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Tuesday, July 27, 2021

Masters Degrees Are the Second Biggest Scam in Higher Education

Jordan Weissmann Slate

Date Posted:: July 27, 2021
Is Database:: Database

@JordanWeissmann Masters degrees have become a lucrative venture for universities, with students accumulating unsustainable debt. Stronger regulation is needed, including a cap on borrowing & loan forgiveness, to prevent excessive student debt.

The master's degree market has become a lucrative venture for universities, blurring the line between nonprofit and for-profit education. With no federal cap on loans for graduate programs, students often accumulate unsustainable debt, as seen in cases like Columbia's MFA program where debt can reach $300,000. This situation is exacerbated by online program managers (OPMs) that take up to 70% of tuition revenue, funded largely by government loans. The lack of transparency and regulation allows institutions to exploit their brand names without accountability. To mitigate this, stronger regulation is needed, including a cap on borrowing and loan forgiveness, and increased transparency on program effectiveness and selectivity. This would prevent the federal government from funding programs that lead to excessive student debt, ultimately protecting both students and taxpayers.

Takeaways by Macro Roundup^® AI

Graduate borrowing faces no federal loan cap, allowing master's students to accumulate debt reaching $300,000 in cases such as Columbia's MFA program, according to the author.
Online program managers capture up to 70% of tuition revenue from master's programs funded largely by government loans, according to the author.
The author calls for stronger regulation of master's programs, including a borrowing cap, loan forgiveness, and disclosure of program effectiveness and selectivity, to stop federal funding of programs that produce excessive student debt.

Jordan Weissmann, "Master’s Degrees Are the Second Biggest Scam in Higher Education," Slate, July 16, 2021, https://slate.com/business/2021/07/masters-degrees-debt-loans-worth-it.html

Master’s Degrees Are the Second Biggest Scam in Higher Education

Last week, the Wall Street Journal published a troubling exposé on the crushing debt burdens that students accumulate while pursuing master’s degrees at elite universities in fields like drama and film, where the job prospects are limited and the chances of making enough to repay their debt are slim. Because it focused on MFA programs at Ivy League schools—one subject accumulated around $300,000 in loans pursuing screenwriting—the article rocketed around the creative class on Twitter. But it also pointed to a more fundamental, troubling development in the world of higher education: For colleges and universities, master’s degrees have essentially become an enormous moneymaking scheme, wherein the line between for-profit and nonprofit education has been utterly blurred. There are, of course, good programs as well as bad ones, but when you scope out, there is clearly a systemic problem.

Few have written more convincingly on this topic than Kevin Carey, director of the education policy program at New America. As a journalist and think tanker, he’s argued for years that “universities see master’s degree programs as largely unregulated cash cows that help shore up their bottom line,” and shown how even schools like Harvard offer effectively predatory programs. The rise of online learning has only supercharged the problem, by allowing universities to parlay their brands nationally and internationally in order to enroll students at an industrial scale.

In 2019, Carey took a long, dispiriting look at the rise of so-called online program managers, or OPMs—the private companies like 2U that major universities from Yale to small schools like Oregon’s Concordia University use to build their online offerings. These companies design and operate courses on behalf of schools—sometimes essentially offering a class in a box—that the university can slap its branding on. The OPM then takes as much as 70 percent of tuition revenue. That money is largely being funded with government loans, which may never be paid back.

After reading the Journal’s article, I called up Carey to ask him his thoughts on the current state of the master’s degree market, and what should be done to fix it.

Jordan Weissmann: I feel like you have been methodically building up this thesis—which you would never quite put this bluntly—that master’s degrees are basically the biggest scam in higher education, and it seems like prestigious nonprofit universities are in on the grift along with for-profits. Would you say that’s an accurate description of your take at this point, or have I wildly distorted it?

Kevin Carey: Probably the biggest scam in higher education remains one-year certificates offered by shady for-profit colleges that cost, like, $25,000 and don’t lead to a job. Master’s degrees are probably No. 2. Certainly, within the confines of colleges that are not legally for-profit, they are the biggest scam by far.

Right, and what makes master’s degrees a little different from those one-year certificate programs that are offered by fly-by-night schools is that they are being delivered by Ivy League universities and online schools alike.

