This paper uses the labor queue theory to examine the changing influence of race on the employment status and earnings of African immigrant men in the United States between 1980 and 2008. The results show that the white advantage echoed in previous research has diminished. Black African immigrant men's chance of being employed is now greater than that of their white counterparts when their sociodemographic characteristics are taken into consideration. However, when human capital factors are included in the regression models, white African immigrant men still maintain a significant advantage in earnings. This study also uncovered differential impacts of marriage and school enrollment on white and black African immigrant men's employment and earnings. These results challenge the use of labor queue theory as a framework for explaining immigrants' experience in the US job market.
Despite the increasing diversity of people living in the United States today, race remains a factor of interest and controversy among researchers and lay persons. This is probably more evident in the labor market where the person’s race is said to affect their chance of employment and earnings. According to the labor queue theory, employers follow a preference ordering in selecting their workforce. In the United States, such preference is primarily based on race [
The composition of the African immigrant male population in the United States has shifted from a white majority (56%) to a black majority (72%) during the period from 1980 to 2008. The change became apparent since 1990 as a result of a sustained economic growth in the United States (pull factor) and mounting economic and political crises in Africa (push factor). Further, it was sustained by the introduction in 1995 of the annual immigration program through which some 50,000 foreign-born people are admitted to the United States and granted permanent resident status.
What effect, if any, does such a changing racial composition have on African immigrant men’s chance of employment and earnings in the United States? In this study, we use data from the US Census and American Community Survey to examine the influence of race on employment status and earnings of African immigrant men for the period of 1980–2008. Our key hypothesis is that the influence of race on African immigrant men’s labor force participation and earnings may have changed or even diminished with shifting racial composition of the African immigrant population.
Immigration remains one of the issues that draw the most passionate and often divisive political debates in the United States [
The DV program has been the major contributing factor of the increase in number of Africans in the United States. For example, 18,000 of the 105,915 African immigrants (19%) who obtained legal permanent resident status in 2008 were DV recipients [
This increase in number of African immigrants has led to the insurgence of immigration studies in recent decades. Most studies based on the 1980–1990 census data revealed a significant racial effect on both employment opportunity and earnings. Analyzing the 1990 census data, Djamba [
This white African immigrants’ advantage was also found in a study on socioeconomic patterns of Africans in the United States. Using the 1990 US census data, Kollehlon and Eule found that white African men and men from English-speaking Africa had higher net hourly earnings than their nonwhite and non-English-speaking counterparts [
Certainly, all new arrivals encounter difficulties in integrating into the new labor market, finding employment that is suitable to their professional qualifications, and attaining adequate economic returns [
Some studies have noted the variations among groups in terms of the economic cost of immigration [
This study extends previous research by examining the changing influence of race on the employment status and earnings of African immigrant men in the United States between 1980 and 2008. Due to gender segregation, nature of employment, and earnings, we focus only on men in this study and examine the situation of women in a separate article.
While the study of migration has become a major focus in social research, there have been only relatively few studies on African immigration to the United States.
Some studies that have examined African emigration have looked at the negative effects of such spatial mobility on countries of origin [
Like other immigrants, African-born populations face challenges and opportunities when they arrive in the US. Their success or failure to adapt to the new environment depends on their premigration cultural legacies, the selectivity of migration, and prevailing job market practices at the place of destination [
Under the premigration cultural legacies, the observed differences in labor force conditions and earnings are said to reflect each group’s particular attitudes toward employment, schooling, family, kinship, and migration itself [
The second explanation about the differences in labor force participation and earnings between African immigrants and other groups can be found in the selectivity of migration argument. The basic assumption is that people who migrate are usually among the most talented and ambitious [
The third type of explanation is based on the labor queue theory, which refers to the “amount of discrimination or favoritism particular groups encounter” [
This study focuses on the labor queue theory, according to which Whites are advantaged in the United States labor market. Therefore, we expect white African immigrant men to have higher labor force participation rate and higher earnings than black African immigrant men, net of their other sociodemographic characteristics.
Two types of data are used in this study: (1) the decennial census data (1980, 1990, and 2000), and (2) the 2008 American Community Survey data. These data were drawn from the five percent Integrated Public Use Microdata Series (IPUMS). IPUMS is a collection of microdata, where each record is a person with all the characteristics numerically coded [
We use these data to show the trends and changing composition of the male population of African origin in the United States in the last three decades. Further analyses were conducted on the working age population to determine the importance of race through the labor queue theory, according to which white African immigrant men will have a better chance of employment and higher earnings than black African immigrant men.
There are two dependent variables: (1) labor force participation and (2) personal income. Labor force was measured by a dummy variable which indicates whether the person was working at the time of the data collection or not. To use the census definition,
There are two sets of independent variables: (1) race, which divides the study population into three racial categories (Black, White, and other), and (2) the sociodemographic variables, including the duration of immigration (the number of years immigrants have lived in the United States).
The “Black” category is for persons born in Africa and who identified themselves as Black on the census or American Community Survey questionnaire. The “White” category includes all persons born in Africa and who identified themselves as White on the census or American Community Survey questionnaire. The third category, which includes all people whose races were not recorded either as Black or White, was classified as “other.” The latter represents about five percent of male African immigrant population in the United States in 2008. Focusing on the labor queue theory, only the two main racial categories (Black and White) are analyzed in detail in this paper.
We recognize that the chances in the way the race variable has been measured in different censuses and American Community Survey may affect some outcomes. For example, the definition of race changed in 2000 with the introduction of individuals indicating two or more races. This may explain the increase in the “other race” category from less than 9 percent in the 1980 and 1990 to nearly 17 percent in 2000. Thus, to reduce the potential bias that may result from changes in racial categorization, we limit our analysis to individuals who self-identified themselves as black or white.
Most work in the area of labor force participation follows the functionalist and human capital frameworks which suggest that family and individual resources determine skills, and skills increase the chance of occupational opportunity and earnings. This reasoning constitutes the benchmark of most current theories of labor force differences. In fact, many of the cultural and selectivity of migration assumptions are based on the influence of human capital variables on individuals’ labor force participation and earnings. In light of the above theoretical framework and past studies on socio-economic conditions of immigrants [
We measured human capital through the following variables:
Education was coded in years of schooling as follows: 0–8 years, 9–12 years, and 13 years and more. This educational classification is preferred to that of level of schooling (e.g., primary, secondary, and higher) because of differences in meaning across countries. We also included the
Other important variables included in this study are
The residence variable was used to control for possible geographic effects which may increase or otherwise decrease the likelihood of entering into the labor force and of earning an income as a result of available local market opportunities. Following the classification used in previous research [
Two types of analyses were performed: descriptive and multivariate. In the descriptive section, we examined the changes in the size and composition of the African immigrant male population, and their sociodemographic characteristics during the four periods considered in this study. The bivariate differences in labor force participation and average earnings were also analyzed. The multivariate section focused on the racial differences in labor force participation and earnings, controlling for the effects of the sociodemographic variables. More specifically, we estimated the probability of being in labor force using logistic regression models. Because personal earning was measured in dollars, we used multiple regression equations to determine the association between earning and sociodemographic variables of interest.
As noted earlier, the following questions are examined using the 5 percent IPUMS data from the last three censuses (1980, 1990, and 2000) and the 2008 American Community Survey: (1) Between black and white African men, which racial group has the best chance of employment and higher personal income in the United States? and (2) what effects, if any, do their social and human capital factors have on their chance of employment and personal income (earnings)?
Data in Table
Distribution of the African immigrant male population (all ages) by race, 1980–2008.
Black | White | Other | Total | |||||
Year | % | % | % | % | ||||
1980 | 42,940 | 35.0 | 69,340 | 56.5 | 10,360 | 8.4 | 122,640 | 100.0 |
1990 | 117,393 | 51.1 | 97,551 | 42.4 | 14,897 | 6.5 | 229,841 | 100.0 |
2000 | 288,019 | 57.7 | 129,179 | 25.9 | 82,285 | 16.5 | 499,483 | 100.0 |
2008 | 565,421 | 71.8 | 183,473 | 23.3 | 39,037 | 5.0 | 787,931 | 100.0 |
Annual growth rate between 1980 and 2008 (%) | 43.46 | 5.88 | 9.89 | 19.37 |
Source: compiled from IPUMS data sets.
Note: some percentages do not sum to 100.0 due to rounding.
