Race

“…“We find,” they wrote in the abstract of their paper, “that this one test score explains all of the black-white wage gap for young women and much of the gap for young men.” With their approach, antiblack bias played no role in the divergent wages among women; a black woman with the same qualifications as a white woman made slightly more money. And it accounted for at most 29% of the racial difference among men, with 71% traceable to disparate performance on the AFQT. The AFQT itself was evaluated by the Pentagon, which found that black and white military recruits with similar AFQT scores performed similarly on the job—indicating no racial bias….”

Roland Fryer, "Disparity Doesn’t Necessarily Imply Racism,"Wall Street Journal, November 25, 2022, https://www.wsj.com/articles/disparity-doesnt-necessarily-imply-racism-skills-wages-black-white-afqt-research-discrimination-jim-crow-grandmother-11669383926

Disparity Doesn’t Necessarily Imply Racism

I was raised, in part, by my paternal grandmother—a phenomenal black woman born in 1925 who came of age during Jim Crow, attended Bethune-Cookman University in the early 1940s, and experienced both the promise and limitations of the civil-rights era when integrating schools in Florida in 1969. She did her best to teach sixth-graders subject-verb agreement minutes after being spat on by their parents. Her life’s journey provided unlimited content as we sat together for nearly three decades, stuck to the plastic slipcovers on her sofa, playing cards, drinking sweet tea, and talking uninhibitedly about race in America.

The first discussion I can remember happened in 1988, when I was 11, after a visit to McDonald’s. After ordering, my grandmother paid with a crisp $20 bill from her pocketbook, and the cashier put the change directly on the counter. When we got to the parking lot, she was incensed. “You see that? White woman didn’t want to touch me.” I had noticed it too, but thought the cashier was being nice, trying to avoid passing on her own germs.

My grandmother—no doubt based in part on her experiences—saw racism everywhere, in every inequity, every statistic. Racial differences in wages? Racism. Racial differences in educational achievement? Racism. Racial differences in teen birth rates? Racism. This sort of casual empiricism—which has crept back into mainstream media and other institutions—was a competitive sport among my family and friends. Did you see the way that white woman tightened her grip on her purse, because I was behind her? Does this guy follow everyone around the store?

A decade after the McDonald’s incident, in graduate school, I read a 1995 paper titled “The Role of Premarket Factors in Black-White Wage Differences.” Using a nationally representative sample of more than 12,000 14- to 17-year-olds from 1979, Derek A. Neal and William R. Johnson estimated that blacks earned between 35% to 45% less than whites on average.

They examined how much of the wage gap could be attributed to present-day discrimination in the workplace versus differences in skill as measured by the Armed Forces Qualification Test, a cousin of the ACT and SAT. Importantly, they weren’t trying to measure the effect of America’s racist history running back to the early 17th century, when the first African slaves were brought to work the tobacco farms of Virginia, only the extent to which blacks earn lower incomes because employers today make racially discriminatory decisions. Nor did they focus on prejudice more broadly: They ignored that my grandmother was spat on in the parking lot at work and concentrated on whether she was treated fairly once she entered the building.

“We find,” they wrote in the abstract of their paper, “that this one test score explains all of the black-white wage gap for young women and much of the gap for young men.” With their approach, antiblack bias played no role in the divergent wages among women; a black woman with the same qualifications as a white woman made slightly more money. And it accounted for at most 29% of the racial difference among men, with 71% traceable to disparate performance on the AFQT. The AFQT itself was evaluated by the Pentagon, which found that black and white military recruits with similar AFQT scores performed similarly on the job—indicating no racial bias.

The paper felt like an attack on what I knew. An assault on all those conversations with my grandmother, which taught me that racism—present-tense racism—dictated black-white inequality. I told myself that Messrs. Neal and Johnson, both of whom were white, were probably bigots, and I set out on a mission to disprove their work.

I vented about my battle with Messrs. Neal and Johnson to a fellow graduate student at Penn State, a white guy from the cornfields of Southern Illinois. He was no more at home at a top-25 economics doctoral program than I was, and we spent a fair amount of time together during our first year, staring at Euler equations in our favorite textbook, “Recursive Methods in Economic Dynamics.”

I told him I was sure discrimination was a bigger factor than Messrs. Neal and Johnson were letting on, but “I just can’t get this data to cooperate.” He asked why I was so convinced, and I erupted in a rant about the prevalence of racism and recognizing bigots on sight. My grandmother would’ve nodded rhythmically along. My friend responded with a burst of loud, sharp laughter in my face.

He pointed out how far I was straying from our Euler equations. How on any subject other than race, I would have never given in to such sloppy thinking. The double identity—a classically trained economist taught to tease out causal relationships and a black Southern boy taught that discrimination is ubiquitous—had lived seamlessly inside me until that moment. Messrs. Neal and Johnson, as it turns out, aren’t bigots, and their conclusions have stood the test of time and my attempts to disprove them. I extended their analysis to unemployment, teen pregnancy, incarceration and other outcomes—all of which follow the same pattern. Moreover, the relationship between skills and wages has been confirmed by study after study, even when using different data and methods. Kevin Lang, an economist at Boston University, corrects important issues in Messrs. Neal and Johnson’s work but finds the same relationship between AFQT scores and wages. Taken together, an honest review of the evidence suggests that current racial inequities are more a result of differences in skill than differences in treatment of those with the same skill.

I write this with some degree of trepidation, in part because I still have my grandmother in my ear and in part because I am keenly aware of the harm in underestimating bias. But there is also a cost to overemphasizing its impact. A black kid who believes he will face daunting societal obstacles is likely to underinvest in trying to climb society’s rungs. Every black student in the country needs to know that his return on investment in education is, if anything, higher than for white students.

The solution is neither to stop fighting biased behavior nor to curb honest inquiry about race in America. We shouldn’t stop searching for and penalizing discriminatory employers, or trying to reduce racial differences in police brutality, or estimating whether the value of a home appraisal depends on the race of the homeowner, or reducing bias in bail decisions by using artificial intelligence. I could go on, like the conversations stuck to those slipcovers. The solution isn’t to look away from discrimination. It does exist. But we also can’t point at every gap in outcomes and instantly conclude it’s racism. Prejudice must be measured rigorously. Statistically. Disparity doesn’t necessarily imply racism. It may feel omnipresent, but it isn’t all-powerful. Skills matter most.

Fryer“Disparity doesn’t necessarily imply racism. It may feel omnipresent, but it isn’t all-powerful. Skills matter most. ”

We are all familiar with comparisons showing that Black people earn 50% less than white peers and women earn 70 cents for every dollar that a man earns. However, the most popular tools used to combat disparities in the workplace have produced almost no measurable results. The average impact of corporate diversity, equity, and inclusion (DEI) training is zero and some evidence suggests that the impact can become negative if the training is mandated.One of the most important developments in the study of racial inequality has been the quantification of the importance of pre-market skills in explaining differences in labor market outcomes between Black and white workers. In 2010, using nationally representative data on thousands of individuals in their 40s, I estimated that Black men earn 39.4% less than white men and Black women earn 13.1% less than white women. Yet, accounting for one variable-educational achievement in their teenage years--reduced that difference to 10.9% (a 72% reduction) for men and revealed that Black women earn 12.7 percent more than white women, on average. Derek Neal, an economist at the University of Chicago, and William Johnson were among the first to make this point in 1996: “While our results do provide some evidence for current labor market discrimination, skills gaps play such a large role that we believe future research should focus on the obstacles Black children face in acquiring productive skill.”

Roland Fryer, "It’s time for data-first diversity, equity, and inclusion,"Fortune, June 20, 2022, https://fortune.com/2022/06/20/data-first-diversity-equity-inclusion-careers-black-workers-gender-race-bias-dei-roland-fryer/

It’s time for data-first diversity, equity, and inclusion

I have spent hundreds of hours in diversity training over the past two decades-from descriptions of federal anti-discrimination laws to academic-style seminars on the perils of implicit bias, microaggressions, or misgendering.

Advocates of this kind of training have their hearts in the right place. We are all familiar with comparisons showing that Black people earn 50% less than white peers and women earn 70 cents for every dollar that a man earns.

However, the most popular tools used to combat disparities in the workplace have produced almost no measurable results.

The average impact of corporate diversity, equity, and inclusion (DEI) training is zero and some evidence suggests that the impact can become negative if the training is mandated.

“Statistical Snapshots,” which describe how employee outcomes differ by demographic group, are another popular tool. These numbers cannot provide proof of bias. Simple averages often mislead and, importantly, crafting strategies based on misleading data often does more harm than good.

Some business leaders, in their determination to increase diversity, leap directly from observing raw disparities to removing some information from application forms, another common practice meant to make workplaces more equitable. However, hiding information on applications often leads to worse outcomes for those it was intended to help-likely because hiring managers use race itself as a proxy for the information they’re no longer allowed to see.

Our intuition for how to decrease race and gender disparities in the workplace has failed us for decades. It’s time to stop guessing and start using the scientific method. Remember when we thought that the Bubonic Plague was caused by a triple conjunction of Saturn, Jupiter, and Mars in the 40th degree of Aquarius?

Here is a three-step approach that can turn earnest intentions into good science.

Understand disparities

For decades, social scientists have shown that raw gaps in employment outcomes like hiring or wages-the type of data typically provided to C-suite executives-misstate the amount of actual bias in an organization. This data omits many factors that are key to personnel decisions, factors that often vary by group, owing to disparities in society at large. Business leaders can and should work to address inequality in their communities, but they should not mistake society-wide gaps for bias by their own employees.

One of the most important developments in the study of racial inequality has been the quantification of the importance of pre-market skills in explaining differences in labor market outcomes between Black and white workers. In 2010, using nationally representative data on thousands of individuals in their 40s, I estimated that Black men earn 39.4% less than white men and Black women earn 13.1% less than white women. Yet, accounting for one variable-educational achievement in their teenage years--reduced that difference to 10.9% (a 72% reduction) for men and revealed that Black women earn 12.7 percent more than white women, on average. Derek Neal, an economist at the University of Chicago, and William Johnson were among the first to make this point in 1996: “While our results do provide some evidence for current labor market discrimination, skills gaps play such a large role that we believe future research should focus on the obstacles Black children face in acquiring productive skill.”

Recently, I worked with a network of hospitals determined to rid their organization of gender bias. The basic facts were startling: Women earned 33% less than men when they were hired and their wages increased less than men once on the job. Yet, accounting for basic demographic variables known about individuals prior to hiring, these differences decreased by 74%. A problem remained, but it was an order of magnitude smaller than the unadjusted numbers implied.

Find the root causes of bias

Social scientists tend to categorize bias into one of three flavors: preference, information, and structural. Preference bias is good old-fashioned bigotry. If company A prefers group W over group B then they will hire and promote them more even if they are similarly qualified.

Information bias arises when employers have imperfect information about workers’ potential productivity and use observable proxies, like gender or race, to make inferences (gender stereotypes are a classic example).

Structural bias occurs when companies institute practices, formally or informally, that have a disparate impact on particular groups, even when the underlying practices are themselves group blind. Employee referral programs can fall into this category.

Over the past fifty years, economists and other social scientists have developed brilliant ways of statistically distinguishing between different types of bias. Gary Becker, in his 1993 Nobel Prize acceptance speech, outlined one such statistical procedure known as the “outcomes test.” It operates by comparing the success rate of decisions across groups and then inferring whether different decision rules were used for different groups. For example, if women CEOs statistically outperform male CEOs, all else equal, that would suggest that a higher standard was applied to women in the selection process. This type of statistical test can be used for hiring, promotions, and attrition across an organization.

In 2013, collaborators and I developed a similar test to detect information-based bias. Our approach uses the insight that if employers have information-based biases at the time of hiring, but then learn more about an employee’s productivity once they are on the job, one would expect to see the returns to tenure within the company to be higher for the group that faced the initial bias. Using a nationally representative dataset of thousands of individuals, we found that there was a significant gap at the time of hiring for Black candidates relative to white peers but that, as predicted, Black candidates experienced a 1.1 percentage point higher return to tenure.

With the aforementioned hospital network, the data pointed to a structural bias in scheduling. Women and men who worked the same number of hours earned exactly the same wage, but men worked more hours due to how the company assigned schedules, not women’s desire to work less.

This is the key step that is missing in every DEI initiative I have seen in the past 25 years: a rigorous, data-driven assessment of root causes that drives the search for effective solutions. In other aspects of life, we would not fathom prescribing a treatment without knowing the underlying cause. Hiding information on resumes when information bias is present is as effective as using alcohol baths to treat fever.

Evaluate

We must rigorously evaluate what works and what doesn't. The old cardiac test-where you “feel it in your heart”-is not good enough. Once we know where potential biases exist, determine what caused them, and curate a set of solutions to test, we must meticulously evaluate what’s working and what’s not.

Solutions that yield measurable results can be substantiated into company policy, while those that don't should be discarded. In the case of the hospital network, once a small change was made to the structure of their scheduling, gender differences were reduced. Despite countless hours spent in training and seminars, their results were unchanged for years. The solution was hidden in plain data.

This will seem heretical to some-but it barely scratches the surface of what's possible with a data-first approach to diversity, equity, and inclusion. More corporate leaders should be trying to solve diversity challenges in the same way they solve problems in every other aspect of their business: through intelligent use of data, rigorous hypothesis testing, and honest inference about what is working.

During the nation’s job recovery (April 2020 to April 2022), the overall civilian unemployment rate fell from 14.7% to 3.6%. Payroll employment returned almost to its pre-pandemic level. The employment-to-population ratios of some noncollege-educated men and women have returned to or exceeded their pre-pandemic levels; however, this literally happened since December 2021.* For example, as the following figure shows, the employment-to-population ratio of young Black men reached 62.1%, exceeding its pre-pandemic value of 56.3% and its December 2021* level of 56.1%.3 In recent months, the ratio for young Latino men recovered to its pre-pandemic level.The next figure reports that the employment-to-population ratio of young Black women recovered to 60.0%, 7.4 percentage points above its pre-pandemic value of 52.6%. The ratios of both young white men and young white women were 2.1 percentage points and 3.2 percentage points, respectively, below their pre-pandemic values. The average employment gap—the difference between their employment-to-population ratios—between young Black men and young white men since January 2020 to March 2022 is 15.0 percentage points. Over the same period, the average employment gap between adult Black men and adult white men was 10.9 percentage points, and it was 8.6 percentage points between young Black women and young white women. The only group near parity with its white counterpart is adult Black women. The figure below shows that, throughout the jobs recovery following the COVID-19 recession, the employment gap between young Black men and young white men stayed between about 8.2 percentage points and 21.0 percentage points. The gap between adult Black men and adult white men ranged from 8.1 percentage points to 13.1 percentage points.

William Rodgers III, "Young Adults without College Education See Uneven Jobs Recovery,"Federal Reserve Bank Of St. Louis, May 17, 2022, https://www.stlouisfed.org/on-the-economy/2022/may/young-adults-without-college-see-uneven-jobs-recovery

Young Adults without College Education See Uneven Jobs Recovery

Relief and recovery investments during the pandemic have made this most recent job recovery unique relative to others in past decades. In fact, major efforts to restore the economy’s health and commitments to lessen racial inequality, combined with widespread labor shortages, have helped to create a “moment of opportunity” for young noncollege-educated adults.1 This moment is especially crucial for young noncollege-educated adults because early career opportunities are key for their long-term earnings prospects. They tend to have fewer skills and credentials and weaker job search networks, and face geographic isolation from job opportunities, making a potential toehold in the labor market all the more important to securing their economic futures. Young Black and Latino adults also face additional hurdles, such as discrimination.

The challenges are even greater for the current cohort of noncollege-educated youth who have entered the labor force during the pandemic. Research has shown that in the aftermath of a recession, youth who enter labor markets experience lower income over their lives. The COVID-19 pandemic is the latest example of a public health and economic shock that could adversely impact their futures. This blog post, the first in a two-part series, assesses how well young noncollege-educated adults, especially young Black adults and young Latino adults, are faring during the recovery.2 Simply put, their recovery is one of unevenness.

Noncollege-Educated Youth Experienced an Uneven Recovery

During the nation’s job recovery (April 2020 to April 2022), the overall civilian unemployment rate fell from 14.7% to 3.6%. Payroll employment returned almost to its pre-pandemic level. The employment-to-population ratios of some noncollege-educated men and women have returned to or exceeded their pre-pandemic levels; however, this literally happened since December 2021.* For example, as the following figure shows, the employment-to-population ratio of young Black men reached 62.1%, exceeding its pre-pandemic value of 56.3% and its December 2021* level of 56.1%.3 In recent months, the ratio for young Latino men recovered to its pre-pandemic level.

The average employment gap—the difference between their employment-to-population ratios—between young Black men and young white men since January 2020 to March 2022 is 15.0 percentage points. Over the same period, the average employment gap between adult Black men and adult white men was 10.9 percentage points, and it was 8.6 percentage points between young Black women and young white women. The only group near parity with its white counterpart is adult Black women.

These gaps are surprising because of the relative gains we would expect young Black and Latino adults to experience as a result of relief and recovery efforts (e.g., the American Rescue Plan and CARES acts) during the pandemic that were particularly impactful for families with the greatest need as well as the “Great Resignation” and its contribution to labor shortages. Furthermore, this most recent recovery is inconsistent with the historical link between overall economic conditions and outcomes for young noncollege-educated adults. In the past, strong economic recoveries have had a disproportionate positive impact on employment among young, noncollege-educated Black men.

Unfortunately, as of March 2022, these conditions did not lead to a narrowing in racial and ethnic employment gaps, and Black and Latino people have experienced persistent disadvantages. The figure below shows that, throughout the jobs recovery following the COVID-19 recession, the employment gap between young Black men and young white men stayed between about 8.2 percentage points and 21.0 percentage points. The gap between adult Black men and adult white men ranged from 8.1 percentage points to 13.1 percentage points.

The second part of this blog series will explore how strong the macroeconomy must be to improve not only the absolute but also the relative economic status of noncollege-educated youth. The post will also discuss ways to narrow the employment gap.

*Editor's Note: This article was updated to correct the year.

Notes and References

1 These young adults are 16- to 24-year-olds who are not enrolled in school, have no more than a high school degree and have no more than 10 years of potential labor market experience. Potential labor market experience equals age minus years of schooling minus 6.

2 This series is published as part of the vulnerable workers initiative from the Institute for Economic Equity. The initiative examines the challenges facing vulnerable workers during the recovery and discusses potential ways to improve their economic security and resiliency in an economy reshaped by the pandemic.

3 These estimates are through March 2022 because the micro data from which these estimates are calculated are only publicly available through March 2022 and not April 2022.

Adam Coleman, "Anti-cop pols yawn as slay spike hits black Americans hardest,"New York Post, May 20, 2022, https://nypost.com/2022/05/20/anti-cop-pols-yawn-as-slay-spike-hits-black-americans-hardest/

Anti-cop pols yawn as slay spike hits black Americans hardest

There are no guarantees in life, yet we still have expectations. We expect our elected leaders to behave selflessly so they can keep the American people in mind first.

But when politicians ignore our humanity in pursuit of their own agenda, the American people tend to suffer the consequences, even with their lives.

Much like everyone else, I watched as America entered a phase of demoralization as they lost faith in institutions of immense importance and questioned the intentions of their neighbors. 2020 was not only a year to decide who our next president would be but also to determine what kind of society we want to maintain.

The elite decided that the safety of the American people is subject to political expedience. Our humanity no longer mattered.

During the nationwide riots, small business owners and residents pleaded for enforcement, and yet police officers were ordered to stand down. In the aftermath, the American people suffered the consequences with their livelihoods and, for some, their lives while our politicians were left mostly unscathed.

Innocents suffer

Centers for Disease Control and Prevention data show the murder rate between 2019 and 2020 increased by an astonishing 30%, and I place some of the blame on many of our supposed leaders.

Once Derek Chauvin became public enemy No. 1, city governments used his actions as an officer as the impetus to begin defunding police departments to appease activists instead of the residents who depend on their presence. Those efforts contributed to the rise in deaths — but not equally.

But, as the Manhattan Institute’s Christos Makridis and Robert VerBruggen note, “since the black homicide rate was already many times higher than the white one, this translated into 8 additional black deaths for every 100,000 population” while “the death rate for whites rose by only 0.5 per 100,000.”

With these numbers, it is understandable why Pew Research recently found that the most important issue for black Americans was related to violence and crime.

While politicians were marching in arms with protesters claiming to care about black lives, they were actively making their lives worse. For black Americans who live in dangerous neighborhoods, removing the police has made them even more unsafe and has emboldened the criminal element that resides alongside the innocent.

If everyone believes that we are in constant danger by the very people we need the support of, then no one will care when mayors across the nation remove them from street corners. No one will care when the few activists speak for the many whose safety is at risk.

As a result, an assessment of a college’s performance would be more relevant, transparent, and likely to lead to improvement if it considered the performance of institutions enrolling comparable groups of at-risk students. To illustrate that the elephant in the room (demographics) is easy to see, I identified 444 public and 688 private, not-for-profit, primarily four-year colleges and universities for which the relevant data on wages and demographics were available These colleges were divided into quintiles based on average median earnings of students who are working 10 years after they first enrolled. The available data include all students, whether they graduated or dropped out and with no regard to the time they spent in school. To test how strongly demographics are correlated with postcollegiate earnings, four student groups likely to experience lower earnings were chosen: Black, Hispanic, low-income (whose federal Pell Grant eligibility serves as a proxy for socioeconomic status), and marginally prepared academically students (those facing a greater risk of not graduating). I also included a group that could be expected to experience higher earnings: students coming from households in the highest quintile of income distribution. Table 1 shows the results, with the selected demographic characteristics correlated with average median earnings that are reported for the institutions’ former students. As Table 1 makes evident, the percentage of Black students increases from the institutions with the highest earnings among former students to those with the lowest earnings. Likewise, for the percentage of Pell-eligible (i.e., low-income) students, the percentage increases as earnings decline. And the reverse takes place when observing overall graduation rates: The higher the earnings quintile that an institution is in, the higher the percentage of completers. In this case, limited to students in four-year schools, Hispanic students are primarily concentrated in states with high levels of wage inequality, which have been shown to correlate with high levels of social mobility for college-educated populations.6 Consequently, Hispanic students represent the inverse of what is found for Black students, although this phenomenon is likely not reflected for the two-year colleges many low-income Hispanic students attend

Jorge Klor de Alva, "Accounting for Demographics and Risk in Postcollege Earnings,"American Enterprise Institute, May 2022, https://www.aei.org/wp-content/uploads/2022/05/Accounting-for-Demographics-and-Risk-in-Postcollege-Earnings.pdf

The average Black student went to a district that spent $14,385 per student, while the average white student went to one that spent $14,263 per student. When the expenditures were narrowed to spending on instruction, the figures were $7,169 per student and $7,329 per student, respectively. Rather than looking at spending on an average student, they examined the relationship between per-student spending on instruction and the share of Black students in a district. The results are shown in the figure below. “There is a small but statistically significant negative relationship; on average, schools with a 10-percentage-point higher share of Black students spend $140 less per student on instruction…The data on total expenditures are similar; schools with a 10% higher share of Black students spend $160 less per student.”What explains this negative correlation when average spending is very similar? The scatter plot reveals significant variation in school spending and that districts spending the most tend to have a mostly white student body, the authors pointed out. Thus, this negative correlation is driven by small, predominantly white school districts with large spending that don’t educate a big share of the student body nationally

Hannah Rubinton and Maggie Isaacson, "Is There a Racial Gap in School District Spending?,"Federal Reserve Bank Of St. Louis, May 5, 2022, https://www.stlouisfed.org/on-the-economy/2022/may/racial-gap-school-district-spending

Is There a Racial Gap in School District Spending?

Efforts have been made to address the race gap in educational achievement by targeting public school funding. A recent Economic Synopses essay explored the relationship between school districts’ budget and the racial composition of their student bodies.

St. Louis Fed Economist Hannah Rubinton and Research Associate Maggie Isaacson analyzed the revenue and spending per student for school districts in the 2017-18 academic year, comparing the data for Black students and for white students.1

Spending Levels Were Similar, But Funding Sources Weren’t

The authors found that the numbers were very similar: The average Black student went to a district that spent $14,385 per student, while the average white student went to one that spent $14,263 per student. When the expenditures were narrowed to spending on instruction, the figures were $7,169 per student and $7,329 per student, respectively.

However, Rubinton and Isaacson noted that the funding sources were different. While states contributed 46% of funding for these school districts, regardless of the average student’s race, the average white student attended a district that received 47% of revenue from local sources and 7% from the federal government. For the average Black student, the district obtained 44% of revenue from local sources and 10% from the federal government.

“Because federal funding often comes as grants or for specific programs, school districts that serve predominantly Black student bodies may have less control over how these funds are spent,” they pointed out.

Instructional Spending Was Unequal

The authors then conducted a different analysis. Rather than looking at spending on an average student, they examined the relationship between per-student spending on instruction and the share of Black students in a district. The results are shown in the figure below.

What explains this negative correlation when average spending is very similar? The scatter plot reveals significant variation in school spending and that districts spending the most tend to have a mostly white student body, the authors pointed out. Thus, this negative correlation is driven by small, predominantly white school districts with large spending that don’t educate a big share of the student body nationally, Rubinton and Isaacson concluded.

Notes and References

The authors excluded districts in the top 1% and bottom 1% of spending per student; they also omitted the 1% of districts with the smallest numbers of students.

"…In this paper, we study declines in women’s labor force participation by race and ethnicity as well as the presence of children. We find that increases in labor force exits were larger for Black women, Latinas, and women living with children. In particular,we find larger increases in pandemic-era labor force exits among women living with children under age 6 and among lower-earning women living with school-age children after controlling for detailed job and demographic characteristics. Latinas and Black women also had larger increases in labor force exits during the pandemic relative to White women. Differences in the presence of children and household structure explain one-quarter of the excess labor force exits among women of color.... Table 3, which displays coefficients from our models on indicators for identifying as a certain race or ethnicity, shows that covariates can explain about two-thirds of increased exits for Latinas and a little less than one-half of the excess exits for Black women. Columns 1 and 2 show specifications where all exits are the dependent variable. In these specifications, Latinas had a 6.0 percentage point higher rate of exits compared with White women without including controls and a 2.4 percentage point higher exit rate after including controls shown in column 2 of table 2. Black women had a 4.0 percentage point higher exit rate without controls and a 2.9 percentage point higher exit rate with controls. Similar disparities and reductions upon including controls apply for exits attributed to caregiving in columns 3 and 4. When examining differences in exits combined with an interest in working, we see that Latinas are 2.5 percentage points more likely than White women to have exited the labor force, but are interested in work and Black women are 2.4 percentage points (column 5). With controls, these differences fall to statistically insignificant 1.5 percentage points and 1.8 percentage points, respectively. Increases in labor force exits were also larger among Latinas and Black women during the pandemic relative to White women..."

Katherine Lim and Mike Zabek, "Women's Labor Force Exits during COVID-19: Differences by Motherhood, Race, and Ethnicity," Federal Reserve, October 2021, https://www.federalreserve.gov/econres/feds/womens-labor-force-exits-during-covid-19-differences-by-motherhood-race-and-ethnicity.htm

"... As we show in figure 2, women in households with young and schoolage children saw larger and more sustained participation declines than women without children under age 13 in their household. In June of 2021, labor force participation rates among women in households with children aged 6 to 12 were 3 percentage points lower than in January 2020, while women in households with children aged 0 to 5 saw a 2 percentage point decline. The plot also demonstrates the wide temporal variation in participation that existed even before the pandemic, particularly for women with young children...."

"... As we show in figure 1, these employment losses translated into higher percentagepoint declines in labor force participation among prime-working-age Black women and Latinas relative to White women. The initial decline in labor force participationamong these groups was more than 4 percentage points, while White women experienced a decline of around 2 percentage points, and Asian women saw no decline in labor force participation. In the fall and winter of 2020, Black women saw an increase in their labor force participation, while participation among Latinas remained persistently lower. While men of color also experienced large declines in both employment and labor force participation early in the pandemic, labor force participation changes among men of different races and ethnicity converged beginning in the fall of 2020...."

Summary statistics, "...Table 1 shows averages and standard deviations for our outcome variables and covariates collected in the CPS. To show how differences in covariates could contribute to varying levels of exits during the pandemic, table 1 presents statistics for all women (overall) and then breaks out statistics for Latinas, Black women, and White women who were employed before the pandemic. The first two rows of table 1 show that 8 percent of women left the labor force in 2020 after being previously employed and that 5 percent did so and named caregiving as a reason. Women attributed more than one-half of labor force exits to caregiving, highlighting the importance of childcare and schooling disruptions during the pandemic. Latinas were especially likely to name caregiving, both in terms of having the highest overall proportion of exits attributed to childcare and these exits making up a high proportion of all exits. Around 3 percent of women who were previously employed exited the labor force but expressed interest in working. Like exits overall, the percentages were higher for Latinas and Black women relative to White women. These figures represent increases when compared with previous years. During the pandemic, there was a 1.7 percentage point rise in the overall share of women previously employed who, while out of the labor force, responded that they wanted a job or planned to look for work in the next year-from 1.3 to 3.0 percent of all prime-working-age women.10 Consistent with the patterns of labor force exit, the increases were larger for Black women and Latinas who saw a 3.1 percentage point and 3.0 percentage point increase in the share out of the labor force but indicated interest in working, respectively. Women living with children under age 13 had around a 3.2 percentage point increase in the share who exited the labor force but expressed interest in working compared with a 1 percentage point increase among women with no children under age 13 in the household.11 Latinas, in particular, also had covariates that predicted much more severe labor market impacts of COVID-19. Table 1 shows that Latinas were employed in occupations and industries that had larger employment declines, lower prevalences of remote working, and higher shares of workers who lost work as a result of the pandemic. Around 15 percent of Latinas had less than a high school degree, compared to 5 percent of Black women and 2 percent of White women. Additionally, Latinas earned an average weekly wage of $780 compared with $1,040 for White women. Latinas are also more likely to be living with children compared with other groups of women. Black women also had covariates predicting more severe impacts, though to a smaller degree than for Latinas. Table 1 shows that the average predicted occupational job loss for a Black woman was 23 percent, compared with 24 percent for a Latina and 19 percent for a White woman. Black women were also less likely than White women to be in occupations and industries where working from home as a result of COVID-19 was common. Black women also earn substantially less and have lower average levels of education compared with White women, and they are less likely to be married..."

Matthew Thomas reports some evidence from the recent New York election that suggests the GOP gains in 2020 with Hispanic and Asian voters are ongoing, "... This country is in the midst of a profound realignment along axes of culture and education that are about to make race and class seem like yesterday’s news...In August, I reported that Hispanic and Asian areas of Queens swung heavily toward Donald Trump in last year’s presidential election, powering the best performance by a Republican nominee in the World’s Borough since 2004. Precincts that are more than 75% Asian swung toward Trump by 16 points in 2020. Precincts that are more than 75% Hispanic saw an even more dramatic pro-Trump swing of 25 points. Now, an analysis of Tuesday night’s returns in combination with demographic data from the 2020 census reveals that these patterns are trickling down to local elections too. In 2017, Bill De Blasio won Queens precincts that are over 75% Asian by 34 points against Republican nominee Nicole Malliotakis. This week, Adams won them by just 20 points over Sliwa: a 14-point swing toward Republicans. De Blasio won Queens precincts that are over 75% Hispanic by a whopping 70 points over Malliotakis. Adams won them by 40 points over Sliwa: a 30-point swing toward Republicans...."

Another remarkable result from Tuesday was that Vallone lost his own bid for a Civil Court judgeship to a Republican candidate with virtually no resources or professional campaign infrastructure. The Queens County judiciary is notoriously corrupt; the nomination process is tightly controlled by the Democratic machine, and judgeships are given away as patronage to party insiders. Although the judicial district that Vallone ran in does not overlap with his base in Northeast Queens - and in fact includes some of the most conservative neighborhoods in the borough - that wouldn’t have been a problem in a less polarized era. His defeat is the first time a Democratic judicial nominee anywhere in Queens has lost to a Republican in recent history.

That’s quite the statement considering that for many years, progressive pundits, consultants, and other politicos have been arguing that the Democratic Party must deepen its dependence on voters of color in order to liberate itself from the need to appeal to white people. Very often, they say this is not only a strategic necessity in an age of deepening racial polarization, but a moral imperative that is the party’s only means of honoring its commitments to black and brown people. Yet Springer portrays growing Democratic reliance on voters of color not as a vindication of that analysis, but as a burden placed on them by unreliable whites.

Here in Queens, some on the left have expressed similar sentiments. On Tuesday, Republicans held on to their last remaining City Council seat in the borough when Joanne Ariola defeated progressive Democratic nominee Felicia Singh in a 36-point landslide. Aleda Gagarin, a left-wing organizer and former City Council candidate in Central Queens, commented: “I also don't want to see any ‘hot takes’ on D32 that don't include taking white supremacy and white rage to task. Republicans didn't just ‘keep a seat.’ They used overtly racist fearmongering as a key strategy and tactic. White rage was on full display throughout this election.”

What’s doubtful is how much of a role all this played in Singh’s defeat. Majority-white precincts did indeed swing 10 points to the right over 2017, when Democrat Mike Scala lost to Republican incumbent Eric Ulrich. But Scala didn’t exactly have his finger on the pulse of Breezy Point either; he and Singh both got less than a quarter of the white vote in their respective races. In other words, Singh’s platform and identity didn’t provoke especially strong blowback in terms of actual votes, though clearly they heightened the visceral hatred Republicans felt for her. But the results from elsewhere in Queens suggest that her slide among white voters probably wasn’t even about her at all. Deepening partisan polarization is coming for everyone trying to keep the flame of outer borough Democratic viability burning, regardless of ideology or identity.

But the left can’t afford to chalk up all of our defeats to whitelash alone. This country is in the midst of a profound realignment along axes of culture and education that are about to make race and class seem like yesterday’s news. If we want to prevent this outcome, the first step is admitting that it’s happening.

This should disturb both conventional Democrats merely concerned with the party’s short-term electoral prospects as much as socialists hoping to build mass support for radical politics. At the moment, all that either faction can boast is that the other is flailing just as desperately as they are. But if the conservative turn among non-white, non-college voters continues to gain momentum, the only group that will succeed in building a party of the multiracial working class will be Republicans.

This year, Avella attempted a comeback by running for the overlapping City Council seat, currently occupied by fellow Moderate White Man Paul Vallone. But this time, his constituents decided that if they wanted a Republican to represent them, they might as well go with the real McCoy - or the real Paladino, anyway. In 2017, Vallone sailed to victory against only nominal Republican opposition and a progressive third party candidate. But this week, Democratic support collapsed among every major demographic group in the district.

Matthew Thomas, "New York Democrats Keep Losing Ground with Hispanic and Asian Voters,"Vulgar Marxism, November 4, 2021, https://vulgarmarxism.substack.com/p/new-york-democrats-keep-losing-ground

New York Democrats Keep Losing Ground with Hispanic and Asian Voters

Eric Adams cruised to victory in New York City’s mayoral election, besting Republican nominee Curtis Sliwa by nearly 40 points - a commanding performance by any standard. But Adams’ margin wasn’t uniform throughout the city. In Queens, for example, he prevailed by only 22 points - five points fewer than Bill De Blasio’s margin here in 2017. Though Adams still made an impressive showing, New York Democrats shouldn’t pop the vegan wine just yet. Troubling demographic patterns are brewing just below the top-line numbers that could indicate tougher battles are yet to come.

In August, I reported that Hispanic and Asian areas of Queens swung heavily toward Donald Trump in last year’s presidential election, powering the best performance by a Republican nominee in the World’s Borough since 2004. Precincts that are more than 75% Asian swung toward Trump by 16 points in 2020. Precincts that are more than 75% Hispanic saw an even more dramatic pro-Trump swing of 25 points. Now, an analysis of Tuesday night’s returns in combination with demographic data from the 2020 census reveals that these patterns are trickling down to local elections too.

In 2017, Bill De Blasio won Queens precincts that are over 75% Asian by 34 points against Republican nominee Nicole Malliotakis. This week, Adams won them by just 20 points over Sliwa: a 14-point swing toward Republicans. De Blasio won Queens precincts that are over 75% Hispanic by a whopping 70 points over Malliotakis. Adams won them by 40 points over Sliwa: a 30-point swing toward Republicans.