Arthur Sakamoto, Ernesto F. L. Amaral, Sharron Xuanren Wang, and Courtney Nelson, "The Socioeconomic Attainments of Second-Generation Nigerian and Other Black Americans: Evidence from the Current Population Survey, 2009 to 2019,"Socius: Sociological Research for a Dynamic World, 2021, https://journals.sagepub.com/doi/pdf/10.1177/23780231211001971

Racism clearly a major headwind to the success of the Nigerian-American community, using CPS data researcher look at wage data by generation“…The results indicate thatthe 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…”

Wage data shows Nigerian American men are ~ on par with Asian and White Men, “…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. 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)…”
Core takeaway, “…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….”

“…Despite Australia’s official skills-based migration policy,Asian immigrants are less hyper-selected in Australia compared to the United States.On its face, this finding is surprising because it upends conventional wisdom and “grand narratives” about the role of immigration policy in selecting immigrants. However, among immigrants with post-high school educational qualifications, immigrants from Asia are more likely to arrive via humanitarian or family-based mechanisms, compared to immigrants from English speaking countries (Kler 2006). In addition, Australia’s skilled-based migration programs over the years have included a wide range of occupations, from highly skilled cardiologists and other medical practitioners that require lengthy postgraduate training, to cabinetmaker, chef and other skilled workers whose educational qualifications could be fulfilled through a few years of technical apprenticeship. As a result, Australia’s skilled-based immigration policy does not necessarily result in higher levels of hyper-selectivity in education than in the United States. Second, we find mixed evidence for our second hypothesis. While Asian immigrants have fared well in both societies, their level of achievement is significantly higher in the United States with regards to labor market outcomes. Asian immigrants in our study did not achieve better socioeconomic outcomes compared to their U.S. counterparts. Even with considerably high educational attainment, Asian immigrants in Australia are less likely to convert their human capital to advantageous labor market outcomes and economic success. This finding provides useful data for the debate on the effects of multiculturalism policy. Because Australia’s “White Australia” policy was abolished only in the 1970s, its racist legacy may still affect the social and economic outcomes of non-European immigrant groups. Third, we find some evidence for our third hypothesis: the level of income inequality in the host society plays some role in affecting migrants’ socioeconomic outcomes. While Asian immigrants are more hyper-selected in the United States, they report a disadvantage in labor market outcomes, including earnings in comparison to whites, despite their significant educational advantage. This reflects the higher level of inequality in the United States, where the white advantage cuts across multiple domains. In contrast, Asian immigrants are less hyper-selected in Australia, but they report less of a disadvantage in the labor market and earnings than Australiaborn non-Indigenous Europeans, because the gaps among ethnoracial groups are also more compressed there….”

Suspect this is driven by the size of American market and innovation clusters.

Counterintuitive but maybe not surprising, new paper finds that to this point the US family based migration system yields more skilled immigrants than Australia's skill based system (would be interesting to see if this holds true for Canada as well) and once here Asian immigrants get better labor market outcomes than they do in AU.

“….It appears that cognitive skills during adolescence, as measured by scores on the Armed Forces Qualification Test (AFQT), are strongly associated with these gaps. For example, conditional on having the median AFQT score, the racial gaps in both upward and downward mobility are relatively small. Consistent with previous studies linking AFQT scores to racial differences in adult outcomes (for example, Neal and Johnson, 1996; Cameron and Heckman, 2001), I do not interpret these scores as measuring innate endowments but rather as reflecting the accumulated differences in family background and other influences that are manifested in test scores.6 If these results are given a causal interpretation, they suggest that actions that reduce the racial gap in test scores could also reduce the racial gap in intergenerational mobility…. The effects of including one’s AFQT score on rates of upward mobility are shown in panel C of figure 8. Here, the results provide a relatively clean and compelling story. For both blacks and whites, upward mobility rises with AFQT scores in a fairly similar fashion. There are especially sharp gains in upward mobility associated with increases in test scores at the low end of the AFQT distribution. Upward mobility continues to rise at a somewhat slower but still strong rate in the middle and in the upper half of the AFQT distribution. Remarkably, the lines for blacks and whites are relatively close throughout the AFQT distribution. For example, the black-white gap in moving out of the bottom quintile is only 5.2 percentage points for those with median AFQT scores, compared with the unconditional gap of 27 percentage points. This suggests that cognitive skills measured at adolescence can “account” for much of the black-white difference in upward mobility. This result echoes previous findings by Neal and Johnson (1996) and Cameron and Heckman (2001), who have also found that AFQT scores can account for much of the racial gap in adult earnings and college enrollment rates. As with these aforementioned studies, I interpret this finding as reflecting the cumulative effect of a broad range of family background influences rather than reflecting only innate differences.28 The effects of AFQT scores on downward mobility (panel D of figure 8) are also quite striking. The lines for whites and blacks converge quite a bit and for a broad swath of the AFQT distribution, the racial gap is below 10 percentage points and is not statistically different from 0. Therefore, as was the case with upward mobility, test scores during adolescence are strongly associated with rates of downward mobility….”