In addition, there have been significant changes in the composition of the male African immigrant population. For example, in 1980, the majority of male African immigrants were White (56.5%), but since 2000, Blacks have outnumbered Whites. By 2008, this racial makeup shifted to 71.8 percent Black, 23.3 percent White, and the remaining 5.0 percent for those male African immigrants whose racial identities were not specified (see Table
Data in Table
Percentage distribution of Black and White African immigrant men aged 16 to 64 by selected sociodemographic characteristics.
1980 | 1990 | 2000 | 2008 | |||||
Characteristics | Black | White | Black | White | Black | White | Black | White |
Age group | ||||||||
16 to 24 years | 22.4 | 24.8 | 11.6 | 14.0 | 15.2 | 10.6 | 16.4 | 9.2 |
25 to 34 years | 55.7 | 30.2 | 45.3 | 34.5 | 27.4 | 23.8 | 25.1 | 19.9 |
35 to 44 years | 17.3 | 23.4 | 33.2 | 27.7 | 35.3 | 32.4 | 26.4 | 29.6 |
45 to 64 years | 4.6 | 21.6 | 9.9 | 23.8 | 22.2 | 33.2 | 32.1 | 41.3 |
Education | ||||||||
Less than 9 years | 1.5 | 4.4 | 2.7 | 2.2 | 3.6 | 1.7 | 4.2 | 1.4 |
9 to 12 years | 16.4 | 29.4 | 17.3 | 19.7 | 31.5 | 23.3 | 29.4 | 22.0 |
13 years or more | 82.1 | 66.3 | 80.0 | 78.2 | 64.9 | 75.0 | 66.4 | 76.6 |
Enrollment | ||||||||
Not in school | 38.2 | 73.2 | 61.1 | 80.6 | 72.5 | 84.9 | 73.2 | 87.7 |
In school | 61.8 | 26.8 | 38.9 | 19.4 | 27.5 | 15.1 | 26.8 | 12.3 |
Marital status | ||||||||
Not married | 51.8 | 39.3 | 49.1 | 37.7 | 46.6 | 34.5 | 48.4 | 32.3 |
Married | 48.2 | 60.7 | 50.9 | 62.3 | 53.4 | 65.5 | 51.6 | 67.7 |
Duration of immigration | ||||||||
Less than 5 years | 57.5 | 37.3 | 31.2 | 24.0 | 30.3 | 20.9 | 21.7 | 14.5 |
5 to 9 years | 35.4 | 32.9 | 51.5 | 36.8 | 26.2 | 16.4 | 33.3 | 21.3 |
10 years or more | 7.1 | 29.9 | 17.3 | 39.2 | 43.5 | 62.7 | 45.0 | 64.2 |
English proficiency | ||||||||
Speaks only English | 21.8 | 38.0 | 22.0 | 39.4 | 19.8 | 37.8 | 20.8 | 36.5 |
Speaks very well | 56.9 | 39.9 | 58.3 | 43.1 | 58.1 | 44.8 | 54.8 | 40.2 |
Speaks well | 19.4 | 19.1 | 16.2 | 14.7 | 17.5 | 14.0 | 19.0 | 17.8 |
Not well | 1.9 | 3.1 | 3.5 | 2.8 | 4.6 | 3.4 | 5.5 | 5.5 |
Region | ||||||||
Northeast | 34.7 | 31.7 | 27.3 | 34.4 | 28.7 | 29.6 | 24.0 | 30.5 |
Midwest | 19.4 | 14.0 | 14.0 | 10.9 | 17.1 | 12.6 | 18.7 | 13.3 |
South | 33.3 | 25.5 | 41.9 | 24.7 | 39.9 | 30.9 | 41.1 | 33.0 |
West | 12.7 | 28.8 | 16.8 | 29.9 | 14.3 | 26.9 | 16.2 | 23.2 |
Labor force participation | ||||||||
Not in labor force | 39.2 | 19.3 | 15.8 | 12.4 | 20.5 | 16.9 | 14.6 | 12.4 |
In labor force | 60.8 | 80.7 | 84.2 | 87.6 | 79.5 | 83.1 | 85.4 | 87.6 |
Average annual personal income | 7,423 | 16,278 | 17,785 | 35,657 | 28,202 | 57,474 | 33,290 | 72,789 |
Total number of cases | 39,620 | 53,940 | 10,6371 | 85,045 | 25,4334 | 10,9243 | 49,1038 | 15,6431 |
Source: analysis based on 5 percent IPUMS data sets.
There were also important racial differences in the educational attainment, school enrollment, and marital status. Black African immigrant men had higher educational attainment than white African immigrant men in 1980 and 1990, but that situation was reversed in 2000 and 2008. For instance, in 2008, 76.6 percent of white African immigrant men had 13 or more years of formal education compared to 66.4 percent for black African immigrant men. In contrast, the percent of black African immigrant men with 13 or more years of formal education in 1980 was significantly higher (82.1%) than that of white African immigrant men (66.3%).
School enrollment rate has significantly decreased over time for both black and white African immigrants. Nonetheless, such enrollment remained relatively higher for black African immigrant men compared to white African immigrant men in all four periods. Such a racial difference in school enrollment suggests that many black African men may be coming to the United States as students or may decide to go to school once they are here in order to prepare themselves for better employment opportunities.
We also uncovered important differences in marital status between the two racial groups. On one hand, more than 60 percent of white African immigrant men were married at each of the four periods. On the other hand, only about half of black African immigrant men were married. Since married men tend to have more resources than unmarried ones [
The regional distribution of the African immigrant population changed over time. As hypothesized, the south has been the top region of residence for black African immigrant men. About 33 percent of all black immigrant men who were living in the United States in 1980 were in the south; the corresponding figure for white African immigrant men was 25.5 percent. The south has even become a preferred region for black African immigrant men in the subsequent periods. In contrast, Northeast was the region of choice for white African immigrant men in 1980 and 1990; 32 and 34 percent of them lived there, respectively, in 1980 and 1990. However, the South became the top region of residence also for white African immigrant men in 2000 and 2008.
What are the prospects of employment and earnings for black and white African immigrant men in the United States? The descriptive statistics in Table
However, white African immigrant men have a significant advantage over black African immigrant men when it comes to earnings. In all the four periods in Table
What chances do these populations have, especially the black African immigrant men in the US labor market? One study that examined the labor force participation of 25–64 years old African immigrant men in 1990 found significant racial differences, with black African men being more likely to be in labor force than white men [
Two models are presented for each of the four years. Model I contains race, age, education, school enrollment, marital status, duration of immigration, English proficiency, and region of residence. Model II includes all the variables in Model I plus two interactions. The first interaction is for marriage and race; the second one is for school enrollment and race. The interaction terms were used to test the following two conditional hypotheses: since in most societies men are usually the breadwinners for their families and households, we expect married men to be more likely in the labor force than unmarried ones; school enrollment is expected to be a deterrent factor of labor force participation. Therefore, we expect men who are not in school to be more likely in labor force than their counterparts who are still in school.
The same interaction terms were also included in the regression equations of earnings to see if the observed racial differences in personal income are mediated by group differences in marriage and school enrollment.
Data in Table
Odd ratios of logistic regression of labor force participation of black and white African immigrant men aged 16 to 64.
1980 | 1990 | 2000 | 2008 | |||||
Characteristics | Model I | Model II | Model I | Model II | Model I | Model II | Model I | Model II |
Race | ||||||||
White | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Black | 0.781*** | 0.660*** | 1.056*** | 0.929*** | 1.053*** | 0.993 | 1.285*** | 1.281*** |
Age group | ||||||||
16 to 24 years | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
25 to 34 years | 1.600*** | 1.589*** | 1.987*** | 1.878*** | 1.675*** | 1.659*** | 3.680*** | 3.674*** |
35 to 44 years | 2.115*** | 2.095*** | 2.231*** | 2.120*** | 1.646*** | 1.624*** | 3.773*** | 3.773*** |
45 to 64 years | 1.481*** | 1.442*** | 1.321*** | 1.139*** | 1.375*** | 1.337*** | 2.082*** | 2.085*** |
Education | ||||||||
Less than 9 years | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
9 to 12 years | 1.895*** | 1.902*** | 1.578*** | 1.479*** | 1.514*** | 1.505*** | 2.313*** | 2.326*** |
13 years or more | 2.642*** | 2.658*** | 3.075*** | 2.847*** | 3.050*** | 3.022*** | 3.902*** | 3.930*** |
Enrollment | ||||||||
Not in school | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
In school | 0.140*** | 0.124*** | 0.199*** | 0.135*** | 0.446*** | 0.310*** | 0.301*** | 0.321*** |
Marital status | ||||||||
Not married | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Married | 1.820*** | 1.771*** | 1.531*** | 2.122*** | 1.432*** | 1.582*** | 1.598*** | 1.532*** |
Duration of immigration | ||||||||
Less than 5 years | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
5 to 9 years | 2.600*** | 2.616*** | 2.527*** | 2.600*** | 1.315*** | 1.324*** | 1.502*** | 1.502*** |
10 years or more | 2.276** | 2.276*** | 1.920*** | 1.966*** | 1.358*** | 1.377** | 1.552*** | 1.550*** |
English proficiency | ||||||||
Speaks only English | 1.999*** | 1.999*** | 2.780*** | 2.877*** | 1.773*** | 1.764*** | 0.705*** | 0.706*** |
Speaks very well | 1.534*** | 1.537*** | 2.583*** | 2.659*** | 1.638*** | 1.630*** | 0.854*** | 0.855*** |
Speaks well | 1.316*** | 1.317*** | 2.090*** | 2.075*** | 1.274*** | 1.269*** | 0.866*** | 0.864*** |
Not well | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Region | ||||||||
Northeast | 0.981 | 0.984 | 0.889*** | 0.880*** | 0.854*** | 0.850*** | 1.100*** | 1.103*** |
Midwest | 1.021 | 1.025 | 0.931** | 0.917*** | 1.025 | 1.020 | 1.099*** | 1.103*** |
South | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
West | 1.501*** | 1.504*** | 0.960* | 0.940* | 0.933*** | 0.927*** | 0.724*** | 0.728*** |
Interactions | ||||||||
Marriage by Race | 1.037 | 0.587*** | 0.864*** | 1.060** | ||||
School Enrollment by Race | 1.258*** | 1.922*** | 1.597*** | 0.924*** | ||||
−2 Log-Likelihood | 72,549 | 72,520 | 125,915 | 125,014 | 324,568 | 323,978 | 433,052 | 433,020 |
Total number of cases | 4,288 | 4,288 | 7,812 | 7,812 | 15,028 | 15,028 | 4,896 | 4,896 |
Source: analysis based on 5 percent IPUMS data sets.
The effects of all other variables were consistent throughout the period under study (1980–2008). For example, having higher educational attainment and being out of school significantly increase African immigrant men’s chances of employment. In addition, age has a reversed U-shaped relationship with labor force participation. Similar to Djamba [
As for language, men with better command of the English language were more likely to be in the labor force than those with limited English language ability for the period of 1980–2000. However, in 2008, the influence of English language on employability significantly diminished; those with limited linguistic ability were more likely to be working than those who spoke English better. This change probably indicates increasing immigration of less skilled people who can take jobs that require little or no English language ability.
The coefficients for the duration of immigration show that immigrants who have been in the country for 5 years or more had a better chance of being in the labor force than the newcomers. The fact that those who lived in the United States for 5–9 years were also significantly more likely to be in labor force than newly arrived immigrants suggests that the work assimilation period for African immigrants is shorter than the 10–15-year period echoed elsewhere [
Marriage and school enrollment can be race-specific conditions that increase or otherwise decrease the likelihood of being in the labor force. Therefore, we examined the interactions between race and marital status and race and school enrollment in Model II. The interaction between race and school enrollment in Table
Access to employment is only one of several factors of economic status. Data in Table
OLS regression of annual personal income of black and white African immigrant men aged 16 to 64 (standardized coefficients).
1980 | 1990 | 2000 | 2008 | |||||
Characteristics | Model I | Model II | Model I | Model II | Model I | Model II | Model I | Model II |
Race | ||||||||
(White) | ||||||||
Black | −0.154*** | −0.108*** | −0.172*** | −0.127*** | −0.179 | −0.106*** | −0.186*** | −0.112*** |
Age group | ||||||||
16 to 24 years | ||||||||
25 to 34 years | 0.046*** | 0.037*** | 0.098*** | 0.072*** | 0.057*** | 0.053*** | 0.051*** | 0.053*** |
35 to 44 years | 0.222*** | 0.206*** | 0.204*** | 0.179*** | 0.132*** | 0.128*** | 0.109*** | 0.109*** |
45 to 64 years | 0.272*** | 0.245*** | 0.279*** | 0.246*** | 0.175*** | 0.168*** | 0.127*** | 0.125*** |
Education | ||||||||
Less than 9 years | ||||||||
9 to 12 years | 0.132*** | 0.134*** | 0.043*** | 0.032*** | 0.025*** | 0.024*** | 0.020*** | 0.014*** |
13 years or more | 0.278*** | 0.281*** | 0.158*** | 0.146*** | 0.162*** | 0.161*** | 0.148*** | 0.139*** |
Enrollment | ||||||||
(Not in school) | ||||||||
In school | −0.214*** | −0.268*** | −0.121*** | −0.231** | −0.079*** | −0.178*** | −0.093*** | −0.233*** |
Marital status | ||||||||
(Not married) | ||||||||
Married | 0.091** | 0.174*** | 0.091*** | 0.183*** | 0.072*** | 0.192*** | 0.088*** | 0.225*** |
Duration of immigration | ||||||||
(Less than 5 years) | ||||||||
5 to 9 years | −0.081*** | −0.091*** | −0.084*** | −0.093** | −0.072** | −0.077*** | −0.087*** | −0.90*** |
10 years or more | −0.041*** | − 0.037* | −0.038*** | −0.039*** | −0.031*** | −0.034** | −0.059*** | −0.63*** |
English proficiency | ||||||||
Speaks only English | 0.187*** | 0.186*** | 0.176*** | 0.184*** | 0.110*** | 0.109*** | 0.177*** | 0.174*** |
Speaks very well | 0.146*** | 0.149*** | 0.122*** | 0.132*** | 0.066*** | 0.067*** | 0.130*** | 0.128*** |
Speaks well | 0.043*** | 0.041*** | 0.039*** | 0.040*** | 0.001 | 0.000 | 0.040*** | 0.040*** |
(Not well) | ||||||||
Region | ||||||||
Northeast | 0.011*** | 0.013*** | 0.024*** | 0.022*** | 0.004** | 0.002 | 0.042*** | 0.038*** |
Midwest | 0.028*** | 0.031*** | −0.005* | −0.007*** | 0.010** | 0.007** | 0.002 | −0.002* |
(South) | ||||||||
West | 0.001 | 0.002 | 0.031** | 0.028*** | 0.034*** | 0.032*** | 0.046*** | 0.040*** |
Interactions | ||||||||
Marriage by race | −0.141** | −0.147*** | −0.166*** | −0.176*** | ||||
School enrollment by race | 0.096*** | 0.147*** | 0.117*** | 0.156*** | ||||
R square | 0.367 | 0.377 | 0.244 | 0.259 | 0.177 | 0.188 | 0.186 | 0.197 |
Degree of freedom | 16 | 18 | 16 | 18 | 16 | 18 | 16 | 18 |
Total number of cases | 85,759 | 85,759 | 191,415 | 191,415 | 363,576 | 363,576 | 647,468 | 647,468 |
Omitted categories in parentheses.
Source: analysis based on 5 percent IPUMS data sets.
Data in Table
The rest of the variables were significantly associated with earnings during all the periods examined in this study. Education and age were positively and significantly associated with earnings. Clearly, education is a key positive factor of earnings. During all four periods examined here, more educated men earned substantially more than less educated ones. The positive association between age and earnings suggests that work experience translates into higher income.
Marriage was also associated with higher earnings for African immigrant men in general. However, when interaction between race and marital status was included in the regression equation, we found that the positive effect of marriage is mostly for white African immigrant men. For black African immigrant men, being married actually meant having lower personal earnings. This explains probably why black African immigrant men earn significantly more than white African immigrant men [
School enrollment was negatively associated with earnings in general. Yet, the analysis of the interaction effect between race and school enrollment showed that black African immigrant men who were in school earned higher income than their white counterparts with the same school enrollment status.
Unlike labor force participation, earning is negatively associated with the duration of immigration in the United States. Those who entered the country in recent years (<5 years) earned significantly more than earlier immigrants. In terms of language, the results in Table
The effect of region of residence shifted over time. In 1980, there was no significant difference in earnings between those African immigrant men who resided in the west and those in the south (reference category). In contrast, those immigrants who lived in the northeast and the midwest earned significantly more income than those in the south. In 1990, the northeast and the west residents earned significantly more, while the midwest residents earned significantly less than the south residents. In 2000, African immigrant men who lived in the south earned less than their counterparts who were living elsewhere in the country. The top earners during that year were those in the west followed by the midwest and then the northeast residents. The west region residents kept their earnings advantage even in 2008. During that year, the residents in the northeast earned significantly more than both the midwest and the south residents, but less than the west residents.
These results show that race remains a determinant factor of earnings for African immigrant men in the United States. Even after controlling for the impact of human capital variables such as education, age, marital status, duration of immigration, English proficiency, and region of residence, white African immigrant men earned significantly more than black African immigrant men.
The study of race and migration is interesting as it offers an important way to look at the assimilation of foreign-born populations in the United States. This study highlights the changes in size and composition of the male African immigrant population in the United States during the periods of 1980–2008, and differences in the labor force participation and earnings between black and white African immigrant men aged 16–64 years. The results show that the number of male African immigrants in the United States continues to increase, and the racial composition is constantly changing.
How do these African immigrant men adapt into the US job market? This question was examined through the analysis of the labor force participation and earnings. The guiding framework was the labor queue theory, which asserts that Whites have a net advantage in the American job market. The results show significant racial differences, but not completely in line with the labor queue assumptions.
Throughout the four periods examined in this study, white African immigrant men were more likely to be in labor force than their black counterparts. However, in multivariate models, which controlled for the effects of other sociodemographic variables, the white advantage was only evident in 1980. Black African immigrant men had a net labor force participation advantage over their white counterparts in subsequent periods (1990–2008). What are the determinant factors of this change? It is possible that the increase in the number of African immigrants in the United States has given the employers chance to become more acquainted with black African immigrants, thus reducing some racial and ethnic prejudices. However, without specific information on employers’ experiences with African immigrants, it is not possible to fully ascertain these changes in racial differences in labor force participation.
While black African immigrant men are apparently gaining more access to the US labor market than their white counterparts, their human capital characteristics have not yet translated into fair earnings. Black African immigrant men continue to earn significantly less than their white counterparts with the same sociodemographic characteristics. The labor queue assumption of a white advantage is therefore confirmed in the case of earnings differences between black and white African immigrant men.
Age, education, and English proficiency were positively and significantly associated with both the labor force participation and earnings. Such findings are consistent with previous research [
As for marriage, this study showed that married men were more likely to be in the labor force and to earn higher income than unmarried ones, which is consistent with earlier results from socio-economic studies [
The analysis of the interaction between school enrollment and race showed that enrollment was associated with negative employment and earnings for white African immigrant men, whereas it had positive effects on both variables for black African immigrant men. The positive effect of the school enrollment and race interaction terms suggests that black African immigrant men who were in school probably had to work to pay their tuitions. On the other hand, white men who were in school may have had scholarships or other support systems that helped them devote more time to learning and less time to earning a living while in school.
Time in the United States was positively associated with the chance of employment and lower earnings. The effect of region of residence changed over time. Results reported here show that human capital characteristics are good predictors of the labor force participation and earnings, but other characteristics such as race and place of residence are still relevant though changing overtime. The labor queue hypothesis, which suggests that Whites are advantaged in the job market was only fully supported in this immigration study for earnings. As for labor force participation, the racial advantage has shifted from Whites to Blacks.
Certainly, other variables not examined in this study, such as other sources of income, and family size could shed more light on the racial differences in labor force participation and earnings uncovered here. Even in the absence of these unobserved factors, the results of this study suggest that the racial impact on the US job market has changed overtime as more people from diverse backgrounds work and live together. This could also be due to employers becoming more acquainted with various racial immigrant groups. More research is needed to understand these changes, especially the persistent white African immigrant men’s earnings advantage over black African immigrant men. Another interesting question to explore in future research is whether race has the same impact on black and white African immigrants’ labor force participation and earnings in Europe and elsewhere as in the United States.
This research was partially supported by a professional development grant from the Population Studies and Training Center at Brown University (USA) to the first author. The authors thank the anonymous reviewers for their helpful comments and suggestions. Thanks also are due to Sylvie Eke Aba, a graduate research assistant, for helping with the construction of statistical tables, and Erin K. Brown, a program assistant, for preparing the list of references.