(0.71) (1.13) 
 20–24 2.69** 3.49** 
(0.86) (1.11) 
 25–29 2.56** 3.43** 
(0.64) (1.10) 
 30–34 2.29** 3.47** 
(0.65) (1.10) 
 35–39 2.15** 3.23** 
(0.41) (1.11) 
 40–44 1.64** 2.75* 
(0.39) (1.12) 
 45–49 0.69* −0.49 
(0.28) (1.46) 
 50–54 0.14 −0.08 
(0.26) (0.28) 
 55–59 −0.79 – 
(0.85)  
Male 1.03** 0.99** 
(0.05) (0.14) 
Ever married 0.15 +  0.16 
(0.08) (0.21) 
Own a family business −0.34** −0.31 
(0.08) (0.22) 
Rural area 0.14 −0.09 
(0.12) (0.23) 
Intercept −10.19** −9.08** 
(0.67) (1.21) 
–2 log likelihood 21,008.8 2,228.4 
N person-years 145,655 11,566 
 Fuzhou-US Mingxi-Europe 
Prior emigrant in household (ref. no prior emigrant) 
 Prior emigrant within 2 years −0.43**,a 0.06b 
(0.08) (0.23) 
 Prior emigrant 2–5 years 0.03 0.35 
(0.07) (0.22) 
 Prior emigrant over 5 years 0.19* −0.05 
(0.09) (0.28) 
Village emigration prevalence ratio 4.17** 4.48** 
(0.32) (0.64) 
Cadre −1.19** −1.68** 
(0.17) (0.64) 
Cadre in household 0.29** 0.16 
(0.06) (0.19) 
Education (ref. no formal education)   
 Elementary school 0.44**,a 0.34b 
(0.13) (0.35) 
 Junior high school 0.67** 0.72 +  
(0.13) (0.37) 
 Senior high school 0.74** 0.60 
(0.14) (0.39) 
 Vocational high school 0.43* −0.26 
(0.20) (0.47) 
 College or above −0.41 +  −0.78 
(0.23) (0.56) 
Age (ref. 60+)   
 15–19 1.76** 2.64* 
(0.71) (1.13) 
 20–24 2.69** 3.49** 
(0.86) (1.11) 
 25–29 2.56** 3.43** 
(0.64) (1.10) 
 30–34 2.29** 3.47** 
(0.65) (1.10) 
 35–39 2.15** 3.23** 
(0.41) (1.11) 
 40–44 1.64** 2.75* 
(0.39) (1.12) 
 45–49 0.69* −0.49 
(0.28) (1.46) 
 50–54 0.14 −0.08 
(0.26) (0.28) 
 55–59 −0.79 – 
(0.85)  
Male 1.03** 0.99** 
(0.05) (0.14) 
Ever married 0.15 +  0.16 
(0.08) (0.21) 
Own a family business −0.34** −0.31 
(0.08) (0.22) 
Rural area 0.14 −0.09 
(0.12) (0.23) 
Intercept −10.19** −9.08** 
(0.67) (1.21) 
–2 log likelihood 21,008.8 2,228.4 
N person-years 145,655 11,566 

Note: Dummy variables of year are omitted. The coefficients generally show patterns similar to those in figure 3.

aJoint tests of education and of household migration networks are significant at 0.001 level.

bJoint tests of education and of household migration networks are insignificant at 0.05 level.

+p<0.1; * p<0.05; ** p<0.01

Despite these differences, there is one notable similarity between Fuzhou and Mingxi—village migration networks substantially increased the probability of further migration. A 10-percent increase in migration prevalence ratio was associated with an over 50-percent increase in the odds of emigration. This finding lends support to the cumulative causation thesis. Regardless of the macro context, earlier migrants from the community provided an important channel of information and support for prospective migrants. They also led to a strong sense of relative deprivation, thereby solidifying the motivation for further migration and creating a local culture of emigration.