Critique of Chetty's mobility data

“…It is often desired to rank different populations according to the value of some feature of each population. For example, it may be desired to rank neighborhoods according to some measure of intergenerational mobility or countries according to some measure of academic achievement. These rankings are invariably computed using estimates rather than the true values of these features. As a result, there may be considerable uncertainty concerning the rank of each population…we consider the problem of accounting for such uncertainty by constructing confidence sets for the rank of each population. We consider both the problem of constructing marginal confidence sets for the rank of a particular population as well as simultaneous confidence sets for the ranks of all populations. We show how to construct such confidence sets under weak assumptions. An important feature of all of our constructions is that they remain computationally feasible even when the number of populations is very large. We apply our theoretical results to re-examine the rankings of both neighborhoods in the United States in terms of intergenerational mobility and developed countries in terms of academic achievement. The conclusions about which countries do best and worst at reading, math, and science are fairly robust to accounting for uncertainty. By comparison, several celebrated findings about intergenerational mobility in the United states are not robust to taking uncertainty into account....In our analysis of data from the 2018 PISA test, we find that the conclusions about which developed countries do best and worst at reading, math, and science are fairly robust to accounting for uncertainty. Both the marginal and simultaneous confidence sets are relatively narrow, especially for the countries at the top and the bottom of the PISA league tables. Indeed, only a small set of countries cannot be ruled out as being among the top or bottom three in terms of scholastic performance. In our analysis of data from Chetty et al. (2014, 2018), we find that several celebrated findings about intergenerational mobility in the United States are not robust to taking uncertainty into account. The most robust findings are obtained if we restrict attention to the 50 most populous commuting zones or counties. In that case, both the marginal and joint confidence sets are relatively narrow, and few places cannot be ruled out as being among the top or bottom five. By comparison, conclusions about the geographic patterns of income mobility become less robust to uncertainty when we consider all commuting zones. While upward mobility appears to be low in the Southeast and high in the Great Plains, it is not clear that the West Coast and the Northeast have particularly high income mobility. Nor is it possible to make a strong case for a lot of within-region variation in income mobility. Furthermore, rankings of places by upward mobility become largely uninformative if one defines neighborhoods with even more granularity by considering all counties or if one uses movers across areas to address concerns about selection.
Magne Mogstad, Azeem M. Shaikh, Joseph P. Romano and Daniel Wilhelm, "Inference for Ranks with Applications to Mobility across Neighborhoods and Academic Achievement across Countries, "University Of Chicago, March 13, 2020, https://statistics.stanford.edu/sites/g/files/sbiybj6031/f/2020-03.pdf

New Ed Lazear paper using OECD data (including hours worked) implicitly supports that idea the labor markets have segmented and high skill (he uses education as a proxy for skill) workers are increasingly servicing other high skilled workers, "..heart of this research, is to show thataggregate productivity masks a key part of the story. The productivity distribution has not merely shifted. It has stretched out. Productivity among more educated workers has grown more rapidly than productivity of less educated workers.This is consistent with growing wage dispersion, where the wages of highly educated, high wage workers have pulled away from wages of less educated, low wage workers..."

“…. heart of this research, is to show that aggregate productivity masks a key part of the story. The productivity distribution has not merely shifted. It has stretched out. Productivity among more educated workers has grown more rapidly than productivity of less educated workers. This is consistent with growing wage dispersion, where the wages of highly educated, high wage workers have pulled away from wages of less educated, low wage workers..."

His core finding,

“…. The commonality of the pattern across countries suggests that global rather than institutional factors are at work. The most obvious candidate is the often discussed biased nature of technological change that has benefitted disproportionately highly skilled workers.....It is difficult to observe productivity at the individual level, but industry data on productivity can be linked with demographic and data on education. This permits an examination of changes in productivity industries that are dominated by highly educated workers versus those dominated by less educated ones. By doing this, it is possible to determine whether the productivity distribution has spread out over time consistent with the changing wage distribution..... Cross-industry data provide evidence that rather than merely displacing upward, the entire distribution has changed shape. The upper tail of the distribution has moved away from the lower tail. The rise in productivity of the high skilled, typically highly educated workers, relative to the low skilled typically less educated workers, has generated an increase in skill disparity, which is consistent with observed increases in inequality over time....There are a number of potential causes of the spreading out of the productivity distribution.... One is the frequently referenced skill-biased technological change that postulates that changes in technology have affected the productivity of highly educated workers more than less educated ones..... Another is that trade has increased in a way that enhances the productivity and wages of the most skilled workers...A third possibility is that schools in the United States may have become more proficient over time in educating the better students relative to the weaker ones.... The main conclusion is that changes in productivity at different educational levels are more than sufficient to account for changes in the wage distribution. The college-high school premium in wages has increased by much less than the college-high school premium in productivity has increased over the past almost-three decades. These changes are consistent with a number of possible causes, which include skill-biased technological change, trade patterns that have altered prices in a non-neutral fashion, and changes in human capital production technologies that favor tertiary over primary and secondary education. The analysis cannot address the specific cause, nor does the fact that non-neutral productivity growth can account for growing skill differences lessen the problem...”

