Abstract
The dramatic increase in Hispanic immigration to the United States in recent decades has been coterminous with fundamental shifts in the labor market towards heightened flexibility, instability, and informality. As a result, the low-wage labor market is increasingly occupied by Hispanic immigrants, many of whom are undocumented. While numerous studies examine the implications for natives’ employment prospects, our understanding of low-wage immigrants themselves remains underdeveloped. Drawing on original data collected in Durham, North Carolina, this article provides a more holistic account of immigrant Hispanic’s labor market experiences, examining not only wages but also employment instability and benefit coverage. The analysis evaluates the role of human capital and immigration characteristics, including legal status, in shaping compensation outcomes, as well as the influence of other employment characteristics. Findings highlight the salience of nonstandard work arrangements such as subcontracting and informal employment to the labor market experiences of immigrant Hispanic men, and describe the constellation of risk factors that powerfully bound immigrant employment outcomes. Keywords: Hispanic; immigration; wages; low-wage labor market; employment relations.
The prospect for the economic integration of foreign-born workers, long a central topic of inquiry in immigration research, is particularly salient for contemporary Hispanics. Recent decades have witnessed a surge in U.S. immigration from Latin America, particularly Mexico; between 1990 and 2010 the number of foreign-born Hispanics more than doubled, from 8.4 to 21.2 million. Within this larger immigrant stream there has been an equally sharp rise in both the absolute and relative share of the undocumented population, currently estimated to number over 11 million, including 6.5 million Mexicans (Passel and Cohn 2011).
Size alone is not the only factor that compels attention to Hispanic immigrant employment; they are also an important and growing segment of the low-wage labor market. The proportion of the low-skill labor force that was foreign born grew from a mere 12 percent in 1980 (Enchautegui 1998) to 50 percent in 2010 (Bureau of Labor Statistics 2011). For 2005 it was estimated that the undocumented alone comprised 23 percent of low-skill workers (Capps, Fortuny, and Fix 2007). These trends have been coterminous with a striking deterioration in the working conditions of the low-skill labor market, such as falling real wages and greater instability and informality in employment relations. Hispanic immigrants are disproportionately exposed to these adverse employment conditions, and register some of the lowest earnings and highest rates of working poverty in the country (Catanzarite and Aguilera 2002; Hauan, Landale, and Leicht 2000).
The growing share of Hispanic immigrants (particularly the undocumented) in the low-wage labor market has garnered intense interest from social scientists. However, more research by far has focused on the possible displacement impact on native workers than on the labor market experiences of the immigrants themselves (Hall and Farkas 2008). Owing in part to data limitations, the small body of research that has directly assessed the employment patterns of low-skilled immigrants largely focused on wages, to the relative neglect of other aspects of compensation. Most studies are also unable to address nonstandard work arrangements such as subcontracting and informal, off-the-books employment that are increasingly common in the low-wage labor market in general, and in particular among Hispanic immigrants. Moreover, our understanding of the determinants of these labor market outcomes, particularly the role of human capital considerations, documentation, and the interrelation of various employment outcomes with one another, remains poorly understood.
This article draws on original data collected in a new immigrant receiving area, Durham, North Carolina, to address these issues. Most studies of immigrant labor market incorporation draw from census and other sources not specifically designed to study immigrants. They thus lack detailed information on the myriad of employment conditions that are particularly salient for low-skilled, foreign-born workers (such as subcontracting, informal employment, or documentation status). Other data sources specifically designed to study immigrants, such as the Legalized Population Survey or the New Immigrant Survey, are better suited to study employment dynamics among the foreign born. However, their exclusive focus on the legal immigrant population necessarily excludes the vast majority of recently arrived entrants to the low-skilled labor market. Our project drew on extensive qualitative research and consultation with community advisors to devise a questionnaire specifically tailored to the immigrant experience. Our data, while not nationally representative, captures a cross-section of a new and highly vulnerable segment of U.S. society, and is uniquely suited to contribute to our understanding of the myriad of human capital, immigration, and employment conditions that structure the labor market position of Hispanic immigrants.
Background
The United States, like other high income countries, has experienced profound changes in its economy, employment structure, and labor market during the past 40 years. Now characterized as a post-industrial economy, the emphasis on free trade, reduced regulation, and greater flexibility (including the greatly diminished strength of labor unions) have combined to radically alter U.S. employment patterns. These trends contributed to a growing disparity between skilled and unskilled workers, in which those at the lower end of the occupational hierarchy have seen their real wages and employment conditions decline substantially (Harrison and Bluestone 1990; Kopczuk, Saez, and Song 2010). Specifically, a large proportion of jobs that once provided good wages, stability, health insurance and pensions, and the potential for upward mobility to U.S. workers, have been replaced by jobs that do not provide any of those things (Hudson 2007). Informal employment (outside the state’s regulatory framework), which was once thought to be a vestige of insufficient integration into the capitalist economy that would inevitably fade with development, has enjoyed a remarkable resurgence in recent years in more and less developed countries alike (Portes and Sassen-Koob 1987; Schneider and Enste 2000). Likewise, nonstandard work arrangements, such as on-call work, temporary help agencies, subcontracting, independent contracting/contingent work, part-time employment in conventional jobs, have also increased dramatically, increasing their dominance in industries where they were already common (such as construction) and spreading to numerous other areas of the economy (Kalleberg 2011), with negative implications for both wages and job quality (Ferber and Waldfogel 1998).
These fundamental changes in employment have been coterminous with the dramatic rise in immigration described above. Scholars have emphasized, though, that structural changes in the advanced capitalist economies have encouraged the growth of employment instability and informality, rather than the presence of immigrants per se. For instance, in cross-national analyses the presence of immigrants is neither necessary nor sufficient to explain outcroppings of informal work in more developed economies (Sassen 1994). Indeed, the deterioration of employment conditions in the low-wage sector is as likely to be a cause of increased immigration as a consequence. Regardless, it is impossible to ignore the coincidence of rising international migration and deteriorating employment conditions in the low-wage labor market.
The rise in undocumented immigration, in particular, is implicated in the growing informality of the low-wage sector. While documentation status is not always explicitly incorporated into discussions of immigrants’ impact on the labor market, the undocumented are by definition at least partially informal workers; even if their incomes are reported their labor is unauthorized. Moreover, recent immigration policies have had a significant impact marginalizing undocumented labor. The 1986 Immigration Reform and Control Act (IRCA) instituted for the first time employer sanctions for the hiring of undocumented labor. And, while the widespread availability of falsified documents undermined some of the deterrent of the law, the 1996 Illegal Immigration Reform and Immigrant Responsibility Act (IIRIRA) further heightened employer sanctions and devoted more resources to enforcement. In tandem with heightened security measures enacted after the September 11, 2001 terrorist attacks, it has become considerably more difficult and dangerous to falsify employment eligibility documents. These policies have had a powerful impact on the segments of the labor market that rely on immigrant labor. Rather than having the intended effect of reducing the size of the undocumented population, they have encouraged employers in immigrant-intensive areas to switch to subcontracting and other practices that insulate them from the risk of sanctions. As entire occupational niches and fields increasingly move to subcontracting, this trend affects both legal and undocumented immigrants alike (Gentsch and Massey 2011; Massey and Bartley 2005).
Again, whether the presence of immigrants encourages the shift to subcontracting or whether the shift to subcontracting increases demand for immigrant labor is difficult to discern. Regardless, the end result is that labor markets are increasingly stratified on the basis of citizenship and documentation status. The low-wage labor market once relied heavily on blacks and women to meet labor demand. As these groups have gradually made incremental progress towards equity with white males, it is increasingly citizenship that channels workers into “bad” jobs (Hudson 2007; Massey 1995; Phillips and Massey 1999). The result of these interrelated trends is a large number of workers who both lack legal protections and labor under increasingly adverse, unstable conditions.