With respect to political capital, in Fuzhou, individuals from cadre households were much more likely to emigrate than others (hypothesis 1.2). This may be because cadres were capable of exploiting their privileged positions to reduce the expenses and risks of emigration for family members. Possible ways to do this include providing crucial migration documents with minimal charges and firsthand information about migration opportunities and policies that would otherwise be difficult and costly to obtain; bargaining for lower smuggling fees through exchange of favors with smugglers; and lifting restrictions and ensuring the cooperation of local law enforcement authorities (Liang et al. 2008). This influence, however, was largely limited in Mingxi (difference in coefficients between Fuzhou and Mingxi is significant at the 0.05 level), where the barriers to emigration were low and the local authorities adopt a proactive approach in enabling emigration. Hence, access to cadre resources no longer provided competitive advantages in migration opportunities.

There is one important similarity with respect to the role of political capital. In both Fuzhou and Mingxi, cadres themselves were much less likely to emigrate than non-cadres. This resonates with previous research showing a persistent advantage of traditional distributive power. In many parts of China, cadres have continued to be important players in local life. Further, they gain substantial benefits from their positional power, sometimes even through abusing their power (e.g., appropriating state loans, extracting illegal taxes). This gives them little incentive to undertake emigration. The potential for cadres to reap economic gains tends to grow with the booming local economy, especially in areas fueled by overseas remittances and investment.

Regarding human capital, we find that when emigration opportunities were readily available, as in Mingxi-Europe migration, there was little individual selectivity on human capital. Of all educational categories, only the coefficient of junior high school was marginally significant (the joint test of all categories was insignificant). However, in Fuzhou-US migration, migration became a selective process based on education (hypothesis 1.3). But the educational selectivity was not strictly linear. People with greater education were generally more likely to migrate up to the postsecondary level. This selectivity may in part reflect the general human capital selectivity of emigration. It may also reflect the cost-benefit calculation in the migration decision-making process. Families would send out relatively well-educated people who had the necessary skills to generate high economic returns. Previous research showed that for Fujianese immigrants in the United States, having secondary school education was positively associated with job and income mobility as well as entrepreneurial attainment (Chunyu 2010). But to maximize overall household income, the most-educated members (those with a college degree) may stay to take advantage of employment opportunities at home.

As for other characteristics, age was significantly associated with emigration in both settings, with younger people having a higher propensity to emigrate (those aged 20–40 were most likely to migrate). Men had a higher likelihood of emigration than women. Marital status was marginally significant in Fuzhou. Family wealth, measured by business ownership, appeared to indicate a lack of motivation for migration in Fuzhou. This was in line with findings from Mexico-US migration, which suggested that people with more physical capital assets were less likely to participate in migration (Massey and Espinosa 1997). The result is also consistent with the new economics of migration, which argues that people do not necessarily migrate to earn higher net incomes, but to secure capital for asset acquisition. However, this result should be interpreted with caution because the data lack more precise measures on family wealth that would allow us to examine what types of households are more likely to own a family business. Therefore, the observed relationship could also be a reflection of the fact that people who are less eager to emigrate may decide to set up a family business as a long-term investment strategy.

We further illustrate the roles of migrant social capital and political capital by examining how they affect emigration costs (table 4). Again, we took the midpoints of the emigration cost categories and converted them into the 2003 constant US dollars. We used log-transformed costs as the outcome and employed Heckman selection models, as emigration costs were observed only for emigrants (Heckman 1979). This method first modeled an individual's likelihood of emigration using the same set of variables as table 3 (selection model). In the second-stage regression, the method estimated emigration costs based on factors that could theoretically be associated with the costs (while controlling for the likelihood of selection into emigration). The Heckman selection model improves estimates in the cost regression by using information from non-emigrants via full-information maximum likelihood estimation. It also tests for correlation between residuals in the selection and cost regression (ρ). A significant test of ρ = 0 suggests a nonignorable sample selection process. The test results are shown at the bottom of table 4. It was significant in Fuzhou but not in Mingxi. This strengthened our hypothesis that emigration was a highly selective process for Fuzhou emigration to the United States (hypothesis 1).

Table 4.