In some ways, they’re more similar than they might seem. Many of them are one-year certificate programs. We don’t call them that. We call them master’s degrees, but that’s part of the problem. They are in fact often one-year job-oriented programs that are heavily debt-financed, marketed very aggressively through online web advertising. They purport to provide very specific economic opportunities in a given field. It’s just one are being marketed to students who just graduated from high school and the other are being marketed to people who just graduated with bachelor’s degrees, but other than that, they’re kind of the same.

Can you give me examples of the types of programs you’re talking about?

The Columbia School of Journalism offers what is essentially a 10-month master’s degree that costs $70,000 or something like that. It starts in September, ends in June. You can only do so much in less than a year. It’s completely a career-oriented degree. There are thousands upon thousands upon thousands of programs out there.

One of the reasons that universities are able to be exploitative in the master’s degree market is because they’re not constrained in the same way that they are in the market for bachelor’s degrees. If you’re offering bachelor’s degrees, they all have to be four years long. You don’t have a two-year bachelor’s degree or a six-year bachelor’s degree. You have to publicly publish your acceptance rates, your average SAT scores, so to the extent that you’re selling selectivity, you actually have to back it up with data, whereas in the master’s degree market, you can call almost anything a master’s degree. Master’s degree programs do not have to publish their admission statistics, which creates, I think, an enormous temptation for institutions that have very attractive brand names, that are attractive in no insignificant part because their undergraduate programs are very selective, to open up the floodgates on the master’s side and pay no penalty in the market because people don’t know they’re doing it.

I want to talk a little bit about the Wall Street Journal article that drew so much attention last week. It was focused very much on MFA programs at Columbia University, specifically, and the gargantuan sums of debt some of those students pursuing degrees in film were accumulating…

I think there are two big things that are both very risky for students overlapping in the case of those Columbia MFAs. First, just in general across higher education, charging people a ton of money to go into any artistic career is often super problematic. If you look at the list of schools that have just terrible outcomes in terms of earnings, loan default rates—art schools across the board, for-profit, nonprofit. The phrase starving artist exists in the vernacular for a reason.

Then, the other is just the broader ability of institutions of all kinds to set up profit-making master’s degrees, which is fueled in no small part because of the very specific change that was made in federal student loan policy about, I guess it was now 15 years or so ago, where the federal government removed any limits on how much money you can borrow to go to graduate school, other than whatever graduate school happens to cost.

If you are an undergraduate, you can only borrow a certain amount of money from the federal government to go to get a bachelor’s degree, and that’s very specifically because they don’t want people to overborrow. In graduate school, you can borrow not only the full cost of tuition, but also room, board, living expenses, which, in a city, could be tens of thousands or more dollars per year, regardless of how much money you already owe to the federal government, by the way, and regardless of whether you have any real prospect of paying it back.

Why did they make that change?

Well, partly for a really bad reason, which is that grad students in the main pay their loans back at a higher rate than people in other parts of the system, and they charge higher interest rates, which means it’s scored as making money for the federal government. So you throw this into the mix of whatever budget deal you’re striking and there was more money to spend.

Also, there’s this sort of sense of, well, if they don’t do this, they’re going to go and get private loans, and so better to have a federal loan where you have a lot more options than with private loans in terms of forbearance and deferments and income-based repayment and potentially loan forgiveness. But what has happened is that you’re essentially creating an unlimited spigot of money that can be used to fund graduate programs, which just creates an enormous moral hazard for colleges and universities when it comes to creating these programs.

Do you think there is something to that argument, though, that if the federal government did not offer unlimited student loans to grad students that they would go to a company like SoFi or to whichever private lender would offer it?

You’re just making trade-offs at that point, and I would say you’re balancing access and the cost of borrowing against the danger of overborrowing, and I would argue that if you want to go to a graduate program and you exhaust what would surely be pretty generous limits on federal graduate lending, if we go back to limits, and you can’t find anyone to lend you the rest of the money on nonterrible terms, maybe you shouldn’t go to that graduate program.

One point you’ve made at length, especially in a really great article for HuffPost a couple years back, is that it’s gotten really difficult to tell the difference between for-profit and not-for-profit higher education when it comes to master’s degrees. You write about the rise of online program managers—essentially private companies that operate behind the scenes and quietly run online master’s degrees on behalf of nonprofits, whether it’s USC or a small Bible college. Do you think for-profit and not-for-profit institutions should be regulated the same way now?

I think, from a regulatory standpoint, all master’s programs should be treated as for-profit, because I think they essentially are. I think if you just look at what nonprofit and public institutions charge in master’s programs, they are charging market rates. There’s really not much difference between them, just whatever the market can bear. I think they act like for-profit companies in the way they engage in marketing. They are profit maximizers. Their pricing decisions don’t reflect some public mission in terms of affordability or anything like that.

Here’s a funny example. The Obama administration promulgated these regulations called the gainful employment rule, which only applied to for-profit colleges or programs that were specifically job-oriented at nonprofit schools. It’s pretty simple. It just looks at how much money people borrow from a program and how much they earn after they finish. If the debt is so much bigger than the earnings that students could never pay their loans back, that’s bad.

Harvard University, when this regulation was first put in place, had an MFA program that showed up as being bad under the gainful employment regulations. I wrote about it in the New York Times. Only because the program happened to be affiliated with a drama school that was sort of Harvard-adjacent, because of this quirky legal status, they were subject to these new regulations and the numbers looked just as bad as the ones from Columbia that we saw last week: Lots and lots of debt for very little earnings because, news flash, there’s not a lot of money in being a stage actor, particularly early in your career, or probably any point in your career.

So, yes, I think any master’s program should be treated as for-profit and regulated that way. The fact that colleges have, over the last five or 10 years, very aggressively moved to enter into business relationships with for-profit corporations that do all of the marketing, much of the online infrastructure, all of the things that you have to do to basically grow a program at scale, and in some cases give those corporations up to 70 percent of their tuition revenue, just, I think, underlines the fact that this distinction between for-profit and nonprofit in the master’s degree space especially has become all but meaningless.

To what extent are online master’s degrees driving the market now?

The statistic you hear is that we’re at the point where, probably, at least half of all enrollments in master’s degree programs are online, way more than undergraduate, and probably more than doctoral programs, because doctoral programs tend to be much more intensive with relationships to mentors and faculty, and a lot of doctoral students teach on campus. A lot of that just makes total sense and has nothing to do with anyone doing anything dastardly. It’s just people who get master’s degrees often or maybe even usually are working. They’re older. They often have families. They have jobs. It’s a lot more convenient for them to study online.

However, where online does intersect with the more troublesome parts of this issue is that gave colleges with valuable brand names an opportunity to monetize those brands in a way that never existed before, so you could be a very famous college that everyone knows about and people want to attend, but before the internet you could only more or less serve people who were able to travel and live where you were. Now, all of a sudden, you can have a global brand name that can be accessed globally, and the whole premise of the for-profit OPM market is that all of the money in online education is at scale.

Who do you think the victims are here, in the end? Is it students? Is it the taxpayer? Is it all of the above? Who gets the worst end of this deal?

It’s certainly not the colleges. They get paid upfront. They bear no risk in these transactions. The students certainly are being victimized. Their trust is being exploited. People have been instructed by the culture that they should trust colleges, and that trust is being turned into money by colleges with very little thought for the consequences, and the American taxpayer is going to pay part of this bill.

It is the federal government that is lending the vast majority of loans used to pay for graduate school. Anyone who reads about how we have $1.7 trillion in outstanding student loan debt should always keep in mind that almost half of all new student loans in particular are for graduate school, not for undergraduate. You hear somebody that’s got $200,000 or $300,000 in debt, they almost surely went to graduate school. They didn’t borrow that much money from the Department of Education to get a bachelor’s degree. How it’s going to play out in terms of who’s actually going to pay their loans back is complicated both because it takes a long time to pay loans back, also because so many graduate students are now enrolled in income-based loan repayment programs that in theory offer the promise of loan forgiveness.

What is your ideal solution here? We’ve talked about limiting federal loans for graduate degree programs. You’ve mentioned possibly regulating master’s degree programs the same way you would a for-profit college program, so kind of a gainful employment rule. But what’s your ideal solution?

We need a stronger regulatory hand in the master’s degree market. One, I would put a cap on how much money you can borrow to go to graduate school. I would put a cap on how much of your graduate school loans can be forgiven under any kind of loan forgiveness program, so we’re not in this situation of unlimited money, because I think unlimited money is a moral hazard. We need more transparency around how selective are graduate programs, how effective are they in helping people get jobs in their field and pay their loans back, and we need to regulate programs around their effectiveness.

We can’t just rely on the market to provide all of the quality discipline that master’s programs need. I think it’s completely reasonable to say that if a master’s degree program consistently induces students to borrow far more money than they can ever afford to pay back, the federal government should not be in the business of lending those students money.

Pretty much the end of the MFA program.

Maybe. Again, the numbers have to add up somehow, and right now they’re just not adding up for the students. They’re adding up for the colleges. The only defense the colleges really have to offer is “We need the money.” Well, everyone needs money. That’s not a defense in and of itself.

“…We need a stronger regulatory hand in the master’s degree market. One, I would put a cap on how much money you can borrow to go to graduate school. I would put a cap on how much of your graduate school loans can be forgiven under any kind of loan forgiveness program, so we’re not in this situation of unlimited money, because I think unlimited money is a moral hazard. We need more transparency around how selective are graduate programs, how effective are they in helping people get jobs in their field and pay their loans back, and we need to regulate programs around their effectiveness.We can’t just rely on the market to provide all of the quality discipline that master’s programs need. I think it’s completely reasonable to say that if a master’s degree program consistently induces students to borrow far more money than they can ever afford to pay back, the federal government should not be in the business of lending those students money….”

Ed Comment:"Sadly, I think this is just another liberal lie--that raising the lending and loan forgiveness for college is necessary a good thing. What also saddens me is that liberals can spread destructive lies like this and be lauded by the media and IUs, while the same group of people condemn Trump for spreading lies about election fraud for example. Surely Trump sees the thick lies intentionally spread by the Dems (and Repubs) and says: why shouldn't I engage those tactics too? Trump is condemned, but no one else is, at least not to the same degree despite the lies being so thick. I'm not debating which is worse. Nor am I defending Trump. I'm condemning the destructiveness of the double standard and the inability of the media and UIs to recognize the similarity, and to demand what's best for America in all cases. The government loaning students $70k to buy a useless Columbia credential, that really isn't a credential because there are no standards and then forgiving the loan is nothing more than buying votes and lining the pockets of special interests using the media to propagandize the UIs. It's a blatantly cynical scam. (I don't understand why you two guys fall for it, i.e.,, as seeing Trump as different vs just more of the same.) Ben, I vaguely recall you telling me for-profit colleges were different the other day. From my perspective, I don't see much, if any, difference."

Is a Graduate Degree Worth the Debt? Check It Here

James Benedict Wall Street Journal

Date Posted:: July 27, 2021
Is Database:: Database

@WSJ highlights the economic implications of pursuing a graduate degree by examining the debt-to-income ratio across various programs and degree levels. Data reveals that graduates from prestigious institutions often incur debt exceeding their post-graduation earnings capacity.

The Wall Street Journal highlights the economic implications of pursuing a graduate degree by examining the debt-to-income ratio across various programs and degree levels. Data reveals that graduates from prestigious institutions often incur debt exceeding their post-graduation earnings capacity. For instance, University of Southern California (USC) graduates with a master's in public health have 2.5 times more debt than their counterparts from California State University, Long Beach, despite similar earnings two years post-graduation. This disparity underscores the importance of evaluating the long-term financial impact of graduate education. The Journal's tool allows users to compare debt-to-income ratios for undergraduate, master's, doctorate, and professional degrees nationwide, providing valuable insights for prospective students, economists, and policymakers. Such data-driven analysis is crucial for understanding the broader economic effects of educational debt and guiding informed decision-making regarding higher education investments.

Takeaways by Macro Roundup^® AI

University of Southern California graduates with a master's in public health carry 2.5 times more debt than California State University, Long Beach graduates in the same field, despite similar earnings two years after graduation, according to The Wall Street Journal.
The Wall Street Journal's data indicate that graduates of prestigious institutions often incur debt exceeding their post-graduation earnings capacity.
The Wall Street Journal's tool lets users compare debt-to-income ratios nationwide across undergraduate, master's, doctorate, and professional degree programs.

James Benedict, Andrea Fuller and Lindsay Huth, "Is a Graduate Degree Worth the Debt? Check It Here,"Wall Street Journal, July 15, 2021, https://www.wsj.com/articles/is-a-graduate-degree-worth-the-debt-check-it-here-11626355788

Is a Graduate Degree Worth the Debt? Check It Here

Lured by the aura of degrees from top-flight institutions, many master’s degree students have taken on debt beyond what their pay after graduation would support. This finding was detailed recently in an article published by The Wall Street Journal.

Unanswered was the question of how all the nation’s universities compare. To answer it, the Journal assembled data that allows you to look up students’ earnings after graduation, compared to their debt load, for most universities. You can use the tool to compare undergraduate degrees, as well as master’s, doctorate and professional degrees.

Here’s an example of how it works. The University of Southern California and California State University, Long Beach, both located in Los Angeles County, each offer a master’s in public health—a degree that became increasingly popular during the pandemic.

These are students’ median earnings two years after graduation at every one of 123 schools that offer a master’s in public health, according to Education Department data on federal loan borrowers.

Recent graduates of the California State University, Long Beach and the University of Southern California made similar incomes.

But USC graduates left school with 2.5 times as much debt as Cal State Long Beach graduates.

In other words, USC’s debt-to-income ratio is far greater than Cal State Long Beach’s. The higher the ratio, the higher the debt is compared to the income students earn after graduation.

USC said tracking graduates after a longer period will better measure the impact of the public-health program, and that the school’s own salary figures are higher than those published by the Education Department, which come from federal tax records.

Cal State Long Beach said the school’s graduates benefit from low debt loads across the board and that their degrees lead to enhanced lifetime earnings.

The tool below compares the debt-to-income ratio of programs around the country.

Thursday, July 29, 2021

Task-Based Discrimination

Erik Hurst National Bureau of Economic Research

Date Posted:: July 29, 2021
Is Database:: Database
Is Important:: Important

Research by @ErikHurst highlights task-based discrimination, where Black workers are underrepresented in high-paying Abstract tasks, widening the racial wage gap since 1980.

The wage return to cognitive skills is higher for Black men than for White men with the same occupation and education, highlighting task-based discrimination. Between 1980 and 2018, the racial wage gap narrowed by 8% due to declining discrimination and narrowing racial skill gaps. However, the increasing return to Abstract tasks widened the gap by 7%, disadvantaging Black workers who are underrepresented in these tasks. Despite declining race-specific barriers, the rising return to Abstract tasks has favored Whites since 1980, preventing Black-White wage convergence. The data from the U.S. Department of Labor's DOT and O*NET reveal that occupations demanding Abstract tasks, such as those requiring analytical flexibility and complex interpersonal communications, have seen increased returns, underscoring the systemic challenges Black workers face in achieving wage parity.

Takeaways by Macro Roundup^® AI

Declining discrimination and narrowing racial skill gaps shrank the Black-White wage gap by 8% between 1980 and 2018, while the rising return to Abstract tasks widened it by 7%.
The wage return to cognitive skills is higher for Black men than for White men in the same occupation with the same education, a pattern characterized as task-based discrimination.
Data from the U.S. Department of Labor's Dictionary of Occupational Titles and O*NET show rising returns to occupations demanding Abstract tasks such as analytical flexibility and complex interpersonal communication, in which Black workers are underrepresented.

Erik Hurst, Yona Rubinstein and Kazuatsu Shimizu, "Task-Based Discrimination," National Bureau Of Economic Research, July 2021, https://www.nber.org/papers/w29022

“…Manual: measures the degree to which the task demands eye, hand, and foot coordination. Occupations with high measures of Manual tasks include athletes, police and fire fighters, drivers (taxi, bus, truck), skilled construction (e.g, electricians, painters, carpenters) and landscapers/groundskeepers….”

“…Routine: measures the degree to which the task requires the precise attainment of set standards and/or repetitive manual tasks. Occupations with high measures of Routine tasks include secretaries, dental hygienists, bank tellers, machinists, textile sewing machine operators, dressmakers, x-ray technology specialists, meter readers, pilots, drafters, auto mechanics, and various manufacturing occupations….”

New Erik Hurst on the black-white wage gap finds that the narrowing of the wage gap btw black and white men prior to 1980 has been offset post 1980 by an increase in the returns to abstract tasks, "... the wage return to cognitive skills is higher for Black men than for White men with the same occupation and education. This is suggestive of the possibility that missing traits associated with Abstract tasks differ systematically between Black and White men…we find that the stagnation in the racial wage gap post-1980 is a product of two offsetting forces. On the one hand, a narrowing of racial skill gaps and declining discrimination between 1980 and 2018 caused the racial wage gap to narrow by 8 percentage points during this period, all else equal. On the other hand,the changing returns to tasks since 1980 - particularly the increasing return to Abstract tasks - widened the racial wage gap by about 7 percentage points during the same period. A rise in the return to Abstract tasks disadvantages Black workers because they are underrepresented in these tasks due to a combination of racial skill gaps and discrimination. In sum, the estimated model highlights that race-specific barriers have continued to decline in the U.S. economy post-1980 but the rising relative return to Abstract tasks has favored Whites since 1980. As a result, the Black progress stemming from narrowing racial skill gaps and/or declining discrimination did not translate into Black-White wage convergence during this period. On the other hand, we show that the relative wage gains of Black men relative to White men during the 1960 to 1980 period stemmed solely from declining discrimination and a narrowing of racial skill gaps; changing task prices did not undermine any of these gains during this earlier period...."

The data, "...we measure the skill demands in each occupation using the following data: (i) the U.S. Department of Labor’s Dictionary of Occupational Titles (DOT) and (ii) the Occupational Information Network (O*NET) sponsored by the U.S. Department of Labor/Employment and Training Administration (USDOL/ETA). The DOT was constructed in 1939 to help employment offices match job seekers with job openings. It provides information on the skills demanded in over 12,000 occupations. The DOT was updated in 1949, 1964, 1977, and 1991, and replaced by the O*NET in 1998. We focus on four occupational task measures that are relevant for our study: Abstract, Routine, Manual and Contact...."

The four measures:

“…Abstract: indicates the degree to which the occupation (i) demands analytical flexibility, creativity, reasoning, and generalized problem-solving and (ii) requires complex interpersonal communications such as persuading, selling, and managing others. Occupations with high measures of Abstract tasks include accountants, software developers, high school teachers, college professors, judges, various medical professionals, engineers, and managers...”

Ed Comment:That outcome seems at odds with the study which says there is discrimination against blacks in abstract/conceptual jobs. Ben, how does it reconcile? Is there an appendix that shows the wages of black and whites vs their relevant abstract/conceptual credentials? They mention AFQT test scores but don't seem to use them. Do they use them? Does the appendix show wages vs test scores (and other credentials), which they clearly seems to have? Using education as a measure of intelligence is problematic because blacks are admitted to colleges with substantially lower scores. Let's read and summarize the reference Neal (decker) 2006, which accompanies their claim that blacks in abstract jobs are paid more than whites. I glanced at it. It's pretty direct. It says: "Some may argue that what matters is not realized returns to investments but rather the fact that many blacks believe that the labor market will not reward them or their children for investing in human capital. The literature on statistical discrimination began with Arrow (1973) and Phelps (1972). When Arrow and Phelps first developed these ideas, existing cohorts of adult workers had lived most of their lives in labor markets where blacks may well have earned lower returns to education and skills than whites workers.28 However, it is hard to find data that would support this belief today. Blacks who reached adulthood after the Civil Rights Act of 1964 have not earned systematically lower returns to attainment or achievement than their white peers. During the past decade or more, black adult respondents in the NLSY79 have earned relatively high returns to skills measured by the AFQT.29 Further, as I demonstrate below, apparent returns to formal schooling among black young adults have typically been as large or larger than the corresponding returns among whites since at least 1970....In the balance of this section, I present details concerning the relationship between education and labor market outcomes among black and white adult men. Existing panel data sets do not provide information concerning racial differences in the relationship between cognitive skills and adult outcomes for birth cohorts other than those included in the NLSY79.30 However, census data document the relationship between educational attainment and labor market outcomes for a broad range of cohorts. Education differs from reading and math scores in that it is an indirect measure of skill that is easily observed by employers, but the patterns documented below are similar to those in Figures 4(a)-5(b). Correlations between schooling and positive labor market outcomes among blacks are as strong or stronger than the corresponding correlations among whites, especially in recent decades. 30 The National Educational Longitudinal Surveys provide panel data on high school experiences and adult labor market outcomes. However, these samples are drawn from students who are enrolled in specific grades at a point in time, and thus they provide select samples from persons born in a set of birth years. In addition, the follow-up data on earnings and employment are of relatively poor quality in the NELS surveys when compared to NLSY79 on work outcomes...." Has he updated this or related work? Has he written anything else of high interest? Maybe I'll talk to him at U Chicago.

Ben Comment:From the paper: "The differences in pre-labor market skills and discrimination facing Black workers give rise to differential sorting into tasks between Black and White individuals in the spirit of Roy (1951). These sorting differences need to be accounted for in order to parse out the effects of race-neutral driving forces (such as time trends in task specific returns) and race-specific driving forces (such as a narrowing of racial skill gaps and/or declining discrimination) when explaining changes in racial wage gaps over time. Using the model structure and empirical moments on the differential occupational sorting of Black and White men, the changing returns to various tasks, and the evolution of the aggregate racial wage gap, we estimate the key driving forces of the model.” Most black people are kept out of the abstract jobs because of discrimination/skill differences (below I pull a quote where they say these skill differences are the result of discrimination, too). The only black people who move into the abstract job categories are the ones who have overcome these. If you think in terms of diminishing marginal productivity, only the absolutely most productive black workers are in the “abstract” category, so they’re making higher wages than the average. If more black people entered abstract jobs, they would bring down the average productivity and (presumably) the wage. The issue of discrimination has to do with the number (or proportion) of black people who enter the field, not with what they make conditional on entering the field. From the discussion of figure 4: "Most of the racial gaps in the average task returns are slightly negative suggesting that the task return to Black men are systematically smaller than White men of similar age and education.” So it’s only in “abstract” tasks that we see a “premium” paid to black workers. And according to this graph it’s very small. Additionally, in the model section: "First, the average skill level may differ across race groups stemming from current or past discrimination.” The assumption is that skill differences are because of discrimination and are not “genetic”. I didn’t see any graphs in the appendix like the ones you asked about.
Ed Comment:I’m still confused (about the math). (I don’t care what the paper says about the causes of racial difference in test scores. The authors are not experts in that. That’s obligatory BS to cover their asses. …and let’s not debate it. It would help my understanding of the paper if you ignored your hypotheses and focused on what the data and the math in the paper actually measure and the limitation of those measures. My own view, from a lot of experience hiring people into highly analytical jobs, is that there is great demand and competition between employers for scarce analytical blacks because of societal demands. Like colleges, we greatly lowered the bar. And their chances of success were far greater as a result of these allowances. So I’m not surprised blacks are paid more than whites in analytical jobs, with the premium growing larger the more precisely they can measure the true analytical capacity of the person and the extent to which the job demands analytical capability.) I don’t see how they measure the extent to which selection bias improves the pool of workers relative to their credentials. I would think they merely have their wage, some measure of the pedigree of their analytical credentials, and measures of the extent to which the job they perform is analytical. If not, please explain. What measures do they use for the pedigree of their analytical credentials? How do they measure the extent to which the job is analytical? My guess is that unless they are using their AFQT score—something the most analytical people are unlikely to have—they don’t have a measure of the analytical skill beyond yes/no. What measure of analytical prowess do they use? Similarly, is the analyticalness of the job measured or is it just yes/no? If it’s measured, how so? Are they merely saying, black people in analytical positions get paid more than white people in analytical positions, on average, without regards to measures of the person’s or the job’s anlysiticalness? Or are they making some adjustment for the extent to which the job is analytical and for the pedigree of the person’s analytical credentials? If black people are paid more than whites on average in analytics jobs, how do they measure discrimination in analytical jobs? I don’t see how the math works or could work unless the measurements are quite precise. For example, they could say black people in analytical jobs have test scores of 125, whereas white people in analytical jobs only have test scores of 115. Blacks are paid more than whites, but, given their higher test scores, they should be paid even more than they are being paid. Show me if and how they do that. If they have all these measures and wages, I’d love to see a graph of x - measure of analyticalness (e.g., test score), and y - wages for blacks vs whites. Again, let’s not speculate about what the data means. Let’s focus on the data they have and how they use it to calculate how people who are paid more are discriminated against.
Ben Comment:They do not create the graph “ x - measure of analyticalness (e.g., test score), and y - wages for blacks vs whites”. Looking at the BLS website it seems like that might be something we can do (it’ll take time, I don’t know these data at all). Other questions below, without my commentary - all language is drawn from the paper. ED: How do they measure the extent to which the job is analytical? "we measure the skill demands in each occupation using the following data: (i) the U.S. Department of Labor’s Dictionary of Occupational Titles (DOT) and (ii) the Occupational Information Network (O*NET) sponsored by the U.S. Department of Labor/Employment and Training Administration (USDOL/ETA) … The first three measures are taken exactly from Autor and Dorn (2013) and Deming (2017) using the DOT data. Below, we provide a brief summary of these measures. The last task measure is new and was created specifically for this paper to help get at the concept of taste-based discrimination. Building on the work in Deming (2017), Contact measures the extent to which an occupation requires interaction and communication with others within the organization (co-workers) or outside the organization (customers/clients) …
