Sage Journals: Discover world-class research

Abstract

Second-generation black Americans have been inadequately studied in prior quantitative research. The authors seek to ameliorate this research gap by using the Current Population Survey to investigate education and wages among second-generation black Americans with a focus on Nigerian Americans. The latter group has been identified in some qualitative studies as having particularly notable socioeconomic attainments. The results indicate that the educational attainment of second-generation Nigerian Americans exceeds other second-generation black Americans, third- and higher generation African Americans, third- and higher generation whites, second-generation whites, and second-generation Asian Americans. Controlling for age, education, and disability, the wages of second-generation Nigerian Americans have reached parity with those of third- and higher generation whites. The educational attainment of other second-generation black Americans exceeds that of third- and higher generation African Americans but has reached parity with that of third- and higher generation whites only among women. These results indicate significant socioeconomic variation within the African American/black category by gender, ethnicity, and generational status that merits further research.

Keywords

second generation Nigerian Americans African Americans black immigrants wages educational attainment immigration assimilation

The socioeconomic circumstances of the African American/black population are often portrayed in monolithic terms (e.g., Massey and Denton 1993; Pettit 2012; Saenz and Morales 2005; Western 2006). Although these studies are important and informative of the majority of the African American/black population, the latter is nonetheless increasing in its demographic heterogeneity and ethnic diversity in the twenty-first century (Kebede 2019). One major source of demographic heterogeneity is immigration (Hamilton 2019). In the 1960 U.S. census, before the Immigration and Naturalization Act of 1965, only about 0.7 percent of the African American/black population was foreign born (i.e., first generation), while another 0.7 percent was native born with foreign-born parents (Sakamoto, Woo, and Kim 2010). By the second decade of the twenty-first century, however, a sizable population of first-generation and second-generation black Americans has accumulated (Capps, McCabe, and Fix 2012). The number of immigrants from Africa doubled from about 800,000 in 2000 to more than 1.6 million in 2010, with particular increases from West Africa and East Africa (American Immigration Council 2012). By 2016, the first generation had grown to about 10 percent, while the second generation had grown to 8 percent of the African American/black population (Anderson and López 2018).¹ The foreign stock (i.e., first-generation and second-generation immigrants) has thus become a significant component of the African American/black population in twenty-first-century America.²

Some prior studies investigated the labor market outcomes of first-generation black immigrants whose earnings trajectories differ from those of third-generation African Americans (e.g., Hamilton 2014, 2019). Quite neglected in the demographic literature on black immigrants, however, are the socioeconomic attainments of second-generation black Americans. The socioeconomic incorporation of second-generation Hispanic and Asian Americans has been widely researched (e.g., Kao and Thompson 2003; Lee and Zhou 2015; Portes and Zhou 1993; Tienda and Mitchell 2006; Tran and Valdez 2017), but demographic studies using representative data on second-generation blacks are still very scarce. As stated by Kebede (2019), “the New African Diaspora’s second generation in the United States is one of the least studied groups” (p. 119).

In the following, we seek to help fill this obvious research gap by investigating racial, ethnic, and generational socioeconomic differentials using data from the Current Population Survey (CPS). Our primary focus is on second-generation Nigerian Americans, but for comparative purposes we consider a variety of reference groups, including other second-generation black Americans (i.e., all second-generation black Americans except those who are Nigerian). The socioeconomic differentials associated with a second-generation group will vary depending on which reference group is being considered (e.g., Park, Myers, and Jiménez 2014).

At the outset, we emphasize that future research should investigate all discernable groups in the African American/black category to better appreciate its heterogeneity. Our findings below focusing on Nigerian Americans are only a first step toward that broader research agenda. Because of space limits, we center this analysis on understanding the socioeconomic outcomes of second-generation Nigerian Americans because prior qualitative research has identified them as being particularly notable with regard to education and labor market attainments (Arthur 2000; Casimir 2018; Chua and Rubenfeld 2014; Imoagene 2017). In general, sociologists from very different methodological approaches agree that research findings that are seemingly unexpected in terms of prevailing perspectives often have substantial potential for promoting theoretical advancement (Habermas and McCarthy 1984; Kuhn 1962; Merton 1968; Weber 1958). In future research, we plan to investigate other African American/black groups for whom adequate sample sizes are available, but currently most lacking are analyses of second generation black Americans, among whom Nigerian Americans may be exceptional (Tran et al. 2018).³

Prior Literature

With regard to educational attainment, second-generation black Americans are more likely to attend college than third-generation African Americans (Sakamoto et al. 2010). Furthermore, the “overrepresentation of immigrants is higher in private than in public institutions and within more selective rather than less selective schools” (Massey et al. 2007:243). Using data from the National Longitudinal Study of Freshmen, Massey et al. (2007) concluded that the greater preponderance of second-generation black Americans over third-generation African Americans is not explained by higher socioeconomic background factors because there are “few differences in the social origins of black students from immigrant and native backgrounds” (p. 243).

Using more detailed data from the National Education Longitudinal Study, Bennett and Lutz (2009) reached similar conclusions about the higher educational attainment of second-generation black Americans over third-generation African Americans. That observed advantage is not explained by higher socioeconomic background. Bennett and Lutz also found that after controlling for socioeconomic background, second-generation black Americans are more likely to attend selective colleges than whites.

Tran et al. (2018) used nationally representative data on educational attainment from the 2008–2012 CPS to obtain a sample size of 44 second-generation Nigerian Americans aged 25 and older. That study revealed that 73.5 percent of second-generation Nigerian Americans were college graduates, in comparison with 32.9 percent of whites and 18.9 percent of third-generation African Americans (Tran et al. 2018:195). These findings suggest that specifically second-generation Nigerian Americans have particularly high levels of educational attainment in recent years.

As for labor market outcomes, Kalmijn (1996) used data from the 1990 U.S. census to investigate immigrant black Americans versus third-generation African Americans. However, Kalmijn’s (1996) analysis grouped first-generation with second-generation black immigrants, so that the net effect of being second generation per se is not identified in his results. Although somewhat dated, his results suggest that immigrant blacks from the English-speaking Caribbean nations have slightly higher socioeconomic outcomes relative to immigrant blacks from French-speaking and Spanish-speaking Caribbean nations as well as relative to third-generation African Americans. Hamilton (2014) similarly concluded that first-generation blacks from English-speaking backgrounds may adjust faster to the U.S. labor market than other first-generation blacks, which may be advantageous for their second-generation offspring. With regard to Nigerian Americans, English is an official language and the lingua franca in Nigeria because of its British colonial history.

To our knowledge, the only multivariate analysis focusing specifically on the wages of second-generation black Americans is that of Sakamoto et al. (2010). Using data from the CPS from 1994 to 2006, they found that net of education, age, disability status, region, and metropolitan status, second-generation black American women have wages that have reached parity with those of white women and are about 8 percent higher than those of third-generation African American women. The multivariate results for second-generation black American men indicate that they have about 5 percent higher wages than third-generation African American men but about 16 percent lower wages than white men. These findings suggest that although second-generation black American women have reached wage parity with white women, second-generation black American men are still disadvantaged relative to white men (i.e., more similar to third-generation African American men) in the labor market. Because of limitations of sample size, Sakamoto et al. did not investigate ethnic differentials among second-generation black Americans.

Data and Methods

We build upon Sakamoto et al. (2010) as well as Tran et al. (2018) by investigating more recent data from the CPS. We study educational and wage attainment among second-generation black Americans with a focus on Nigerian Americans in the twenty-first century. Specifically, we use the March Annual Social and Economic Supplement files for the CPS from 2009 to 2019 to obtain a substantially larger sample size of second-generation black Americans than did Sakamoto et al. and Tran et al. Because of the rotational sampling design of the CPS, about one third of the sample overlaps between successive years. To obtain a larger sample size, we included each year of CPS data but deleted duplicate records of the same individual in adjacent years by using the unique identifiers for individuals that are available in every year of the data that we investigate. This approach has been successfully used in prior research (Sakamoto and Hsu 2020).⁴

Among second-generation black Americans, we identify a separate category consisting of second-generation Nigerian Americans. We refer to all other (i.e., non-Nigerian) second-generation black Americans as other second-generation black Americans. Although the CPS includes data on racial categories, no question on the survey directly asks about ethnic identity. We therefore define second-generation Nigerian Americans as U.S.-born individuals who identify as African American/black and who have at least one parent who was born in Nigeria.

This “place of parental birth” approach differs from a purely subjective self-assessment of ethnic identity. With regard to the latter, prior studies consistently describe how second-generation black Americans (including Nigerian Americans) often adopt more ethnic and “foreign” identities for strategic purposes to avoid racial discrimination (e.g., Arthur 2000; Imoagene 2017; Ludwig 2019; Waters 1990). As stated by Berthelemy (2019), “the ethnic label tends to be perceived as a kind of ‘exit option’ under the threat of being racially discriminated” (p. 175). According to Sall (2019), “embracing Black ethnic identities might provide a buffer from racial discrimination” (p. 140).

More generally, Saperstein and Penner (2012) argued that ethnic identities are themselves influenced by having particular socioeconomic outcomes. All of these aforementioned studies imply that ethnic identity may sometimes be partially “endogenous” (i.e., simultaneously defined) with respect to educational and income attainments (Liebler et al. 2017; Villarreal 2014). These processes may apply not only to blacks but also to other minority groups such as Hispanics and Native Americans (Duncan and Trejo 2011; Liebler et al. 2017).

Although we believe that self-assessed indicators of ethnic identity need to be investigated in future research, in the following we restrict our analysis to the place of parental birth approach. The latter is more appropriate for our research purposes because place of parental birth occurs prior to and is therefore exogenous with respect to socioeconomic attainments of an individual. Using place of parental birth to categorize second-generation Nigerian Americans allows us to use conventional methods for recursive regression models. By contrast, analyzing the socioeconomic outcomes of minorities on the basis of purely subjective self-assessment of ethnic identity may be better studied using nonrecursive statistical models (Villarreal and Bailey 2020) or data linking racial/ethnic identities across generations (Duncan and Trejo 2011; Liebler et al. 2017).

Other groups that we investigate include third- and higher generation African Americans (which we have been referring to as “third-generation African Americans”), second-generation non-Hispanic whites (hereafter “second-generation whites”), third- and higher generation non-Hispanic whites (hereafter “whites”), third- and higher generation non–African American/black Hispanics (hereafter “Hispanics”), and second-generation Asian Americans (hereafter “second-generation Asians”).⁵ To obtain an adequate sample size, we additionally include persons who are 1.5th generation (i.e., foreign-born but arrived in the United States at age 15 or younger). As is customary, these 1.5th-generation individuals are grouped together with their respective racial/ethnic category of second-generation individuals (Portes and Rumbaut 2005; Sakamoto et al. 2010). For convenience, our use of the term second-generation actually refers to 1.5th generation and second generation combined.⁶ Because of our focus on second-generation individuals, who are generally younger because they are mostly post-1965 immigrants, we restrict our target population to persons aged 25 to 54.

Our first dependent variable of interest is ordinal and is the highest level of educational attainment completed. The levels include less than high school, high school, associate degree or two years of college, bachelor’s degree, and PhD or professional degree. This outcome is investigated using an ordered logit model that incorporates the ordinal ranking inherent in the educational levels. The control variables used in this regression include age, age squared, and disability status.

The second dependent variable is the hourly wage, which is recognized as a critically important indicator of labor market outcomes that is central to the stratification system (e.g., Cheng 2016; England et al. 1988; Mouw and Kalleberg 2010; Stolzenberg 1975; Western and Rosenfeld 2011), including with regard to racial and ethnic inequalities (e.g., Grodsky and Pager 2001; Hamilton 2014; Sakamoto et al. 2010; Western 2002; Wang, Takei, and Sakamoto 2017). It is computed as total labor earnings (i.e., salary earnings as well as income from self-employment) from the previous calendar year divided by total hours worked during that year.⁷ Dollar values were adjusted to the 2019 price level using the Consumer Price Index. Total hours worked is calculated as the total number of weeks worked multiplied by the “usual hours worked per week.” In the specification of our regression models, we follow conventional practice by investigating the log of hourly wage because of the skewed distribution of the unlogged hourly wage (Mouw and Kalleberg 2010).

Some prior studies using the CPS delete cases for which the computed hourly wage is deemed to be very small or very large (Mouw and Kalleberg 2010). We do not follow this practice for several reasons. First, it unnecessarily reduces the sample size. Second, it underestimates the level of inequality (i.e., to the extent that hourly wages that are very small or very large are not totally invalid values). Third, the ordinary least squares estimation of regression coefficients requires a conditional random sample of the dependent variable to be best linear unbiased estimates, and the deletion of such cases can lead to bias to the extent that they derive from values that have a greater variance than the other observed values (Bollinger and Chandra 2005; Bollinger et al. 2019).

Although following usual practice, our calculation of the total hours worked in the previous calendar year necessarily involves some measurement error because “usual hours worked per week” is not exactly precise given some variability across weeks during the course of a year. Some resulting measurement error in the hourly wage is therefore unavoidable, but rather than deleting extreme cases, we recoded values less than $1 to be equal to $1 and values greater than $750 to be equal to $750. We believe that this approach is preferably because if the African American/black population is more likely to have extremely lower values than whites (Kim and Tamborini 2014), then deleting extreme values would generate downward bias in the estimate of racial inequality. As an additional methodological precaution, our sample for the wage analysis is limited to persons who worked at least 500 hours during the year.

Because our analysis includes so many different demographic groups, large variations in labor supply are typical depending on a variety of factors such as age, gender, marital status, family size, education, generational status, region of residence, and other factors (e.g., Bowen and Finegan 2015). As an essentially exploratory analysis of racial and ethnic differentials relating to second-generation black Americans versus a wide variety of reference groups, the investigation of wages is preferable to annual earnings because the latter might obscure discrimination due to immigrants’ and minorities’ having to work longer hours to obtain the same earnings (net of other relevant factors) as whites. Although no single measure of labor market outcomes is perfect or completely informative, wages are increasing in their socioeconomic significance, while occupations are becoming more heterogeneous (Sakamoto and Wang 2020). Relatedly, wage differentials between whites and African Americans persist even within detailed occupational categories (Grodsky and Pager 2001).

With regard to our statistical models, different specifications are estimated that increasingly include additional independent variables that may be argued to be less exogenous with respect to the hourly wage in the previous year (Wang et al. 2017). Model 1 is the baseline (i.e., bivariate) specification, which includes only the racial/ethnic and generational categories. Model 2 includes age, age squared, disability status, dichotomous variables indicating the highest level of education completed, and a dichotomous variable indicating the receipt of any self-employment income. The rationale for the latter variable is because prior research has shown that self-employed persons tend to have lower hourly wages (Portes and Zhou 1996), which may be related to a certain degree of underreporting of earnings (Hurst, Li, and Pugsley 2014).⁸ Model 3 adds marital status and the presence of own children to the model 2 specification. Model 4 adds region and metropolitan status to the model 3 specification.

Because of well-known differences between men and women in terms of educational attainment, labor force participation and labor market outcomes, our analyses are broken down by gender (Valdez and Tran 2020). Furthermore, racial and ethnic differences are known to vary substantially by gender (Greenman and Xie 2008) as is also evident in the literature review discussed above with regard to second-generation black Americans (Sakamoto et al. 2010). In general, however, our focus is on racial and ethnic differentials within gender rather than on explicating gender differentials per se.

Wang et al. (2017) found some empirical evidence that the cost of living associated with one’s place of residence is to some degree endogenous with respect to one’s hourly wage; regional migration is not random with respect to one’s income (e.g., Frey 1995; Ganong and Shoag 2017). If that conclusion is generally valid, then model 3 (which does not include region or metropolitan status) might be the most appropriate specification. Having said that, the empirical results indicate that our major conclusions are not sensitive to the particular multivariate specification (i.e., they are evident throughout our models 2, 3, and 4) which suggests a certain degree of robustness.

Empirical Results

Table 1 shows the sample sizes for the analysis of educational attainment. In this case, the dependent variable is defined whether the individual is employed in the labor force. For men aged 25 to 54, the sample sizes are 109 second-generation Nigerian Americans, 1,083 other second-generation black Americans, 22,363 third-generation African Americans, 10,408 Hispanics, 6,569 second-generation whites, 149,223 whites, and 5,805 second-generation Asians. Table 1 also shows that among women aged 25 to 54, the sample sizes are 89 second-generation Nigerian Americans, 1,315 other second-generation black Americans, 29,548 third-generation African Americans, 11,772 Hispanics, 6,693 second-generation whites, 157,332 whites, and 5,771 second-generation Asians.

Table 1.

Sample Sizes for the Analysis of Educational Attainment for Persons Aged 25 to 54 by Racial/Ethnic/Generational Group and Gender, 2009 to 2019.

Race/Ethnicity/Generation	Male		Female		Total
Race/Ethnicity/Generation	n	Percentage	n	Percentage	n	Percentage
3rd+ generation non-Hispanic whites	149,223	76.31	157,332	74.03	306,555	75.12
3rd+ generation blacks (including Hispanics)	22,363	11.44	29,548	13.90	51,911	12.72
3rd+ generation nonblack Hispanics	10,408	5.32	11,772	5.54	22,180	5.44
2nd-generation non-Hispanic Asians	5,805	2.97	5,771	2.72	11,576	2.84
2nd-generation Nigerians (including Hispanics)	109	0.06	89	0.04	198	0.05
2nd-generation non-Hispanic whites	6,569	3.36	6,693	3.15	13,262	3.25
2nd-generation blacks (including Hispanics)	1,083	0.55	1,315	0.62	2,398	0.59
Total	195,560	100.00	212,520	100.00	408,080	100.00

Source: Current Population Survey, 2009 to 2019.

Table 2 shows the sample sizes for the analysis of the hourly wage. These sample sizes are somewhat smaller for each respective group in Table 1 because some persons aged 25 to 54 are not employed as workers in the labor force (although they have completed educational attainment). For men aged 25 to 54 with an hourly wage, Table 2 shows that the sample sizes are 93 second-generation Nigerian Americans, 893 other second-generation black Americans, 17,274 third-generation African Americans, 8,817 Hispanics, 5,887 second-generation whites, 132,743 whites, and 5,083 second-generation Asians. Table 2 also shows that among women aged 25 to 54 with an hourly wage, the sample sizes are 71 second-generation Nigerian Americans, 1,061 other second-generation black Americans, 22,134 third-generation African Americans, 8,752 Hispanics, 5,308 second-generation whites, 122,329 whites, and 4,610 second-generation Asians.

Table 2.

Sample Sizes for the Analysis of Hourly Wage for Workers Aged 24 to 54 by Racial/Ethnic/Generational Group and Gender, 2009 to 2019.

Race/Ethnicity/Generation	Male		Female		Total
Race/Ethnicity/Generation	n	Percentage	n	Percentage	n	Percentage
3rd+ generation non-Hispanic whites	132,743	77.72	122,329	74.47	255,072	76.13
3rd+ generation blacks (including Hispanics)	17,274	10.11	22,134	13.47	39,408	11.76
3rd+ generation nonblack Hispanics	8,817	5.16	8,752	5.33	17,569	5.24
2nd-generation non-Hispanic Asians	5,083	2.98	4,610	2.81	9,693	2.89
2nd-generation Nigerians (including Hispanics)	93	0.05	71	0.04	164	0.05
2nd-generation non-Hispanic whites	5,887	3.45	5,308	3.23	11,195	3.34
2nd-generation blacks (including Hispanics)	893	0.52	1,061	0.65	1,954	0.58
Total	170,790	100.00	164,265	100.00	335,055	100.00

Source: Current Population Survey, 2009 to 2019.

Table 3 shows descriptive statistics for the sample for the educational analysis. For both men and women, Table 3 shows that second-generation Nigerians, other second-generation black Americans, and second-generation Asians are younger on average than third-generation African Americans, Hispanics, and whites. The average age of second-generation whites does not differ significantly from that of whites, perhaps because many second-generation whites were able to immigrate to the United States before the 1965 Immigration and Naturalization Act.

Table 3.

Descriptive Statistics for Persons Aged 25 to 54 in the Analysis of Educational Attainment by Racial/Ethnic/Generational Group and Gender, 2009 to 2019.

Variable	3rd+ Generation Non-Hispanic Whites	3rd+ Generation Blacks (Including Hispanics)	3rd+ Generation Nonblack Hispanics	2nd-Generation Non-Hispanic Asians	2nd-Generation Nigerians (Including Hispanics)	2nd-Generation Non-Hispanic Whites	2nd-Generation Blacks (Including Hispanics)
Men
Age (y) (mean)	39.41	38.87	37.16	35.34	33.02	39.14	33.88
Any disability (%)	7.16	9.74	8.00	3.48	.83	5.95	5.92
Educational attainment (%)
Less than high school	6.04	11.16	13.50	3.64	.44	4.24	8.19
High school or GED	30.66	40.44	38.48	16.41	5.74	21.35	26.90
Some college	27.79	30.35	30.75	24.16	25.64	26.76	33.56
Bachelor’s degree	24.56	12.91	13.07	36.51	44.14	30.93	22.38
Master’s degree	7.68	4.19	2.97	11.96	9.96	11.03	6.80
Professional/PhD degree	3.26	.95	1.22	7.32	14.08	5.69	2.17
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Women
Age (y) (mean)	39.63	38.91	37.36	35.47	32.80	39.41	34.73
Any disability (%)	7.36	9.35	7.64	2.71	7.06	5.44	6.53
Educational attainment (%)
Less than high school	4.49	9.01	12.48	3.66	1.28	2.94	6.10
High school or GED	24.17	31.51	32.27	14.59	6.84	17.27	20.21
Some college	30.41	35.10	33.67	22.09	20.74	26.31	35.17
Bachelor’s degree	27.24	15.66	15.31	38.40	36.25	32.22	23.65
Master’s degree	10.93	7.38	5.13	13.40	22.17	16.07	10.60
Professional/PhD degree	2.75	1.35	1.13	7.85	12.72	5.20	4.29
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00

Source: Current Population Survey, 2009 to 2019.

With regard to educational distributions, Table 3 is consistent with the well-known findings that second-generation Asians have higher educational attainment than whites and that the latter group has higher educational attainment than third-generation African Americans. The results for other second-generation black Americans are also consistent with prior studies discussed above, which demonstrated that this group has higher educational attainment than Hispanics and third-generation African Americans. Relative to whites, the educational attainment compared with other second-generation black Americans is generally slightly lower. The only slight exception to this generalization is that other second-generation black American women are more likely to have professional or PhD degrees than white women.

The group with the highest level of educational attainment is second-generation Nigerian Americans. For both men and women, the educational distributions for second-generation Nigerian Americans are generally more highly concentrated at the upper levels than for any of the other groups shown in Table 3. For example, among second-generation Asian men, 3.6 percent dropped out of high school and 7.3 percent obtained PhD or professional degrees, whereas the corresponding figures for second-generation Nigerian American men are 0.4 percent and 14.1 percent, respectively. The specific group with the highest level of educational attainment is arguably second-generation Nigerian American women; Table 3 indicates that 71.1 percent of them have bachelor’s or higher degrees in comparison with 68.2 percent for second-generation Nigerian American men.⁹

Table 4 shows the results for the ordered logit regression of highest educational level. Model 1 is the bivariate specification, while model 2 controls for age and age squared. Model 3 then adds disability status to the model 2 specification. The coefficient for each group indicates the multiplicative change in the odds ratio for completing a higher level of education relative to the reference category, which is whites. The ordered logit model stipulates a constant (i.e., proportional) change across each educational level for each group. The descriptive results in Table 3 suggest that this proportionality assumption is not always precisely, accurate so the estimates in Table 4 may be interpreted as an average across the different educational levels for each group.

Table 4.

Odds Ratios Estimated from Ordered Logistic Regressions of Educational Attainment for Persons Aged 25 to 54, 2009 to 2019.

Independent Variable	Men			Women
Independent Variable	Model 1	Model 2	Model 3	Model 1	Model 2	Model 3
Race/ethnicity/generation
3rd+ generation non-Hispanic whites	Reference	Reference	Reference	Reference	Reference	Reference
3rd+ generation blacks (including Hispanics)	.495***	.492***	.499***	.535***	.525***	.532***
3rd+ generation blacks (including Hispanics)	(.008)	(.007)	(.008)	(.007)	(.007)	(.007)
3rd+ generation nonblack Hispanics	.464***	.455***	.457***	.437***	.419***	.418***
3rd+ generation nonblack Hispanics	(.011)	(.011)	(.011)	(.010)	(.009)	(.009)
2nd-generation non-Hispanic Asians	2.218***	2.146***	2.103***	1.954***	1.824***	1.772***
2nd-generation non-Hispanic Asians	(.063)	(.061)	(.060)	(.056)	(.053)	(.051)
2nd-generation Nigerians (including Hispanics)	3.762***	3.584***	3.474***	3.684***	3.404***	3.612***
2nd-generation Nigerians (including Hispanics)	(.660)	(.631)	(.613)	(.776)	(.714)	(.779)
2nd-generation non-Hispanic whites	1.669***	1.670***	1.659***	1.662***	1.661***	1.639***
2nd-generation non-Hispanic whites	(.048)	(.048)	(.048)	(.049)	(.049)	(.049)
2nd-generation blacks (including Hispanics)	.916	.883*	.887*	1.004	.930	.933
2nd-generation blacks (including Hispanics)	(.060)	(.058)	(.058)	(.061)	(.057)	(.057)
Age		1.069***	1.064***		1.085***	1.080***
Age		(.006)	(.006)		(.006)	(.006)
Age squared		.999***	.999***		.999***	.999***
Age squared		(7.35e-05)	(7.39e-05)		(7.04e-05)	(7.06e-05)
Any disability			.352***			.349***
Any disability			(.008)			(.007)
No disability			Reference			Reference
Cut point 1	.065***	.192***	.165***	.050***	.164***	.144***
Cut point 1	(.001)	(.021)	(.018)	(.001)	(.017)	(.015)
Cut point 2	.564***	1.670***	1.485***	.392***	1.289**	1.163
Cut point 2	(.004)	(.182)	(.163)	(.003)	(.135)	(.122)
Cut point 3	1.858***	5.519***	4.987***	1.469***	4.876***	4.490***
Cut point 3	(.013)	(.603)	(.547)	(.009)	(.511)	(.472)
Cut point 4	8.325***	24.770***	22.630***	6.262***	20.920***	19.500***
Cut point 4	(.080)	(2.696)	(2.473)	(.053)	(2.189)	(2.047)
Cut point 5	30.460***	90.670***	83.050***	34.180***	114.400***	107.000***
Cut point 5	(.494)	(9.942)	(9.147)	(.578)	(12.100)	(11.360)
Observations	195,560	195,560	195,560	212,520	212,520	212,520

Source: Current Population Survey, 2009 to 2019.

Note: Exponential of robust standard error in parentheses.

p < .10. **p < .05. ***p < .01.

The results for model 1 for men indicate that, relative to white men, the differentials in educational attainment are statistically significant for third-generation African Americans, Hispanics, second-generation Nigerian Americans, second-generation Asians, and second-generation whites. The educational differential between whites and other second-generation black Americans is not statistically significant for the bivariate model. Table 4 shows the same conclusion for women on the basis of model 1; relative to white women, the differentials in educational attainment are statistically significant for third-generation African Americans, Hispanics, second-generation Nigerian Americans, second-generation Asians, and second-generation whites but not for other second-generation black Americans.

After controlling for age and disability in model 3 in Table 4, the coefficients indicate the net differential for each group relative to whites. The results for model 3 indicate that this net differential for other second-generation black Americans relative to whites is statistically significant for men but not for women. The groups with the lowest levels of educational attainment are third-generation African Americans and Hispanics, while second-generation Asians and second-generation whites have higher educational attainment than whites.

The group with the largest differential is second-generation Nigerian Americans. The coefficient for model 3 in Table 4 indicates that second-generation Nigerian American men have 247.4 percent (i.e., 3.474 – 1.000) greater odds than white men of achieving a higher educational level, while second-generation Nigerian American women have 261.2 percent (i.e., 3.612 – 1.000) greater odds of achieving a higher educational level than white women. The coefficients for second-generation Nigerian Americans are statistically significant at any conventional level.

To accurately assess the statistical significance across different groups, Table 5 shows some of the results for the ordered logit regression of highest educational level after varying the reference category to be some group other than whites. Table 5 shows the coefficient and its statistical significance for second-generation Nigerian Americans using different reference categories. For example, model 3 in Table 5 indicates that second-generation Nigerian American men have 596 percent greater odds of achieving a higher educational level than third-generation African American men, while second-generation Nigerian American women have 579 percent greater odds of achieving a higher educational level than third-generation African American women. These effects are statistically significant at any conventional level.

Table 5.

Odds Ratio for the Nigerian Coefficient Estimated from Ordered Logistic Regressions of Educational Attainment Using Different Racial/Ethnic/Generational Reference Groups for Persons Aged 25 to 54, 2009 to 2019.

Reference Category	2nd-Generation Nigerian Men (Including Hispanics)			2nd-Generation Nigerian Women (Including Hispanics)
Reference Category	Model 1	Model 2	Model 3	Model 1	Model 2	Model 3
3rd+ generation non-Hispanic Whites	3.762***	3.584***	3.474***	3.684***	3.404***	3.612***
3rd+ generation non-Hispanic Whites	(.660)	(.631)	(.613)	(.776)	(.714)	(.779)
3rd+ generation blacks (including Hispanics)	7.603***	7.290***	6.958***	6.883***	6.486***	6.787***
3rd+ generation blacks (including Hispanics)	(1.337)	(1.287)	(1.230)	(1.451)	(1.362)	(1.466)
3rd+ generation nonblack Hispanics	8.112***	7.876***	7.598***	8.436***	8.115***	8.635***
3rd+ generation nonblack Hispanics	(1.434)	(1.397)	(1.350)	(1.785)	(1.710)	(1.871)
2nd-generation non-Hispanic Asians	1.696***	1.670***	1.652***	1.886***	1.866***	2.038***
2nd-generation non-Hispanic Asians	(.301)	(.297)	(.295)	(.400)	(.395)	(.443)
2nd-generation non-Hispanic whites	2.254***	2.147***	2.094***	2.217***	2.050***	2.203***
2nd-generation non-Hispanic whites	(.400)	(.383)	(.374)	(.471)	(.434)	(.480)
2nd-generation blacks (including Hispanics)	4.108***	4.059***	3.918***	3.670***	3.661***	3.872***
2nd-generation blacks (including Hispanics)	(.768)	(.761)	(.735)	(.803)	(.799)	(.868)
Control variables		Age	Age		Age	Age
		Age squared	Age squared		Age squared	Age squared
			Disability			Disability

Source: Current Population Survey, 2009 to 2019.

Note: Exponential of robust standard error in parentheses.

***

p < .01.

Table 5 further indicates that the higher educational attainment of second-generation Nigerian American women is statistically significant relative to every other female group for each of the model specifications. For example, model 3 shows that second-generation Nigerian American women have 104 percent greater odds of achieving a higher educational level than second-generation Asian women, and that coefficient is statistically significant at any conventional level. For second-generation Nigerian American men, Table 5 indicates that their higher educational attainment is also statistically significant relative to all of the other male groups for each of the model specifications. For example, model 3 shows that second-generation Nigerian American men have 65 percent greater odds of achieving a higher educational level than second-generation Asian men, and that coefficient is statistically significant.

Table 6 shows descriptive statistics for the sample that is used in the analysis of the hourly wage. The results are generally similar to those shown earlier in Table 3 for the sample used in the analysis of educational attainment. Some additional data considered in Table 6, however, are family- and geography-related variables. The male groups that are most likely not to have children are second-generation Nigerian Americans and other second-generation black Americans. Among women, the groups that are most likely not to have children are second-generation Nigerian Americans and second-generation Asians. Hispanic women and third-generation African American women are the groups that stand out as being the most likely to be residing with an own child. A commonality across all three African American groups is that they are less likely than the other racial/ethnic groups (within the same gender) to be currently married.

Table 6.

Descriptive Statistics for Workers Aged 25 to 54 with an Hourly Income by Racial/Ethnic/Generational Group and Gender, 2009 to 2019.

Independent Variable	3rd+ Generation Non-Hispanic Whites	3rd+ Generation Blacks (Including Hispanics)	3rd+ Generation Nonblack Hispanics	2nd-Generation Non-Hispanic Asians	2nd-Generation Nigerians (Including Hispanics)	2nd-Generation Non-Hispanic Whites	2nd-Generation Blacks (Including Hispanics)
Men
Self-employment income (%)	8.12	5.77	6.01	6.94	5.56	8.16	5.55
Age (y) (mean)	39.23	38.65	36.99	35.54	33.79	39.13	33.68
Any disability (%)	3.78	3.41	3.96	1.89	1.03	3.05	1.64
Married (%)	59.55	41.65	47.37	50.22	43.84	54.54	31.54
No own child in household (%)	51.65	59.20	51.72	60.32	67.52	57.41	65.86
Educational attainment (%)
Less than high school	4.56	7.05	11.24	2.79	.55	3.11	6.26
High school or GED	29.31	38.34	37.85	15.59	3.64	20.06	25.03
Some college	28.05	32.98	31.90	23.88	24.97	26.37	33.77
Bachelor’s degree	26.20	15.35	14.34	37.20	44.47	32.62	24.95
Master’s degree	8.33	5.16	3.33	12.59	12.39	11.65	7.26
Professional/degree	3.54	1.13	1.34	7.93	13.98	6.20	2.73
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Live in metropolitan area (%)	82.61	90.89	90.74	97.78	99.45	93.44	98.60
Women
Self-employment income (%)	6.54	3.99	4.60	5.85	3.35	6.45	4.08
Age (y) (mean)	39.43	38.66	37.25	35.44	33.21	39.34	34.67
Any disability (%)	3.65	3.82	4.34	1.54	5.36	2.63	3.74
Married (%)	59.83	31.65	46.38	53.44	38.96	56.39	33.59
No own child in household (%)	45.42	39.22	37.19	53.97	59.62	50.11	48.26
Educational attainment (%)
Less than high school	2.62	5.58	8.41	2.53	1.68	1.64	4.04
High school or GED	21.75	28.82	30.87	13.08	7.72	14.95	17.63
Some college	30.53	37.00	35.63	21.06	18.09	25.53	36.19
Bachelor’s degree	29.41	18.20	17.69	39.97	37.84	34.08	24.68
Master’s degree	12.53	8.79	6.13	14.45	17.98	17.88	12.34
Professional/PhD degree	3.16	1.61	1.27	8.90	16.69	5.92	5.13
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Live in metropolitan area (%)	82.28	91.00	90.04	97.76	98.95	92.76	98.98

Source: Current Population Survey, 2009 to 2019, using constant 2019 dollars.

Table 7 shows statistics regarding the hourly wage in constant 2019 dollars. These are bivariate statistics (i.e., not controlling for other characteristics), so they should not be used to make conclusions about overall patterns of wage determination in the labor market. Nonetheless, not surprisingly, the average wage is higher at higher levels of education for each demographic group. Married persons have a higher average hourly wage than unmarried persons across all demographic groups.

Table 7.

Average Hourly Income for Workers Aged 25 to 54 by Racial/Ethnic/Generational Group and Gender, 2009 to 2019.

Variable	3rd+ Generation Non-Hispanic Whites	3rd+ Generation Blacks (Including Hispanics)	3rd+ Generation Nonblack Hispanics	2nd-Generation Non-Hispanic Asians	2nd-Generation Nigerians (Including Hispanics)	2nd-Generation Non-Hispanic Whites	2nd-Generation Blacks (Including Hispanics)
Men
Hourly income (average)	32.47	23.95	25.97	35.86	40.05	36.85	24.81
Hourly income (SD)	38.46	33.74	34.01	40.50	60.36	41.48	24.50
Hourly income (median)	24.45	17.99	19.92	25.90	27.10	27.66	19.17
Log of hourly income (average)	3.20	2.89	2.98	3.27	3.31	3.31	2.96
Wage and salary income	32.27	23.55	25.74	35.29	41.68	36.74	23.44
Self-employment income	34.77	30.52	29.63	43.43	12.38	38.07	48.19
Any disability	26.60	22.97	22.34	33.14	22.76	29.25	30.77
No disability	32.71	23.99	26.12	35.91	40.23	37.09	24.71
Married	36.66	27.51	28.79	41.55	37.90	43.06	29.77
Not married	26.31	21.41	23.43	30.11	41.72	29.40	22.53
Have own child in household	36.62	26.74	28.45	42.79	42.63	44.34	27.64
No own child in household	28.59	22.03	23.66	31.30	38.81	31.29	23.34
Educational attainment
Less than high school	20.30	15.66	20.51	20.13	56.44	32.85	16.10
High school or GED	24.05	20.57	23.31	19.89	10.82	24.01	16.48
Some college	27.80	22.72	24.81	24.17	26.11	28.93	22.07
Bachelor’s degree	39.53	31.96	34.34	39.31	44.39	41.20	31.44
Master’s degree	47.79	37.04	41.60	48.35	41.14	49.91	41.28
Professional/PhD degree	66.56	58.10	46.20	71.94	57.14	66.64	50.65
Live in metropolitan area	33.99	24.12	26.29	36.12	40.19	37.64	24.83
Do not live in metropolitan area	25.26	22.26	22.80	24.13	15.09	25.64	23.87
Women
Hourly income (average)	25.48	20.59	21.34	31.96	31.24	30.24	23.23
Hourly income (SD)	30.64	23.33	30.78	39.73	27.37	33.60	17.64
Hourly income (median)	19.71	16.49	16.58	23.68	19.71	22.92	19.19
Log of hourly income (average)	2.97	2.78	2.80	3.17	3.17	3.14	2.94
Wage and salary income	25.29	20.45	21.10	31.54	31.33	30.07	23.20
Self-employment income	28.09	23.80	26.42	38.74	28.65	32.73	23.82
Any disability	21.51	20.26	17.64	23.20	12.81	24.26	23.16
No disability	25.63	20.60	21.51	32.10	32.28	30.40	23.23
Married	26.92	23.38	23.08	35.67	38.17	32.73	27.76
Not married	23.33	19.29	19.84	27.71	26.81	27.03	20.94
Have own child in household	26.15	20.03	20.62	35.01	32.02	31.00	24.17
No own child in household	24.66	21.45	22.56	29.37	30.70	29.49	22.22
Educational attainment
Less than high school	14.50	13.44	12.19	14.91	5.16	13.46	20.10
High school or GED	17.90	15.74	17.00	17.99	15.57	20.66	15.17
Some college	20.99	18.99	20.55	22.40	22.63	23.53	18.92
Bachelor’s degree	30.08	26.00	28.92	32.59	27.20	31.74	27.95
Master’s degree	34.86	33.00	34.22	42.50	35.31	39.58	30.69
Professional/PhD degree	50.05	39.87	42.11	60.03	55.20	51.26	43.12
Live in metropolitan area	26.67	21.06	21.93	32.14	31.05	30.94	23.26
Do not live in metropolitan area	19.93	15.75	16.08	24.26	48.74	21.28	20.59

Source: Current Population Survey, 2009 to 2019, using constant 2019 dollars.

As is evident in Table 7, three groups have an average hourly wage greater than $35, including second-generation Asian men, second-generation white men, and second-generation Nigerian American men. The mean wage of the latter group is the highest in Table 7 (i.e., $40.05). The female group with the highest average wage is second-generation Asian women (i.e., $31.96). The mean wage for second-generation Nigerian American women closely follows (i.e., $31.24).

Table 8 shows the estimates for the log wage regressions. Because the dependent variable is logged, the coefficients are approximately equal to percentage effects for a unit change in the independent variable.¹⁰ For example, controlling for self-employment, age, education, and disability in model 2, other second-generation black American men have about 14 percent lower wages than white men, while third-generation African American men have about 21 percent lower wages than white men. Both of these negative effects are statistically significant at the .01 level, as shown in Table 8.

Table 8.

Coefficients Estimated from Ordinary Least Squares Regression Models of Log Hourly Income for Workers Aged 25 to 54 by Gender, 2009 to 2019.

Independent Variable	Men				Women
Independent Variable	Model 1	Model 2	Model 3	Model 4	Model 1	Model 2	Model 3	Model 4
3rd+ generation non-Hispanic whites	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
3rd+ generation blacks (including Hispanics)	−.306***	−.210***	−.178***	−.181***	−.190***	−.093***	−.075***	−.080***
3rd+ generation blacks (including Hispanics)	(.007)	(.007)	(.007)	(.007)	(.006)	(.005)	(.006)	(.006)
3rd+ generation nonblack Hispanics	−.217***	−.065***	−.056***	−.086***	−.176***	−.026***	−.019**	−.053***
3rd+ generation nonblack Hispanics	(.010)	(.010)	(.010)	(.010)	(.009)	(.009)	(.009)	(.009)
2nd-generation non-Hispanic Asians	.070***	.001	.018	−.028**	.193***	.119***	.121***	.060***
2nd-generation non-Hispanic Asians	(.013)	(.012)	(.012)	(.012)	(.013)	(.012)	(.012)	(.012)
2nd-generation Nigerians (including Hispanics)	.106	−.033	−.002	−.025	.198**	.077	.085	.065
2nd-generation Nigerians (including Hispanics)	(.095)	(.090)	(.088)	(.088)	(.092)	(.077)	(.077)	(.077)
2nd-generation non-Hispanic whites	.113***	.037***	.052***	.023**	.164***	.081***	.084***	.049***
2nd-generation non-Hispanic whites	(.012)	(.011)	(.011)	(.011)	(.013)	(.012)	(.012)	(.012)
2nd-generation blacks (including Hispanics)	−.235***	−.135***	−.096***	−.131***	−.032	.013	.027	−.018
2nd-generation blacks (including Hispanics)	(.028)	(.025)	(.025)	(.025)	(.022)	(.021)	(.021)	(.020)
Control variables		Self-employmentincome	Self-employmentincome	Self-employmentincome		Self-employmentincome	Self-employmentincome	Self-employmentincome
		Age	Age	Age		Age	Age	Age
		Age squared	Age squared	Age squared		Age squared	Age squared	Age squared
		Education	Education	Education		Education	Education	Education
		Disability	Disability	Disability		Disability	Disability	Disability
			Marital status	Marital status			Marital status	Marital status
			Child inhousehold	Child inhousehold			Child inhousehold	Child inhousehold
				Region				Region
				Metropolitan area				Metropolitan area

Source: Current Population Surveys, 2009 to 2019, using constant 2019 dollars.

Note: Robust standard errors in parentheses.

p < .05. ***p < .01.

After further controlling for marital status and the presence of own children in model 3, and then region and metropolitan status in model 4, the negative effects of other second-generation black American men and third-generation African American men change slightly but not very much. Relative to white men, other second-generation black American men still have 13 percent lower wages, while third-generation African American men have about 18 percent lower wages after all of the control variables are included in model 4. These negative coefficients remain statistically significant at the .01 level.

Table 8 also shows, however, that these negative effects are not evident for second-generation Nigerian American men. Their coefficient in model 2, which controls for self-employment, age, education, and disability, is –3 percent, but it is not statistically significant at any conventional level or even at the 10 percent level using a one-tailed test. The coefficient is still –3 percent in the full specification of model 4, but its standard error remains much larger than that regression coefficient. In other words, we fail to reject the null hypothesis that the net effect for second-generation Nigerian American men relative to white men is zero. We interpret these findings as indicating that the wages of second-generation Nigerian American men are not statistically disadvantaged relative to white men. This conclusion for men contrasts with the wages of other second-generation black American men and third-generation African American men in Table 8.

Table 8 furthermore shows that relative to white women, second-generation Asian women and second-generation white women have slightly higher wages. Third-generation African American women have about 8 percent lower wages than white women, which is fairly close to the disadvantage reported by Sakamoto et al. (2010). Other second-generation black American women have reached parity with white women, as their coefficient is not statistically significant in any of the models in Table 8, and that conclusion is also consistent with Sakamoto et al. (2010). Second-generation Nigerian American women have also reached parity with white women, because their coefficients are not statistically significant at any conventional level in models 2, 3, and 4 in Table 8. The coefficient for second-generation Nigerian American women is statistically significant in model 1, but that result indicates that this group, before controlling for any independent variables, has a higher average wage than white women.

To readily assess the statistical significance across different groups, Table 9 shows the coefficient and its statistical significance for second-generation Nigerian Americans using different reference categories in the log hourly wage regressions. Among women, the coefficient for second-generation Nigerian Americans is not negative and statistically significant in the multivariate models (i.e., models 2, 3, and 4) in Table 9. That is, after controlling for education and other demographic characteristics, the labor market does not disadvantage second-generation Nigerian American women in comparison with other female groups. The only theoretically important differential is that second-generation Nigerian American women are consistently paid more than third-generation African American women, because those coefficients are positive and statistically significant for each of the multivariate models in Table 9.

Table 9.

Estimated Nigerian Coefficient from Ordinary Least Squares Regression Models of Log Hourly Income for Workers Aged 25 to 54 Using Different Racial/Ethnic/Generational Reference Groups by Gender, 2009 to 2019.

Reference Category	2nd-Generation Nigerian Men (Including Hispanics)				2nd-Generation Nigerian Women (incl. Hispanics)
Reference Category	Model 1	Model 2	Model 3	Model 4	Model 1	Model 2	Model 3	Model 4
3rd+ generation non-Hispanic whites	.106	−.033	−.002	−.025	.198**	.077	.085	.065
3rd+ generation non-Hispanic whites	(.095)	(.090)	(.088)	(.088)	(.092)	(.077)	(.077)	(.077)
3rd+ generation Blacks (including Hispanics)	.412***	.177**	.177**	.156*	.387***	.169**	.160**	.145*
3rd+ generation Blacks (including Hispanics)	(.095)	(.090)	(.088)	(.089)	(.092)	(.077)	(.077)	(.077)
3rd+ generation nonblack Hispanics	.323***	.033	.054	.061	.374***	.103	.104	.118
3rd+ generation nonblack Hispanics	(.095)	(.090)	(.088)	(.089)	(.092)	(.077)	(.077)	(.078)
2nd-generation non-Hispanic Asians	.036	−.034	−.020	.003	.005	−.043	−.036	.005
2nd-generation non-Hispanic Asians	(.096)	(.091)	(.089)	(.089)	(.093)	(.078)	(.077)	(.078)
2nd-generation non-Hispanic whites	−.007	−.070	−.054	−.048	.034	−.005	.002	.015
2nd-generation non-Hispanic whites	(.096)	(.091)	(.089)	(.089)	(.093)	(.078)	(.077)	(.078)
2nd-generation blacks (including Hispanics)	.341***	.102	.094	.106	.229**	.064	.059	.083
2nd-generation blacks (including Hispanics)	(.099)	(.093)	(.091)	(.092)	(.095)	(.080)	(.079)	(.080)
Control variables		Self-employmentincome	Self-employmentincome	Self-employmentincome		Self-employmentincome	Self-employmentincome	Self-employmentincome
		Age	Age	Age		Age	Age	Age
		Age squared	Age squared	Age squared		Age squared	Age squared	Age squared
		Education	Education	Education		Education	Education	Education
		Disability	Disability	Disability		Disability	Disability	Disability
			Marital status	Marital status			Marital status	Marital status
			Child inhousehold	Child inhousehold			Child inhousehold	Child inhousehold
				Region				Region
				Metropolitan area				Metropolitan area

Source: Current Population Survey, 2009 to 2019, using constant 2019 dollars.

Note: Robust standard errors in parentheses.

p < .10. **p < .05. ***p < .01.

Among men as well, the results for the multivariate models in Table 9 show that second-generation Nigerian Americans have higher wages than third-generation African Americans (i.e., 18 percent according to model 3). However, after accounting for education and other demographic characteristics, second-generation Nigerian American men have wages that are not statistically different from those of other second-generation black Americans, second-generation Asians, second-generation whites, Hispanics, and whites. The coefficient for second-generation Nigerian American men is statistically significant only relative to third-generation African American men in models 2, 3, and 4 in Table 9.

Discussion

A common critique of discussions of the higher average socioeconomic attainments of Asian Americans is that that category is heterogeneous and includes some ethnic groups that are disadvantaged (Kao and Thompson 2003).¹¹ We agree, but we would also suggest that all broad racial/ethnic categories may have some degree of internal heterogeneity. In the foregoing, the empirical evidence suggests that the socioeconomic characteristics of second-generation black Americans exceed third-generation African Americans. Other second-generation black American women have achieved educational and wage parity relative to white women. Second-generation Nigerian Americans stand out as having higher educational attainment not only compared with other second-generation black Americans but also compared with whites and even second-generation Asians. Second-generation Nigerian Americans (including men) have reached wage parity compared with whites with similar characteristics such as age, education, and disability status. Other second-generation black Americans and second-generation Nigerian Americans are thus a source of heterogeneity in the socioeconomic profile of the African American category.

Our findings from the CPS are nationally representative, but they are consistent with various qualitative studies in which the high socioeconomic attainments of second-generation Nigerian Americans is a consistent theme (Arthur 2000; Casimir 2018; Chua and Rubenfeld 2014; Imoagene 2017; Waters 1990). Our results are also consistent with prior studies finding a high level of educational attainment among second-generation black Americans compared with third-generation African Americans (Bennett and Lutz. 2009; Massey et al. 2007; Sakamoto et al 2010; Tran et al. 2018). Given these prior studies, our results do not appear to be prima facie problematic, although they are clearly different from the socioeconomic patterns of third-generation African Americans.

The finding that second-generation Nigerian Americans have reached parity with whites in the labor market might be contested on the grounds that the sample size is relatively small. However, a small sample size does not lead to bias in the ordinary least squares estimate of a regression coefficient or its standard error. Although a small sample size does reduce statistical power, the Nigerian sample sizes do not prevent any of their coefficients from being statistically significant at the .01 level in the educational attainment regressions in Table 4. Furthermore, the point estimates for second-generation Nigerian American men in the wage regressions are all substantively close to zero, while for second-generation Nigerian American women, the point estimates are actually slightly positive in Table 8. Despite the more modest sample sizes for second-generation Nigerian Americans, their coefficients are consistently statistically significant relative to third-generation African Americans (who have smaller sample sizes relative to whites), as shown in Table 9. For these reasons, we believe that our finding of wage parity for Nigerian Americans relative to whites is worthy of consideration rather than being dismissed as a statistical artifact.

In general, groups with extremely high levels of educational attainment might be somewhat less selective on unobserved variables (Mare 1980), which could lead to a slightly negative net effect in the multivariate analysis of wages (Kim and Sakamoto 2014). Relative to individuals with the same level of education from a less selective group, individuals from a very high achieving group may have slightly lower wages, as in the case of Japanese Brazilians versus white Brazilians (Maia, Sakamoto, and Wang 2015). The fact that none of the coefficients for second-generation Nigerian American men or women is ever statistically significant and that all of these coefficients are close to zero or slightly positive is therefore especially notable in the wage regressions in Table 8.

Conclusion

As stated by Hamilton (2014), “Black immigrants are one of America’s most diverse immigrant subgroups” (p. 1000). Our findings are consistent with that assessment. As the African American population is becoming increasingly diverse in the twenty-first century, we encourage further research on all discernable black ethnic groups. Particularly second-generation black Americans have been neglected in prior studies. Further research on black immigrant groups would be informative for understanding the growing diversity of the African American population while providing new insights into diverse assimilation processes (Hamilton 2019; Imoagene 2017; Waters 1990).

Our results show that other second-generation black Americans have higher levels of educational attainment and wages compared with third-generation African Americans. However, the group that stands out the most is second-generation Nigerian Americans. Their educational attainment exceeds all other racial/ethnic groups, including Asian Americans. To our knowledge, second-generation Nigerian Americans are the only group of African Americans for whom systematic evidence indicates that they have higher educational attainment than whites while also achieving wage parity with whites using these sorts of standard demographic models.

Future research should investigate the sources of the high socioeconomic attainments of second-generation Nigerian Americans. Selective immigration of more highly educated first-generation immigrants is likely an important factor.¹² According to Hamilton (2019:11), 63 percent of first-generation Nigerian immigrants to the United States are college educated, which is significantly higher than in the U.S. population (Tran et al. 2018). This selectivity is further evident because “in Nigeria, however, only 7 percent of the population had earned a bachelor’s degree” (Hamilton 2019:11), as is also emphasized in Tran et al.’s (2018) discussion of “hyper-selectivity” (p. 188).

However, as discussed by Sakamoto and Wang (2021), these typical discussions are not very clear about the specific variables that are being selected. Because the selectivity is usually being measured with regard to educational attainment, perhaps an important characteristic that is being selected is educational aspirations. High educational expectations for second-generation children seem to be common among Nigerians (Imoagene 2017), as is typically the case for Asian Americans (Xie and Goyette 2003), which is another group with high educational attainment among both the first and second generations. As shown by Xie and Goyette (2003) and Hsin and Xie (2014), high educational expectations affect behavior in schools as well as educational outcomes. How Nigerian American families foster these high levels of educational aspirations might be investigated in future research.¹³

Educational aspirations relate to the more general issue of cultural effects, including “cultural differences in work orientation” (Hamilton 2019:15) and “differing perceptions of the benefits of U.S. employment” (Hamilton 2019:13). These cultural aspects are in turn shaped by actual opportunities and experiences with discrimination (Hamilton 2019:79–80), which are themselves influenced by the historical context (Hamilton 2019:13). Addressing these complex issues is beyond the scope of our analysis, which lacks information beyond basic socioeconomic and demographic variables for a few years of data. However, we concur that the study of educational expectations needs to be contextualized to account for the broader societal and historical conditions. As stated by Hamilton (2019), “much of the extant work on disparities among blacks implicitly assumes that a single factor—culture, selection, or discrimination—is the primary driver of disparate outcomes across groups. The three mechanisms, however, are not mutually exclusive” (p. 61).

Footnotes

Acknowledgements

This material is based upon work supported by the National Science Foundation under grant 1757813. All opinions stated herein are the sole responsibility of the authors.

ORCID iDs

Arthur Sakamoto

Ernesto F. L. Amaral

Notes

Author Biographies

Arthur Sakamoto is a professor in the Department of Sociology at Texas A&M University. His research interests include social stratification and inequality, racial/ethnic relations, and economic sociology. His research has recently appeared in the Brazilian Journal of Political Economy, the Chinese Journal of Sociology, Demography, Ethnic and Racial Studies, Population Research and Policy Review, Research in Social Stratification and Mobility, Sociological Science, and Socius.

Ernesto F. L. Amaral is an associate professor in the Department of Sociology at Texas A&M University. His research is related to social demography, migration, and public policy analysis. His teaching interests include demography, migration, methods, social statistics, and public policy analysis. He was an associate sociologist at the RAND Corporation from 2014 to 2017. He served as an assistant professor and then as an associate professor at the Federal University of Minas Gerais in Brazil from 2009 to 2014. He received his PhD in sociology with a concentration in demography from the University of Texas at Austin in 2007. More information about his work can be found at .

Sharron Xuanren Wang is an assistant professor in the Department of Sociology and Criminal Justice at Delaware State University. Her research focuses on social inequality, race/ethnicity, and immigrant incorporation. Her research has appeared in Social Currents, Sociology of Race and Ethnicity, and Socius, among other journals.

Courtney Nelson received her bachelor’s degree in 2020 from Sweet Briar College. As an undergraduate student majoring in sociology in 2018, she participated in the Research Experience for Undergraduates program at Texas A&M University. She is currently a graduate student at Virginia Commonwealth University studying mental health counseling.

References

American Immigration Council. 2012. “African Immigrants in America: A Demographic Overview.” Retrieved March 5, 2021. https://www.americanimmigrationcouncil.org/sites/default/files/research/african_immigrants_in_america_a_demographic_overview.pdf.

Anderson

Monica

López

Gustavo

. 2018. “Key Facts about Black Immigrants in the U.S.” Pew Research Center. Retrieved March 5, 2021. http://www.pewresearch.org/fact-tank/2018/01/24/key-facts-about-black-immigrants-in-the-u-s/.

Arthur

John A.

2000. Invisible Sojourners: African Immigrant Diaspora in the United States. Westport, CN: Praeger.

Bennett

Pamela R.

Lutz

Amy

. 2009. “How African American Is the Net Black Advantage? Differences in College Attendance among Immigrant Blacks, Native Blacks, and Whites.” Sociology of Education 82(1):70–100.

Berthelemy

Clémentine

. 2019. “Second-Generation African College Students and the American Ethnoracial Pentagon: Self-Identification, Racial Labeling and the Contouring of Group Boundaries.” African and Black Diaspora: An International Journal 12(2):171–88.

Bollinger

Christopher R.

Chandra

Amitabh

. 2005. “Iatrogenic Specification Error: A Cautionary Tale of Cleaning Data.” Journal of Labor Economics 23(2):235–57.

Bollinger

Christopher R.

Hirsch

Barry T.

Hokayem

Charles M.

Ziliak

James P.

2019. “Trouble in the Tails? What We Know about Earnings Nonresponse Thirty Years after Lillard, Smith, and Welch.” Journal of Political Economy 127(5):2143–85.

Bowen

William G.

Finegan

T. Aldrich

. 2015. The Economics of Labor Force Participation. Princeton, NJ: Princeton University Press.

Capps

Randy

McCabe

Kristen

Fix

Michael

. 2012. “Diverse Streams: African Migration to the United States.” Unpublished report, Migration Policy Institute, Washington, DC.

10.

Casimir

Leslie

. 2018. “Data Show Nigerians the Most Educated in the U.S.” Houston Chronicle, January 12. Retrieved March 5, 2021. https://www.chron.com/news/article/Data-show-Nigerians-the-most-educated-in-the-U-S-1600808.php

11.

Cheng

Siwei

. 2016. “The Accumulation of Disadvantage: The Intersection of Gender and Race in the Long-Term Wage Effect of Marriage.” American Sociological Review 81(1):29–56.

12.

Chetty

Raj

Hendren

Nathaniel

Jones

Maggie R.

Porter

Sonya R.

2020. “Race and Economic Opportunity in the United States: An Intergenerational Perspective.” Quarterly Journal of Economics 135(2):711–83.

13.

Chua

Amy

Rubenfeld

Jed

. 2014. The Triple Package: How Three Unlikely Traits Explain the Rise and Fall of Cultural Groups in America. New York: Penguin.

14.

Duncan

Brian

Trejo

Stephen J.

2011. “Tracking Intergenerational Progress for Immigrant Groups: The Problem of Ethnic Attrition.” American Economic Review 101(3):603–608.

15.

England

Paula

Farkas

George

Kilbourne

Barbara S.

Dou

Thomas

. 1988. “Explaining Occupational Sex Segregation and Wages: Findings from a Model with Fixed Effects.” American Sociological Review 53(4):544–58.

16.

Frey

William H.

1995. “Immigration and Internal Migration Flight: A California Case Study.” Population and Environment 16(4):353–75.

17.

Ganong

Peter

Shoag

Daniel

. 2017. “Why Has Regional Income Convergence in the US Declined?” Journal of Urban Economics 102(1):76–90.

18.

Greenman

Emily

Xie

. 2008. “Double Jeopardy? The Interaction of Gender and Race on Earnings in the United States.” Social Forces 86(3):1217–44.

19.

Grodsky

Eric

Pager

Devah

. 2001. “The Structure of Disadvantage: Individual and Occupational Determinants of the Black-White Wage Gap.” American Sociological Review 66(4):542–67.

20.

Habermas

Jürgen

McCarthy

Thomas

. 1984. The Theory of Communicative Action. Boston: Beacon.

21.

Hamilton

Tod G.

2014. “Selection, Language Heritage, and the Earnings Trajectories of Black Immigrants in the United States.” Demography 51(3):975–1002.

22.

Hamilton

Tod G.

2019. Immigration and the Remaking of Black America. New York: Russell Sage.

23.

Hsin

Amy

Xie

. 2014. “Explaining Asian Americans’ Academic Advantage over Whites.” Proceedings of the National Academy of Sciences 111(23):8416–21.

24.