Coefficients from Heckman Selection Models of Factors Predicting Emigration Cost (Standard Errors in Parentheses)

 Fuzhou-US Mingxi-Europe 
Own a family business 0.18 +  0.04 
(0.10) (0.05) 
Cadre −0.40 0.26 
(0.27) (0.19) 
Cadre in household −0.28** 0.05 
(0.07) (0.05) 
Prior emigrant in household (ref. no prior emigrant) 
 Prior emigrant within 2 years −0.16 +  −0.05 
(0.09) (0.06) 
 Prior emigrant 2–5 years −0.43** −0.07 
(0.08) (0.06) 
 Prior emigrant over 5 years −0.69** 0.12 
(0.08) (0.09) 
Village emigration prevalence ratio 2.74** 0.06 
(0.31) (0.16) 
Rural area 0.01 0.06 
(0.12) (0.06) 
–2 log likelihood 13,444.8 1,168.2 
Likelihood ratio test of ρ = 0 (p-value) <0.001 0.22 
N 6,632 1,296 
 Fuzhou-US Mingxi-Europe 
Own a family business 0.18 +  0.04 
(0.10) (0.05) 
Cadre −0.40 0.26 
(0.27) (0.19) 
Cadre in household −0.28** 0.05 
(0.07) (0.05) 
Prior emigrant in household (ref. no prior emigrant) 
 Prior emigrant within 2 years −0.16 +  −0.05 
(0.09) (0.06) 
 Prior emigrant 2–5 years −0.43** −0.07 
(0.08) (0.06) 
 Prior emigrant over 5 years −0.69** 0.12 
(0.08) (0.09) 
Village emigration prevalence ratio 2.74** 0.06 
(0.31) (0.16) 
Rural area 0.01 0.06 
(0.12) (0.06) 
–2 log likelihood 13,444.8 1,168.2 
Likelihood ratio test of ρ = 0 (p-value) <0.001 0.22 
N 6,632 1,296 

Note: We took the midpoints of the emigration cost categories and converted them into the 2003 constant US dollars. They are log transformed and included in the regression. We used the Heckman selection model because emigration costs were observed only for emigrants. The first-stage regression (which predicts emigration status) is not shown. The predictors included in the first regression are the same as those included in table 3.

+p<0.1; * p<0.05; ** p<0.01

Results from the cost regressions showed substantial differences between the two migration streams. In Mingxi-Europe migration, the costs did not appear to be associated with household resources because the low expenses were readily affordable by average families. However, for US migration from Fuzhou, having previous emigrants in the households and access to cadre resources significantly lowered the costs of migration. Specifically, living in a cadre household reduced emigration fees by 28 percent, consistent with our speculation regarding the role of political capital. Individuals from households with previous emigrants also enjoyed lower costs, in part because they were better able to negotiate the costs with smugglers, especially after previous migrants paid off the fees and successfully settled down at the destination. Also, these households were often more trusted by the brokers and likely offered lower fees. Another reason may be that some of these later emigrants migrated legally as family members of earlier migrants who were legalized in the United States, which incurred much lower migration expenses. Interestingly, village migration networks seemed to increase emigration costs in Fuzhou. This may suggest that in places where both the demand for and barriers to migration were high, the fees were unlikely to be substantially reduced. They may even increase, as government enforcement tended to be more stringent in these areas, leading to more sophisticated smuggling operations and thus higher fees. Finally, family wealth was positively (but weakly) related to emigration costs in Fuzhou, presumably because these households may be willing to pay higher fees to improve their chance of success.

Over-Time Patterns in Fuzhou-US Migration

Because Fuzhou migration to the United States is characterized by a changing sociopolitical environment, especially at the level of local sending areas, we further explore how contextual changes shape the social process of emigration over the three stages of Fuzhou emigration to the United States (table 5). We did not conduct a similar analysis of Mingxi-Europe migration, because of its shorter history and relatively small temporal variability in underlying contexts.

Table 5.

Coefficients of Discrete-Time Event History Analysis Predicting First Overseas Trip in Fuzhou-US Migration by Period (Standard Errors in Parentheses)

 Stage 1 (1978–1985) Stage 2 (1986–1995) Stage 3 (1996–2003) 
Education (ref. no formal education) 
 Elementary school 0.32 0.29 +  0.54** 
(0.37) (0.15) (0.18) 
 Junior high school 0.77 0.56** 0.75** 
(0.40) (0.16) (0.18) 
 Senior high school 0.45 0.60** 0.78** 
(0.45) (0.18) (0.19) 
 Vocational high school 1.44 0.37 0.30 
(0.91) (0.29) (0.25) 
 College or above −0.49 −0.42 −0.41 
(1.16) (0.33) (0.27) 
Cadre −0.26 −1.05** −1.45** 
(0.49) (0.26) (0.28) 
Cadre in household 0.35 0.31** 0.26** 
(0.25) (0.09) (0.07) 
Prior emigrant in household (ref. no prior emigrant) 
 Prior emigrant within 2 years 1.07* −0.33** −0.70** 
(0.53) (0.10) (0.11) 
 Prior emigrant 2–5 years 2.61** 0.17 −0.25* 
(0.38) (0.12) (0.10) 
 Prior emigrant over 5 years 3.15** 0.55** −0.20* 
(0.24) (0.14) (0.09) 
Village emigration prevalence ratio a 6.42** 3.19** 
 (0.54) (0.36) 
Number of emigrants in each period 123 1,207 1,327 
Average cost of emigration ($) 13,413 29,376 50,839 
Percent likely through formal channels 66 29 34 
 Stage 1 (1978–1985) Stage 2 (1986–1995) Stage 3 (1996–2003) 
Education (ref. no formal education) 
 Elementary school 0.32 0.29 +  0.54** 
(0.37) (0.15) (0.18) 
 Junior high school 0.77 0.56** 0.75** 
(0.40) (0.16) (0.18) 
 Senior high school 0.45 0.60** 0.78** 
(0.45) (0.18) (0.19) 
 Vocational high school 1.44 0.37 0.30 
(0.91) (0.29) (0.25) 
 College or above −0.49 −0.42 −0.41 
(1.16) (0.33) (0.27) 
Cadre −0.26 −1.05** −1.45** 
(0.49) (0.26) (0.28) 
Cadre in household 0.35 0.31** 0.26** 
(0.25) (0.09) (0.07) 
Prior emigrant in household (ref. no prior emigrant) 
 Prior emigrant within 2 years 1.07* −0.33** −0.70** 
(0.53) (0.10) (0.11) 
 Prior emigrant 2–5 years 2.61** 0.17 −0.25* 
(0.38) (0.12) (0.10) 
 Prior emigrant over 5 years 3.15** 0.55** −0.20* 
(0.24) (0.14) (0.09) 
Village emigration prevalence ratio a 6.42** 3.19** 
 (0.54) (0.36) 
Number of emigrants in each period 123 1,207 1,327 
Average cost of emigration ($) 13,413 29,376 50,839 
Percent likely through formal channels 66 29 34 

Note: Other covariates, the same as those in table 3, were omitted from this table. We performed formal tests of differences across three periods. The role of human capital across three stages is quite similar (statistically insignificant). The role of cadre status and household cadre status in stages 2 and 3 is similar, but its role in stage 1 is significantly different from that at later stages (p<0.05). For family emigration social capital, the difference is significant between stages 1 and 2 (p<0.05), and between stages 2 and 3 (p<0.1). The role of village emigration prevalence ratio in stages 2 and 3 is significantly different at the 0.05 level.

aWe exclude the village prevalence ratio in the first stage to provide more stable results, though including this variable does not change the substantive story. This is because the aggregated village-level emigration was very low and household migration was highly correlated with village migration at this stage.

+p<0.1; * p<0.05; ** p<0.01

Fuzhou experienced increasing emigration over time, from about 120 in the earliest stage to over 1,200 by the mid-1990s in our data, though the increase seemed to have leveled off since then. The average costs of migration also grew considerably, roughly fourfold by the mid-2000s. We sought to examine the legality of emigration using information on the type of departure documents. This analysis was quite crude but was the best we could do given the data. Migrants departing with US citizen status, a green card, or for reasons of contracted labor or family visit were considered legal migrants. Others (those with tourist visas or unreported reasons) were treated as illegal migrants. In the first stage, 66 percent of emigrants held legal documents. When we restricted the sample to an earlier period (before the early 1980s) when the vast majority of emigrants had overseas connections, the percentage increased to over 80 percent. Since the mid-1980s, the majority of emigrants have been illegal, and the percentage of regular migrants has dropped by over half. After the mid-1990s, regular migrants increased slightly, presumably because an increasing number of Fujianese emigrated as family members of earlier (legalized) migrants.

We next turn to the cross-temporal variation in the social process of emigration. In Fuzhou, given the increasingly high barriers to emigration, and especially the tightening measures against illegal emigration in the sending areas, there exist strong and persistent roles of social and political resources and human capital selectivity (hypothesis 2). During the first stage, household migration networks significantly promoted further emigration because migration followed primarily from overseas family connections. For this reason, other personal and household factors did not play a clear role. After this initial phase, the importance of sociopolitical resources and human capital became more evident. The positive effect of migrant social capital was depressed in the short term but manifested over time. But as the legal toll and financial costs for sending additional migrants increased, families needed more time to be able to support further emigration. Hence, the positive role of migrant social capital became increasingly constrained, as shown in the most recent stage of emigration. In spite of the rising barriers, people appeared to continue to pool resources and take on debt to pursue their American dreams. The institutionalization of migration in local communities (village migrant prevalence ratio) has continued to sustain the motivation and act of emigration.

As emigration became subject to rising costs and pressures since the mid-1980s, the benefits and privileges conferred by political capital persisted, in terms of both garnering economic benefits and sanctioning the unauthorized act of emigration. Cadre households continued to be more likely to engage in emigration. The difference in the coefficients of household cadre status between the last two phases was insignificant. This observation suggested that, while deepening restrictions against illegal emigration and the rising expenses gradually dimmed the prospect of emigration for ordinary households, such difficulties could be largely overcome by the power vested in local cadres, who were able to circumvent state regulations and pave the way for their family members' emigration. The slightly larger coefficient in the second stage may be because some local authorities were even peripherally involved in the smuggling business during this period. In addition, cadres themselves were consistently less motivated to emigrate, suggesting that the inherent advantages conferred to cadres have persisted.

The pattern of human capital selectivity was similar. Except for the first period, emigrants were drawn largely from relatively well-educated people, though not the small group with tertiary education. This suggested that because of the persistently high barriers, migration continued to involve a high degree of self-selection, and/or that cost-benefit strategizing was occurring based on personal characteristics, especially those rewarded in the labor market.

Conclusion and Discussion

Chinese international migration has increased in scale, as well as in the range of origins and destinations, since the beginning of the economic reforms. A prominent component of this is emigration from the Fujian Province, which consists of different migration streams that are embedded in distinct destination geopolitical environments and local sending state practices. In the present study, we develop a comparative analysis to examine how the combination of origin and destination contexts shapes the emigration process across context and over time. To our knowledge, this is the first systematic comparison of different streams of Chinese international migration.

The findings add to our understanding of the role of contexts in conditioning the migration process. Emigration from the Fuzhou region to the United States has largely resorted to clandestine channels because of the long geographical distance and restricted means of migration to the United States. This has led to high costs and stakes of migration. By contrast, the geopolitical environment in Europe offers shorter distances, flexible routes, and a range of immigration policies to exploit. This has reduced the barriers to legal migration and substantially lowered the associated expenses and risks. In addition, local governments in the sending communities have developed differential responses to emigration. State authorities in Mingxi have adopted highly proactive and open practices to promote and enable international migration, whereas the Fuzhou government has stepped up stringent measures to combat irregular emigration, especially since the mid-1990s.

Correspondingly, we observe substantial differences in the process of international migration, as reflected in the differential roles of resources conferred by migrant social capital, political capital, and human capital–based characteristics. When barriers to migration are high, as the receiving and sending governments enact restrictive migration policies (i.e., in the case of clandestine migration from Fuzhou to the United States), migrant social capital (family migration networks), political capital (cadre resources), and human capital (education) all play crucial roles in the emigration process. This is because these resources help circumvent formal state institutions and reduce the costs while enhancing the potential benefits of migration. However, the importance of these resources in the migration process is much less pronounced when immigration policy conditions are relatively lax and when local sending governments adopt proactive emigration policies (i.e., migration from Mingxi to Europe). Across different stages of emigration from Fuzhou over time, the importance of migrant social capital, political capital, and human capital has strongly persisted as a result of tightening migration restrictions and escalating costs.

