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Recent studies on Black immigrants in the USA report higher than average socioeconomic and educational attainment for African immigrants. Based on an intra-African immigrant comparison, we examine the generalizability of this finding to African groups of differing countries and regions using data from the 1990 and 2000 5 % U.S. census Integrated Public Use Microdata Samples (IPUMS). We specifically ask whether hourly earnings of African immigrants vary by region of origin. Results of our analyses reveal that sending region is an important determinant of variations in hourly earnings among African immigrants. Also, data indicate that both race and gender are significantly associated with earning outcomes; with the earnings of males and whites significantly higher than those of females and blacks. We discuss these findings in the context of the so-called “black immigrant success story.”
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Socioeconomic Diversity Among African Immigrants
in the United States: An Intra-African Immigrant
Comparison
Abdi M. Kusow &Sitawa R. Kimuna &Mamadi Corra
#Springer Science+Business Media Dordrecht 2014
Abstract Recent studies on Black immigrants in the USA report higher than average
socioeconomic and educational attainment for African immigrants. Based on an intra-
African immigrant comparison, we examine the generalizability of this finding to
African groups of differing countries and regions using data from the 1990 and 2000
5 % U.S. census Integrated Public Use Microdata Samples (IPUMS). We specifically
ask whether hourly earnings of African immigrants vary by region of origin. Results of
our analyses reveal that sending region is an important determinant of variations in
hourly earnings among African immigrants. Also, data indicate that both race and
gender are significantly associated with earning outcomes; with the earnings of males
and whites significantly higher than those of females and blacks. We discuss these
findings in the context of the so-called black immigrant success story.
Keywords African immigrants .Earnings .Educational attainment .Intra-African
comparison .USA
Introduction
For more than half a century, the literature on black immigrants in the USA has
consistently shown that first and second generation black immigrants are more educat-
ed and economically successful than their African American counterparts (Reid 1939;
Sowell 1978; Glazer and Moynihan 1963; Moynihan 1965;LoganandDeane2003;
Int. Migration & Integration
DOI 10.1007/s12134-014-0377-x
A. M. Kusow
Department of Sociology, Iowa State University, 216 East Hall, Ames, IA 50011, USA
e-mail: kusow@iastate.edu
S. R. Kimuna (*):M. Corra
Department of Sociology, East Carolina University, Brewster 416A, Greenville, NC 27858, USA
e-mail: kimunas@ecu.edu
M. Corra
e-mail: corram@ecu.edu
Kent 2007; Bryce-Laporte 1972). More recent studies further show that such immi-
grants (and/or their offspring) are overrepresented in selective colleges and universities
relative to African Americans (Massey et al. 2006;Haynie2002), or are the most
educated immigrant group in the USA (Logan and Deane 2003;Kent2007).
In this study, we examine the extent to which the so-called black immigrant success
storyis directly relevant to U.S. African immigrants from the various African regions.
Do the socioeconomic trajectories of immigrants from countries in the southern region
of Africa, for example, differ markedly from those immigrants from countries in the
Northern African region? How about immigrants from countries in the western region
of Africa? Do West African immigrants exhibit patterns of socioeconomic attainment
that are different from East African immigrants? More specifically, we use a key
indicator of socioeconomic attainment, income, to address one research question
controlling for human capital variables including education. Do hourly earnings of
African immigrants significantly differ by region? If this question is answered in the
affirmative, this would indicate that Black immigrants should not be lumped in a single
black immigrantcategory and treated as a homogeneous group.
Background
In his now classic work, The Negro Immigrant:His Background Characteristics and
Social Adjustment, 18991937, the late Ira Reid made the seminal observation that high
schools and colleges in New York City have an unusually high foreign-born Negro (sic)
representation(1937:416) and that nearly one third of New Yorks black professionals,
including physicians and lawyers, is foreign-born. More than three decades later, Thomas
Sowell (1978) reiterated Reids observation. Sowell (1978) concluded that English
speaking West Indian immigrants outperform African Americans in almost all indicators
of socioeconomic achievement (see also Moynihan 1965; Glazer and Moynihan 1963).
In order to further demonstrate that this success was not confined to the first generation
alone, Sowell used the 1970 census data to analyze the relative success of second
generation West Indian immigrants and noted that they too exceeded the socioeconomic
status of first generation English speaking West Indians, native blacks, and the U.S.
population as a whole in terms of education, income, and proportion in the labor force.
The most recent, and relatively widely reported finding regarding the achievement of
black immigrants, is provided by Logan and Deane (2003; see also Kent 2007;
Rumbaut 1996). In a report titled Black Diversity in Metropolitan America,Logan
and Deane compare socioeconomic attainment levels across African immigrants, Afro-
Caribbean, and African Americans with a comparative sample of major U.S. ethnic
groups. They report that Median Household Income for Afro-Caribbean immigrants
and African immigrants was $43,650 and $42,900, respectively, compared to $33,700
for African Americans. Moreover, the average number of years of education completed
by African immigrants (14) was higher than not only the African Americans (12.4) and
Afro-Caribbean (12.6), but also that of whites (13.5) and Asian Americans (13.9).
Collectively, the above literature and the preceding socioeconomic statistics support the
increasing assertion that black immigrants, not only outperform native African
Americans, but all other ethnic groups, including native whites, giving further support
to the so-called black immigrant success story.
A.M. Kusow et al.
The higher than average socioeconomic and educational attainment of black immi-
grants has been extended to the observation that the first and second generation black
immigrants are overrepresented in Ivy League colleges and universities. In an article
titled Top Colleges Take More Blacks, But Which Ones?,the New York Times
reported a discussion that took place at a gathering of Harvard Universitysalumni
where two prominent African American scholars, Henry Louis Gates and Lani Guinier,
noted that more than half of black students at Harvard are of first and second generation
West Indian and African immigrant families, or children of bi-racial couples (Rimer and
Anderson 2004; see also Banerji 2007;Glenn2007; Johnson 2005).
Empirically, much of the Ivy League overrepresentation argument is informed by the
results of two recent sociological studies. The first (Haynie 2002) concentrates on
Harvards black student population, and despite its limited scope, provides an
interesting insight into the ethnic background of black students at Harvard. Based on
a sample of 170 students, Haynie (2002) found that those who identified themselves as
African or Afro-Caribbean made up nearly one third of the student population. When
the biracial category is included in the figures, the number jumped to more than two
thirds of the black student population. Comparing the ethnic backgrounds of Harvards
black student population as a proportion of the total U.S. black population, Haynie
(2002) found that while the first, second, and third generation black immigrants
represent only 10 % of the total U.S. black population, they accounted for more than
55 % of Harvards black student body. In contrast, the fourth and plus generation
African Americans who represented nearly 90 % of the total U.S. Black population
accounted for only 45 % of Harvards black population.
The second and perhaps most important recent sociological study on this topic is that
of Douglas Massey and associates (2006). Using data from the National Longitudinal
Survey of Freshmen (NLSF), Massey et al. (2006) examined the extent to which first
and second generation black immigrants were overrepresented in selective colleges and
universities as compared to African Americans. They found that the black immigrants
were overrepresented throughout elite academia and that this overrepresentation is
greatest in the most exclusive colleges and universities. According to Massey et al.
(2006), students of immigrant background made up 41 % of entering black freshmen in
Ivy League institutions. Despite this immigrant overrepresentation, however, the au-
thors found very few differences between black immigrants and African American
students, except, fathers of black immigrant freshmen are more likely to be college
graduates and hold advanced degrees than those of African American students.
Taken together, the foregoing literature suggests two key findings. First, African
immigrants are much more successful in socioeconomic attainment than not only their
native-born counterparts, but also other American ethnic groups. Second, that the
higher than average socioeconomic and educational attainment of black immigrants
extends to the overrepresentation of such immigrants in Ivy League colleges and
universities in the USA. Not previously addressed is whether or not such generaliza-
tions apply to all Africans or are only limited to Africans from certain regions of Africa.
Some recent studies have examined socioeconomic variation among African immi-
grants by race (Djamba and Kimuna 2011; Borch and Corra 2010;Dodoo2000;Dodoo
and Takyi 2002), or trends or motives of migration among African immigrants in the
USA (Thomas 2011). Notably, none of these studies has directly examined the impact
of region of origin on earnings among African immigrants. One exception is the work
Socioeconomic Diversity Among African Immigrants
of Kusow (2007), which used the country of origin-based census data from 1980 to
2000 to compare socioeconomic achievement levels of immigrants from Kenya,
Uganda, Tanzania, Ethiopia, Somalia, and Sudan. He found significant socioeconomic
and cultural differences. Kusow (2007) specifically found that immigrants from Kenya,
Tanzania, and Uganda have much higher socioeconomic achievement levels than those
from Ethiopia, Somalia, and Sudan. Kusows(2007) study, however, was geographi-
cally limited to the East African region and was also based on descriptive analysis. His
work, therefore, does not capture the achievement patterns of immigrants from other
regions of origin in Africa. In what follows, we discuss the importance of the regional
and racial geography of African emigration to the USA.
Geography of Origin and Destination
Following Suzanne models(2008) comment that an important way to examine
variations in socioeconomic achievement among the black population in the USA is
to carry out an intra-African immigrant comparison, we suggest that geography or the
racial, economic, and political context of emigration is an important framework for
understanding variation in labor market outcomes among African immigrants in the
USA. First, African regions are culturally and ethnically distinct from each other such
that the aggregation of all immigrants from different African countries into one large
sample minimizes the level of socio-economic diversity across African immigrants.
The four regions, East Africa, West Africa, North Africa, and Southern Africa, upon
which our current analysis is based, are different from each other economically, racially,
and politically. In terms of economic development indicators, Southern Africa and
North Africa show higher levels of per capita income, life expectancy, and lower child
mortality rates as compared to East and West African regions (Word Bank 2011).
More importantly, the racial demographic composition of the four regions from
which our data are derived is different from each other. North African immigrants are
primarily of Middle Eastern background, and are therefore considered as white in the
USA. On the other hand, South Africa has had a long history of white racial domination
that dates back to the early seventeenth century continuing to the last decade of the
twentieth century. As recent as 1991, South Africas multi-racial population consisted
of 12 % white, 9 % colored, roughly 3 % Asian, and 75 % black African (Marger
2008). In East Africa, aside from a small population of Asian Indian descent in Kenya,
Uganda, and Tanzania, the population of the greater East African region is black.
Similarly, the population of West Africa is primarily black African (Kusow 2007). In
other words, Africas regional population ranges from a majority middle Eastern/white
in North Africa, multi-racial in Southern African region (e.g., South Africa and
Zimbabwe), and primarily black African in the West and East African regions.
The racial geography of Africa has important implications for the economic inte-
gration of African immigrants in the context of reception (Portes and Borocz 1989;
Bashi and McDaniel 1997). The ways in which the racial and ethnic structure of the
USA organizes the nature of economic integration among white immigrants differently
than that of black immigrants is partially articulated in segmented assimilation theory
(Portes and Zhou 1993). Segmented theory generally predicts that the racial structure of
the American society shapes immigrant opportunities along racial lines such that white
immigrant opportunities will converge with the white native-born and vice versa even
A.M. Kusow et al.
when education and skill are controlled (Nawyn 2010; Bashi and McDaniel 1997). This
observation is confirmed by an increasing number of scholars who find that black
African immigrants earn less than white African immigrants even when human capital
variables including education are controlled for (Dodoo 2000; Dodoo and Takyi 2002;
Kollehlon and Eule 2003; Borch and Corra 2010;Nawyn2010; Moore and Amey
2002;BideshiandKposowa2012). Borch and Corra (2010) found that, in general,
black immigrants do have significantly lower earnings than white African immigrants
such that race is an important factor in determining black African immigrant earnings in
the USA (see also Bideshi and Kposowa 2012).
We join the recent literature that disaggregate African immigrants along racial and
gender lines to include the impact of region of origin on African immigrant earnings
while controlling for human capital variables including education and the demographic
variable of race and gender. The intra-African region comparison is important for a
number of reasons, including the fact that different regions of Africa under consider-
ation have different levels of racial heterogeneity from mostly white in the case of
North Africa to mostly black in the case of West and East Africa with South Africa in
the middle. Given the foregoing scenario, we hypothesize that even after controlling for
human capital and demographic measures, the hourly earnings of immigrants from the
various regions of Africa will differ markedly from one another.
Data and Methods
The data used in this paper are drawn from the 1990 and 2000 U.S. population censuses.
The sample consists of 26,976 African immigrants in the USA from 20
1
African countries
taken from the 1990 and 2000 5 % Integrated Public Use Microdata Samples (IPUMS).
2
Following previous studies (Dodoo 1997 and 2000; Dodoo and Takyi 2002;Corraand
Kimuna 2009; Borch and Corra 2010; Corra and Borch 2014), the sample was restricted to
individuals who recorded their birthplace in any of the 20 African countries in the census
documents, were employed at least part-time (which we define as 50 hours per year or
more), age 25 to 64, were not enrolled in school, and reported positive annual earnings.
Theselectionofagegroup25to64isbasedonthe lower labor force participation of older
people 65 and above and the age groups different labor force behaviors compared to those
below age 65 (Djamba and Kimuna 2012).
The 20 countries included in our sample were further categorized into four African
regions: those in East Africa (Ethiopia, Kenya, Somalia, Sudan, Tanzania, and
Uganda), West Africa (Cameroon, Cape Verde, Ghana, Liberia, Nigeria, Senegal, and
Sierra Leone), North Africa (Algeria, Egypt, Libya, Morocco, and Tunisia), and
Southern Africa (South Africa and Zimbabwe). Thus, of the 26,976 persons included
in our two samples (1990 and 2000), 6,209 are from East Africa, 10,983 are from West
Africa, 6,913 are from North Africa, and 2,871 are from Southern Africa. Further, data
1
The 20 countries included in our sample are, alphabetically, Algeria, Cameroon, Cape Verde, Egypt,
Ethiopia, Ghana, Kenya, Liberia, Libya, Morocco, Nigeria, Senegal, Sierra Leone, Somalia, South Africa,
Sudan, Tanzania, Tunisia, Uganda, and Zimbabwe.
2
For a complete description of the IPUMS dataset (including sample and variable descriptions, data
compilation, and storage), see the IPUMS website at www.ipums.org .
Socioeconomic Diversity Among African Immigrants
in Table 1indicate that West Africa region was the top sending region in both 1990 and
2000 samples; however, East Africa had the highest percent change increase (167 %) in
immigrants to the USA between 1990 and 2000.
Var i ab le s
The variables used in the analyses assess variations in hourly earnings, with the main
explanatory variable being region of origin (East, West, North, or South). We created
dummy variables for East Africa, North Africa and West Africa, with Southern Africa
treated as the reference category. Our dependent measure (Hourly earnings) was
computed by taking the annual amount earned and dividing it by the number of hours
worked per year. Because initial statistical tests revealed the income measure to be
somewhat skewed, we minimized the effect of skewness by logging hourly earnings.
Hence, the regression models noted in Table 3are on logged hourly earnings.
The independent variables indicating human capital are educational attainment in
categories (reference group = those with less than a high school diploma), a continuous
number of years of schoolingvariable,
3
labor market experience (calculated as age
minus number of years in school minus 6; and its squared term; for similar computa-
tions, see Borch and Corra 2010; Corra and Borch 2014; Corra and Kimuna 2009;
Dodoo 1997;Mincer1974), occupation (reference group = management), English
language proficiency (reference group = speaks English wellor speaks only
English),
4
and citizenship (naturalized citizen=1; reference group = non-citizen). A
three-category race variable was created by grouping respondents into black, white, and
other.
5
Dummies controlled for black and other, with white treated as the reference
category. Gender was also controlled, with the female treated as the reference category
(male=1). Variables that measure other demographic factors include a binary variable
for marital status (reference group = non-married) and a variable delineating respon-
dentsU.S. region of residence at the time of the census (reference group = South).
In the first regression model (Model I of Table 3), we regress our key independent
variable (region of origin) on logged hourly earnings. We present this model to set the
baseline difference in earnings between the four regional groups before controlling for
variables pertaining to demographic and human capital factors (Models II, III, and IV).
3
The estimation of number of years of schooling follows Kalmijns(1996) formulation, where Kindergarten is
estimated to equal to 0 years of schooling; grades 1 to 4 equals to 2.5 years; grades 5 to 8 equals to 6.5 years;
grade 9 equals to 9 years; grade 10 equals to 10 years; grade 11 equals to 11 years; grade 12 and high school
graduate equals to 12 years; partial college and associate degree in an occupational program translates to
13 years; associate degree in an academic program translates to 14.5 years; bachelors degree equals to
16 years; masters degree translates to 18 years; and professional and doctorate degrees translate to 22 years.
4
English proficiency, or the ability to understand and speak English well, varies across immigrant groups. It
should be noted that this variable is self-reported in the census documents.
5
Of the 8,500 cases in our 1990 sample, 4,023 (47.33 %) identified themselves as white, 3,754 (44.16 %) as
black, 8 (0.09 %) as American Indian, 7 (0.08 %) as Chinese, 1 (0.01 %) as Japanese, 478 (5.62 %) as Other
Asian or Pacific Islander, and 229 (2.69 %) as other race (nec). Combining the latter five comes to a total of
723 included in our othercategory of race for 1990. Of the 18,476 cases in our 2000 sample, 5,066
(27.42 %) identified themselves as white, 10,299 (55.74 %) as black, 11 (0.06 %) as American Indian, 33
(0.18 %) as Chinese, 737 (3.99 %) as Other Asian or Pacific Islander, 507 (2.74 %) as other race (nec), 1,792
(9.70 %) as two major races, and 31 (0.17 %) as three or more races. Combining the latter six comes to a grand
total of 3,111 cases included in our othercategory for 2000. Thus, the total sample (1990 and 2000 data)
consists of 9,089 whites, 14,053 blacks, 3,834 included in our othercategory, for a total sample of 26,976.
A.M. Kusow et al.
In Model II, we add controls for race and gender, while Model III includes measures
for human capitaleducational attainment, work experience, and occupation. Finally,
Model IV adds dummies for marital status (married=1), region of U.S. residency
(reference group = South), citizenship, and English ability.
Results
Descriptive Analysis
Tab le 2presents descriptive statistics on all variables for 1990 and 2000 samples. Data
show that in the 1990 sample, the mean age of immigrants from East and West Africa
was in the mid-thirties except for immigrants from North and Southern Africa, whose
mean age was 40 years. In 2000, the mean age only slightly increased for immigrants
from all sending regions except for immigrants from West Africa, whose mean age
increased by almost six years (5.5 years). Data in Table 2also provide an overall
demographic and socio-economic profile of the African immigrants in the USA.
Clearly, African immigrants to the USA are a growing and diverse population. The
1990 annual wages for immigrants from Southern African region were higher than
other regions, on average. Immigrants from Southern Africa earned on average $20.72
an hour, the only sending region, whose immigrants in the USA earned slightly more
than $20.00 an hour. The rest of the sending regionshourly earnings were on average
less than $14.50. Amongst all regions, West Africa region had the lowest average
hourly earnings of $12.62 in 1990.
In the 2000 sample, the average hourly earnings increased by $5.00 to $9.00 for
sending regions. For instance, immigrants from East Africa hourly earnings increased
by only $5.00; immigrants from West and North African regionshourly earnings
increased by $7.00, and immigrants from Southern Africa region had the highest
increase, $9.00, which was two to four dollars more than East, West, and North
Africa. This increase in hourly earnings translated to substantial increases in annual
earnings for immigrants from some regions of Africa. The region that saw the greatest
boost in annual earnings was Southern Africa, which had an increase of more than
$19,000 and East Africa saw the lowest increase in annual earnings, an increase of less
than $9,000 only, from 1990 to 2000. West Africa and North Africa saw a $13,000
increase in their annual earnings in 1990 to 2000.
Tab l e 1 Sample frequencies and mean age for African regions (5 % IPUMs for 1990 and 2000)
Region IPUMS 1990 IPUMS 2000
Total population Mean age Total population Mean age
East Africa 1,693 36.46 4,516 38.39
West Africa 3,002 35.66 7,981 40.21
North Africa 2,836 40.61 4,077 41.99
Southern Africa 969 39.69 1,902 40.80
Socioeconomic Diversity Among African Immigrants
Tab l e 2 Descriptive statistics for all variables in the model (IPUMS 1990 and 2000) region
Indicators East Africa West Africa North Africa Southern Africa
1990 2000 1990 2000 1990 2000 1990 2000
Means
Age 36.11 37.97 35.50 40.10 40.02 41.70 39.52 40.37
Annual wage/salary 24,090.78 32,947.95 20,626.42 33,765.15 32,544.10 45,680.31 42,171.10 61,539.83
Hours worked 1848.65 1883.52 1818.88 1896.48 1942.62 1956.97 2019.00 2055.42
Hourly earnings 13.56 18.69 12.62 19.56 16.87 23.91 20.72 30.01
Logged hourly earnings 2.32 2.59 2.26 2.67 2.51 2.80 2.65 2.98
Years of schooling 14.74 12.11 14.75 12.51 15.32 14.03 15.81 14.33
Years of experience 15.37 19.86 14.72 21.55 18.70 21.64 17.72 20.04
Experience 2 311.50 503.10 286.10 569.30 458.83 588.00 415.60 529.01
Occupation (%)
Management 68.78 49.39 58.04 60.68 73.26 56.59 89.16 69.10
Service 14.66 11.77 20.05 9.44 12.22 11.46 4.95 3.99
Farming 0.30 10.86 0.33 6.67 0.21 11.07 0.32 8.86
Laborer 16.26 27.98 21.58 23.21 14.31 20.88 5.57 18.05
Level of education (%)
Less than HS 8.62 10.92 10.03 9.76 6.77 5.64 3.30 3.36
HS graduate 11.75 18.11 12.13 16.21 13.90 12.73 12.60 10.94
Some college 31.72 31.31 25.88 27.50 24.05 21.66 25.80 24.76
Bachelors degree 26.58 23.16 26.32 24.80 29.27 35.81 27.66 32.02
>Bachelors degree 21.32 16.50 25.65 21.73 26.02 24.16 30.65 28.92
A.M. Kusow et al.
Tab l e 2 (continued)
Indicators East Africa West Africa North Africa Southern Africa
1990 2000 1990 2000 1990 2000 1990 2000
Citizenship (%)
Naturalized 30.60 36.87 23.55 39.42 53.88 53.42 43.65 40.12
Non-citizen 69.40 63.13 76.45 60.58 46.12 46.58 56.35 59.88
English ability (%)
No English 0.00 0.49 0.80 0.51 0.21 0.34 0.00 0.05
Only English 17.78 12.38 26.02 23.55 25.46 18.49 73.48 64.46
Speaks very well 60.43 58.57 57.12 60.88 52.71 53.01 23.32 32.18
Speaks well 18.31 23.25 12.36 11.98 18.55 22.96 2.68 3.05
Speaks poorly 3.48 5.31 3.70 3.08 3.07 5.20 0.52 0.26
Married (%) 62.67 62.89 73.03 74.51 59.39 65.00 71.89 70.35
U.S. Regions %
Northeast 20.50 16.70 36.91 36.31 38.65 38.36 23.22 18.30
Midwest 11.75 15.94 9.96 12.07 9.23 10.28 11.15 10.99
West 29.42 26.68 12.06 10.50 28.60 24.26 37.15 35.01
South 38.33 40.68 41.07 41.12 23.52 27.10 28.48 35.70
Socioeconomic Diversity Among African Immigrants
On examining educational attainment, data from the 1990 sample show that immi-
grants from the sending regions of North and Southern Africa regions had slightly more
years of schooling than immigrants from East and West African regions. Thus, it is not
surprising that the income figures reported show lower mean hourly earnings as well as
low annual wage for immigrants from the East and West African regions. When the
level of education is examined, immigrants in the 1990 sample were better off than
immigrants in the 2000 sample. This is true for all educational categories except for the
bachelors degree category for immigrants from North and Southern Africa. Whereas
the percent of immigrants with a bachelors degree declined for immigrants from East
and West Africa regions, immigrants from North and Southern Africa had an increase
in the percent of bachelorsdegreeholders.
Intra-comparisons of the sending regions reveal that for the sending regions of East
and West Africa in the 1990 sample, college degrees did not translate into a higher
average annual wage and neither did it translate into management positions in the labor
force. North and Southern Africa sending regionsimmigrants were concentrated in
management. However, in the 2000 sample, a decrease is noted in the proportion of
immigrants in management and an increase in the percent engaged in laborer, service
and farming categories respectively for all sending regions. There are also differences
in the other labor participation characteristics among immigrants from the different
sending regions. For instance, some sending regionsimmigrants have more schooling
than others and more experience than others. Others are similar in terms of labor market
experience.
There is substantial variability across the four regions in terms of the other consid-
ered variables. In fact, immigrants from Southern Africa continued to outperform all
other sending regions in the sample by huge margins. If we examine the number of
hours worked and the hourly earnings, immigrants from Southern Africa sending
region reported high numbers of hours worked even when years of schooling and
experience are considered. This disparity may be due to the difficulty with the English
language proficiency that contributes also to the observed differences in earnings.
Research indicates that knowing English is a key component to improving an immi-
grantslife prospects, including labor force participation and earnings. There have been
significant changes in language skills over the 10-year period of 1990 to 2000. For
instance, immigrants from East Africa and North Africa sending regions reported an
increase in poor English proficiency. In 1990, 3.5 % of immigrants from East Africa
and 3.1 % of immigrants from North Africa reported poor English proficiency; this
figure increased to 5.3 % and 5.2 %, respectively, in 2000. Data on marital status of
African immigrants show that African immigrants are more likely to be married. These
immigrants are also less likely to be naturalized citizens. Further, in the 10-year period
1990 and 2000, the trend show African immigrants from the East and West Africa
region settling in the South in both 1990 and 2000 samples; whereas, the majority of
immigrants from North Africa settled in the Northeast, Southern African immigrants
settled in the West and South.
Multivariate Analysis
Tab le 3presents OLS regression results for four models that examine logged hourly
earnings for African immigrants in the 5 % IPUMS data from 1990 and 2000 census.
A.M. Kusow et al.
Tab l e 3 OLS regression of logged hourly earnings for African immigrants (5 % IPUMS of 1990 and 2000 US Census)
5 % IPUMS 1990 5 % IPUMS 2000
Model I Model II Model III Model IV Model I Model II Model III Model IV
Intercept 13.14 (.024)*** 11.22 (.026)*** 3.65 (.063)*** 3.45 (.066)*** 18.73 (.017)*** 17.58 (.019)*** 8.94 (.036)*** 8.82 (.038)***
Region of origin
East Africa 0.28 (.031)*** 0.21 (.037)*** 0.12 (.035)*** 0.10 (.034)** 0.32 (.021)*** 0.20 (.025)*** 0.12 (.023)*** 0.11 (.023)***
Wes t A fri c a 0.32 (.027)*** 0.24 (.037)*** 0.13 (.035)*** 0.13 (.035)*** 0.27 (.019)*** 0.12 (.025)*** 0.11 (.023)*** 0.11 (.023)***
North Africa 0.13 (.028)*** 0.16 (.027)*** 0.11 (.026)*** 0.12 (.026)*** 0.17 (.021)*** 0.18 (.021)*** 0.15 (.021)*** 0.15 (.021)***
Black 0.16 (.032)*** 0.12 (.030)*** 0.09 (.030)** 0.22 (.021)*** 0.17 (.020)*** 0.14 (.020)***
Other 0.03 (.039) 0.01 (.036) 0.02 (.036) 0.22 (.020)*** 0.13 (.019)*** 0.12 (.019)***
Male 0.32 (.017)*** 0.22 (.016)*** 0.23 (.016)*** 0.22 (.011)*** 0.12 (.011)*** 0.12 (.011)***
Human capital
Years of schooling 0.04 (.005)*** 0.04 (.005)*** 0.01 (.002)*** 0.01 (.001)***
Years of experience 0.03 (.003)*** 0.03 (.003)*** 0.02 (.002)*** 0.01 (.002)***
Experience
2
0.00 (.000)*** 0.00 (.000)*** 0.00 (.000)*** 0.00 (.000)***
High school graduate 0.05 (.038) 0.06 (.038) 0.06 (.023)** 0.04 (.023)
Some college 0.05 (.039) 0.05 (.039) 0.21 (.021)*** 0.16 (.021)***
Bachelors degree 0.00 (.048) 0.00 (.048) 0.42 (.024)*** 0.34 (.024)***
More than Bachelorsdegree 0.18 (.062)** 0.18 (.062)** 0.75 (.027)*** 0.65 (.028)***
Occupation
Service 0.27 (.023)*** 0.25 (.023)*** 0.31 (.020)*** 0.28 (.019)***
Farming 0.42 (.138)*** 0.38 (.137)*** 0.17 (.020)*** 0.16 (.019)***
Laborer 0.16 (.023)*** 0.15 (.022)*** 0.15 (.014)*** 0.15 (.014)***
Married 0.04 (.016)* 0.08 (.011)***
Socioeconomic Diversity Among African Immigrants
Tab l e 3 (continued)
5 % IPUMS 1990 5 % IPUMS 2000
Model I Model II Model III Model IV Model I Model II Model III Model IV
U.S. Regions
Wes t 0.10 (.020)*** 0.12 (.014)***
Northeast 0.21 (.019)*** 0.10 (.013)***
Midwest 0.04 (.026) 0.03 (.017)
Citizenship
Naturalized 0.11 (.016)*** 0.11 (.011)***
English ability
Speaks no English 0.31 (.126)** 0.07 (.080)
Good English 0.07 (.021)*** 0.11 (.015)***
Poor English 0.11 (.045)** 0.10 (.029)***
Adjusted R
2
0.0333 0.0665 0.2001 0.2201 0.0229 0.0475 0.1709 0.1847
DF 3 6 16243 6 1624
N8500 8500 8500 8500 18476 18476 18476 18476
Note: Unstandardized coefficients. Standard errors are in parentheses
*p<.05; **p<.01; ***p< .001 (some variables are not includedbecause of multicollinearity)
A.M. Kusow et al.
Model I measures the region of origin on logged hourly earnings. In model II, we add
race and gender to the region of origin variables controlled for in model I. In model III,
we add human capital variables, which include educational attainment, occupation, and
experience to the measures controlled for in model II. Model IV controls for the net
effects of all our independent variables, which include marital status, U.S. regions of
residence, citizenship, and English ability.
In model I, immigrants from East Africa, West Africa, and North Africa are
significantly less likely to report higher hourly earnings than immigrants from
Southern Africa. These findings are affirmed in all models in both 1990 and 2000
samples. In model II, after we include the race and gender variables in both
1990 and 2000 samples, results show that black African immigrants are signif-
icantly less likely to report higher hourly earnings than white African immi-
grants, but there is variability in the sample when the category otheris
considered. In the 1990 sample, the othercategory was not significant, but
this outcome changed in the 2000 sample. In the 2000 sample, similar to
blacks, African immigrants in the othercategory were also significantly less
likely to report higher hourly earnings than white African immigrants. Further,
in both samples, males were significantly more likely to report higher hourly
earnings than female immigrants.
In model III, we add the human capital characteristics of educational attainment,
occupational characteristics, and experience. In both 1990 and 2000 samples, there is
no change from the results in model I and II even after controlling for all other
variables. Immigrants from East Africa, West Africa, and North Africa still significantly
earned less in hourly wages than those from Southern African countries of South Africa
and Zimbabwe. Further, immigrants with work experience were significantly more
likely to report higher hourly earnings than those with less experience. Higher educa-
tion is considered to be one of the best measures for higher earnings; but for African
immigrants in the U.S., having more than a bachelors degree was a disadvantage; those
immigrants with more than a bachelorsdegreeaswellasthoseinserviceand
laborer/farming occupations were significantly less likely to report higher hour-
ly earnings. However, this outcome did not remain constant in model III in the
2000 sample. All educational categories: having a high school diploma, some
college, a bachelors and more than a bachelorsdegreeweresignificantly
associated with higher earnings.
In model IV, after adding marital status, U.S., regions of residence, citizenship, and
English ability, results indicate that married immigrants in both the 1990 and 2000
samples were significantly more likely to report higher hourly earnings than
non-married immigrants. There is variability in earnings in the U.S. regional
variables. In both the 1990 and 2000 samples, immigrants residing in the West
and Northeast were significantly more likely to report higher hourly earnings
than immigrants from the South; earnings for immigrants residing in the
Midwest was not significantly different. Further, African immigrants who were
naturalized citizens were more likely to report higher hourly earnings than non-
naturalized immigrants. Another point worth noting is the English ability,
immigrants whose English ability was goodwere significantly less likely to
report higher hourly earnings in both the 1990 and 2000 samples compared to
immigrants who spoke English very well.
Socioeconomic Diversity Among African Immigrants
Discussion and Conclusion
In this study, we examine socioeconomic variations among African immigrants in the
U.S. from different sending African regions of origin focusing mostly on the effects of
sending African region on immigrantssocioeconomic achievement. We specifically
asked whether or not the logged hourly earnings of African immigrants significantly
differ by African region of origin. Our findings reveal that the sending region is an
important factor in examining variations in earnings among African immigrants in the
USA. Immigrants from the Southern African region still out performed in hourly
earnings. For example, the Southern African region was the only region to earn more
than $20.00 per hour; $6.00 more than the other sending African regions.
Other factors that contribute to the observed variation among African immigrants
across different regions of origin include race, gender, ability to speak English, marital
status, and education. White African immigrants are more likely to report higher hourly
earnings than Black African immigrants, men are more likely to report higher hourly
earnings than women, and those who speak English more fluently are more likely to
report higher hourly earnings. Education for immigrants from East and West African
regions did not translate into higher annual earnings.
However, as we pointed out earlier, sending region of origin remains the most
important factor in structuring socioeconomic attainment among U.S. African immi-
grants. This finding is very important in that until now, most studies have concentrated
on racial differences and socioeconomic achievement among immigrants, racial differ-
ences in employment and earnings, labor force participation and earnings gap among
African immigrant women, and immigrant selectivity and labor market experiences
among black immigrants and black natives. None, however, has examined the impact
of region of origin on earnings and economic achievement among African immigrants
in the USA. The intra-Africa comparison is very important in that it moves us away
from treating all immigrants from Africa as an undifferentiated body and allows
comparison of African immigrants across different regions. This is important because
African immigrants come from different social, racial, language, religious, and colonial
historical backgrounds. More importantly, the intra-African comparison adds to in-
creasing findings of socioeconomic variations among African immigrant along racial
and gender lines and serves as a further caution against the treatment of immigrants
from the African continent as one homogenous body that can be compared to other
immigrants or African Americans.
Limitations and Future Research
An important limitation of this study is that a more comprehensive articulation of
socioeconomic variation among African immigrants across different regions of origin
obviously requires a more robust data than we provide here. The census data from
which our analysis is derived do not have enough properly designed variables to fully
address the questions at hand. Such an endeavor requires both historical and contextual
variables that can address the different social and political contexts that inform emi-
gration from Africa and the context of reception in host communities. It is important to
compare the implications of country of origin on assimilation experiences among
immigrants who both embrace religion similar to that of the host community, or speak
A.M. Kusow et al.
English prior to emigration as well as those who do not. Such contextual variables may
serve an important role as control variables so that the direct effects of context can be
statistically ascertained.
Future research could consider examining the impact of motives of migration,
political versus economic on variation in socioeconomic achievement among
African immigrants across different regions of origin. For instance, certain coun-
tries like Ethiopia, Somalia, and Sudan have been characterized by a permanent
and acute political instability. The political instability may have forced most people
to migrate to other countries, including USA. On the other hand, immigrants from
Kenya, Tanzania, and Uganda, emigrate from a somewhat politically and econom-
ically stable environment. Therefore, immigration from these countries may not
produce the same type of political immigrants and refugees.
We, therefore, suggest that future research should not desegregate African immi-
grants along racial, gender, and region of origin alone, but in terms of the (a) linguistic
identity of former colonial master, English-speaking countries versus non-English-
speaking countries, (b) differences in colonial strategy of the mother country, presence
or absence settler colonial community, and (b) political versus economic motives/
degrees of political stability in the home country.
Despite such limitations, however, we believe findings presented from the current
analysis further our understanding of variations in socioeconomic achievement among
U.S. black African immigrants from different regions of origin.
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