He hypothesis these returns could be caused by several factors but doesn’t attempt to attribute causality

“…The most obvious illustration of the correlation between wages and productivity at the aggregate level comes from cross country comparisons. OECD data on wages and productivity across countries make immediately apparent that the two are linked. Figure 2, which plots the 2017 average wage against labor productivity (the last year for which data are available),defined as output in US dollars per hour worked, reveals the close co-movement of wages with productivity. Countries like Switzerland, Norway, Denmark and Luxembourg all have both high productivity and high wages. At the other end, countries like Mexico, Chile, Latvia, Poland and Estonia have low productivity and low wages.The correlation coefficient between log wage and log productivity in 2017 is.84…The most important finding is summarized in figure 9. Forty 3-digit industries for which complete productivity, wage and education data are available over the period 1989-2017 are split into two groups. The top and bottom groups are comprised of industries that rank in the highest and lowest 50% based on average education level in the industry in 1989. The left two bars compare productivity growth in logs for high and low education industries. Productivity in the top education industries grew by over.34 log points between 1989 and 2017. Productivity in the bottom education industries grew only.20 log points during that same 30 year period. The right two bars compare wage growth in those same industries. The same pattern holds, but not with the same force. Wages in highly educated industries grew by.26 log points while those in the low education half grew by.24 log points during the 1989-2017 period. The difference in productivity between the groups is more pronounced than the difference in wages. This simple comparison suggests that productivity growth differences between groups is more than sufficient to explain the better wage growth that more educated workers enjoyed as compared with less educated workers….”

This is supported by his Figure 2 figure for the cross country comparisons, which mirrors our relationship btw skill and hourly wage (which was the OECD math/hourly wage data) which we used in the chapter and Figure 9 is his charting of the change in the relationship btw productive and wages in the US by skill level

"....There is compelling evidence that productivity and wages are linked. The pattern is clear in cross-country comparisons and for virtually all individual countries over time. Furthermore, all parts of the income distribution seem to benefit from increases in aggregate productivity. The mean wage rises as does the wage of the lowest wage earners and highest wage earners when productivity increases. It is also true that wages have spread out over time in most OECD countries where data are available. The ratio of wages of the 90th percentile worker to the median worker has risen in 15 out of 17 countries between 1997 and 2015. In the United States, this is apparent not only in percentiles, but in returns to education. The education coefficient in log wage regressions is significantly higher in 2017 than it is in 1989…”

He uses several series as proxies for productivity, though he does note in terms of individuals, “It is virtually impossible to measure individual productivity except in some firm-based data where measures of output are readily available”For outside the US“…The productivity data are from the OECD Compendium of Productivity Indicators….”Where the “GDP per hour worked” series lives. For the US he uses BLS’s “Nonfarm Business Sector: Real Output Per Hour of All Persons” At the industry level for the US he uses data from The Division of Industry Productivity Studies (DIPS) in the Office of Productivity and Technology at the Bureau of Labor Statistics who have an industry series “Labor Productivity and Costs Measures” which has value added per hour in absolute terms and a productivity index.

Marieke Vandeweyer, "OECDL Survey Of Adult Skills,” Organisation for Economic Co-operation and Development, August 8, 2019, https://www.oecd.org/skills/piaac/

"...Average annual hours worked is defined as the total number of hours actually worked per year divided by the average number of people in employment per year. Actual hours worked include regular work hours of full-time, part-time and part-year workers, paid and unpaid overtime, hours worked in additional jobs, and exclude time not worked because of public holidays, annual paid leave, own illness, injury and temporary disability, maternity leave, parental leave, schooling or training, slack work for technical or economic reasons, strike or labour dispute, bad weather, compensation leave and other reasons. The data cover employees and self-employed workers. This indicator is measured in terms of hours per worker per year. The data are published with the following health warning:The data are intended for comparisons of trends over time; they are unsuitable for comparisons of the level of average annual hours of work for a given year, because of differences in their sources and method of calculation...."

"...Household gross adjusted disposable incomeis the income adjusted for transfers in kind receivedby households, such health or education provided for free or at reduced prices by government and NPISHs. This indicator is presented both in terms of annual growth rates (for real net disposable income)and in terms of USD per capita at current prices andppPs (gross adjusted disposable income).All OECD countries compile their data according to the 2008 System of National Accounts (SNA 2008)...."

Note this series should be capturing health care, "...Household gross adjusted disposable income is the income adjusted for transfers in kind received by households, such health or education provided for free or at reduced prices by government and NPISHs....."

https://data.oecd.org/emp/hours-worked.htm

Breaking embargo on sending you things as I finally got the PIAAC/Hourly Wage data by race for the US that you have been looking for. Stijn and his colleague Marieke @ OECD were kind enough to carve it out for us as it's not available publicly via the data portal. They cautioned that the sample size is small for Blacks and Hispanics (N=357/308 versus almost 2000 for whites) however they also noted they will have new US data this November with a much larger sample size. This is the same series (numeracy proficiency) and hourly wages (USD, expressed inppP) that they used in their paper we've been using. I've plotted the new points on a chart against the other countries; note the regression is currently capturing all points including the US. Striking: