Preparing Blacks and Latinx for Workforce 2000: Unfulfilled Promises and Lost Opportunities

Abstract

In the mid-1980s, economists made predictions about what the workforce would look like in the 21st century and the implications for employees and employers. For example, it was predicted that due to demographic shifts in the overall population, blacks and Latinx would represent a large share of the new entrants into the workforce. The primary purpose of this article is to examine whether the economic forecasts for workforce 2000 were accurate. The findings of this study suggest economic forecasting was inaccurate and that public and private sectors failed to prepare blacks and Latinx for the modern economy.

Keywords

black employment skills mismatch perpetuating inequality

In the mid-1980s, demographers and economists began to make predictions about what the workforce would look like in the 21st century and the implications of the forecasted changes for employees and employers. For example, in 1987, Johnston and Packer, predicted in Workforce 2000: Work and Workers for the Twenty-first Century, that due to demographic shifts in the overall population, the social makeup of the American workforce would change in several important ways by the year 2000. They claimed inter alia that (a) fewer young white males would enter the workforce, and (b) blacks and Latinx would represent a large share of the new entrants into the workforce.¹ This and other policy analyses projected that the demographic changes would result in blacks and Latinx, overall, holding a higher share of jobs in the 21st century, with whites losing a fraction of the jobs. Additional economic forecasts, based on the transition from a manufacturing-based to service- and technology-based economy, included that there would be a shift in the skills needed for the new jobs in the 21st century (see, e.g., De Vita, 1989; Cantrell & Clark, 1982). Taken together, economists and demographers called on governments and private sector organizations to develop policies and programs to prepare workers for these new jobs. Educational and training opportunities would be needed to provide blacks and Latinx with the necessary skills. But, have the predictions regarding changes to the workforce and concomitant recommendations been realized and achieved? Have investments been made in the human capital of blacks and Latinx?

The primary purpose of this article is to examine whether the economic forecasts for workforce 2000 were accurate. This inductive study finds that they were not and that the public and private sectors failed to prepare blacks in particular for the jobs of today. Government and private sector organizations were nudged to provide these workers with the tools and skills needed for a changing workforce, characterized by increased automation, technological advances, and global competition. This issue is significant because the lost opportunity created a skills mismatch and skills deficit for workers of color, which has led to racial gaps in workers’ occupations and concomitantly wages. This has implications for diversifying the workplace (Harris, 2013; Sabharwal, 2014; Sowa & Selden, 2003), but more significantly, it perpetuates wage inequalities between whites and blacks and Latinx, which will be addressed in the final section.

Assessing the Accuracy of Predictions for the 21st Century Workforce

Economic forecasts for jobs of 2020 were made in a number of areas, including new entrants to the workforce, changes in employment status and occupational classifications based on race, ethnicity, and gender and increased job opportunities for women and persons of color in technology. Each is addressed below in terms of whether the predictions have been realized.

New Entrants to the Workforce

Research on the demographics of new entrants to the workforce of the 21st century predicted that blacks and Latinx would represent a large share of the new entrants into the workforce, and that fewer young white men would enter the workforce. According to the U.S. Bureau of Labor Statistics (BLS), “new entrants” to the workforce are unemployed people looking for their first job. They have no previous work experience. As Table 1, indicates from 1990 to 2006, the highest percentage of new entrants into the workforce was Latinx—men constituted 78.3% of the new entrants and women constituted 74.2%. This may reflect the fact that Latinx was the fastest-growing demographic group in the overall population. The demographic group with the next highest percentage of new entrants between 1990 to 2006 was Asians—men at 41.2% and women at 50%. A much smaller percentage of blacks entered the workforce during that same time period—1.9% black men and 13% black women. As Table 1 illustrates white men and women’s share was 3.7% and 4.8%, respectively. Women did not account for two thirds of the new entrants to the workforce, as demographers expected.²

Table 1.

New Entrants to the Civilian Labor Force, 1990, 2006, 2016, and Projected 2026 (Percentages).

	1990	2006	Change 1990–2006	2016	Change 2006–2016	2026	Projected change 2016–2026
Black	10.7	11.4	6.5	12.3	7.9	12.7	3.3
Men	5.3	5.4	1.9	5.9	9.3	6	1.7
Women	5.4	6.1	13	6.5	6.6	6.7	3.1
Hispanic origin	7.7	13.7	77.9	16.8	22.6	20.6	22.6
Men	4.6	8.2	78.3	9.7	18.3	11.6	19.6
Women	3.1	5.4	74.2	7.2	33.3	8.9	23.6
Asian	3.1	4.4	41.9	6	36.4	7.2	20
Men	1.7	2.4	41.2	3.2	33.3	3.8	18.7
Women	1.4	2.1	50	2.8	33.3	3.4	21.4
All other groups^a	n.a	2.3	—	3.3	43.5	3.9	18.2
Men	n.a	1.3	—	1.7	30.8	2	17.6
Women	n.a	1.1	—	1.6	45.5	1.9	18.7
White	78.5	81.8	4.2	78.3	−4.3	76.2	−2.7
Men	43.1	44.7	3.7	42.4	−5.1	40.8	−3.8
Women	35.4	37.1	4.8	35.9	−3.2	35.3	−1.7

Source. U.S. BLS (n.d.-a). U.S. BLS (n.d.-c).

Note. This table omits “leavers” or those departing the labor force.

“All other groups” includes those classified as of multiple racial origin and the race categories of American Indian, Alaska Native, Native Hawaiian, and Other Pacific Islanders (totals then will not equal 100).

Table 1 also presents data on new entrants to the workforce between 2006 to 2016. Here we see that fewer white men and women entered the workforce between these time periods, where there was a 5.1% drop in white men’s entry into the workforce, and a 3.2% drop for white women. Among all the other groups, blacks share of new jobs was lowest—black men accounted for only 9.3% of the new entrants and black women for only 6.6% between 2006 and 2016. Certainly, the recession in 2008 contributed to job loss for all races; unemployment peaked at 10.2%. However, for blacks, unemployment reached 11.5% compared to 8.9% for Latinx and 6.3% for whites. Overall, blacks and Latinx were at least more than 40% more likely than whites to experience unemployment by the end of 2008 (Logan & Weller, 2009).³

Predicted change to new entrants from 2016 to 2026 is also presented in Table 1. It shows that whites will constitute fewer new entrants into the workforce, as demographers predicted. But here, as with trends from 2006 forward, blacks constitute the smallest percentage of new entrants to the workforce. As noted, new entrants have no previous work experience, but this does not connote an absence of training or education, and this is perhaps key to the reason that so few blacks account for new entrants into the workforce. These patterns and phenomena cannot be divorced from the broader picture of the socioeconomic status of blacks in America. As Austin (2011) points out,

African Americans live in communities that lack access to good jobs and good schools and suffer from high crime rates. African-American adults are about twice as likely to be unemployed as Whites, black students lag their White peers in educational attainment and achievement, and African-American communities tend to have higher than average crime rates. (p. 1; also see Perry, 2019)

Moreover, even a strong economy does not lead to labor market gains for blacks in particular.

These issues and problems are interrelated and interconnected: poor economic conditions create and cause poor educational opportunities and high crime rates which are correlated with high unemployment rates for blacks. Education and training opportunities could lead to blacks being “new entrants” into the workforce, but they are deprived of both, as will be further addressed later in the text. Increased employment would then enable blacks to acquire the experience needed to improve their economic and social status, including job status, as addressed in the following section.

Changes in Employment Status

Table 2 presents changes in the employment status from 2001 to 2018 based on race, ethnicity, and gender. As the data show, whites lost the greatest share of jobs, as demographers predicted. Although they represent the largest share of job holders, they did drop their participation rate during this time period. For white men, employment dropped by 20.8%, and for white women it dropped by 19.8%. Blacks also lost a share of jobs in the workforce between 2001 and 2018, with black men’s employment dropping by 3.8% and black women’s dropping by 8.2%. The overall employment status for Latinx showed improvement, where Latina women increased their share of jobs by 53.1% and Latino men by 37.5%. Again, however, the overall share of jobs help by Latinx in the workforce is relatively small. The same can be said for Asian men and women—Asian men increased the share of jobs from 2010 to 2018 by 27.3%, and Asian women’s presence in the workforce increased by 31.6%.

Table 2.

Employment Status of Civilian Population by Race, Ethnicity, and Gender, 2001, 2010, and 2018.

	Number percent 2001	Number percent 2010	Number percent 2018	Percent change 2001–2018
African American
Women	8,774	9,447	10,720
	6.1	5.3	5.6	−8.2
Men	7,647	8,415	9,694
	5.3	4.8	5.1	−3.8
Latinx
Women	7,049	9,238	12,223
	4.9	5.2	7.5	53.1
Men	10,279	13,511	16,113
	7.2	7.6	9.9	37.5
Asian
Women	n.a	3,355	4,790
		1.9	2.5	31.6^a
Men	n.a.	3,893	5,346
		2.2	2.8	27.3^a
White
Women	54,433	57,356	57,732
	37.8	32.5	30.3	−19.8
Men	64,966	67,728	68,082
	45.2	38.3	35.8	−20.8

Source. U.S. BLS (n.d.-b).

Percent change for Asians is 2010 to 2018.

Contrary to the expectations, blacks presence in the workforce in the 21st century did not increase but rather decreased as Table 2 indicates. Joblessness among blacks offers a starting point for understanding this trend, as was previously discussed. The high rate of joblessness among blacks results from a lack of skills and a lack of opportunities to acquire skills, education and experience; job restructuring and geographical disparities also explain the loss of jobs for black women and men. Job restructuring results when there is a reduced demand for workers with low levels of skill and education, which was a prediction for workforce 2000; this then begs the question, which will be further addressed later, why were not training and educational programs put in place for blacks, which was strongly urged by demographers in the 1980s. The problem is further compounded by the fact that the geographic areas hit harder by restructuring are those where low-income blacks are most likely to reside. Manufacturing jobs traditionally held by blacks have moved away from the Northeast and Midwest—where many blacks reside—to the South, the Sunbelt states or out of the country (Browne, 1997; Kasarda, 1988).

Geographic disparities in the sense of “separate and unequal communities” pose even larger problems. As Austin (2011) observes, in

2010, in the 100 metropolitan areas with the largest black populations, 62.5 percent of blacks would have had to move to achieve full black-White integration. In some of the largest metropolitan areas, the degree of segregation is significantly above the average. In the New York, Chicago, and Detroit metropolitan areas, for example, more than 75 percent of African Americans would have had to move to achieve residential integration. (p. 2; also see Alkadry et al., 2017)

Blacks living in these areas already face economic distress, where unemployment rates are persistently higher than the national average. Just prior to the recession in 2007, the unemployment rate for blacks in the Chicago metropolitan was 10.3% and 3.6% for whites. In the Detroit metropolitan area black unemployment was 14.9% while for whites the rate was 5.8%.

Occupational Classifications

Demographers also predicted that the fastest-growing jobs would be at the professional and managerial levels. As Table 3 shows, whites hold a large share of the management and professional jobs compared with other groups, even though they experienced some loss between 2005 and 2018: white women lost 3.8% of those jobs, while white men lost 7.8%. Although the numbers are small, Asian women and Latinx men and women increased their share of management and professional jobs by almost 50% over that time period. Black women’s share of those jobs increased by 16% between 2005 and 2018, while black men’s share increased by 19.4%.

Table 3.

Total Management and Professional Occupations by Race, Ethnicity, and Gender, 2005, 2010, and 2018.

	2005 (%)	2010 (%)	2018 (%)	Percent change 2005–2018
African American
Women	5.0	5.3	5.8	16.0
Men	3.1	3.1	3.7	19.4
Latinx
Women	3.3	3.8	5.0	51.5
Men	3.1	3.5	4.6	48.4
Asian
Women	2.6	2.8	3.9	50.0
Men	3.3	3.3	4.6	39.4
White
Women	41.9	42.4	40.3	−3.8
Men	42.3	41.2	39.0	−7.8

Source. U.S. Bureau of Labor Statistics, Unpublished Tables. Table for 2000 was unavailable.

Occupational segregation by race and gender has existed historically for certain groups. For example, in 2018, Asians held the highest share of management, professional, and related occupations; for men it was 54.9% of employed Asian men; and 52.5% of employed Asian women worked in these occupations (see Table 4). These are the highest paying occupations according to the BLS; the median annual wage for management occupations was $104,240 in May 2018 (U.S. BLS, 2018e). Around 37% of employed white men held those jobs and for employed white women is was 45.2%. Close to 26% of employed black men and 36.2% of employed black women filled these occupations, compared to 18.8% of employed Latino men and 27.2% of employed Latina.

Table 4.

Occupational Status of Employed Persons by Race, Ethnicity, and Gender, 2018 (Percentages).

	Black		Latinx		Asian		White
	Men	Women	Men	Women	Men	Women	Men	Women
Management, professional, and related occupations	25.9	36.2	18.8	27.2	54.9	52.5	37.1	45.2
Management, business, and financial operations occupations	10.8	11.4	9.1	9.8	18.2	17.8	18.6	16.3
Professional and related occupations	15.1	24.8	9.7	17.4	36.7	34.7	18.5	28.9
Service occupations	20.3	28	19	31.4	12.7	20.8	12.8	19.6
Sales and office occupations	17.3	27	13.9	29.5	13.9	20	15.6	28.7
Sales and related occupations	8.3	9.8	7.7	12.1	8.4	8.8	10	10.9
Office and administrative support occupations	9	17.2	6.2	17.4	5.5	11.3	5.6	17.8
Natural resources, construction, and maintenance occupations	11.5	0.6	28.2	2.3	5.7	0.5	18.1	1.1
Farming, fishing, and forestry occupations	0.3	0.1	2.4	1.2	0.3	0.2	1.2	0.4
Construction and extraction occupations	6.3	0.2	19.4	0.8	2.5	0.1	10.7	0.4
Installation, maintenance, and repair occupations	4.9	0.3	6.3	0.2	2.9	0.2	6.2	0.2
Production, transportation, and material moving occupations	25	8.1	20.2	9.6	12.8	6.2	16.4	5.4
Production occupations	8.4	4.1	8.6	5.9	5.6	4.5	7.4	3.1
Transportation and material moving occupations	16.6	4.1	11.6	3.7	7.2	1.6	9.1	2.2

Source. U.S. BLS (2018b).

Table 4 presents greater detail on the extent of occupational segregation by race, gender, and ethnicity for 2018. For example, Table 4 presents data for management and professional positions separately, which show that women tend to be in the lower rung of management positions, in “professional and related occupations,” where the pay is lower than jobs filled by men, “management, business, and financial operations.” Employed black women, for instance, filled 24.8% of professional posts in the “management” category, while they held only 11.4% of the posts that were geared toward business and financial operations. Employed Latinx women held only 9.8% of the business/financial positions but 17.4% of the professional jobs. Similarly, employed white women were more likely to be found in professional positions (28.9%), and less so in business/financial positions (16.3%). Employed Asian women as well as men held more professional jobs compared to business/financial positions. The median annual wage for business and financial occupations was $68,350 in 2018 (U.S. BLS, 2018a); for professional and related occupations, it was.

Table 4 also shows that the highest percentages of employed women of every race are in service related or office and administrative support occupations. Twenty-eight percent of employed black women are in service occupations, and 17.2% are in office and administrative support occupations. For Latinx women, it is 31.4 and 17.4, respectively. For Asian women, it is 20.8% and 11.3%, respectively, and 19.6% of employed white women hold service jobs, and 17.8% hold jobs in office and administrative support.

As Table 4 further shows, employed women hold far fewer jobs in construction and extraction occupations, which include such jobs as carpenters, construction, and building inspectors, electricians, and construction equipment operators (U.S. Bureau of Labor Statistics, 2018c). As the data show, 19.4% of employed Latino men held jobs in construction and 10.7% of white men held jobs in this occupation. Only 6.3% of employed black men held jobs in construction, and only 2.5% of employed Asian men held these jobs. The median annual wage for all construction and extraction occupations was $46,010 in May 2018, which was higher than the median annual wage for all occupations, at of $38,640 (U.S. BLS, 2018d). The median annual wage for office and administrative support occupations, which are filled mostly by women, was $35,760 in May 2018, also lower than the median annual wage for all jobs (U.S. BLS, 2018d).

Table 4 also shows that employed black men held 16.6% of the transportation and material moving occupations in 2018. For Latino men, it was 11.6% and for white men, 9.1%. The median annual wage for transportation and material moving occupations was $32,730 in May 2018, also below the median annual wage for all occupations. Table 4 also shows relatively high percentages of all workers in an aggregated category, “service occupations,” but the categories range from high-paying jobs such as firefighters and police officers to low paying jobs such as cooks, waitresses, and janitors, without reference to race, ethnicity, or gender. Additional tables published by the BLS indicate that occupations such as police and firefighters are filled predominately by white men. Just over 87% of firefighters in the United States are white; only 5.1% are women. And 81.7% of police and sheriff patrol officers are white; only 15.4% are women (U.S. BLS, 2018f).⁴

Technology Jobs

A major prediction for the 21st century workforce was that new jobs in service industries would be in high demand, requiring higher skills levels than the jobs of today. Least-skill job classes would no longer exist. Technology jobs would be among those new jobs. Johnston and Packer (1987) point out that the “occupational changes will present a difficult challenge for the disadvantaged, particularly for black men and Hispanics, who are underrepresented in the fastest-growing professions and overrepresented in the shrinking job categories” (p. 96). They go on to suggest that unless blacks and Hispanics are provided with adequate training for the new jobs, they will not only have a small share of those jobs, they will lose jobs that become obsolete. The first question then is, have women and men of color moved into these new jobs? Addressed later will be the investments made in training and educating them for these jobs.

A plethora of technology jobs emerged at the turn of last century, and therefore, for the purposes of this article, I examine only a cross-section of technology jobs, namely information technology (IT) jobs. These occupations rose from 450,000 in 1970 to 4.6 million in 2014 (Beckhusen, 2016). It should be noted at the outset that between 1980 and 2000, the few detailed IT occupations in existence split into eight. Additional changes to IT occupations were made later (Beckhusen, 2016). These changes certainly reflect the creation of new technology jobs for workforce 2000. As such, precise assessments of the progress made by women and people of color in these particular jobs are challenging. Moreover, comprehensive data are not disaggregated by race, gender, and ethnicity.⁵ Nonetheless some analysis is in order (also see Muro et al., 2018). It should further be noted that some of the IT jobs included here (e.g., computer programmers and computer systems analysts) are considered tech jobs in the context of STEM.

Table 5 provides data on the IT jobs held in 1980 and 2014 by gender. As the data show, in both time periods, males held the largest share of both Computer Systems Analyst⁶ and Computer Programmers.⁷ While they lost 18.5% of the former, they increased their share of Computer Programmer jobs by 13.8%. Where men lost a share of jobs as Computer Systems Analysts, women increased their share by 63.6%. However, women lost 30.4% of the jobs as Computer Programmers between 1980 and 2014.⁸

Table 5.

Technology Jobs by Gender, 1980 and 2014, Percentages.

	1980		2014		Percent change
	Women	Men	Women	Men	Women	Men
Computer systems analysts	22.5	77.5	36.8	63.2	63.6	−18.5
Computer programmers	31.2	68.8	21.7	78.3	−30.4	13.8
Computer operators	59.1	40.9	n.a		n.a.

Source. U.S. Census Bureau (1980).

Women dominated the position of Computer Operators (59.1%) in 1980, but a comparable job classification for comparison purposes was not available in 2014. The median pay for Computer Operators was $16,172 in 1985, compared to $31,304 for Computer Systems Analysts, and $26,104 for Computer Programmers for that same year.⁹ For all newly classified IT positions, even Computer Systems Analysts, where women made some gains in share of jobs, they continue to earn less than men in every job category (see Figure 1). Indeed, as seen in Figure 1, for every category of IT job, women’s salaries are lower than men for the same job, signifying a pay gap.

Figure 1.

Distribution by sex for all information technology (IT) workers and median earnings, by sex of full-time, year-round IT workers: 2014.

Table 6 provides data by race for IT jobs in 1980 and 2014. In both 1980 and 2014, whites held the largest share of the higher paying jobs, Computer Systems Analysts, and Computer Programmer positions, even though they lost a share in both job categories: 22.2% and 15.3%, respectively.¹⁰ The data also show that for Computer Systems Analysts, blacks, Latinx, and Asians increased their share of jobs between 1980 and 2014. Asians experienced the largest increase in their share of those jobs: 406.1%, followed by Latinx at 205.4%. Blacks, by comparison, experienced a smaller increase in those jobs at 85.7%.

Table 6.

Technology Jobs by Race and Ethnicity, 1980, 2014, Percentages.

	1980				2014				Percent change
	African American	Latinx	Asian	White	African American	Latinx	Asian	White	African American	Latinx	Asian	White
Computer systems analysts	4.6	2.4	3.6	89.1	8.5	7.3	18.2	69.3	85.7	204	405.6	−22.2
Computer programmers	5.6	2.8	4.5	86.8	3.9	5.5	19.1	73.6	−30.4	96.4	324.4	−15.2
Computer operators	11.6	5.2	2.5	80	n.a.				n.a.

In Computer Programmer positions, blacks lost 30.4% of the jobs between 1980 and 2014. Latinx increased their share by 96.4%, but here, too, Asians experienced the largest job growth at 324%. These data suggest that blacks may not have been prepared for IT jobs, in comparison to Latinx and Asians. A survey by the Pew Research Center (2018b) suggests that blacks are underrepresented in the tech industry because organizations to not devote enough attention to increasing racial and ethnic diversity. Alternatively, blacks may have incurred higher levels of bias or there was no improvement in their access to suburban labor markets where much of the job growth has occurred (Austin, 2011). The following section looks more closely at the training and educational opportunities that were provided to prepare for workforce 2000.

Training and Education

Demographers argued that to prepare for workforce 2000, black and Latinx workers would need to be fully integrated in the economy. Johnston and Packer (1987), for example, stated that the

shrinking numbers of young people, the rapid pace of industrial change, and the ever-rising skill requirements of the emerging economy make the task of fully utilizing minority workers particularly urgent between now and 2000 . . . education and training investments will be needed to create real equal employment opportunity. (p. xiv)

They go on to say, however, that

traditional job training and employment programs by themselves are unlikely to have profound impacts on the future success of minority youth. Unless the $127 billion public educational system can somehow be better harnessed to serve minority youth. . . [job training programs] can make only a small dent in the problem. (Johnston & Packer, 1987, p. 115)

This study does not seek to empirically link lack of proper education and training to skills match for 21st century jobs; nor does it link economic climate. Rather, the study seeks to show that investments in training and education failed to provide blacks and Latinx with the skills sets for jobs of the 21st century. But certainly, some inferences can be drawn from the investments, or lack thereof, in education and training programs that would prepare workers for 21st century jobs because they follow historical trends and patterns. In addition, however, proper education or training will not necessarily lead to job acquisition or placement because the problem of job discrimination in recruitment in hiring persists in our society. Thus, even if young blacks or Latinx graduate from college with 4-year degrees in computer science, they are not necessarily guaranteed jobs.

School Financing

Funding for K-12 education comes from a combination of state, local, and federal dollars. The federal government contributes about 10% of the total, primarily in the form of categorical grants to state education agencies. Federal dollars fund such programs Head Start and free and reduced lunch programs, and states and local governments split the rest. In FY 2000–2001, a total of approximately $442 billion was spent on elementary and secondary education (U.S. Department of Education, n.d.). In FY 2015-16, total expenditures reached about $706 billion (National Center for Education Statistics [NCES], n.d.). Again, the purpose is not to link funding to a skills match for black and Latinx in the United States, but rather to infer from these data that school expenditures were not necessarily “better harnessed to serve minority youth” in this nation, as predicted by demographers.

One significant explanation for this relates to inequities in school financing (Alemán, 2007; Baker & Corcoran, 2012; Curtis, 2012; Wood & Theobald, 2003). Urban schools tend to be underfunded because school districts’ resources depend on how wealthy an area is and how much residents pay in taxes. Inequities occur largely because public school districts in most states in America are run by local cities and towns and are funded by local property taxes. High-poverty areas have lower home values and collect less taxes, and so these districts cannot raise as much money as cities or towns where homes are worth millions of dollars. Nationally, high-poverty districts spend 15.6% less per student compared to low-poverty districts. A 20% increase a year in spending per-pupil for poor children can result in an additional year of completed education, 25% higher earnings, and a 20-percentage point reduction in the incidence of poverty in adulthood (Jackson et al., 2015). Education is paid for with the amount of money available in a district, which doesn’t necessarily equal the amount of money required to adequately teach students.

Related, more than half of students in the United States go to segregated or “racially concentrated,” schools where more than 75% of students are Non-white, or conversely, are white (EdBuild, 2020). In addition, as Barnum (2019) points out, there is extreme variance in educational spending between and among the states, which drives inequities in educational funding. For example, Connecticut, one of the wealthiest states in the union, spends more than twice as much as Mississippi, which has a greater proportion of poor students. Many states, such as Massachusetts, Minnesota, and New Jersey that spend the most per student have relatively fewer low-income students. And states such as Arizona, Mississippi, and California that spend less on education tend to serve low-income students and students of color (Barnum, 2019).

Inequitable funding patterns also exist in higher education. There are gaps in educational spending at public colleges and universities by state and local governments, which tend to disadvantage students of color who are concentrated at lower-resourced, or less prestigious institutions. A study by the Center for American Progress found that education spending at public 2- and 4-year colleges was more than $1,000 less per year per black and Latinx students compared with white students. This disparity in spending can result in fewer support services for students of color, including opportunities to work with advisers and tutors as well as access to mental health services (Garcia, 2018).

