Occupational Dissimilarity between the American Indian/Alaska Native Workforce and White Workforce in the Contemporary United States

Abstract

Who has which job? When this answer differs by race group or sex, inefficiencies such as labor market discrimination or suboptimal investment in education may be impeding productivity and sustaining inequities. We use US census data to analyze the occupational structure of American Indian/Alaska Native (AIAN) workers, relative to non-Hispanic White workers. AIAN workers (especially men and single-race AIAN workers) are generally overrepresented in low-skilled occupations and underrepresented in high-skilled occupations, relative to White workers. AIAN occupational dissimilarity does not appear to have declined substantially since 1980. Sex-specific multivariate analyses do not remove the significant inequalities in observed occupational outcomes.

INTRODUCTION

Occupational structure is a useful social indicator. Group differences in occupational attainment may signal inefficiencies that significantly reduce economic productivity, such as labor market discrimination or suboptimal investment in education. Occupational differences can also mediate other adverse social and economic disadvantages because occupations differ in average pay, sensitivity to business cycles, health risks, prestige, status, and authority.

We analyze the occupational structure of the non-Hispanic American Indian and/or Alaska Native (AIAN) workforce in the United States to understand this social indicator for this important but understudied group. We compare AIAN occupational structures to those of the non-Hispanic White workforce and other specific comparison groups.¹ Racial, ethnic, and sex differences in occupational patterns have been documented and analyzed for decades,² but few studies have focused on the occupational structure of the AIAN workforce. No other studies that we know of have examined occupations of both single-race and multiple-race AIAN workers.

A detailed analysis of AIAN occupational structure is timely in light of economic and social changes that have affected the AIAN workforce in recent decades. The economies of many reservations and homeland areas have grown rapidly (albeit from a low base) in recent decades.³ This growth directly affects many AIANs -- about one-fifth of AIAN individuals (single-race and multiple-race combined) lived on a reservation or other homeland as of 2010,⁴ and at least as many lived in nearby counties.⁵ Since 1970, tribal colleges have expanded significantly,⁶ and there has been a general increase in AIAN educational attainment (as we show in Figure 6, below). At the same time, in the broader economy, the occupational distribution of the general workforce has changed significantly in response to deindustrialization and rising service employment.

Figure 6.

Educational attainment of labor force participants by race, 1980–2010.

AIAN = Non-Hispanic American Indian/Alaska Native (1980, 1990) and non-Hispanic single-race American Indian/Alaska Native (2000, 2010)

AIAN+ = Non-Hispanic multiple-race American Indian/Alaska Native

White = Non-Hispanic White (1980–1990) and non-Hispanic single-race White (2000–2010)

Occupation and race are both time-specific concepts that undergo periodic changes in measurement. This adds to the value of our updated analysis of links between occupation and race. Partly as a result of the shift in the general occupational distribution, the Standard Occupational Classification system used by federal agencies and developed in 1977 was updated as of 1980, 2000, and 2010.⁷ Meanwhile, in 1997 the federal government broadened the 1977 definition of AIAN to include Central and South American indigenous people and began to require that multiple-race responses be allowed.⁸ In the censuses of 2000 and 2010, individuals were instructed to “mark one or more" races. In the 2010 Census, there were about 2.3 million individuals who reported AIAN as well as another race or races, and 2.9 million who identified as single-race AIAN.⁹

In this paper, we address three research questions about AIAN occupational stratification. First, is the occupational distribution of AIAN workers different from that of Whites, now and since 1980? Using decennial census data and the American Community Survey, we show that it is and that AIAN workers share many occupational patterns long observed among other racial and ethnic minorities. We find that the pattern of occupational dissimilarity between AIAN workers and White workers is stronger among men than among women (although still significant among women). We do not find that AIAN occupational dissimilarity has declined substantially since 1980, though results about changes over time are relatively tenuous due to changes in measurement (mentioned above) and racial identification (discussed below).

Second, in which occupations are AIAN workers underrepresented relative to White workers? In which are they overrepresented? We make comparisons between single-race White workers, single-race AIAN workers, and multiple-race AIAN workers, including sex-specific comparisons. Using Census 2000 and the 2008–2012 American Community Survey (ACS), we find that AIAN workers of both sexes are generally overrepresented in low-skilled occupations and underrepresented in high-skilled occupations, relative to White workers. This distinction is less pronounced for multiple-race AIAN workers than for single-race AIAN workers.

Third, we ask: Do standard demographic factors account for the underrepresentation of AIAN workers in high-education occupations, relative to White workers? Among the observable factors that may account for sex-specific AIAN-White occupational differences (including age, location, and language proficiency), we find that gaps in educational attainment are the most important. Controlling for individual differences in these factors reduces the degree of AIAN underrepresentation in high-education occupations, but fails to fully account for it. We regard the remaining occupational structure differences between AIAN and White workers as a call for more research on the deeper social and economic issues that continue to restrain the well-being of AIAN workers.

COMPLEX ISSUES OF DEFINING WHO IS IN THE AIAN POPULATION

We use US Census Bureau data for this study because of its level of coverage of “the AIAN population,” but who is in the census-defined population and who is excluded? The Census Bureau uses the current federal definition of “American Indian or Alaska Native,” which is: “A person having origins in any of the original peoples of North and South America (including Central America), and who maintains tribal affiliation or community attachment.”¹⁰ Maintenance of tribal affiliation or community attachment is not checked in any way, however; responses to the census are (usually) given by individuals in the privacy of their homes. The elements that go into self-definition are distinct from the types of procedures and checks used by most tribes to determine tribal enrollment status eligibility. Therefore, someone in the census as AIAN may or may not be enrolled in a tribe or even have tribal affiliation or community attachment.¹¹

Censuses and surveys also have issues of undercount, which can exclude AIAN people from studies such as ours.¹² Undercounts are higher in rural areas because standard enumeration strategies rely on mailing addresses and door-to-door follow-ups.¹³ Those AIANs who live in cities are not usually residentially segregated,¹⁴ which may be part of why neighbors of non-responsive households are unlikely to report AIAN individuals as AIAN.¹⁵

Note that people who reported AIAN (whether single-race or multiple-race) in one census or survey did not necessarily give the same race report in another census or survey.¹⁶ Several studies show a net increase in the AIAN population that can only be due to change in how an individual reported their race.¹⁷ There is also evidence that some who reported AIAN in 1990 reported a non-AIAN race in 2000.¹⁸ Note that response change also happens in other race groups.¹⁹ In acknowledgement of response change, we urge readers to interpret our samples as point-in-time populations of people who self-reported (or were reported by someone else in their home) as American Indian or Alaska Native to the Census Bureau.

PREVIOUS STUDIES

In their landmark 1967 study The American Occupational Structure, Peter Blau and Otis Dudley Duncan documented basic occupational differences between Whites and non-Whites (94 percent of whom were “Negro”; 207) in the 1960s in the U.S.²⁰ After ranking seventeen occupations primarily by the median income and education of incumbents in 1962, they found that the occupation status typical for non-Whites was not only different from that of Whites but also “far inferior to that of whites” (209). Although lower educational attainment explained part of this difference, it remained large “even when the lower social origin, education, and first occupation of Negroes [had] been taken into account” (209). Furthermore, “the difference between mean occupational status of whites and nonwhites increase[d] with higher educational levels” (210). The occupational differences both illustrated and exacerbated social inequalities between Whites and non-Whites.

Blau and Duncan's key results have been confirmed in multiple subsequent studies – minority workers have different occupational patterns than majority workers, with minority workers generally holding lower status or lower paid occupations.²¹ The lower educational attainment of minorities explains much of the occupation gap, but not all of it.²² Comparisons of people with similar human capital shows that racial and ethnic disparities within occupational subcategories rise, or at least do not steadily decline, at higher levels of education and skill.²³

There have been several expansions on Blau and Duncan’s findings. In the U.S., Tomaskovic-Devey and colleagues²⁴ find that the degree of racial/ethnic occupational separation declined most rapidly in the 1970s “during the peak period of regulatory enforcement” and then “stalled or nearly stalled,” though other researchers report evidence of further declines.²⁵ In Australia, the degree of racial/ethnic occupational dissimilarity is substantially lower in the female indigenous workforce than in the male indigenous workforce.²⁶

Although the literature on U.S. racial and ethnic differences in occupational structure is long and rich, few results are available for the AIAN workforce. A notable exception is recent (2012) work by Olga Alonso-Villar, Coral Del Rio, and Carlos Gradin.²⁷ These researchers included “Native Americans” among the six racial/ethnic groups in their study using the 2007 ACS data. They defined “Native Americans” as non-Hispanic individuals who reported one of the following as their single race: American Indian, Alaska Native, Native Hawaiian, or another Pacific Islander group. They classified all individuals who reported a Hispanic ethnicity as “Hispanic” (regardless of their race response) and included all non-Hispanic multi-racial individuals in the “other" category. They found substantial occupational dissimilarity between “Native Americans” and the overall population (about to the same degree as for other minority groups), with “Native Americans … concentrated in lower-paid occupations” (190). They also found mostly higher occupational segregation for Native American women than Native American men (194),²⁸ though this result did not hold in their regression analyses of the differences in a segregation index across 260 regional labor markets in the U.S. (198–200).

Our research is similar to that conducted by Alonso-Villar, Del Rio, and Gradin (ADG), but is different in at least five ways that allow us to build on their results. First, our analysis is more narrowly focused on the AIAN²⁹ workforce, as opposed to “Native Americans”³⁰ and five other race/ethnic groups. Accordingly, we do not benchmark relative to the overall workforce, a technique ADG introduce to facilitate simultaneous comparisons of multiple racial/ethnic groups. Instead, we rely mainly on the familiar Index of Dissimilarity, with the White workforce as our comparison group. Second, we implement recently developed statistical tests to assess the significance of the differences in dissimilarity we report.³¹ Third, because occupational patterns remain quite different by sex, when we present occupational dissimilarity results by sex, we compare only within sexes (e.g., AIAN women versus White women) rather than comparing to the overall workforce of both sexes, as in ADG. Fourth, we examine not only the single-race AIAN workforce but also present separate results for the multiple-race AIAN workforce. ADG study only single-race AIANs; they include multiple-race AIANs in an “other” category that includes a variety of groups. Because single-race AIANs are not representative of the entire AIAN group,³² the omission of multiple-race AIANs can introduce bias. Finally, when modeling factors associated with occupational differences, we use an education-based ranking of occupations as the dependent variable (rather than regional differences), so that our regressions directly shed light on factors related to the tendency for AIAN workers to be concentrated in low-skill sectors.

DATA

We focus our analyses on the American Community Survey five-year pooled sample from 2008–2012, which hereafter we will refer to as 2010, its middle year. For a few analyses, we draw on other public-use data sets collected by the Census Bureau: decennial census data from 1980, 1990, and 2000 (5 percent samples). We accessed all data through IPUMS-USA.³³ We used person weights (the PERWT variable) to create statistics that are nationally representative of persons in the U.S. in that year.³⁴ Throughout the paper we include all workers ages sixteen and over.³⁵ We used the statistical software R for all analyses.³⁶

Our ability to detect changes over time in occupational dissimilarity is limited by changes in categorizations over time mentioned above. Specifically, the race categorization system has changed substantially, allowing us to begin tracking the multiple-race responses separately from single-race responses in 2000. As noted above, the AIAN category does not include an entirely consistent set of individuals across the decades even before this change allowing multiple-race reporting. In addition, the categorization of occupations was changed fundamentally between the 1990 Census and the 2000 Census, and was modified again by 2010.³⁷

To measure “occupation group,” we used a twenty-six-category constructed variable that attempts to map the earlier occupational categories into the contemporary categories.³⁸ Changes in definitions as well as the evolving nature of jobs make perfect mapping impossible, though cross-time differences in measurement are reduced by using only twenty-six categories. We present the occupation groups in descending order of “occupational education,” as shown in Table 3, below.

Table 3.

Occupational education and occupational income by occupation group, all workers in 2010

Occupation Group	Occupational Education*		Occupational Income**
Architecture & Engineering	high	93%	high	$ 78,296
Life, Physical, & Social Science	high	92%	high	$ 60,251
Legal	high	92%	high	$ 92,550
Healthcare Practitioners & Technical	high	90%	high	$ 67,493
Education, Training, & Library	high	90%		$ 39,477
Financial Specialists	high	90%	high	$ 66,827
Computer & Mathematical	high	90%	high	$ 72,010
Community & Social Services	high	88%		$ 38,332
Arts, Design, Entertainment, Sports, & Media	high	81%		$37,760
Business Operations Specialists	high	80%	high	$ 58,952
Management, Business, Science, & Arts	high	76%	high	$ 76,252
Technicians		63%		$ 49,100
Protective Service		61%		$ 44,791
Military		59%		$ 43,189
Sales & Related		55%		$ 36,657
Office & Administrative Support		54%		$ 29,813
Healthcare Support		48%		$ 22,451
Personal Care & Service		45%		$ 14,393
Installation, Maintenance, & Repair		38%		$ 39,324
Food Preparation & Serving		33%		$ 14,383
Production		28%		$ 32,429
Transportation & Material Moving		28%		$ 29,042
Construction		26%		$ 29,982
Extraction		22%		$ 49,235
Building & Grounds Cleaning & Maintenance		22%		$ 17,563
Farming, Fishing, & Forestry		17%		$ 18,955

^*Percentage of workers in this occupation group who completed at least one year of college. Occupation groups with "high education" in later analyses are ones in which at least 75% of incumbents have completed at least one year of college.

^**Average wage and salary income of workers in this occupation group. Most occupations with high occupational education also have high occupational income.

Our focus in this research is on two categories of (non-Hispanic) AIAN workers: those who reported being single-race American Indian or Alaska Native, and those who reported being American Indian or Alaska Native in combination with one or more other races. People who report AIAN and Hispanic are especially unlikely to give the same race response in another census but are likely to consistently report being Hispanic.³⁹ We show results for Hispanic single-race and multiple-race AIANs only in Table 1 and Table 8; in Table 2, we combine Hispanic AIANs with other Hispanic people.

Table 1.

Percentage breakdown of the labor force by race, 2000 and 2010.

Race	2000	2010
American Indian / Alaska Native (AIAN) groups
Non-Hispanic single-race AIAN	0.66	0.58
Non-Hispanic multiple-race AIAN	0.55	0.58
Hispanic single- or multiple-race AIAN	0.19	0.27
Other groups*
White	72.71	65.99
Black or African American	10.57	11.45
Asian or Pacific Islander	3.75	5.14
Some Other Race or multiple races	1.09	1.04
Hispanic^	10.48	14.95
Total in the labor force	100	100

Note: Labor force participants must be 16 years or older.

^*All groups are single-race non-Hispanic unless specified.

^{^}"Hispanic" includes all people of Hispanic origin (besides AIAN), regardless of race(s)

Table 8.

Unadjusted and adjusted binomial regressions predicting employment in a highly educated field,^* for the year 2010, including Hispanic AIANs.

	Estimate	SE	z value	Pr(>|z|)
(Intercept)	−0.502	0.0010	−564.9	<2e-16***
AIAN	−0.625	0.0110	−58.28	<2e-16***
AIAN+	−0.4	0.0100	−38.86	<2e-16***
Hispanic AIAN	−0.948	0.0170	−55.23	<2e-16***

	Male workers		Female workers
	Estimate	SE	Estimate	SE
(Intercept)	−4.4942	0.0135***	−4.3748	0.0147***
AIAN	−0.2856	0.0186***	−0.0681	0.0161***
AIAN+	−0.1832	0.0175***	−0.0953	0.0160***
Hispanic AIAN	−0.3189	0.0289***	−0.2325	0.0274***
High school graduate	1.0531	0.0095***	1.3151	0.0113***
Bachelor's degree	2.8666	0.0096***	3.0225	0.0115***
More than a Bachelor's	4.1753	0.0105***	4.3416	0.0124***
In a metropolitan area	0.1558	0.0025***	0.1558	0.0025***
In a homeland	−0.0186	0.0028***	−0.0186	0.0028***
Age	0.0839	0.0005***	0.0839	0.0005***
Age2	−0.0008	0.0000***	−0.0008	0.0000***
Not proficient in English	−0.9845	0.0179***	−0.9845	0.0179***
Mostly proficient in Englis	−0.5448	0.0113***	−0.5448	0.0113***

AIAN = Non-Hispanic single-race American Indian / Alaska Native

AIAN+ = Non-Hispanic multiple-race American Indian / Alaska Native

Hispanic AIAN = Hispanic single- or multiple-race American Indian / Alaska Native

^*A field with high occupational education, as shown in Table 3

Table 2.

Index of dissimilarity (and standard errors) for workers in 26 occupation categories, split by race/Hispanic origin and decade.

	AIAN	AIAN+	Asian/PI	African American	Hispanic	Remainder
1980	17.79 (0.37)		16.55 (0.21)	21.62 (0.08)	20.5 (0.10)	9.98 (2.00)
1990	18.16 (0.32)		15.97 (0.14)	20.35 (0.06)	22.43 (0.09)	14.91 (2.03)
2000	16.54(0.27)	10.15 (0.47)	17.78 (0.12)	19.09 (0.08)	23.3 (0.07)	10.07 (0.31)
2010	16.47 (0.38)	9.92 (0.55)	18.13 (0.11)	19 (0.07)	24.14 (0.07)	10.33 (0.35)

Note: Comparisons are to non-Hispanic single-race Whites

AIAN = Non-Hispanic American Indian / Alaska Native (1980, 1990) and non-Hispanic single-race American Indian / Alaska Native (2000, 2010)

AIAN+ = Non-Hispanic multiple-race American Indian / Alaska Native

Hispanic = all workers of Hispanic origin (including AIAN), regardless of race(s)

RESULTS

Is the AIAN Occupational Distribution Different from that of Whites?

In Table 1 we show a race-specific disaggregation of the U.S. labor force in 2000 and 2010, where each category (except where noted) includes only people who were single-race and non-Hispanic. From the results in Table 1 we see that there are relatively few AIAN workers; AIAN single-race and multiple-race individuals together comprised 1.43 percent of the (age sixteen and older) labor force.⁴⁰ In most of our analyses, we compare single-race AIAN and multiple-race AIAN workers to the largest race group in the workforce – single-race, non-Hispanic Whites – who made up about two-thirds of the workforce in 2010 (Table 1).

Our first research question is: Is the occupational distribution of AIAN workers different from that of single-race White workers, now and since 1980? We begin to address this question using Figure 1, in which we plot the distribution of the workforce in 2010 across occupation groups, separating out the results by sex and by race group.

Figure 1.

Percent of workers in each race group who worked in each type of occupation in 2010, by race and sex.

For example: 1.2% of single-race AIAN male workers were in an architecture or engineering field in 2010, as compared to 1.6% of multiple-race AIAN male workers and 2.7% of White male workers.

Some general patterns are evident. The distribution of women across occupations is very different from the distribution of men, and differences by sex are generally large relative to differences by race, which motivates our decision to present results separately for men and women. Multiple-race AIAN workers have an occupational distribution that is generally between that of single-race AIAN workers and White workers; the share for multiple-race AIAN workers lies between the shares for single-race AIAN and White workers in eighteen of the twenty-six career categories for men and seventeen of twenty-six for women. Also noteworthy is a tendency toward underrepresentation of all AIAN workers of both sexes in traditional “white-collar” occupation categories, such as management, financial specialists, and legal professions, and their overrepresentation in traditional “blue/pink-collar” fields such as construction, healthcare support, and building/grounds cleaning and maintenance.

Figure 1 presents a mixed answer to our first research question – whether the occupational distribution of AIAN workers is different from White workers. On the one hand, the patterns suggest broad racial similarities in how workers in each sex sort across the groups. The occupations that account for large (small) shares of workers within one racial group tend to also account for large (small) shares within the other groups (e.g., construction is a large occupation for men of all three of the race groups). On the other hand, the racial differences in shares within many occupations appear large, at least proportionately (e.g., the share of single-race AIAN men in construction is over 50% greater than the corresponding White share), raising the possibility that within-occupation share differences add up to a pattern of dissimilarity.

For a more rigorous test, we use data across all the occupations in Figure 1 to calculate an overall index of occupational dissimilarity between each AIAN group and the corresponding group of single-race White workers.⁴¹ This index can be interpreted as a percentage that represents the proportion of workers who would need to change careers in order to make the AIAN and White occupational distributions identical. In 2010, the index is about 16.5 percent for single-race AIAN workers and about 10 percent for multiple-race AIAN workers. Furthermore, both percentages are very significantly different from zero (p < 0.001 for both), according to the likelihood ratio test described by Allen and colleagues.⁴²

In Table 2, we show this index of dissimilarity for AIAN and other race/ethnic groups over four decades, for males and females combined, compared to Whites. All of the index values in the table are significantly different from zero. As in 2010, there is less dissimilarity to single-race White workers in 2000 for multiple-race AIAN workers than for single-race AIAN workers. For both 2000 and 2010, the degree of dissimilarity for single-race AIAN workers is closer to that of African American or Asian/Pacific Islander workers than to the value for multiple-race AIAN workers, and is about halfway between the values of multiple-race AIAN workers and Hispanic workers.

The degree of AIAN occupational dissimilarity from Whites changed little between 2000 and 2010 and we see no clear AIAN trend overall since 1980. This is in contrast to the small but steady decrease for African Americans and the steady increase for Hispanics. However, change in the census race question and occupational classification undermines strong intertemporal comparisons.

Men and women tend to choose different occupations (as highlighted in Figure 1) and thus may have different within-sex occupational dissimilarities. Accordingly, we also calculate the dissimilarity index (AIAN vs. White) separately for men and women in 2010. Similar to the findings reported by Taylor for the distribution of indigenous Australian workers across broad occupational categories, we find a lower occupational dissimilarity index between single-race AIAN women and White women (14.5 percent) than between single-race AIAN men and White men (19.8 percent), and this difference is statistically significant. However, for women as well as men, the answer to our first question is the same -- AIAN workers have a different occupational distribution than White workers.

We are also interested in whether the overall difference between AIAN and White workers' occupations varies by place. Dissimilarity indices for single-race AIAN and multiple-race AIAN workers appear to vary substantially by location within the U.S. In Figure 2, we show the occupational index of dissimilarity for single-race AIAN people and multiple-race AIAN people in 13 regions.⁴³

Figure 2.

Occupational dissimilarity indices (D) by region

Note: These regional aggregations are defined and justified in Eschbach (1992).

AIAN = Non-Hispanic single-race American Indian/Alaska Native

AIAN+ = Non-Hispanic multiple-race American Indian/Alaska Native

White = Non-Hispanic single-race White

The single-race AIAN occupational dissimilarity index is higher in areas with relatively many AIAN workers than in areas with relatively few of them. For single-race AIAN workers, the Southwest and North Carolina stand out as having the highest degree of occupational dissimilarity with Whites in the same region. Alaska, California, and the Basin-Mountain, Northern Plains, and Great Lakes regions also show high levels of occupational dissimilarity between Whites and single-race AIAN workers. For multiple-race AIAN workers, Alaska and the Northern Plains stand out as regions of higher occupational dissimilarity from local Whites.

We found significant disparities between single-race AIAN and multiple-race AIAN workers in the Southwest and North Carolina (tests not shown). In the South, the dissimilarity from local Whites is relatively low for both AIAN groups, and in Alaska the dissimilarity is relatively high for both.⁴⁴

In sum, our analyses show that the answer to our first question is clear: the AIAN occupational distribution was significantly different from the White occupational distribution in 2010 and each of the three preceding decades. There are also notable variations in occupational distributions by single- or multiple-race, by sex, across time, and by geographic location.

Over- and Under-representation in Occupations

Our second research question is: In which occupations are AIAN workers underrepresented relative to White workers? In which are they overrepresented? To begin answering this question, we return to Figure 1. The occupational categories there are ordered by the fraction of incumbents who had completed at least one year of college, based on the data from 2010 (for all workers) – in other words, in order of “occupational education.” For example, 92.9 percent of workers in architecture and engineering occupations had attended college. This was the highest rate of college attendance by labor force participants in any of the occupation groups, so it is at the top of the chart. Those in the farming, fishing, and forestry occupation category, shown at the bottom, had the lowest percentage of incumbents who attended college (16.9 percent). See Table 3 for details.

With this ordering, Figure 1 suggests a racial occupation gap related to education, with AIAN workers overrepresented towards the bottom (low-education occupations) and underrepresented at the top. To investigate the statistical significance of this apparent link between race and the educational ranking of occupations, we display in Figure 3 an index based on the ratio of the single-race AIAN employment proportion to the employment proportion of the White workforce (for men and women combined). Specifically, for single-race AIAN workers our index for any single occupation takes the value (expressed as a percentage):

$$\frac{(\text{share of single}-\text{race AIAN workers in the occupation})}{(\text{share of White workers in the occupation})}-1$$

Figure 3.

Under-/over-representation of non-Hispanic single-race American Indian/Alaska Native workers in each occupation group, 2010.

* Difference relative to proportional representation. Calculations and style based on that of John Fox in “Effect Displays in R for Generalised Linear Models,” Journal of Statistical Software 8, no. 15

Figure 3 includes thin lines showing the 95 percent confidence interval for each career category and maintains the education-based ordering of the careers. In the careers in the bottom half of the occupational education distribution, where individuals typically have less education, there is generally overrepresentation of AIAN workers. This tendency disappears for careers in the middle, whose incumbents tend to have moderate levels of education, and transitions to underrepresentation in fields where higher levels of education are common.

The ratios in Figure 3 display a distinct “tilt” in the occupational representation of single-race AIAN workers. In nine of the ten lowest categories on the education scale, there is statistically significant overrepresentation of single-race AIAN workers. In fields such as building and grounds cleaning there are twice as many single-race AIAN workers employed, relative to the proportion of Whites in that sector (i.e., the index exceeds 100 percent). Single-race AIAN individuals are underrepresented in ten of the top eleven most highly educated occupation categories (the exception being community and social services). For legal professions in particular, there were 50 percent fewer single-race AIAN workers (our “parity” index is −50.45 percent) than there would have been if their occupational participation were proportional to participation by single-race Whites.

We expand these statistics in Figure 4 to include multiple-race AIAN workers and to include data for both 2000 and 2010. The results for multiple-race AIAN workers (in the right-hand panel) show a pattern of statistically significant, education-based occupational disparity that is qualitatively similar to the pattern for single-race AIAN workers in the left-hand panel (as seen by the visual “tilt” of both panels). However, the pattern is quantitatively milder for multiple-race AIAN workers than single-race AIAN workers. In both panels of Figure 4, the observed changes in career categories between 2000 and 2010 are small (with the largest differences, like those in extraction occupations, mainly due to small cell counts).

Figure 4.

Under-/over-representation on non-Hispanic single-race and multiple-race AIAN workers in 2000 and 2010, by occupation group.

Notes: Lighter lines represent 2000 and darker liner represent 2010 (2008-12ACS). AIAN = Non-Hispanic single-race American Indian/Alaska Native. AIAN+ = Non-Hispanic multiple-race American Indian/Alaska Native. All values are statistically significantly different from zero except: Community & Social Services (AIAN+ in 2000); Arts, Design, etc (AIAN+ in 2000 & 2010); Technicians (AIAN & AIAN+ in 2000 & 2010); Office & Admin. Support (AIAN & AIAN+ in 2010); and Installation, etc (AIAN & AIAN+ in 2010).

Figure 5 is parallel to Figure 3 but is separated by gender, comparing female single-race AIAN workers to female White workers (and male multiple-race AIAN workers to male White workers). Low cell counts hinder interpretation for some categories. For example, we estimated that female single-race AIAN workers were 268 percent overrepresented in the extraction group; however, because few women work in extraction in either race, our 95 percent confidence interval for this estimate ranges from 76 to 654 percent. The basic pattern is the same as Figure 3, but a few subtleties emerge. For example, AIAN underrepresentation in the legal professions is larger for men than for women. Also, although AIAN workers overall are overrepresented in protective services, this is even more true for AIAN women relative to White women than it is for AIAN men relative to White men.

Figure 5.

Under-/over-representation of non-Hispanic single-race American Indian/Alaska Native workers in 2010, by sex.

Notes: All values are statistically significantly different from zero except: Community & Social Services (men); Technicians (women & men); Office & Administrative Support (men); and Installation, Maintenance & Repair (men).

Adjusting the Dissimilarity Index for Educational Attainment

To explore the relationship between education and occupation further, we calculated the AIAN-White indices of dissimilarity within each of five education categories: less than high school, high school degree, some college or associate's degree, bachelor's degree, and more than a bachelor's degree. We show these results for 2010 in Table 4. For example, 12.23 percent of single-race AIAN workers in the lowest education category would need to change fields in order for their occupational distribution to match that of White workers in the same education category. Again in Table 4 we find that all occupational dissimilarity values represent a statistically significant dissimilarity between (single-race and multiple-race) AIAN workers and White workers (p < 0.001).

Table 4.

Occupational dissimilarity index in 2010 comparing non-Hispanic single-race and multiple-race AIAN workers to non-Hispanic single-race White workers with similar

	AIAN
	Male workers	All workers	Female workers
All Education Levels	19.77 (0.58)	16.47 (0.38)	14.47 (0.53)
No High School Degree	11.98 (2.25)	12.23 (1.69)	13.60 (2.44)
High School Graduate	13.64 (1.02)	12.95 (0.74)	12.90 (1.16)
Some College or Associate's Degre	12.91 (1.85)	10.97 (1.17)	11.43 (1.41)
Bachelor's Degree	18.18 (2.94)	13.70 (2.05)	8.97 (2.78)
More than a Bachelor's Degree	12.43 (4.29)	9.79 (2.81)	9.72 (3.41)
	AIAN+
	Male workers	All workers	Female workers
All Education Levels	12.61 (0.78)	9.92 (0.55)	10.32 (0.73)
No High School Degree	9.40 (3.00)	7.86 (2.44)	7.45 (3.33)
High School Graduate	7.29 (1.66)	7.69 (1.15)	10.24 (1.46)
Some College or Associate's Degre	7.60 (2.15)	6.88 (1.32)	9.01 (1.58)
Bachelor's Degree	13.03 (2.72)	10.87 (1.85)	8.27 (2.57)
More than a Bachelor's Degree	12.50 (3.34)	8.48 (2.27)	8.84 (2.80)

Note: Standard Errors are shown in parentheses.

AIAN = Non-Hispanic single-race American Indian / Alaska Native

AIAN+ = Non-Hispanic multiple-race American Indian / Alaska Native

White = Non-Hispanic single-race White

From the calculated statistics shown in Table 4 we notice that racial comparisons restricted to like-educated workforce members often produce a smaller index of occupational dissimilarity than for the general workforce (shown in the last row). This indicates that differences in educational attainment partly explain the high overall occupational dissimilarity between the AIAN workforce and the White workforce. However, the index of dissimilarity is still quite high within education categories, especially among individuals with a bachelor's degree but no further education.⁴⁵ Also, in each educational category, the results for multiple-race AIAN workers again lie between the results for White and single-race AIAN workers.⁴⁶

In Which Occupations Are AIAN Workers Underrepresented: A Formal Test

Figures 3, 4, and 5 already show a statistically significant pattern of AIAN overrepresentation in low-education occupations and underrepresentation in high-education occupations. To provide a clear test of this overall tendency, we construct a binomial regression model, predicting the probability that a given individual is employed in a highly educated field (the binomial “success”) or not. In defining highly educated fields, we sort military workers (an industry) back into their original occupation groups. We code “high” education fields as “Architecture and Engineering” through “Management in Business, Science, and Arts” and “low” education fields as “Technicians” through “Farming, Fishing, and Forestry;” see Table 3. This dichotomy roughly corresponds to careers with a higher/lower fraction of college-educated participants than in the general workforce. In Table 3 we also show the occupational income (average income of incumbents) of each of the twenty-six broad occupation groups which illustrates that ranking occupations by income instead of education would result in a generally similar definition of high-ranked versus low-ranked occupations.

For the binomial regression analyses, first we created a basic regression predicting whether a worker is in a high-education occupation based only on their race response; see Table 5. The coefficient estimate for the intercept (− 0.502) implies that a White worker in the year 2010 had a e^{− 0.502}/(1 + e^{− 0.502}) = 37.71 percent chance of being employed in a highly educated field. The coefficient for multiple-race AIAN workers (− 0.396) implies an e^{− 0.396} − 1 = − 32.70 percent difference in the odds of a multiple-race AIAN worker being in a highly educated field relative to the odds for Whites. Thus, a multiple-race AIAN worker in the labor force has 28.95 percent probability⁴⁷ of being employed in a highly educated field. The results also show that single-race AIAN workers have an even lower probability, just 24.47 percent, of being employed in a highly educated field.

Table 5.

Binomial regression predicting employment in a highly educated field,^* for 2010.

	Estimate	SE	z value	Pr(>|z|)
(Intercept)	−0.502	0.001	−565.0	<2e-16***
AIAN	−0.625	0.011	−58.29	<2e-16***
AIAN+	−0.396	0.010	−38.87	<2e-16***

AIAN = Non-Hispanic single-race American Indian / Alaska Native

AIAN+ = Non-Hispanic multiple-race American Indian / Alaska Native

^*A field with high occupational education, as shown in Table 3

Notably, the Wald-tests (comparing each coefficient to zero, to which the p-values included refer) and the relative size of the standard errors in Table 5 provide a clear answer to our second question. They show that there are statistically significant racial differences consistent with our earlier visualizations: Both single-race and multiple-race AIAN workers are significantly more likely to be employed in low-education fields relative to Whites, with the disparity significantly smaller for the multiple-race AIAN group.

Do Standard Demographic Factors Account for Occupational Disparity?

Having established that the occupational distribution of AIAN workers differs from that of single-race White workers and is tilted toward low-education fields, we now turn to our third research question: Do standard demographic factors account for the underrepresentation of AIAN workers in high-education occupations (relative to White workers)? To answer this question, we add additional explanatory variables, beyond race, to the regression framework introduced in the previous section.

Measures of educational achievement are, on the one hand, natural variables to add because of the obvious ties between education attainment and many occupations. On the other hand, using an individual's education to predict whether they are in a high-education occupation may seem circular and thus merits some discussion. To define the dependent variable in our regressions, we classify occupations as high- or low-education based on whether a high or low percentage of incumbents have at least some college education. Thus, on average over the full sample of Whites and AIAN workers, there must be a positive overall average relationship between an individual’s education attainment and whether that person is in a high- or low-education field. However, it need not automatically be true that each additional level of education will further increase the odds that an individual will hold a high-education occupation. Nor must individual education be related to occupation on average in the AIAN portion of our sample --- this population is very small relative to the White population and thus has little influence on how occupations are ranked. Therefore, the race coefficients in a regression of occupational outcome (high- or low-education field) on individuals’ race and education can meaningfully show that (holding the effects of individuals’ educational attainment constant) AIAN workers are less likely to hold high-education occupations than White workers.⁴⁸

Other factors besides educational attainment may also be related to whether a person has a high-education occupation. For example, compared to jobs in rural areas, proportionately more jobs in metropolitan areas require high levels of education. Because multiple-race AIAN workers are more likely than single-race AIAN workers to live in metropolitan areas,⁴⁹ controlling for location may account for some of the differences in outcomes between these two groups. In Table 6 we show basic summary statistics on the variables we use in our calculations, including sex, location in a metropolitan area, presence of an American Indian or Alaska Native homeland in the individual's Public Use Microdata Area,⁵⁰ age, English proficiency, and educational attainment.

Table 6.

Labor force participants in 2010, by race.

	Race	N	Mean	SD	Min	Max
Female
	AIAN	54,534	0.4988	0.500	0	1
	AIAN+	45,154	0.4963	0.500	0	1
	White	5,397,814	0.4687	0.499	0	1
Lives in a Metro Area
	AIAN	54,534	0.5045	0.500	0	1
	AIAN+	45,154	0.7216	0.448	0	1
	White	5,397,814	0.7353	0.441	0	1
Lives in a Homeland
	AIAN	54,534	0.6361	0.481	0	1
	AIAN+	45,154	0.3007	0.459	0	1
	White	5,397,814	0.1774	0.382	0	1
Age
	AIAN	54,534	39.5640	13.636	16	94
	AIAN+	45,154	39.2964	14.131	16	94
	White	5,397,814	42.3989	14.246	16	95
Not English Proficient
	AIAN	54,534	0.0060	0.077	0	1
	AIAN+	45,154	0.0041	0.064	0	1
	White	5,397,814	0.0049	0.070	0	1

	Race	N	No High School Degree	High School Graduate	Some College or Assoc.	Bachelor's Degree	More than Bachelor's Degree
Educational Attainment
	AIAN	54,534	12%	42%	30%	11%	5%
	AIAN+	45,154	9%	36%	33%	14%	8%
	White	5,397,814	6%	34%	26%	22%	12%

Note: We report weighted statistics (using PERWT in IPUMS-USA) and the unweighted N.

AIAN = Non-Hispanic single-race American Indian / Alaska Native

AIAN+ = Non-Hispanic multiple-race American Indian / Alaska Native

White = Non-Hispanic single-race White

We next (in Figure 6) show a plot of the educational attainment of single-race AIAN, multiple-race AIAN, and White workers in 1980, 1990, 2000, and 2010, because it is a primary independent variable of interest. Note that these data include multiple-race responses only in 2000 and 2010.

We see in Figure 6 that, compared to Whites in each year, a lower proportion of AIAN labor force participants completed each educational level. In 2000 and 2010, both single-race and multiple-race AIAN workers are more highly concentrated in the high school graduate category than are Whites, with fewer college degrees and greater numbers without high school education. We can also see a general increase in graduation rates for all groups over time. Although AIANs are keeping up with overall educational increases, they are not catching up to close the gaps. The AIAN labor force in aggregate is more educated today than in 1980, but AIAN workers are still less educated than White workers.

Our predictors of a worker being employed in a highly educated field include those shown in Table 6 as well as age squared. In Table 7, we show our results of a single binomial regression model in terms of fitted coefficients, net of interaction effects, with separate columns for men and women as a way of displaying interaction effects.⁵¹ As in Table 5, the coefficients in Table 7 sum to the log-odds of a particular worker being employed in a highly educated field.

Table 7.

Adjusted binomial regression predicting employment in a highly educated field,^* f

	Male workers		Female workers
	Estimate	SE	Estimate	SE
(Intercept)	−4.4954	0.0136***	−4.3760	0.0147***
AIAN	−0.2935	0.0186***	−0.0752	0.0161***
AIAN+	−0.1825	0.0175***	−0.0944	0.0160***
High school graduate	1.0581	0.0095***	1.3205	0.0113***
Bachelor's degree	2.8712	0.0097***	3.0272	0.0115***
More than a Bachelor's	4.1782	0.0105***	4.3453	0.0125***
In a metropolitan area	0.1517	0.0025***	0.1517	0.0025***
In a homeland	−0.0171	0.0028***	−0.0171	0.0028***
Age	0.0836	0.0005***	0.0836	0.0005***
Age2	−0.0008	0.0000***	−0.0008	0.0000***
Not proficient in English	−0.9483	0.0183***	−0.9483	0.0183***

AIAN = Non-Hispanic single-race American Indian / Alaska Native

AIAN+ = Non-Hispanic multiple-race American Indian / Alaska Native

^*A field with high occupational education, as shown in Table 3

Regardless of race, education is the best predictor of employment in a high-education field, and we show in Figure 6 that AIAN workers lag in education relative to White workers. This means differences in education between AIAN men and White men are responsible for a significant share of the differences between AIAN and White men's occupational structure. The same patterns are evident for women. The education coefficients increase sharply with each level of educational attainment for both men and women, and these increases are statistically significant.⁵² The effect of education on the odds of working in a highly educated field is stronger for women than for men. Age predicts a maximum probability of high-education employment just above age 50, falling off quadratically. Living in a metropolitan area, not living near a homeland,⁵³ and being proficient in English are also statistically significant predictors of working in a “highly educated field,” although their coefficients show much smaller effects than for education.

After adjusting for these other factors, including educational attainment, all the race coefficients are smaller than their values in the previous race-only regression (Table 5). However, all the race group coefficients remain statistically different from zero, implying that the factors we considered did not fully account for the underrepresentation of AIAN workers in high-education occupations. Compared to the disparities for AIAN men, disparities between AIAN women and White women are much smaller, or more nearly eliminated, after controlling for our additional factors, but even they remain statistically significant.⁵⁴

One difference within the AIAN workforce itself does disappear with our additional controls. Comparing female single-race AIAN workers to female multiple-race AIAN workers, the difference in their log-odds (and thus probability) of being in a high-education occupation is no longer statistically significant in our adjusted regression (with the additional explanatory variables). This is not true for male AIAN workers.⁵⁵ For males, we can only say that the differences between single- and multiple-race AIAN workers are substantially smaller in the adjusted model (Table 7) than in the unadjusted model (Table 5). This indicates that much, but not all, of the observed difference in employment in a high or low education field between single- and multiple-race male AIAN workers is accounted for by the additional factors included in Table 6.