These issues are particularly significant for Hispanics since they are the largest immigrant group. A number of previous studies have highlighted the challenges faced by the immigrant Hispanic workforce. These studies document that while foreign-born Hispanics have high rates of labor force participation, they also have difficulty maintaining stable, full-time employment and are dramatically more likely than non-Hispanic whites to be among the working poor even when working full time (Hauan et al. 2000). These disparities attenuate but are far from eliminated when differences across groups in human capital endowments are taken into consideration. While immigrants of all origins have a difficult time translating human capital acquired abroad into higher wages in the United States, Hispanics seem particularly disadvantaged. Thus, assessing the factors that structure labor market outcomes for immigrant Hispanics is essential both for understanding the low-wage labor market and for weighing the prospects for economic incorporation of the nation’s largest minority group.
Conceptual Framework
While our understanding of the processes shaping the economic incorporation of Hispanic immigrants is growing, a number of critical gaps remain. Data limitations have prevented simple descriptions of the extent of precarious employment conditions, such as subcontracting and informal employment. In addition, our understanding of the variability in employment patterns among immigrant Hispanics, and how different facets of employment interrelate and overlap remains incomplete. To address these issues, I formulate a conceptual framework, illustrated in Figure 1, which builds on the literatures on immigrant adaptation and low-wage labor markets.
Figure 1. Conceptual Framework.
Four main objectives guide the analysis. First, while most studies rely exclusively on wages as a summary indicator of the returns to employment, they do not provide a comprehensive account of the economic position of workers in the low-wage sector. In particular, chronic employment instability renders it difficult to estimate annual earnings. Lack of benefit coverage likewise undermines well-being in a way not easily captured by wage data alone. Indeed, for low-wage immigrants, basic provisions such as paid sick leave and vacation are often not available, lowering their effective wage in a way not often considered by studies of wage inequality and working poverty. Incorporating these dimensions of compensation in addition to wages thus provides a more comprehensive gauge of economic position of low-skill Hispanic immigrant workers. I expect that once employment instability and low rates of benefit coverage are taken into account, the financial security of immigrant Hispanics will be considerably lower than that suggested by their relatively meager incomes alone.
Second, it is important to examine the variability among Hispanic immigrants to assess the factors that either heighten or ameliorate exposure to adverse outcomes. In particular, theorists have long debated whether the returns to human capital are suppressed in low-wage segments of the economy (Piore 1970). The low-wage labor market is characterized by instability and limited prospects for upward mobility, calling into question whether workers in this sector of the economy enjoy significant returns to factors such as education and U.S. labor market experience (Zucker and Rosenstein 1981). Among immigrants, in particular, evidence is somewhat mixed. Factors such as education, length of U.S. work experience, and English language ability have been found to predict wages, even among low-skill immigrant workers (Bleakley and Chin 2004; Catanzarite 2002; Chiswick 1984; Hall, Greenman, and Farkas 2010; Kossoudji 1988; Phillips and Massey 1999). However, there is evidence that immigrants’ return to human capital may have lessened in recent years (Catanzarite 2000). A recent examination of wages among natives and immigrants with less than a high school degree, for instance, showed that immigrants overall show positive wage returns to education. An important exception, however, was immigrant Hispanics, for whom the returns to education were essentially absent (Hall and Farkas 2008). Similarly, studies have shown that the positive effect of labor market experience is considerably weaker among undocumented than documented workers (Hall et al. 2010). The impact of education, U.S. labor market experience, and English language ability on other aspects of compensation has received far less attention. If human capital were important to immigrant labor market incorporation, we would expect higher levels of education, U.S. labor market experience, and English language ability to buffer immigrant workers from low-wages, employment instability, and lack of benefits alike.
In a similar vein, while data limitations often prevent a direct analysis, legal status is likely central to immigrant employment outcomes. It would seem obvious that the disproportionate concentration of undocumented workers in the Hispanic immigrant population would be a critical challenge to their economic incorporation, as the employer sanctions instituted under IRCA and IIRIRA were specifically designed to block unauthorized employment. While immigration policies have heretofore failed to staunch the entry and employment of undocumented workers, there is ample reason to expect that unauthorized status is directly related to low wages and other adverse employment outcomes (Donato and Massey 1993). Because many employers are not willing to hire undocumented workers, at least not directly, their bargaining power in employment relations is severely curtailed. Along with the threat of being reported to immigration authorities, this makes the undocumented particularly vulnerable to exploitation. Indeed, a number of previous studies found a direct negative effect of undocumented status on wages, even net of differences among immigrants in human capital considerations such as educational attainment, U.S. labor market experience, and English language ability (Hall et al. 2010; Phillips and Massey 1999; Rivera-Batiz 1999).1 Studies also suggest that the wage penalty for being undocumented increased after the implementation of IRCA and IIRIRA (Donato et al. 2008; Orrenius and Zavodny 2009). Building on these findings we expect undocumented status to undermine wages and benefit provision, and potentially employment stability as well.
Our third objective is to elaborate on the role of employment conditions in shaping wages and other aspects of compensation. This objective is two-fold: we want to assess the direct impact of nonstandard work arrangements on compensation outcomes and evaluate the extent to which they mediate the effect of human capital and immigration characteristics (particularly legal status) on wages, stability, and benefits. Several nonstandard arrangements are especially salient to the immigrant experience. Heightened exposure to nonstandard work arrangements, particularly subcontracting and informal employment, is a central characteristic of the low-wage labor market. While not all forms of nonstandard work arrangements are associated with lower wages among the general population, there is evidence that some types of nonstandard work arrangements increase workers’ risk of low earnings. And nonstandard work arrangements have been consistently found to predict lack of health and retirement benefits (Kalleberg 2003; Kalleberg, Reskin, and Hudson 2000;). In immigrant-intensive labor markets, there is evidence that employers have increasingly turned to subcontracting as a way of circumventing the employer sanctions imposed by IRCA and IIRIRA (Massey 2010). The subcontractor, in return for assuming this risk, then appropriates a share of the worker’s wages as compensation. Similarly, another way for employers to lower the risk of sanctions when hiring undocumented workers (and reduce tax and administrative costs) is to pay their workers off-the-books, usually in cash. Among immigrants, both working for a subcontractor and being paid in cash have been found to be associated with lower wages (Catanzarite and Aguilera 2002; Phillips and Massey 1999). Less is known about the role of subcontracting and informal employment on other dimensions of compensation. Building on the prior evidence, I expect nonstandard work arrangements will also undermine all aspects of compensation considered.
Likewise, Hispanic immigrants are highly concentrated in a handful of niche industries and occupations, and these patterns are likely to shape other employment outcomes. In particular, fields with a high degree of seasonality such as construction and yard work could be particularly vulnerable to employment instability. Given that research has documented the negative impact of small firm size on wages, employment stability, and benefit provision among the general population, I would also expect to see the same pattern of association among immigrant workers. And finally, recently arrived Hispanic immigrants are disproportionately employed in job sites where other Hispanics predominate. The literature is ambiguous about the effect of working with other co-ethnics on employment outcomes. On the one hand, some studies have found that Hispanic men working in blue-collar and service jobs earn less when they were employed in Hispanic concentrated work sites in part as a reflection of the overall vulnerability of the group (Catanzarite 2000; Catanzarite and Aguilera 2002). On the other hand, other studies have posited that in some situations ethnic concentration might improve employment outcomes by diminishing discrimination or the penalties associated with other personal disadvantages, such as lack of English ability. I will evaluate the two expectations in the context of a rapidly growing ethnic economy.
Finally, to the extent that nonstandard work arrangements, seasonal occupations, small firms, or segregated Hispanic work sites expose immigrant Hispanics to lower compensation outcomes a more comprehensive account of the labor market position of immigrant workers needs to also consider the channeling of workers into these particular employment conditions. Our fourth objective is thus to explore more systematically the human capital and immigration characteristics that put migrants at risk of being at risk for low incomes. We do so by separately modeling the likelihood of occupying specific employment conditions. Studies among the general population have found that factors such as age, race, nativity, education, occupation, and family structure shape exposure to nonstandard work arrangements (Kalleberg 2011), as well as occupation and firm size. The literature also suggests that legal status shapes exposure to nonstandard work arrangements such as subcontracting and being paid off-the-books, though few studies have been able to test the relationship empirically. The analysis will assess the extent to which human capital shapes employment conditions among immigrant workers. I expect better-educated migrants with greater U.S. experience and English skills to be less likely to be laboring in segregated and niche occupations, small firms, and nonstandard work arrangements. Those who lack documentation, on the other hand, are expected to be overrepresented among disadvantageous employment conditions. Taken together, the focus on employment outcomes and mediating conditions not only provides a more comprehensive account of the labor market experience of immigrant workers but also adds to our understanding the constellation of factors that constrain the labor market position of Hispanic immigrants.
Data and Methods
We test our framework drawing on original and locally representative data collected among Hispanic immigrants during 2006 and early 2007 in the Durham/Chapel Hill, North Carolina metropolitan area. Durham represents a valuable vantage point to study Hispanic immigrant incorporation for a number of reasons. The overall area has been growing rapidly, as part of the national shift in population from Rustbelt to Sunbelt states. The influx of highly educated workers attracted to growing job opportunities in the nearby Research Triangle Park, universities, and other large employers generated an intense demand for low-skill service and construction labor. Some employers responded by recruiting Hispanic immigrant laborers from more traditional receiving areas or even directly from Mexico (Flippen and Parrado forthcoming; Johnson-Webb 2003; Parrado, Flippen, and Uribe 2010). Once early migrants became established, a cycle of chain migration began that resulted in dramatic population growth; the Hispanic population rose from a mere 1 percent of the total population of Durham in 1990 to nearly 9 percent by 2000 and 11.9 percent by 2007.
While our data are only locally representative, a similar scenario played out in other “new destinations” throughout the American southeast. For instance, cities like Charlotte, North Carolina and Atlanta, Georgia, differ from Durham in their industrial structure; Durham relies more on education and research and technology as opposed to banking and commerce. Nevertheless, all three cities shared the experience of robust native population growth and rapidly expanding construction industries that served as magnets for Hispanic immigrants. Likewise, all of these new destinations by definition lacked a sizeable Hispanic community in the 1990s, making Hispanic population growth a sudden phenomenon. Also, most of the Hispanic growth was driven by the foreign born, mainly recently arrived low-skilled immigrants, some of whom, especially at early stages of the flow, arrived to the Southeast via another U.S. location. Thus, even though the analysis is at the local level, it arguably has implications for understanding the larger experience of Hispanic immigrants in new destinations.
Sample
The precarious position of Hispanic immigrants in Durham presented unique challenges for approximating a locally representative sample. Community involvement and original sampling techniques were necessary to enhance coverage and reduce underrepresentation, especially of the undocumented, as well as to facilitate reaching the still relatively small Hispanic immigrant population in a new destination. Our study relied on a combination of community-based participatory research (CBPR) and targeted random sampling to overcome these difficulties. CBPR is a participatory approach to research that incorporates members of the target community in all phases of the research process (Israel et al. 2005). In our case, a group of 14 community members assisted in the planning phase of the study, survey construction and revision, and devising strategies to boost response rates and data quality. In addition, CBPR members were trained in research methods and conducted all surveys. Finally, through ongoing collaborative meetings, they were also influential in the interpretation of survey results. It is difficult to overstate the wealth of culturally grounded understanding that they brought to project findings.
At the same time, the relatively recent nature of the Hispanic community in Durham rendered simple random sampling prohibitively expensive. We therefore employed targeted random sampling techniques (Watters and Biernacki 1989). Based on our knowledge of the community, we identified 49 apartment complexes and blocks that house large numbers of immigrant Hispanics. We then collected a census of all the apartments in these areas and randomly selected individual units to be visited by interviewers. Using community members as interviewers helped achieve a refusal rate of only 9 percent, and a response rate, which also discounted randomly selected units in which contact was not made after numerous attempts, of over 72 percent. The end result was a sample of 355 Hispanic immigrant men between the ages 18 and 49.2 All interviews were conducted in Spanish, usually in the homes of respondents, with interviewers filling out paper surveys that included a mix of close-ended and open-ended questions. Only 16 (4 percent) immigrant Hispanic men were not working at the time of the survey. Given our focus on employment conditions we restrict our analysis to the 339 men working at the time of interview.
To evaluate potential biases arising from targeted random sampling, we compared our sample with data on Hispanic immigrant men from the 2000 Census. The results show that the vast majority of Durham’s Hispanics, close to 80 percent, live in areas similar to those in which our targeted samples are located, i.e., in blocks that are between 25 and 60 percent Hispanic. This figure would likely be even higher if block-level data identifying the foreign born were available. Moreover, there were no statistically significant differences between our data and Census data on a number of sociodemographic characteristics such as age, employment status, hourly wages, marital status, and year of arrival in the United States. Our respondents were slightly less educated than Hispanic immigrants enumerated in the Census, reflecting differences in question wording and the tendency for less well-educated individuals to be underenumerated in the Census (Parrado, McQuiston, and Flippen 2005). Thus, over all we are confident that targeted sampling provided a reasonable approximation of the Durham immigrant community.
A main advantage of the original data collected as well as the collaboration with community members was the ability to develop a questionnaire specifically tailored to assess the experience of immigrants in the low-wage labor market. Standard and nationally representative data sources, such as the Census or Current Population Survey, for the most part, do not contain the necessary information to assess the dimensions highlighted in our conceptual framework. However, even though we included a wide diversity of neighborhoods in our sampling strategy we cannot rule out the possibility that more established immigrants might be underrepresented in the study. While it is important to consider this limitation, the targeted approach is better suited to the study of immigrant populations than other methods of recruitment such as snowball or convenience samples. Moreover, our focus is on variation within the low-wage labor market that overwhelmingly recruits workers residing in the areas included in our targeted sampling design.
Model Specification
The three main dependent variables describing the employment position of Hispanic immigrants in our conceptual framework are hourly wages, employment stability, and benefit receipt. Hourly wages were measured directly through a question in the survey.3 Our measure of employment stability is the self-reported number of weeks that respondents did not work during the previous year. To aid in recollection, the survey collected retrospective information separating the prior year into four seasons and asked separately for each season whether there was a period of time that respondents were involuntarily without work, and if so for how long. This strategy was specifically designed to capture the seasonal variability in employment in the construction and landscaping industries, where immigrant men in Durham are concentrated. Responses for the four seasons were summed to produce the yearly estimate. Lack of benefits is measured as a dummy variable indicating whether respondents currently receive any of four employer sponsored benefits: paid vacation, sick leave, overtime compensation,4 or health insurance. Those who reported none of the above were deemed lacking in benefits. Together the three employment outcomes more precisely reflect the overall economic standing of Hispanic immigrants.
Our model includes three measures of human capital: age, educational attainment, and rural origins. We also include a squared term to capture nonlinear age effects. Educational attainment is measured by a set of dummy variables distinguishing between those with six or fewer, seven and nine years, and ten or more years of completed schooling. These distinctions correspond to primary, secondary, and above secondary education in Mexico. Finally, we include a dummy variable indicating whether respondents originated from a rural area in Latin America, which may signal a background that is less conducive to the transition to U.S. employment.
Immigration-related characteristics include indicators of family structure, migration histories, English ability, and legal status. First, marital status is captured by a set of dummy variables that reflect the particular dynamics of male migration from Latin America to the United States. Specifically, we distinguish between three categories of men: unmarried (single or divorced), unaccompanied married (whose wives were residing in their country of origin), and accompanied married men (whose wives resided with them in Durham). We also include a dummy variable for whether the respondent reported having family residing in the area, over and above members of their household. In addition, we include a variable capturing self-reported number of years of residence in the Durham area, and a dummy variable indicating if the person migrated to Durham via another U.S. city, and thus has additional American labor market experience.5 English ability is measured by a dummy variable indicating whether the respondent reported being able to speak English well or very well (as opposed to more or less or not at all).6 Finally, a dummy variable for undocumented status reflects the response from a direct question on legal status.
As elaborated in the conceptual framework, we also consider five mediating employment characteristics: occupation/industry, firm size, ethnic concentration, subcontracting, and informal employment. Occupation/industry is captured by a set of dummy variables reflecting whether the respondent’s primary job is in construction, yard work, food preparation, or other occupations.7 Firm size is a dummy indicator of whether a person is working in a firm with ten or fewer workers (including all locations or branches). Ethnic concentration is also captured through a dummy variable indicating whether the respondent reported working at a site that is majority Hispanic. Exposure to nonstandard work arrangements, particularly subcontracting and informal employment, are measured through dummy indicators constructed from self-reports to direct questions about whether the person is working for a subcontractor and is being paid in cash, respectively.8
Analytic Strategy and Methods
We separate the analysis into two parts. We first describe and model our three measures of employment compensation, namely wages, employment stability, and benefit provision. For each dependent variable we estimate two models, the first including only human capital and immigration characteristics and a second adding the mediating employment characteristics. Because employment characteristics such as subcontracting, informal employment, occupation, working in a segregated Hispanic work site or small firm are central determinants of compensation, the second part of the analysis models them as dependent variables as well. We are primarily concerned with which, if any, human capital and immigration characteristics structure employment conditions, and how they relate to one another.
The statistical specification varies depending on the dependent variable under consideration. For hourly wages, which is continuous, we apply standard ordinary least squared regression. For dichotomous outcomes such as lack of benefits, majority Hispanic work site, and working for a subcontractor we apply logistic regression techniques. For occupation, which consists of four mutually exclusive categories, we employ multinomial logistic regression. Finally, the distribution of number of weeks of involuntary job separation in the past year more closely conforms to a negative binomial discrete probability distribution. Accordingly, parameter estimates were obtained using negative binomial regression.
We also evaluated the role of collinearity in affecting model results both investigating the correlation matrix and estimating the variance inflation factor (VIF) in OLS regression models. Results show the correlation between independent variables (reported in Appendix A) to be low or moderate and the VIF was in all cases less than two, except for the obvious association between age and age squared.
Appendix A.
Correlation Matrix of Independent Variables
| Wage | Wks Unemp | No Ben. | Constr. | Yard | Food | Other Occ. | Subc. | Paid in Cash | Small Firm | Hisp. Site | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Hourly wage | — | −.087 | −.112 | .269 | −.105 | −.252 | −.041 | −.092 | −.120 | −.015 | −.016 |
| Weeks unempoyed | — | .150 | .101 | −.059 | −.176 | .085 | .197 | .092 | .129 | .026 | |
| No benefits | — | .081 | −.024 | −.052 | −.044 | .411 | .264 | .301 | .242 | ||
| Construction | — | — | — | — | .289 | .090 | −.063 | .249 | |||
| Yard | — | — | — | −.071 | −.044 | .098 | .072 | ||||
| Food | — | — | −.216 | −.067 | −.011 | −.356 | |||||
| Other occupation | — | −.137 | −.024 | .023 | −.047 | ||||||
| Subcontracting | — | .336 | .200 | .204 | |||||||
| Paid in cash | — | .231 | .217 | ||||||||
| Small firm | — | .166 | |||||||||
| Hispanic work site | — |
Descriptive Results
Table 1 presents descriptive results for the variables in our analysis for all respondents and separately by legal status, in order to get a preliminary sense for the salience of documentation to employment outcomes. Results show that on average Hispanic workers earn just over $11 an hour. Assuming a full work year at 40 hours per week, this wage rate would translate into an annual income of $22,968, which is just above the poverty threshold for a family of four. There is nearly a $2 deficit in hourly wages associated with undocumented status. A difference-in-means test (t) indicates that this difference is statistically significant. Given full-time, full-year employment this difference yields nearly $4,000 (18 percent) less in annual income for those lacking legal status. Results also show considerable employment instability. Nearly 62 percent of workers reported being without work at some point during the previous year. The average time out of work (including all respondents, even those who were never without work) was 3.8 weeks. Applying similar assumptions, this implies that employment stability reduces workers’ annual income by over $1,700 (8 percent). If we divide the sample between those who did and did not experience joblessness during the previous year, those with a spell of instability experienced an average of six full weeks of job separation, reducing their yearly income by over $2,600 (12 percent).
Table 1.
Descriptive Results by Documenation Status
| Documented
|
|||
|---|---|---|---|
| Yes | No | ||
| Compensation outcomes | |||
| Hourly wages (mean) | $11.07 | $12.75 | $10.89** |
| s.d. | (3.1) | (4.0) | (2.9) |
| Employment instability/involuntary job separation | |||
| Number of weeks in the past year (mean) | 3.8 | 3.7 | 3.9 |
| s.d. | (6.0) | (6.2) | (6.0) |
| No employment benefits (%) | 52.2 | 25.0 | 55.1** |
| No paid overtime | 63.4 | 34.4 | 66.4** |
| No sick leave | 82.6 | 68.7 | 84.0** |
| No paid vacation | 73.4 | 46.9 | 76.2** |
| No health insurance | 80.5 | 71.9 | 81.4 |
| Personal human capital charactersitics | |||
| Age (mean) | 30.9 | 37.2 | 30.3** |
| Educational attainment (%) | |||
| 6 years or less | 40.1 | 34.4 | 40.7 |
| 7–9 years | 28.0 | 25.0 | 28.3 |
| 10 or more years | 31.9 | 40.6 | 30.9* |
| Rural origin (%) | 28.6 | 25.0 | 29.0 |
| Immigration characteristics | |||
| Marital status (%) | |||
| Accompanied married | 45.4 | 59.4 | 44.0* |
| Unaccompanied married | 20.4 | 18.8 | 20.5 |
| Single | 34.2 | 21.9 | 35.5* |
| Family in Durham (%) | 61.7 | 59.4 | 61.9 |
| Years in Durham (mean) | 4.9 | 7.5 | 4.6** |
| Direct migrant to Durham | 50.1 | 34.4 | 51.7* |
| English proficiency (%) | 7.7 | 25.0 | 5.9** |
| Undocumented (%) | 90.6 | — | — |
| Mediating work conditions | |||
| Occupation/industry (%) | |||
| Construction | 68.1 | 53.1 | 69.7** |
| Yard work | 7.4 | 12.5 | 6.8 |
| Restaurant/food preparation | 13.0 | 12.5 | 13.0 |
| Other | 11.5 | 21.9 | 10.4** |
| Small firm (%) | 35.4 | 25.0 | 36.5 |
| Majority Hispanic work site (%) | 77.3 | 50.0 | 80.1** |
| Paid by subcontractor (%) | 27.1 | 15.6 | 27.0* |
| Paid in cash (%) | 18.0 | 3.1 | 19.5* |
| N | 339 | 32 | 307 |
p < .10
p < .05 (two-tailed tests)
The low rate of benefit coverage among immigrant workers evident in Table 1 is another serious impediment to security. In the larger labor market, “benefit coverage” usually refers to employer subsidized health care or retirement contributions. For Hispanic immigrants, however, basic provisions such as paid sick leave and vacation time are often lacking. In fact, just under 37 percent of immigrant Hispanic men received paid overtime, less than 18 percent paid sick leave, less than 27 percent paid vacation, and less than 20 percent employer-sponsored health insurance. Just over half (52.2 percent) of all men report having no benefits whatsoever. Once again there were sizeable differences in benefit coverage according to legal status, with the undocumented more than twice as likely as those with legal papers to report having no benefits (55.1 percent relative to 25.0 percent).
The human capital and demographic characteristics of the sample are typical of Hispanic immigrant populations:9 the men are relatively young, with an average age of only 30.9 years, and poorly educated. With an average of just over eight years of completed schooling, 40.1 percent of immigrant men in Durham did not complete more than a primary education, an additional 28.0 percent finished between seven and nine years of schooling, and just under one-third (31.9 percent) completed ten or more years of education. The fact that 28.6 percent of immigrant Hispanic men herald from rural areas no doubt contributes to their limited educational attainment.
Immigration characteristics reported in Table 1 also reflect disadvantage. A mere 45.4 percent of men are married and currently residing with their spouse. An additional 20.4 percent of men are married but unaccompanied by their wives, who continue to reside in their countries of origin. Over one-third of the immigrant men in our sample are either single or divorced. Consistent with the importance of family networks in migration flows close to 62 percent of men report having family in the Durham area. Most Hispanic immigrant men in Durham are recently arrived, averaging a scant 4.9 years in the area. Over half (50.1 percent) of the sample moved to Durham directly from their countries of origin. These characteristics help explain why English language proficiency is low (only 7.7 percent of men report speaking English well), and why the overwhelming majority, 90.6 percent, of the men are undocumented. As with compensation outcomes, human capital and immigration characteristics also vary markedly by documentation status. The undocumented are significantly younger, less likely to be well educated, and less likely to be married and accompanied by their wives. They also average far less time in Durham and lower English fluency than their peers with legal status, and are more likely to have migrated directly to Durham from their countries of origin, and thus to lack additional U.S. experience.
Table 1 also documents a very high degree of occupational concentration among Durham Hispanic men. Nearly 89 percent of all immigrant men are employed in only three areas: construction, where a staggering 68.1 percent of immigrant men labor; yard work, where an additional 7.4 percent of men work; and food preparation (which includes restaurant work), where an additional 13.0 percent of men are employed. Only 11.5 percent of men work outside of these areas, in a diverse array of fields including retail, the military, entertainment, and mechanics of various types.10 The fact that over one-third (35.4 percent) of all men work in small firms, and over three-quarters (77.3 percent) in predominantly Hispanic work sites is additional cause for concern, along with the incidence of nonstandard work arrangements. Fully 27.1 percent of immigrant men are paid via a subcontractor rather than directly by their de jure employer, and an additional 18 percent report being paid in cash. Once again, documented immigrants show considerable advantage relative to the undocumented with respect to employment characteristics. Documented workers are significantly less likely than their undocumented peers to work in Hispanic occupational niches (53.1 percent in construction versus 69.7 percent), Hispanic work sites (50.0 versus 80.1 percent), for a subcontractor (15.6 versus 27.0), or informally (3.1 versus 19.5 percent).
Multivariate Results
While the overall pattern described above highlights many elements of disadvantage associated with the secondary labor market, there is considerable variation in employment outcomes and work conditions among the Hispanic immigrant population in Durham. It remains unclear, though, which factors undergird this variation and how the different employment conditions relate to one another. The next set of analyses investigates the dimensions affecting the labor market position of Hispanic immigrant men. Following our conceptual framework we first investigate the factors associated with our three main aspects of compensation and then investigate the factors related to mediating employment conditions.
Compensation Outcomes
Table 2 reports results from models predicting hourly wages, employment instability, and benefit coverage. For each variable we report two models, one that controls only for personal human capital and immigration characteristics and a second that adds the role of mediating employment conditions. Results indicate that even in the low-wage labor market in which immigrants concentrate there is still a clear wage return to human capital. OLS estimates in columns one and two show that older workers earn more, on average, than their younger counterparts, though the negative age-squared term indicates this return diminishes at older ages. More importantly, better educated workers earn significantly more than their less educated peers. Those with ten or more years of education earn $1.57 more per hour more than those with only a primary education, and $1.22 an hour more than those with seven to nine years of education.
Table 2.
Coefficients from Models Predicting Hourly Wages, Employment Instability, and Work Benefits
| Hourly Wages1
|
Weeks of Job Separation2
|
Lack of Benefits3
|
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |||||||
| Human capital characteristics | ||||||||||||
| Age | .549 | (.133) | .455** | (.127) | .067 | (.071) | .039 | (.069) | −.076 | (.101) | −.105 | (.116) |
| Age2 | −.008** | (.002) | −.006** | (.002) | −.001 | (.001) | .000 | (.001) | .002 | (.001) | .002 | (.002) |
| Education (ref = 10 years or more) | ||||||||||||
| 6 years or less | −1.566** | (.386) | −1.536** | (.376) | −.017 | (.211) | −.146 | (.204) | .658** | (.289) | .478 | (.343) |
| 7–9 years | −1.218** | (.404) | −1.212** | (.383) | .174 | (.224) | .137 | (.216) | .393 | (.299) | .349 | (.347) |
| Rural origin | −.108 | (.348) | −.022 | (.329) | .057 | (.191) | .109 | (.185) | −.076 | (.260) | −.015 | (.302) |
| Immigration characteristics | ||||||||||||
| Marital status (ref = single or divorced) | ||||||||||||
| Accompanied | .326 | (.376) | .249 | (.359) | −.457** | (.210) | −.458** | (.205) | −.457* | (.281) | −.316 | (.331) |
| Unacc. married | −.278 | (.472) | −.438 | (.448) | .038 | (.264) | .296 | (.257) | −.108 | (.356) | .157 | (.415) |
| Have family in Durham | .009 | (.323) | −.107 | (.307) | .248 | (.183) | .227 | (.181) | −.109 | (.242) | −.068 | (.280) |
| Time in Durham | .196** | (.048) | .177** | (.046) | −.029 | (.028) | −.020 | (.027) | −.060* | (.037) | −.036 | (.042) |
| Direct migrant to Durham | −.223 | (.327) | −.293 | (.309) | −.012 | (.182) | −.073 | (.176) | −.077 | (.245) | −.154 | (.282) |
| Good English | .331 | (.619) | .740 | (.599) | .250 | (.345) | .387 | (.342) | .381 | (.472) | .864 | (.541) |
| Undocumented | −.998* | (.560) | −1.037** | (.538) | −.085 | (.315) | .141 | (.317) | 1.454** | (.479) | 1.468** | (.566) |
| Mediating employment characteristics | ||||||||||||
| Occupation (ref = construction) | ||||||||||||
| Yard | −2.120** | (.570) | −.275 | (.328) | −.240 | (.499) | ||||||
| Food | −2.798** | (.490) | −1.293** | (.309) | .312 | (.429) | ||||||
| Other | −1.635** | (.482) | .198 | (.266) | −.061 | (.433) | ||||||
| Majority Hispanic | −.035 | (.398) | −.386 | (.236) | .689** | (.360) | ||||||
| Small Firm | .430 | (.323) | .524** | (.188) | 1.192** | (.301) | ||||||
| Subcontractor | −.723** | (.369) | .355* | (.203) | 1.973** | (.373) | ||||||
| Paid cash | −.516 | (.418) | −.057 | (.234) | .650 | (.411) | ||||||
| Intercept | 3.219 | (2.219) | 5.634** | (2.154) | .205 | (1.253) | .534 | (1.235) | −.289 | (1.665) | −1.763 | (1.937) |
| Summary statistics | .192 | .302 | .009 | .030 | 3.713 | 117.77 | ||||||
Note: Standard errors in parenthesis.
OLS estimates; summary statistic R2
Negative binomial regression estimates; summary statistic pseudo R2
Negative binomial regression estimates; summary statistic pseudo R3
p < .10
p < .05 (two-tailed tests)
There is also a significant positive return to years in the Durham area, indicating that as immigrants build local labor market experience their wages rise. Specifically, each additional year of Durham residence translates into roughly 19.6 cents per hour in additional wages. Other dimensions of the migration experience, however, such as residing with a spouse, having family in the area, English language ability, or internal migration experience do not significantly translate into wage gains. Consistent with our descriptive results, though, estimates show that even after controlling for other personal characteristics lack of documentation significantly reduces the hourly wage of Hispanic immigrant male workers by nearly $1 (−.998 in column one).
Estimates in column two show that while mediating employment characteristics significantly correlate with hourly wages they do not modify the impact of human capital and immigration characteristics. In particular, even after controlling for other employment conditions, lack of documentation significantly diminishes hourly wages and the effect remains close to $1.00. Estimates for our mediating employment factors show that occupation is clearly related to wages, with construction workers earning significantly more per hour than all other categories. Specifically, on average those in yard, food, and other occupations earn $2.12, $2.80, and $1.64 less per hour, respectively, than those employed in construction. Of the other constellation of employment conditions affecting Hispanic immigrant labor only subcontracting has a direct negative effect on hourly wages. Net of human capital, immigration, and occupational characteristics, those paid via a subcontractor earn 72 cents an hour less than those paid directly by their employer. Given that average wages are only $11 an hour, this represents a nearly 7 percent reduction in wages associated with subcontracting. There does not seem to be any independent earnings penalty associated with off-the-books employment, however, over and above its association with subcontracting.
Columns three and four report estimates from negative binomial regression models predicting the number of weeks of job separation in the previous year. Contrary to the findings for hourly wages, results show that for the most part human capital resources and immigration characteristics do not protect immigrant Hispanic workers against employment instability, highlighting the widespread nature of Hispanic immigrants’ vulnerability to periods of inactivity. Only residing with a spouse decreases the number of weeks with no work, but the direction of causality is unclear. On the one hand, marriage is associated with greater labor force attachment in the general population. It could be that married men living with their wives have stronger intentions to remain in the United States and are more motivated to seek stable employment, and that marriage expands the networks connecting immigrants to jobs. On the other hand, it is also likely that men with more stable employment patterns are better able to afford to send for their wives or form a union in Durham.
Rather than human capital and immigration considerations, the primary determinants of employment instability relate to other employment characteristics (column four). Those employed in food services, mainly restaurants, enjoy far greater employment stability than their counterparts in construction, at least partially offsetting some of the lower hourly wages common in those jobs. To illustrate, compared to the average worker that experiences 3.8 weeks of job separation, those in food services would see the number of weeks without work reduced to 1.04, an incidence rate ratio of .274 (exp(−1.293)). Contrary to employment in food services, workers in small firms are significantly more likely to experience involuntary joblessness than those in larger firms (.524). Again, taking the average of 3.8 weeks, workers in small firms would see their time without work almost double to 6.4 weeks, an incidence rate ratio of 1.68. In addition, subcontracting also increases workers vulnerability to involuntary job separation above and beyond work in small firms (.355). Again, relative to 3.8 weeks of job separation experienced by the average worker those in subcontracting can expect 5.4 weeks of nonwork, an incidence rate ratio of 1.425.
Just as it is important to consider employment instability in addition to wages when assessing the economic security of Hispanic immigrants, it is also critical to consider benefit provision. Columns five and six in Table 2 report results from logit models predicting lack of benefit receipt. Once again human capital and immigration characteristics have little effect on this aspect of employment compensation. Results in column five show that those with very low levels of education are more likely than those completing ten or more years of schooling to lack benefits. At the same time, workers residing with a spouse and with longer stays in Durham are less likely to lack benefits. However, in all these cases, the effects disappear once we control for mediating employment conditions in column six. Additional tabulations (not reported) that consider the determinants of each type of benefit separately reveal that those with 10 or more years of schooling are significantly less likely than their less educated counterparts to lack insurance coverage, even net of other employment characteristics. No other human capital or immigration characteristics predict the receipt of the various benefits when considered separately, however.
As with wages, results show a consistent detrimental effect of lack of documentation on benefit receipt, even net of differences across groups in human capital and immigration characteristics. Estimates in column five show that undocumented workers are 4.5 times (exp(1.454)) more likely to lack benefits than their documented counterparts. Moreover, the detrimental effect remains even after accounting for mediating employment conditions. It is important to note, however, that this effect is largely driven by the lack of overtime compensation among the undocumented. Additional tabulations that model each benefit separately show that legal status does not significantly predict receipt of paid sick leave and vacation time, or insurance coverage, net of other factors.
Column six shows that the main determinants of benefit provision relate to the mediating employment conditions. While occupation does not significantly predict benefit receipt, working in majority Hispanic work sites increases the likelihood of not receiving benefits 2.0 times (exp (.689)), working in a small firm by 3.3 times (exp(1.192)), subcontracting by a dramatic 7.2 times (exp(1.973)), and being paid in cash by 1.9 times (exp(.650)). It is worth noting that these effects are remarkably uniform when each benefit is considered separately. Moreover, they remain significant even controlling for one another, so that immigrants with multiple adverse employment conditions (such as an employee of a small subcontracting firm at a predominant Hispanic work site) are extremely unlikely to have even the most basic benefit provisions.
Thus, overall, our results confirm our expectation that Hispanic workers are subject to a constellation of factors that diminish their employment compensation and heighten their vulnerability in the U.S. labor market. The next set of analyses take these mediating conditions as dependent variables to assess the factors affecting their variation as well as how they relate to one another.
Correlates of Mediating Employment Conditions
Table 3 reports estimates from a multinomial logit model predicting occupation (construction, yard, food services, or other) according to human capital and immigration characteristics. The reference category is construction. Despite the salience of occupation for compensation outcomes documented above, results show very little systematic allocation of Hispanic workers across occupational types. In fact, the process appears to be nearly random with little connection to human capital endowments or immigration characteristics. Part of the reason stems from the high degree of concentration of Hispanic workers in construction, which appears to be a magnet for Hispanic immigrants of all backgrounds. There is some evidence though that employment in “other” occupations outside of traditional Hispanic niches attracts more educated workers with a good command of English. The lack of systematic variation is consistent with segmented labor market descriptions of Hispanic immigrants’ employment position and highlights that Hispanic immigrants work within a relatively narrow set of employment options not easily overcome by higher human capital endowments.
Table 3.
Coefficients from Multinomial Logit Model Predicting of Occupation Type
| Yard | Food | Other | ||||
|---|---|---|---|---|---|---|
| Human capital characteristics | ||||||
| Age | −.249 | (.185) | −.312** | (.137) | .017 | (.159) |
| Age2 | .004 | (.003) | .005** | (.002) | .001 | (.002) |
| Education (ref = 10 years or more) | ||||||
| 6 years or less | .473 | (.597) | −.329 | (.433) | −.820* | (.454) |
| 7–9 years | .440 | (.615) | −.190 | (.439) | −.649 | (.481) |
| Rural origin | .282 | (.467) | .047 | (.387) | −.010 | (.441) |
| Immigration characteristics | ||||||
| Marital status (ref = single or divorced) | ||||||
| Accompanied | .493 | (.544) | −.027 | (.406) | .207 | (.463) |
| Unacc. married | .164 | (.729) | −.599 | (.567) | .103 | (.552) |
| Have family in Durham | −.585 | (.448) | −.332 | (.356) | −.144 | (.389) |
| Time in Durham | .060 | (.062) | −.026 | (.055) | −.054 | (.055) |
| Direct migrant to Durham | −.361 | (.470) | −.323 | (.364) | .413 | (.394) |
| Good English | .196 | (.867) | .541 | (.666) | 1.714** | (.594) |
| Undocumented | −.617 | (.660) | .224 | (.656) | −.340 | (.568) |
| Constant | 1.871 | (3.036) | 3.248 | (2.284) | −2.471 | (2.737) |
| Pseudo R2 | .065 | |||||
Notes: ref = construction; standard errors in parentheses.
p < .10
p <.05 (two-tailed tests)
Table 4 reports estimates from logit models predicting the likelihood of working for a subcontractor, being paid in cash, working at a small firm, and working at a majority Hispanic work site. Results for the model predicting subcontracting, reported in the first column of Table 4, show that human capital characteristics are far less important predictors of entry into subcontracting than they were of wages. Factors such as age and education have little impact; compared to those with ten or more years of education, those with only a primary education are only marginally more likely to work for a subcontractor. Likewise, neither documentation nor ability to speak English predict subcontracting. Only time in Durham helps move immigrants toward direct employment, with each additional year decreasing the likelihood of working for a subcontractor by .89 (exp (−.115)). Once again it is other employment conditions that are the primary predictors of this form of contingent labor. First, subcontracting is heavily concentrated in construction employment; those employed in food and other industries, and to a lesser extent yard work, are significantly less likely than construction workers to engage in subcontracting. Predicted probabilities at the mean show that among construction workers the likelihood of working for a subcontractor is 36 percent, compared to 16 percent for yard workers, and only 2 and 7 percent for food and other workers, respectively. The practice is also significantly more common among employees of small firms and those who are paid in cash.
Table 4.
Coefficients from Logit Models Predicting Subcontracting, Informal Employment, Small Firm, and Hispanic Worksite
| Subcontracting (1) | Paid in Cash (2) | Small Firm (3) | Hispanic Work Site (4) | |||||
|---|---|---|---|---|---|---|---|---|
| Human capital characteristics | ||||||||
| Age | −.057 | (.127) | .317* | (.181) | −.044 | (.108) | −.205 | (.141) |
| Age2 | .001 | (.002) | −.006** | (.003) | .001 | (.002) | .003 | (.002) |
| Education (ref = 10 years or more) | ||||||||
| 6 years or less | .673* | (.376) | .026 | (.439) | −.525 | (.322) | 1.497** | (.423) |
| 7–9 years | .312 | (.398) | .084 | (.451) | −.293 | (.327) | .532 | (.392) |
| Rural origin | .138 | (.323) | .173 | (.373) | −.637** | (.292) | .108 | (.371) |
| Immigration characteristics | ||||||||
| Marital status (ref = single or divorced) | ||||||||
| Accompanied | −.523 | (.360) | −.602 | (.405) | .051 | (.309) | .551 | (.382) |
| Unacc. married | −.441 | (.428) | −.062 | (.491) | −.307 | (.389) | .926* | (.518) |
| Have family in Durham | .535 | (.323) | −.512 | (.349) | −.368 | (.262) | .011 | (.334) |
| Time in Durham | −.115** | (.052) | −.006 | (.062) | .013 | (.041) | −.037 | (.046) |
| Direct migrant to Durham | .131 | (.315) | −.322 | (.358) | −.035 | (.266) | .130 | (.332) |
| Good English | .920 | (.645) | −1.513 | (1.174) | −1.492** | (.641) | .467 | (.575) |
| Undocumented | −.111 | (.603) | .811 | (1.143) | .163 | (.512) | 1.305** | (.472) |
| Mediating employment characteristics | ||||||||
| Occupation (ref = construction) | ||||||||
| yard | −1.098* | (.608) | −.241 | (.731) | 1.210** | (.470) | .332 | (.700) |
| food | −3.439** | (1.069) | .977 | (.630) | .900** | (.434) | −2.394** | (.448) |
| other | −1.687** | (.573) | .583 | (.588) | .755* | (.413) | −.489 | (.479) |
| Majority Hispanic | .463 | (.463) | 2.267** | (.822) | .997** | (.383) | – | – |
| Small Firm | .990** | (.312) | .853** | (.345) | – | – | .875** | (.370) |
| Subcontracting | – | – | 1.600** | (.367) | .979** | (.308) | .433 | (.477) |
| Paid cash | 1.579** | (.365) | – | – | .771** | (.335) | 2.245** | (.840) |
| Intercept | −1.017 | (2.201) | −8.878** | (3.034) | −.947 | (1.834) | 2.377 | (2.315) |
| Chi-square | 96.814 | 81.354 | 53.575 | 97.869 | ||||
Note: Standard errors in parenthesis.
p < .10
p <.05 (two-tailed tests)
Informal employment, in addition to being associated with subcontracting, is also a negative employment outcome in its own right. Accordingly, the second column of Table 4 reports results from a logit model predicting being paid in cash. As was the case for subcontracting, few human capital or immigration characteristics predict cash payment. Older workers are marginally more likely to be paid in cash, though the effect diminishes with age. Neither education nor Durham experience shield workers from informal employment. Surprisingly, neither does legal status. Similar to the models for employment instability, benefit provision, and subcontracting, only other employment characteristics are central determinants of informal employment. In particular, working in small firms and majority Hispanic work sites is strongly related to cash payment, as is working for a subcontractor.
Likewise, working in predominantly Hispanic work sites and small firms are implicated in a number of adverse employment outcomes, including subcontracting, being paid in cash, employment instability, and lack of benefit coverage. With respect to firm size (column three), Table 4 again shows relatively few returns to human capital. Those who report good English skills and herald from rural areas of Latin America are less likely than others to work for a small firm. However, factors such as education, time in Durham, and documentation are unrelated to firm size. Once again it is other employment characteristics that most strongly predict firm size. Specifically, construction workers are less likely than their peers in yard work, food preparation, and other occupations to work for a small firm. There is also a strong association between small firms and majority Hispanic work sites, subcontracting, and being paid in cash.
And finally, in the fourth column of Table 4 we see that few human capital considerations help immigrant men escape predominantly Hispanic work sites. While the least educated are more likely than the most educated to work in a segregated work setting, factors such as age, education, time in Durham, and English ability do not relate to the ethnic composition at work. Unaccompanied married men are more likely than their single counterparts to work in Hispanic jobs, perhaps a reflection of their greater likelihood of being target earners who view their stay as temporary and thus do not work as hard to escape segregated employment settings. Those employed in food preparation also work in more diverse settings than their counterparts in construction. Once again, we see a strong association between Hispanic work sites and being paid in cash. And finally, working at a predominantly Hispanic work site is the only mediating employment outcome to be significantly related to legal status. Compared to their peers with legal authorization to work, the undocumented are 3.7 times (exp(1.305)) more likely to work in Hispanic work sites, even net of other immigration and employment characteristics.
Conclusions
With Hispanic immigrants comprising a large and growing share of the low-wage labor market, it is particularly important to examine the forces that shape economic outcomes among this group. Data limitations have heretofore proved to be a significant obstacle in achieving that goal. Drawing on original data collected in Durham, North Carolina, this article provides a multifaceted account of the labor market position of recent Hispanic immigrant men. Integrating the literatures on immigrant incorporation and low-wage labor markets the primary objective was to consider a multifaceted conception of compensation, including wages, employment stability, and benefit provision, and to assess how they were related to human capital, documentation, and other employment characteristics. We were particularly concerned with the incidence and impact of nonstandard work arrangements and how different employment characteristics related to one another.
One critical question guiding the analysis was whether human capital characteristics such as education, local work experience, and English language ability pay off in the low-wage labor market where immigrants concentrate. Results in this regard are mixed. Human capital considerations, including age, education, and time in Durham, all predicted wages in the expected direction. However, human capital considerations played virtually no role in structuring employment stability or benefit provision, other critical aspects of compensation. Moreover, other employment outcomes were also surprisingly impervious to variation in human capital. Thus, while increased time in Durham helped immigrant men avoid subcontracting, it had no effect on employment stability, benefit coverage, occupation, firm size, working in segregated Hispanic work sites, or informal employment. Likewise, better educated men were less likely to work in segregated jobsites and marginally less likely to work for a subcontractor (in addition to averaging higher wages), but did not differ from their less educated peers with respect to employment stability, benefit provision (with the exception of health insurance), occupation, firm size, or informal employment. Moreover, aside from helping Hispanic men move out of small firms and immigrant niches in construction, food preparation, and yard work into other occupations, English language ability did not affect any of the employment outcomes considered, over and above its association with educational attainment and length of Durham residence. Thus, overall, while human capital does help explain wage variation among migrants, it does a very poor job of explaining other aspects of compensation or nonstandard work arrangements.
The impact of documentation status on labor market incorporation also shows mixed effects. There is a clear negative effect of unauthorized status on wages, and at roughly $1 per hour in lost wages the impact is substantively large. Most other employment outcomes, however, do not vary systematically according to documentation status. The two exceptions are benefit coverage and segregated work sites, with unauthorized workers far more likely than their documented counterparts to lack overtime pay and to work in jobs comprised mostly of other Hispanics. While our small sample size (particularly of legal workers) prevents drawing definitive conclusions from these findings, they suggest that while undocumented status confers a unique penalty in some realms, its lack of consistent effect is also broadly consistent with Douglas Massey and colleagues’ (Gentsch and Massey 2011; Massey and Bartley 2005) assertion that both legal and unauthorized workers are penalized in fields with large numbers of undocumented workers.
Results also underscore the importance of employment characteristics to compensation outcomes among immigrant Hispanic men. It is not difficult to understand why so many of these men are employed in construction. The educational and other human capital requirements are no greater there than in other fields but the pay is significantly higher than even jobs outside of traditional Hispanic niches. However, construction work has its disadvantages as well, particularly greater employment instability and use of labor subcontractors. To illustrate, if men worked full time all year, those in construction would earn roughly $5,845 more per year than their statistically equivalent counterparts in food preparation (using the coefficients in Table 2). Yet, when we account for differential exposure to joblessness, this annual pay gap is reduced by $915, or nearly 16 percent. If we consider that food workers are dramatically less likely to be paid via a subcontractor, which itself is associated with instability, this differential would be reduced further still.
Results also highlight the deleterious impact of nonstandard work arrangements on Hispanic immigrants’ economic well-being. The roughly 27 percent of immigrant Hispanic men employed via a subcontractor lose 76 cents an hour in wages and, along with those paid in cash, are significantly less likely to receive benefits. Moreover, subcontracting and informal work are closely associated with one another and with small firms and predominantly Hispanic work sites. These often-overlapping disadvantages have a serious deleterious impact on compensation. To illustrate, the modal Hispanic male worker in Durham is employed in construction. If he did not experience any of the conditions that adversely affected wages in Table 2, namely lack of documentation and working for a subcontractor, his predicted hourly wage would be $13.60. Assuming a full year of full-time work this would yield an annual income of $28,500. Lacking documentation reduces hourly wages by just over one dollar, decreasing annual income by 7 percent to $26,500. If he were both undocumented and working for a subcontractor, his predicted wage would be reduced by almost $2, and his annual wage of $24,700 would be 13 percent lower.
However, the employment conditions of Hispanic workers affect not only their pay but also the amount of time they work. The seasonality of construction employment translates into an average of 3.3 weeks without work annually for Hispanic immigrant men. Without experiencing any additional disadvantage this would reduce the annual income of the average construction worker by nearly $2,000, to $26,600. However, if he were also employed in a small firm and via a subcontractor, the number of predicted weeks without work would increase to 7.7, reducing annual income to $24,200. Further, if he were to experience all of the above disadvantages both in terms of wages and employment instability (i.e., an undocumented man working for a subcontractor in a majority Hispanic work site) his annual income would decline to $21,200, a 26 percent reduction from what workers with no disadvantage would be earning. This is not an unlikely scenario among Hispanic immigrant workers in Durham; as many as 22 percent of our sample is jointly undocumented and working in a small subcontracting construction firm. Moreover, 90 percent of the workers among this group are predicted to lack all employment benefits compared to only 13 percent among construction workers not suffering those disadvantaged conditions.
A number of caveats are in order. First, our relatively small sample size warrants caution when interpreting results. The lack of variation in legal status and English language ability among this recently arrived population prevents strong conclusions about their lack of effect on employment outcomes. Second, these findings were obtained from a case study of Durham, North Carolina and are not necessarily generalizable to all low-skill Hispanic immigrants living in the United States. However, there is reason to believe that they may be applicable to other new destinations, particularly in the Southeast. For instance, compared to cities like Charlotte, North Carolina and Atlanta, Georgia, Durham is somewhat unique in its industrial composition; education, healthcare, and science occupations are overrepresented while sales, administrative, transportation, and construction occupations are correspondingly underrepresented. Nevertheless, all three metros have grown dramatically in recent decades in their native populations, with attendant growth in construction and other services. Indeed, construction accounts for an even larger share of employment in Charlotte and Atlanta than Durham, and all three metros experienced a rapid influx of Hispanic immigrants after 1990. There is ample reason to expect, then, that the employment dynamics evident in Durham could be comparable across contexts. And finally, our lack of comparison with low-skill whites, blacks, and native-born Hispanics in Durham precludes definitive conclusions that these findings are unique to Hispanic immigrants, though previous research suggests that this is indeed the case (Hall et al. 2010).
Nevertheless, these findings, together with the previous literature, suggest serious impediments to the labor market incorporation of low-skill immigrant Hispanic men emanating from processes that extend far beyond their human capital characteristics. The current anti-immigrant atmosphere and emphasis on employer sanctions has resulted in an extremely precarious labor market position for both legal and undocumented Hispanic immigrants, who suffer from multiple, overlapping elements of disadvantage. The meager impact of time in the United States on employment outcomes suggests that without policy changes contemporary immigrants laboring in the low-wage labor market face the prospect of a lifetime of reduced wages, heightened insecurity, and lack of benefits. To the extent that these penalties undermine savings they diminish immigrants’ ability to contribute to their children’s education and compromise old-age security, seriously affecting chances for intergenerational social mobility.
It is worth emphasizing that the data presented above were collected in 2006 and early 2007, at the height of the construction boom associated with the housing bubble. The subsequent recession that began in late 2007 resulted in a steep rise in unemployment, but particularly rapid job loss in construction. According to the Bureau of Labor Statistics (2011), between December 2007 and 2010 North Carolina lost close to 80,000 jobs in construction, a decline of over 30 percent, and the overall unemployment rate increased from 5 to 10 percent. Thus, serious vulnerability of immigrant Hispanic men, already evident even under peak economic conditions, undoubtedly worsened further still with the recession.
Acknowledgments
This research was supported by grant #NR 08052-03 from the National Institute of Nursing Research, National Institutes of Health (NINR/NIH). The author thanks Emilio Parrado, Chris McQuiston, Leonardo Uribe, Claudia Ruiz, Amanda Phillips Martinez, our CBPR partners, El Centro Hispano, and the Durham Hispanic community for their contribution to this work.
Footnotes
An exception is Massey (1987) who reports that undocumented migrants earn the same as their documented counterparts once time in the U.S. labor market is held constant.
While interviews were also conducted with women, due to considerable differences in industrial representation as well as articulation with family life, the working patterns of female migrants are considered in a separate analysis.
We evaluated the need to log wages for the statistical analysis. In our case the distribution of hourly wages is close to normal, with relatively modest variation over all. As such, logging hourly wages results in significant loss of information (Portes and Zhou 1996).
Respondents were asked if they received pay for extra hours worked, so this measure is less stringent than requiring that pay to be higher than their normal hourly wage.
An alternative approach would have been to model total years of U.S. experience as opposed to time specifically in Durham. Substantively, Durham-specific labor market experience is arguably more likely to predict employment outcomes than more generalized U.S. experience. A comparison of models using time in Durham and total U.S. experience (not reported) show that this is in fact the case, as the former was more predictive of employment outcomes than the latter.
The distribution for this variable is highly uneven, with roughly 70 percent choosing “more or less.” Substantively, one would expect the payoff to speaking English well relative to those who speak a little to be greater than the payoff to speaking a little relative to none. Both those who speak a little and those who speak none will need some form of translation to talk to their employers. Those who are competent to do the translating, on the other hand, could potentially command greater compensation. Once again, I experimented with different specifications and this argument holds up empirically. Of the three possible ways to model English ability (good versus all others; none versus all others; and some and none relative to good), the good versus all others is the specification that most often results in statistically significant findings.
Just under 7 percent of the men in the sample report holding a second job, and just over 30 percent report doing occasional odd jobs. Income from these side jobs is not considered in this analysis.
Respondents were asked if they were paid directly by their employer, through a subcontractor, as a day laborer, or some other way. While 17 percent of men reported having worked as a day laborer at some point after arriving in Durham, only four respondents were engaged in day labor at the time of survey. For the multivariate analyses these respondents were included as working for a subcontractor. Substantive results were not different if these four cases were included with the directly employed, or if they were excluded from the analysis entirely.
The national origins are also typical, with 68.3 percent originating in Mexico, 18.7 percent in Honduras, 5.4 percent from El Salvador, 6.5 percent from Guatemala, and 1.1 percent from other Latin American countries. Differences in compensation outcomes across national origin groups were not statistically significant, net of other human capital and immigration characteristics, and were thus not included in the analysis.
These figures are starkly at odds with employment patterns prior to immigration. Just under 14 percent of immigrant men in Durham had never worked before migrating to the United States, either due to high rates of unemployment or their young age. Only 18.4 percent had worked in construction prior to migrating, and yard work and food services were relatively rare, accounting for only .6 percent and 4.3 percent of prior work experience, respectively. Just under one-third of men worked in agriculture prior to migrating, and the range of jobs held was far more broad, with nearly one-third falling into the “other” category.
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