Diminishing statewide education spending levels affect all public college students. In FY 2016–2017, state and local governments expended only 10% of state and local direct general spending on higher education (Urban Institute, 2019). Today, colleges and universities are tuition driven, which can dissuade students of color without adequate resources from enrolling in even community colleges. Although federal programs such as the Pell Grant Program provides some financial aid to students, the net cost of attending postsecondary schools may be too prohibitive for students of color.¹¹

Ricks (2014) points out that the national discourse on “post-racialism” also compounded the problems and challenges faced by the educational system (p. 10). Postracialism, she goes on to say is

a concept that connotes an imagined era in which issues of race are no longer at the forefront of the national discourse. The term “post-racialism” has been particularly pervasive since the election of President Obama . . . By default, if our nation is post-racial, our educational system must be post-racial, which leads or misleads educators to believe and espouse that racial injustices do not exist in schools. Too much data reveal otherwise, especially when considering the achievement gap, which exists nationally, and in the majority of states and school districts. Black males and females continue to lag behind their White counterparts upon entering school—and the gap widens during the 13 years. (Ricks, 2014, p. 10; also see Barton & Coley, 2009)

In short, gross inequities in educational funding result in inferior and a lower quality of training and education for blacks and Latinx. If economists predicted that blacks and Latinx would be better prepared for workforce 2000, due to education, the record of school financing to underserved and disadvantaged communities certainly nullifies this forecast.

Training

Bartik and Hollenbeck (2000) argued that because of the proven failure of urban schools to provide blacks and Latinx with adequate basic skills, the United States educational system could not be relied upon to prepare them for the jobs of the 21st century. Financial investments and a number of well-intentioned initiatives such as the Race to the Top, My Brother’s Keeper, and the sweeping reform, No Child Left Behind could never overcome the fundamental, systemic complexities that explain why blacks and Latinx experience poor test scores, low grades, achievement gaps, high dropout rates, and high suspension rates—structural and institutional racism (see Figure 2).

Figure 2.

Trends in education based on race and ethnicity.

The federal government has developed a variety of job training programs over the last several decades to provide skills to American youths and as well as unskilled adults. For example, the Comprehensive Employment and Training Act (CETA) was enacted in 1973 to train the economically disadvantaged, unemployed, or underemployed workers and provide them with jobs in the public service. Marred by inefficiency and scandal, CETA was replaced in 1982 with the Job Training Partnership Act (JTPA) of 1982. Under this program, funds were primarily earmarked directly for training purposes, rather than for support and administrative costs. The JTPA was grossly mismanaged and steeped in pork barreling where resources were expended on, for example, teaching Washington D.C. taxi drivers to smile, expanding an Indiana circus museum and providing foreign travel money for state and local politicians (Bovard, 2011).

The JTPA was replaced in 1998 by the Workforce Investment Act (WIA), which also replaced additional job training laws with new workforce investment systems for workforce development. The WIA invests in workforce programs intended to improve the occupational skill levels of participants and “as a result, improve the quality of the workforce, reduce welfare dependency, and enhance the productivity and competitiveness of the Nation” (WIA, Public Law, 1998, online). This last statement speaks volumes about the genuine commitment to developing skills of women and persons of color, especially in the context of workforce 2000. The WIA program has worked in tandem with the 1996 Temporary Assistance for Needy Families (TANF) Program, which was aimed at moving low-income person off the welfare rolls and into jobs. Recall that heretofore, welfare under American Families with Dependent Children (AFDC) was an entitlement program. The WIA and related training programs were aimed more at providing rudimentary levels of skills to welfare recipients with the ultimate goal of getting them off the welfare rolls (hence the moniker, welfare-to-work programs). The jobs are generally minimum wage with little opportunity for upward mobility. These programs could never provide the level of training to meet the demands of the jobs of the 21st century, especially tech jobs.

In 2014, the Workforce Innovation and Opportunity Act (WIOA) replaced the WIA in an effort to provide workers with the skills match needed to compete for jobs in the global economy. But Congress again called for coordination with TANF and the public workforce development system. State officials argued that increased coordination between TANF and WIOA would improve the quality of workforce programs only for job-ready TANF recipients (Cielinski, 2017). Again, this effort would only provide basic skills to workers, especially blacks and Latinx.

As noted, this study does not link, from an explanatory standpoint, job training programs with skills match for blacks and Latinx. However, there are a number of studies that have found that job training programs “have not eliminated, or even substantially reduced, poverty among the working age population” (Burtless, 1984, p. 22; also see Council of Economic Advisors [CEA], 2018, 2019). Also, an early study by the Urban Institute concluded that CETA resulted in “Significant earnings losses for young men of all races and no significant effects for young women (Bassi et al., 1984, p. 177; also see CEA, 2018, 2019; Heinrich et al., 2013).

In short, there may have been good intentions on part of policymakers in implementing job training programs, but they have not been effective in providing blacks and Latinx with the skills needed to acquire the types of jobs economics predicted they would secure in the 21st century.

Discussion and Implications

Much was made of the opportunities that blacks and Latinx would benefit from in the labor market as the demographics of our society shifted. The question raised here was: were the predicted gains in jobs realized in this nation? This analysis suggests that they were not particularly for blacks. Their gains compared to other persons of color in almost every realm were the lowest. And their losses, too, were the highest across various categories (i.e., new entrants, employment status, technology jobs). This has implications for the ability of employers to diversify their workforces (Sabharwal, 2014), but even more profoundly, it perpetuates income inequality between persons of color and whites.

Granted, demographers could not foresee the recession of 2008, but joblessness and unemployment of blacks as well as Latinx is consistently higher than that of whites. As noted earlier, in 2008 unemployment peaked at 10.2%. But, for blacks, unemployment reached 11.5% compared to 8.9% for Latinx and 6.3% for whites. By 2019, the unemployment rate was 3.6%; but for whites it was 3.2%; for blacks, 5.4%, Latinx, 4.1% and for Asians, 2.9%.

In addition, the staggering economic toll of the COVID-19 pandemic is likely to be more harmful to blacks. First, layoffs tend to be made on the basis of seniority, and systematically blacks have less seniority than white workers. Thus, layoffs due to the pandemic will likely disproportionately hurt black workers. Second, unless Congress extends the $600 weekly payments to unemployed workers to subsidize state jobless benefits, blacks will also be affected negatively, as they live disproportionately in states with the lowest benefit levels (Brown, 2020).

Although gender was not the main focus of this study, the analysis also found occupational segregation by gender. For example, women tend to be concentrated in the lower levels of management positions, specifically in “professional and related occupations,” where the pay is lower than jobs filled by men, “management, business, and financial operations.” In addition, the highest percentages of employed women of every race are in service-related or office and administrative support occupations, which also tend to pay lower (see, Schachter, 2017).

The data also suggest that blacks may not have been prepared for IT jobs, in comparison to Latinx and Asians, resulting in a skills mismatch; that is, where a discrepancy exists between the skills possessed by workers and those required to perform the job. Alternatively, blacks may have incurred higher levels of bias or there was no improvement in their access to suburban labor markets where much of the job growth has occurred. For women, the data show that they are concentrated in lower level tech jobs such as database administrators and computer systems analysts, and in every category of IT worker, they are paid lower than their male counterparts. Future research might pay closer attention to the demographics of jobs in the STEM fields, where recent studies have found that women, blacks, and Latinx are underrepresented (see, e.g., Pew Research Center, 2018a).

It seems that there was a failure to train and educate blacks in particular for workforce 2000, creating a portentous skills deficit for blacks. This issue has not been widely addressed or even acknowledged by policymakers or economists. Instead, new reports even today continue to call for the same measures—education and technology training to prepare workers particularly blacks for not workforce 2000, but now for 21st century jobs (see, e.g., Engler et al., 2018). But the calls for greater training and education continue to miss the broader picture of institutional, systemic barriers that work against blacks from enhancing their ability to acquire educational opportunities and good jobs that can lead to pay equality. Residing in separate and unequal communities contributes to the problem, in tandem with high unemployment rates as well as crime rates, as addressed in this study. Band-Aid approaches that have been offered by big business, government and society as a whole will prove ineffective as they have in the past. The problem is cyclical and even when the U.S. economy is strong, the unemployment rate suggests that blacks do not benefit from a booming economy.

Inequality has become the tacit double standard of the American economy, and it has become the new normal. The implication is that until these pervasive, institutional and structural biases are addressed, the employment landscape for blacks will remain static. Structural inequality exists not only in education and employment, but in housing and health care as well. It produces insurmountable obstacles and barriers to blacks in their efforts to achieve social, political, and economic equality with whites (see, Fernandez et al., 2018; Riccucci, 2019). Elected and appointed leaders have long called for addressing the problem of inequality through, for example, investing in low-income neighborhoods to ameliorate poverty, improving health care, child care and welfare benefits, and creating equity in education, the latter of which indeed was strongly urged by economists and demographers to prepare for workforce 2000. If blacks and Latinx were to genuinely benefit from the demographic shifts in the population, the government as well as the private sector would have needed to invest in much more than simply educational and training opportunities.

In this sense, although the approach to this research is inductive, it builds more broadly on and contributes to theories of equality, which emanate from egalitarian and social justice theory. It augments theories of discrimination where certain classes of persons based on such characteristics as race or gender are disadvantaged and disenfranchised because of those very characteristics. For example, a basic premise of Rawls’ (1971) thesis is that justice ensures that everyone is afforded the same rights under the law (Arellano-Gault, 2010; Wooldridge & Gooden, 2009). Rawls’ difference principle suggests that to the extent society distributes more power or resources to some persons, it must ensure that material circumstances for the disadvantaged will be improved. From this perspective, the least advantaged in society will benefit more than under conditions of strict equality (Lopez-Littleton et al., 2018). Unfortunately, Rawls said very little about matters involving race, and Rawlsian justice ultimately relies on the existence of reason, rationality and impartiality, suggesting that other theories of equality and justice must be considered in examining racism, particularly since the economic gap between blacks and whites has widened greatly in the United States.

Until racial discrimination and racism, both overt and covert, are ameliorated, racial inequalities and social injustices will persist. This suggests that public administration needs to embrace and apply theoretical frameworks from other fields in the social sciences in studying this systemic, perennial problem. Issues of race in public administration, especially in the employment context have been examined through conceptual or theoretical frameworks such ethics (Alexander & Stivers, 2010, 2020; Gabard & Cooper, 1998), social justice (Hooker, 2018; Rubin & Chiqués, 2015), and legal (see, Riccucci, 2014; Sterett et al., 2017). While significant, these frameworks do not go far enough to address the broader problem of racial discrimination and racism that pervade the American workplace which perpetuates social and economic inequalities.

One framework that is beginning to emerge as relevant to our field is critical race theory (see, e.g., Heckler, 2019). This theory maintains that our legal, political, and economic institutions are inherently racist. Critical race theorists recognize that race is a socially constructed concept which enables and justifies the ability of whites to promote their own economic, social, and political interests at the expense of people of color (Bell, 1992, 1995). Bell and other legal scholars advanced the theory in the 1970s and 1980s in response to the lack of, or incremental progress being made by the civil rights movement, arguing that white liberal ideals such as equal opportunity, freedom of choice, and merit advanced the interests and privileges of whites while perpetually repressing and oppressing people of color. Critical race theorists call for a deconstruction of the social concept of race, urging a “re-cognizing” of race, not as a product of social practices or processes of power (see, e.g., Chayes, n.d.; Thomas, 2002).

This article showed that blacks in particular continue to make incremental, if any, gains in employment. This is due as suggested to failures in the educational and employment systems in our society. Indeed, structural inequality in the U.S. education and employment systems disproportionately segregates blacks and other communities of color from access to a range of opportunities and to upward mobility. In effect, blacks are deprived of quality education and ultimately good, high-paying jobs. As Crenshaw (1988) argued, “Blacks have been created as a subordinated ‘other,’ and formal reform has merely repackaged racism. Antidiscrimination law. . .has largely succeeded in eliminating the symbolic manifestations of racial oppression, but has allowed the perpetuation of material subordination of Blacks” (p. 1331).

Critical race theory (CRT) maintains that employment policies and practices construct race to disadvantage blacks; it further engages the larger struggle for social transformation beyond the workplace, as discussed in this article. It rejects liberalism’s embrace of the merit principle, which has fostered the white liberal ideal of neutrality. The presumption of a meritocracy has helped to produce school systems, employment settings, mass media outlets, and housing arrangements that are racially segregated or vehicles for white domination of persons of color. Markovits (2019) argues that meritocracy is a myth that promotes eliteness, perpetuates inequalities, and undermines democracy (also see Portillo et al., 2020). Any effort to address this issue has posed a threat to white liberalism. Indeed, consider how Mr. Trump and his administration is threatened by CRT; in September of 2020, he banned federal agencies from including CRT as part of workplace race-sensitivity training. Trump’s Office of Management and Budget (OMB) Director, Russell Vought said federal agencies “have spent millions of taxpayer dollars” on trainings that “engender division and resentment within the federal workforce” (Nagele-Piazza, 2020, online). Vought’s memo to agency leaders stated that the “president has directed me to ensure that federal agencies cease and desist from using taxpayer dollars to fund these divisive, un-American propaganda training sessions” (Ibid). Recognizing that the ban violated the First Amendment rights of the organizations that offer the training, a federal district court judge overturned Trump’s ban on CRT training in December of 2020 (Renda, 2020).

Such invective and alarm point to the importance of relying on CRT to examine issues of racial discrimination and racism not only in the workplace but in the criminal justice system, housing, education, public health, banking, welfare, and the environment. Certainly, these are policies that are fundamentally within the realm of public administration and affairs. But public administration essentially lacks sound theoretical frameworks to the study these issues. Critical race theory is one such framework, but the field can look to other fields including criminal justice, sociology, ethnic studies, social psychology, women’s studies, philosophy and others to apply multilevel theoretical frameworks (including intersectionality) to study these issues.

As racial injustice has been an intractable problem in the United States, CRT within public administration can contribute significantly to its resolution. Relying on CRT to study issues of race would uncover the institutional and structural racism that permeates our society. One theory that emanates from CRT and is gaining popularity in public administration is Victor Ray’s (2019a) “theory of racialized organizations.” Ray, drawing heavily on CRT, theorizes organizations and racial progress through the lens of CRT. Importantly, for public administration, Ray focuses on how organizations, at the meso-level, are affected by macro-level policies and politics (e.g., from government) and micro-level behaviors of individuals (e.g., workers). He contends that the racist systems and structures created by government and individual prejudice are filtered through organizations, and suggests that organizations can moderate or change those racist structures and behaviors through their practices and policies. Ray (2019b, online) recognizes that organizations themselves are the purveyors of racism, and are “central to the reproduction of the racial order as a whole.” He states that “Organizations distribute health care and education, and they organize policing and public safety.” But he also argues that organizations will respond to “pressures from activists or markets” and are thereby “central to the changes in the racial order.” In this sense, his work points to the importance of how research can help to guide organizations in eradicating discrimination and racial animus.

Certainly, there are a number of opportunities for applying a CRT framework in public administration. Generating such research can help to promote our understanding of structural and institutional racism, its affects in creating inequalities, and help point to solutions to those inequities and inequalities in the areas of health, employment, criminal justice (especially policing), education, environmental justice, and other areas that intersect with public administration and policy.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Norma M. Riccucci

Notes

Author Biography

Norma M. Riccucci is Board of Governors Distinguished Professor at the School of Public Affairs and Administration at Rutgers University, Newark. She has published extensively in the areas of diversity, representative bureaucracy and social equity.

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