Because the absolute counts for Hispanic American Indians or Alaska Natives are significantly smaller, we exclude them from the bulk of our analyses. However, in Table 8 we show the fitted coefficients for two regression models that include the Hispanic AIAN group (combining single-race Hispanic AIAN workers and multiple-race Hispanic AIAN workers). When compared to White workers, the disparity in education-ranked occupational outcomes is much larger for Hispanic AIAN workers than for either of the non-Hispanic AIAN groups in the “unadjusted” regression, which includes only race/ethnicity explanatory variables. All non-Hispanic AIAN-White disparities are smaller than Hispanic AIAN-White disparities after adjusting for the other covariates, but none are fully accounted for (each coefficient is statistically different from zero). When comparing non-Hispanic to Hispanic AIAN workers, we find results that differ by sex. Among AIAN women, the Hispanic AIAN coefficient in the adjusted model is still statistically and materially larger than the coefficients for non-Hispanic AIAN workers. Among men, however, the statistical difference between Hispanic AIAN workers and single-race AIAN workers disappears in the adjusted model.

CONCLUSION

Do standard demographic factors account for occupational disparity between AIAN workers and White workers? Our analysis shows that the answer is “no, not completely,” at least for the factors we consider. The raw data on occupational distribution by race reveals a clear disparity between AIAN workers and White workers that has been present since at least 1980. AIAN workers, both single-race and multiple-race, are underrepresented in high-education fields like management, financial services, and legal professions, relative to White workers. AIAN workers are significantly overrepresented in low-education fields like construction, healthcare support, and food preparation. These differences are especially strong when the comparisons are limited to working men.

We find that AIAN-White race-group differences in educational attainment are the single most important explanatory factor in predicting whether a worker is in an occupation group with relatively high education in 2010. Accounting for differences in educational outcomes and other factors markedly reduces all the race coefficients relative to their values in a race-only regression, but the race coefficients remain statistically different from zero. In other words, education and demographic characteristics cannot fully explain the AIAN-White differences in working in a highly educated field. Measured factors explain much (for men) or all (for women) of the tendency for single-race AIAN workers to be less likely than their multiple-race AIAN counterparts to work in a high-education field.

Although American Indians and Alaska Natives have improved their educational attainment in the past decades, White educational levels have also been increasing, and the education gap remains. Over the same decades, the aggregate occupational dissimilarity of the AIAN workforce seems to have changed little (though data issues prevent us from being certain). Our findings suggest that further efforts to close racial gaps in educational attainment can play an important role in narrowing the occupational dissimilarity between White workers and AIAN workers, thus improving lives and eliminating potential inefficiencies in how jobs are allocated.

Other factors causing occupational dissimilarity can also be addressed. For example, Peterson and West (forthcoming) find that on average college completion boosts the earnings of AIAN workers significantly (relative to those with only a high-school education) but less than for White workers and that this disparity persist after controlling for numerous observable demographic factors. They raise the possibility that labor market discrimination plays a role but note that the effect could also be due to unobservable differences in factors such as the quality of K-12 schooling or the extent of labor market networking and mentorship. Liebert⁵⁶ tracks Minnesota college students who enter the Minnesota employment market and finds that college degrees completed after age 30, a condition relatively common among AIAN workers in her sample but one we could not control for here, are less effective in closing racial gaps. She concludes that “Policies aimed at increasing educational attainment are most effective when they target individuals early in their working life, especially before age 30.” In addition, a disproportionate share of AIAN workers live on or near American Indian reservations, where Akee, Mykerezi, and Todd⁵⁷ show that employment opportunities are skewed toward a few large service sectors such as Arts/Entertainment/Recreation (including casinos), Food and Accommodations, and Public Administration (i.e., government). Not surprisingly, then, Liebert⁵⁸ finds that AIAN workers in her sample have a “very high concentration… in jobs in tribal government,” which may skew their occupational choices and opportunities.

Although further research is clearly needed to untangle the factors driving the many differences in occupational and earnings outcomes for AIAN adults, some policy options may be tentatively offered now. We have already endorsed efforts to close racial gaps in education generally. Some tribes have taken specific steps to help young AIAN workers learn about and gain mentoring in well-paid occupations. Tribes such as the Makah, Coeur d’Alene, Chickasaw, Saginaw Chippewa, and Tigua of Ysleta del Sur Pueblo expose their youth to information about prospective careers or work experience, often via internships or summer jobs in tribal government or tribally owned enterprises.⁵⁹ The Makah program specifically promotes science and technical education via a Fisheries Management Internship with hands-on and academic components.⁶⁰ The Indigenous Food and Agriculture Initiative at the University of Arkansas School of Law conducts a multiday Native Youth in Food and Agriculture Leadership Summit each summer.

A second policy option would be to reduce the total cost of training for these occupations. In light of their finding that returns to a college education are not as high for AIAN workers as for White workers, Peterson and West⁶¹ discuss the value of compensating AIAN graduates via loan forgiveness programs, perhaps including tribal programs that target certain occupations or jobs; some tribes are already linking scholarships with subsequent employment.⁶² These and similar efforts seem well targeted but could be strengthened by research to assess their short- and long-term outcomes. A third policy path involves looking for opportunities to diversify reservation economies and thereby provide a broader array of career opportunities for AIAN workers living on or near homelands. Akee, Mykerezi, and Todd⁶³ showed that reservation employment opportunities are skewed to a narrow range of casino and government related sectors.

Inequalities in occupational incumbency create and exacerbate inequalities in pay, health, authority, and opportunities for advancement. Using these and other paths, efforts to reduce occupational disparities between AIAN workers and White workers are likely to have important positive effects on the AIAN workers themselves, as well as their families and communities.