Hurst

Erik

Geng

Pugsley

Benjamin

. 2014. “Are Household Surveys Like Tax Forms? Evidence from Income Underreporting of the Self-Employed.” Review of Economics and Statistics 96(1):19–33.

25.

Imoagene

Onoso

. 2017. Beyond Expectations: Second-Generation Nigerians in the United States and Britain. Berkeley: University of California Press.

26.

Kalmijn

Matthijs

. 1996. “The Socioeconomic Assimilation of Caribbean American Blacks.” Social Forces 74(3):911–30.

27.

Kao

Grace

Thompson.

Jennifer S.

2003. “Racial and Ethnic Stratification in Educational Achievement and Attainment.” Annual Review of Sociology 29:417–42.

28.

Kebede

Kassahun

. 2019. “The African Second Generation in the United States—Identity and Transnationalism: An Introduction.” African and Black Diaspora: An International Journal 12(2):119–36.

29.

Kim

ChangHwan

Sakamoto

Arthur

. 2014. “The Earnings of Less Educated Asian American Men: Educational Selectivity and the Model Minority Image.” Social Problems 61(2):283–304.

30.

Kim

ChangHwan

Tamborini

Christopher R.

2014. “Response Error in Earnings: An Analysis of the Survey of Income and Program Participation Matched with Administrative Data.” Sociological Methods & Research 43(1):39–72.

31.

Kuhn

Thomas S.

1962. The Structure of Scientific Revolutions. Chicago: University of Chicago Press.

32.

Lee

Jennifer

Zhou

Min

. 2015. The Asian American Achievement Paradox. New York: Russell Sage.

33.

Liebler

Carolyn A.

Porter

Sonya R.

Fernandez

Leticia E.

Noon

James M.

Ennis

Sharon R.

2017. “America’s Churning Races: Race and Ethnicity Response Changes between Census 2000 and the 2010 Census.” Demography 54:259–84.

34.

Ludwig

Bernadette

. 2019. “It Is Tough to Be a Liberian Refugee in Staten Island, NY: The Importance of Context for Second Generation African Immigrant Youth.” African and Black Diaspora: An International Journal 12(2):189–210.

35.

Maia

Alexandre G.

Sakamoto

Arthur

Wang

Sharron X.

2015. “The Socioeconomic Attainments of Japanese-Brazilians and Japanese Americans.” Sociology of Race and Ethnicity 1(4):547–63.

36.

Mare

Robert D.

1980. “School Background and School Continuation Decisions.” Journal of American Statistical Association 75:295–305.

37.

Massey

Douglas S.

Denton

Nancy A.

1993. American Apartheid: Segregation and the Making of the Underclass. Cambridge, MA: Harvard University Press.

38.

Massey

Douglas S.

Mooney

Margarita

Torres

Kimberly C.

Charles

Camille Z.

2007. “Black Immigrants and Black Natives Attending Selective Colleges and Universities in the United States.” American Journal of Education 113(2):243–71.

39.

Merton

Robert K.

1968. Social Theory and Social Structure. New York: Simon & Schuster.

40.

Mouw

Ted

Kalleberg

Arne L.

2010. “Occupations and the Structure of Wage Inequality in the United States, 1980s to 2000s.” American Sociological Review 75(3):402–31.

41.

Park

Julie

Myers

Dowell

Jiménez

Tomás R.

2014. “Intergenerational Mobility of the Mexican-Origin Population in California and Texas Relative to a Changing Regional Mainstream.” International Migration Review 48(2):442–81.

42.

Pettit

Becky

. 2012. Invisible Men: Mass Incarceration and the Myth of Black Progress. New York: Russell Sage.

43.

Portes

Alejandro

Rumbaut

Ruben G.

2005. “Introduction: The Second Generation and the Children of Immigrants Longitudinal Study.” Ethnic and Racial Studies 28(6):983–99.

44.

Portes

Alejandro

Zhou

Min

. 1993. “The New Second Generation: Segmented Assimilation and Its Variants.” Annals of the American Academy of Political and Social Science 530(1):74–96.

45.

Portes

Alejandro

Zhou

Min

. 1996. “Self-Employment and the Earnings of Immigrants.” American Sociological Review 61(2):219–30.

46.

Saenz

Rogelio

Morales

M. Cristina

. 2005. “Demography of Race and Ethnicity.” Pp. 169–208 in Handbook of Population, edited by Poston

D. L.

Micklin

New York: Springer.

47.

Sakamoto

Arthur

Hsu

. 2020. “Do Second-Generation Asian Americans Face a Systematic Disadvantage in Occupational Attainment? Comment on Tran, Lee and Huang.” Ethnic and Racial Studies 43(3):516–32.

48.

Sakamoto

Arthur

Woo

Hyeyoung

Kim

ChangHwan

. 2010. “Does an Immigrant Background Ameliorate Racial Disadvantage? The Socioeconomic Attainments of Second-Generation African Americans.” Sociological Forum 25(1):123–46.

49.

Sakamoto

Arthur

Wang

Sharron Xuanren

. 2020. “The Declining Significance of Occupation in Research on Intergenerational Mobility.” Research in Social Stratification and Mobility 70:100521.

50.

Sakamoto

Arthur

Wang

Sharron Xuanren

. 2021. “Deconstructing Hyper-selectivity: Are the Socioeconomic Attainments of Second-Generation Asian Americans Only Due to Their Class Background?” Chinese Journal of Sociology 7(1):3–21.

51.

Sall

Dialika

. 2019. “Convergent Identifications, Divergent Meanings: The Racial and Ethnic Identities of Second-Generation West African Youth.” African and Black Diaspora: An International Journal 12(2):137–55.

52.

Saperstein

Aliya

Penner

Andrew M.

2012. “Racial Fluidity and Inequality in the United States.” American Journal of Sociology 118(3):676–727.

53.

Stolzenberg

Ross M.

1978. “Bringing the Boss Back In: Employer Size, Employee Schooling, and Socioeconomic Achievement.” American Sociological Review 43(6):813–28.

54.

Tienda

Marta

Mitchell

Faith

. 2006. Hispanics and the Future of America. Washington, DC: National Academies Press.

55.

Tran

Van C.

2018. “Social Mobility across Immigrant Generations: Recent Evidence and Future Data Requirements.” Annals of the American Academy of Political and Social Science 677(1):105–18.

56.

Tran

Van C.

Lee

Jennifer

Khachikian

Oshin

Lee

Jess

. 2018. “Hyper-selectivity, Racial Mobility, and the Remaking of Race.” Russell Sage Foundation Journal of the Social Sciences 4(5):188–209.

57.

Tran

Van C.

Valdez

Nicol M.

2017. “Second-Generation Decline or Advantage? Latino Assimilation in the Aftermath of the Great Recession.” International Migration Review 51(1):155–90.

58.

Valdez

Nicol M.

Tran

Van C.

2020. “Gendered Context of Assimilation: The Female Second-Generation Advantage among Latinos.” Journal of Ethnic and Migration Studies 46(9):1709–36.

59.

Villarreal

Andrés

. 2014. “Ethnic Identification and Its Consequences for Measuring Inequality in Mexico.” American Sociological Review 79(4):775–806.

60.

Villarreal

Andrés

Bailey

Stanley R.

2020. “The Endogeneity of Race: Black Racial Identification and Men’s Earnings in Mexico.” Social Forces 98(4):1744–72.

61.

Xie

Goyette

Kimberly A.

2003. “Social Mobility and Educational Choices of Asian Americans.” Social Science Research 32(3):467–98.

62.

Wang

Sharron Xuanren

Takei

Isao

Sakamoto

Arthur

. 2017. “Do Asian Americans Face Labor Market Discrimination? Accounting for the Cost of Living among Native-Born Men and Women.” Socius 3. Retrieved March 5, 2021. https://journals.sagepub.com/doi/full/10.1177/2378023117741724.

63.

Waters

Mary C.

1990. Ethnic Options: Choosing Identities in America. Berkeley: University of California Press.

64.

Weber

Max

. 1958. “Science as a Vocation.” Daedalus 87:111–34.

65.

Western

Bruce

. 2002. “The Impact of Incarceration on Wage Mobility and Inequality.” American Sociological Review 67(4):526–46.

66.

Western

Bruce

. 2006. Punishment and Inequality in America. New York: Russell Sage.

67.

Western

Bruce

Rosenfeld

Jake

. 2011. “Unions, Norms, and the Rise in US Wage Inequality.” American Sociological Review 76(4):513–37.

The Socioeconomic Attainments of Second-Generation Nigerian and Other Black Americans: Evidence from the Current Population Survey,2009 to 2019

Abstract

Keywords

Prior Literature

Data and Methods

Empirical Results

Discussion

Conclusion

Footnotes

Acknowledgements

ORCID iDs

Notes

Author Biographies

References