Note that for Fujianese migration, the macro-level structural features at the origin and destination do not take shape simultaneously, but develop as a series of evolving and adaptive processes. The geopolitical institutions in the destination countries and the socioeconomic context in the origin have pulled or pushed the large exodus of Fujianese emigrants. Varying geopolitical environments and immigration regimes across the United States and Europe have important implications for the barriers (legal, financial, and personal) to immigration. These contexts of reception and the resultant patterns of migration have in turn shaped the strategies of the sending state, and importantly, have been further transformed by the local state in the sending areas. Because of this interdependence of receiving and sending contexts, we cannot definitively identify the respective roles of origin and destination factors. Nevertheless, exploring how political capital intersects with sending contexts in influencing migration outcomes helps improve our understanding of the role of the sending state in the process of migration. This is because the importance of political capital is closely tied to the goals and interests of the local state. When the act of emigration conflicts with formal state institutions, political capital forms a pool of resources for circumventing local state policies and reducing the costs and stakes of unauthorized migration. However, when emigration is congruent with and openly promoted by local state institutions, the privileges and resources conferred by access to political capital tend to be much less important for successful migration.

Several additional observations are worth noting. First, the role of family migration networks is far from uniform. This factor is not necessarily always salient, nor is it always positive. The effect depends largely on macro-level factors that shape the legality of and barriers to emigration. Second, human capital selectivity of migration is closely associated with the contexts, and is not necessarily linearly associated with emigration. The process seems to reflect both the neoclassical model and new economics model of migration: education increases the likelihood of migration up to the secondary level, but then decreases the likelihood at the tertiary level. This is because individuals choose to diversify risks and maximize household welfare, as tertiary education becomes highly rewarded in the local economy.

Despite the substantial differences, the results highlight some fundamental processes that are highly consistent across settings and over time. One is the importance of village migration networks in sustaining emigration from both Fuzhou and Mingxi, as postulated by the cumulative causation thesis. Such networks provide ample information and strongly enduring incentives for migration, as well as create a culture of migration, thereby perpetuating respective migration streams. This helps explain why Fuzhou migrants have continued to follow in the footsteps of their predecessors to the United States (rather than heavily exploring other destinations), in spite of the rising costs and difficulties of illegal migration. Second, the results show a persistent advantage of traditional positional power in China over time and across the two migration settings, as embodied in (individual) cadre status. This engages the market transition debate and suggests that marketization has not completely erased the privileges of positional power. Cadres have continued to enjoy ample local opportunities to benefit themselves, which makes emigration a much less appealing option for them.

It is important to acknowledge that emigrants from Fujian Province are only two of several important streams of emigration from China. The Fujianese case sheds light mostly on low- and semi-skilled labor immigration, as well as emigration from rural areas. It represents most of the irregular emigration from China to the United States (especially New York), and immigration to southern Europe. The migrant flows from Fujian are to a large degree similar to those from Zhejiang Province, another dominant source province of emigration to the United States and Europe. However, the Fujianese case is certainly not representative of all emigration from China, particularly the growing numbers of emigrants as students, skilled workers, and professionals, and those from the northeast provinces heading to non-US and non-European destinations. Nevertheless, we expect that some of what we learned from the Fujianese case would, at least to some degree, help explain other streams of Chinese emigration ingrained in varying macro-level contexts. For example, political capital is likely to be mobilized to facilitate the emigration of students and skilled workers. Also, even among student and skilled emigrants, the level of selectivity is likely to vary by destination contexts, with countries having more restrictive immigration policies (e.g., the United States) drawing more selected immigrants than countries with more lenient immigration policies (e.g., southern Europe, until very recently).

The present investigation joins the growing scholarly emphasis on comparative immigration research that incorporates different sending and receiving contexts within a common framework. The different macro-level factors we delineate and the different selectivity of emigration we observe may very well shape the integration process of Chinese immigrants in the United States and Europe. This question sets a research agenda for the next stage of our comparative analysis—how Chinese immigrants fare under different and shifting contexts of exit and reception in the United States and Europe.

Supplementary Material

Supplementary material is available at Social Forces online, http://sf.oxfordjournals.org/.

Note

1.
Labor market conditions in destination countries are not the focus of this study, because they are similar for Fujianese migrants. In both the United States and Europe, there exist ample employment opportunities for Chinese immigrants in the low- and semi-skilled labor market, and to some extent, in small business niches (Laczko 2003).

References

Alba
Richard
Waters
Mary
, eds.
2011
.
The Next Generation: Immigrant Youth in a Comparative Perspective
 .
New York
:
New York University Press
.
Alexander
Garth
.
1973
.
The Invisible China: the Overseas Chinese and the Politics of Southeast Asia
 .
New York
:
Macmillan Publishing Co., Inc
.
Blatt
Jason A.
2007
. “
Recent Trends in the Smuggling of Chinese into the United States
.” .
Bohra-Mishra
Pratikshya
Massey
Douglas S.
.
2011
. “
Individual Decisions to Migrate During Civil Conflict
.”
Demography
 
48
(2)
:
401
-
24
.
Borjas
Gorge J.
1987
. “
Self-Selection and the Earnings of Immigrants
.”
American Economic Review
 
77
:
531
-
53
.
Cheng
Xi
.
2007
. “
The ‘Distinctiveness’ of the Overseas Chinese as Perceived in the People's Republic of China
.” Pp.
49
-
66
in
Beyond Chinatown: New Chinese Migration and the Global Expansion of China
 , edited by
Thuno
M.
.
Copenhagen
:
Nordic Institute of Asian Societies
.
Chin
James K.
2003
. “
Reducing Irregular Migration from China
.”
International Migration
 
41
:
49
-
72
.
Chin
Ko-Lin
.
1999
.
Smuggled Chinese: Clandestine Migration to the United States.
 
Philadelphia
:
Temple University Press
.
Chunyu
Miao
.
2010
. “
A Comparative Study of Chinese and Mexican Immigrants' Economic Incorporation in the United States
.”
Dissertation
,
State University of New York–Albany
.
Curran
Sara R.
Rivero-Fuentes
Estela
.
2003
. “
Engendering Migrant Networks: The Case of Mexican Migration
.”
Demography
 
40
:
289
-
307
.
Donato
Katharine
Hiskey
Jonathan
Durand
Jorge
Massey
Douglas
.
2010
. “
Migration in the Americas: Mexico and Latin America in Comparative Context
.”
Annals of the American Academy of Political and Social Science
 
630
(1)
:
6
-
17
.
Foner
Nancy
.
2005
.
In a New Land: A Comparative View of Immigration
 .
New York
:
New York University Press
.
Fussell
Elizabeth
Massey
Douglas S.
.
2004
. “
The Limits to Cumulative Causation: International Migration from Mexican Urban Areas
.”
Demography
 
41
:
151
-
71
.
Garip
Filiz
Curran
Sara
.
2010
. “
Increasing Migration, Diverging Communities: Changing Character of Migrant Streams in Rural Thailand
.”
Population Research and Policy Review
 
29
:
659
-
85
.
Heckman
James J.
1979
. “
Sample Selection Bias as a Specification Error
.”
Econometrica
 
47
(1)
:
153
-
61
.
Keefe
Patrick R
.
2009
.
The Snakehead
 .
New York
:
Doubleday
.
Kwong
Peter
.
1997
.
Forbidden Workers: Illegal Chinese Immigrants and American Labor
 .
New York
:
New Press
.
Kyle
David
Koslowski
Rey
.
2001
.
Global Human Smuggling: Comparative Perspectives
 .
Baltimore
:
Johns Hopkins University Press
.
Laczko
Frank
.
2003
. “
Introduction: Understanding Migration between China and Europe
.”
International Migration
 
41
:
5
-
19
.
Levitt
Peggy
DeWind
Josh
Vertovec
Steven
.
2003
. “
International Perspectives on Transnational Migration: An Introduction
.”
International Migration Review
 
37
:
565
-
75
.
Li
Minghuan
.
2005
.
Identities, Networks and Culture: Fieldwork Reports on the Homeland of Overseas Chinese in Fujian
.
Xiamen, China
:
Xiamen University Press
(in Chinese)
.
Liang
Zai
.
2001
. “
Demography of Illicit Emigration from China: A Sending Country's Perspective
.”
Sociological Forum
 
16
(4)
:
677
-
701
.
Liang
Zai
Chunyu
Miao
Zhuang
Guotu
Ye
Wenzhen
.
2008
. “
Cumulative Causation, Market Transition, and Emigration from China
.”
American Journal of Sociology
 
114
:
706
-
37
.
Lin
Sheng
.
2009
.
Irregular Emigration from Fuzhou: Changes and Transformation in Coastal Rural Qiaoxiang
 .
Manuscript
,
University of Hong Kong
.
Massey
Douglas S.
1990
. “
Social Structure, Household Strategies, and the Cumulative Causation of Migration
.”
Population Index
 
56
:
3
-
26
.
Massey
Douglas S.
1999
. “
International Migration at the Dawn of the Twenty-First Century: The Role of the State
.”
Population and Development Review
 
25
(2)
:
303
-
22
.
Massey
Douglas S.
Alarcon
Rafael
Durand
Jorge
Gonzalez
Humberto
.
1987
.
Return to Aztlan: The Social Process of International Migration from Western Mexico
 .
Berkeley
:
University of California Press
.
Massey
Douglas S.
Espinosa
Kristin E.
.
1997
. “
What's Driving Mexico-U.S. Migration? A Theoretical, Empirical, and Policy Analysis
.”
American Journal of Sociology
 
102
(4)
:
939
-
99
.
Massey
Douglas S.
Goldring
Luin
Durand
Jorge
.
1994
. “
Continuities in Transnational Migration: An Analysis of Nineteen Mexican Communities
.”
American Journal of Sociology
 
99
:
1492
-
533
.
Model
Suzanne
Fisher
Gene
Silberman
Roxane
.
1999
. “
Black Caribbeans in Comparative Perspective
.”
Journal of Ethnic and Migration Studies
 
25
:
183
-
208
.
Naim
Moises
.
2005
.
Illicit: How Smugglers, Traffickers, and Copycats are Hijacking the Global Economy
 .
New York
:
Doubleday
.
Nee
Victor
.
1989
. “
A Theory of Market Transition: From Redistribution to Markets in China
.”
American Sociological Review
 
54
:
663
-
81
.
O'Brien
Kevin J.
Li
Lianjiang
.
1999
. “
Selective Policy Implementation in Rural China
.”
Comparative Politics
 
31
:
167
-
86
.
Parish
William L.
Michelson
Ethan
.
1996
. “
Politics and Markets: Dual Transformations
.”
American Journal of Sociology
 
101
:
1042
-
59
.
Pieke
Frank N.
Nyiri
Pal
Thuno
Mette
Ceccagno
Antonella
.
2004
.
Transnational Chinese: Fujianese Migrants in Europe
 .
Stanford, CA
:
Stanford University Press
.
Portes
Alejandro
.
1999
.
“Immigration Theory for a New Century: Some Problems and Opportunities
.” Pp.
21
-
33
in
Handbook of International Migration: The American Experience, edited by
 
Hirschman
C.
Kasinitz
P.
DeWind
J.
.
New York
:
Russell Sage Foundation
.
Poston
Dudley L.
Jr.
Luo
Hua
.
2007
. “
Chinese Student and Labor Migration to the United States: Trends and Policies since the 1980s
.”
Asian and Pacific Migration Journal
 
16
:
323
-
55
.
Van Tubergen
Frank
Maas
Ineke
Flap
Henk
.
2004
. “
The Economic Incorporation of Immigrants in 18 Western Societies: Origin, Destination, and Community Effects
.”
American Sociological Review
 
69
:
701
-
24
.
Walder
Andrew
.
2002
. “
Income Determination and Market Opportunities in Rural China, 1978-1996
.”
Journal of Comparative Economics
 
30
:
354
-
75
.
Winters
Paul
de Janvry
Alain
Sadoulet
Elisabeth
.
2001
. “
Family and Community Networks in Mexico-U.S. Migration
.”
Journal of Human Resources
 
36
:
159
-
84
.
Zhou
Min
.
2009
.
Contemporary Chinese America: Immigration, Ethnicity, and Community Transformation
 .
Philadelphia
:
Temple University Press
.
Zhou
Xueguang
.
2000
. “
Economic Transformation and Income Inequality in Urban China: Evidence from Panel Data
.”
American Journal of Sociology
 
105
:
1135
-
74
.

Author notes

Data collection for this project was supported by grants from the National Institute of Child Health and Human Development (1 R01 HD39720-01), the National Science Foundation (SES-0138016), and the Ford Foundation (1025-1056). Yao Lu gratefully acknowledges support from the Russell Sage Foundation and the Columbia Population Research Center with funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R24HD058486). Zai Liang gratefully acknowledges support from the Center for Social and Demographic Analysis at the University at Albany with funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R24 HD044943). An earlier version of this paper was presented at the American Sociological Association Conference, Atlanta, Georgia, in August 2010. We thank the anonymous reviewers and the editor for their helpful comments.

Supplementary data