Abstract: indicates the degree to which the occupation (i) demands analytical flexibility, creativity, reasoning, and generalized problem-solving and (ii) requires complex interpersonal communications such as persuading, selling, and managing others. Occupations with high measures of Abstract tasks include accountants, software developers, high school teachers, college professors, judges, various medical professionals, engineers, and managers. Routine: measures the degree to which the task requires the precise attainment of set standards and/or repetitive manual tasks. Occupations with high measures of Routine tasks include secretaries, dental hygienists, bank tellers, machinists, textile sewing machine operators, dressmakers, x-ray technology specialists, meter readers, pilots, drafters, auto mechanics, and various manufacturing occupations. Manual: measures the degree to which the task demands eye, hand, and foot coordi- nation. Occupations with high measures of Manual tasks include athletes, police and fire fighters, drivers (taxi, bus, truck), skilled construction (e.g, electricians, painters, carpenters) and landscapers/groundskeepers. Contact: measures the extent that the job requires the worker to interact and communi- cate with others (i) within the organization or (ii) with external customers/clients or potential customers/clients. To create our measure of Contact tasks we use two 1998 O*NET work activity variables taken from Deming (2017). Specifically, we use the variables Job-Required SocialInteraction(Interact)andDealWithExternalCustomers(Customer).4 Interactmea- sures how much workers are required to be in contact with others in order to perform the job. Customer measures how much workers have to deal with either external customers (e.g., retail sales) or the public in general (e.g., police work). To make our measure of the Contact task content of an occupation, we take the simple average of Interact and Customer for each occupation. Occupations with high measures of Contact tasks include various health care workers, waiter/waitress, sales clerks, lawyers, various teachers, and various managers." Appendix A.3 has even more detail and Autor and Dorn (2013) and Deming (2017) have even more detail if you’d like us to find that for you. ED: What measures do they use for the pedigree of their analytical credentials/What measure of analytical prowess do they use? From the ACS: "In most specifications, we control for the worker’s age and accumulated years of schooling.” From the NYSL: "The NLSY data allows us to link a worker’s subsequent occu- pational choice with pre-labor market measures of cognitive, non-cognitive, and social skills… The key reason we use the NLSY data is to have measures of racial differences in pre- labor market skills. We use measures of performance on cognitive test and psychometric assessments to generate a set of unified proxies for cognitive, non-cognitive and social traits across the two NLSY waves. These skill measures were primarily collected before the indi- viduals entered the labor market. Again, our goal is to take these skill measures directly from the existing literature. We summarize these measures briefly here and include a more detailed discussion in the online appendix. Cognitive Skills (COG): We follow the literature and use the respondent’s standard- ized scores on the Armed Forces Qualifying Test (AFQT) as our measure of cognitive skills. The AFQT is a standardized test which is designed to measure an individual’s math, verbal and analytical aptitude. The test score was collected from all respondents in their initial year of the survey and was measured in both the 1979 and 1997 waves.7 Non-cognitive Skills (NCOG): We use the measures of non-cognitive skills created by Deming (2017). Deming (2017) uses questions pertaining to the Rotter Locus of Control Scale and the Rosenberg Self-Esteem Scale for the NLSY79 cohort to make a measure of non- cognitive skills.8 Likewise, for the NLSY97 cohort Deming (2017) uses respondent answers (provided prior to entering the labor market) to the question “How much do you feel that conscientious describes you as a person?” to approximate respondents’ non-cognitive skill. Deming (2017)’s non-cognitive skill measures are expressed in z-score units. Social Skills (SOC): We again follow Deming (2017) to generate a unified measure of social skills using a standardized composite of two variables that measure extroversion in both waves. Specifically, for the NLSY79, we use self-reported measures of sociability in childhood and sociability in adulthood. Individuals were asked to assess their current sociability (extremely shy, somewhat shy, somewhat outgoing, or extremely outgoing) and to retrospectively report their sociability when they were age 6. For the NLSY97, we proxy for social skills using the two questions that were asked to capture the extroversion factor from the commonly-used Big 5 personality inventory. For each wave, we normalize the two questions so they have the same scale and then average them together. We then convert the measures into z-score units. Deming (2017) shows that these measures of social skills positively predict individual wages when they are adults even conditional on controlling for individual measures of cognitive skills (AFQT).” ED: are they making some adjustment for the extent to which the job is analytical and for the pedigree of the person’s analytical credentials?

"wijt is the log wage of individual i working in occupation j during year t. Our coefficients of interest are again the βg ’s, the Mincerian wage premium of task k in year t kt for group g. For this regression, we use are sample of full-time workers… the demographically adjusted Black-White gaps in the wage premium by task requirement (Panel B) … Finally, the racial gaps in the wage premiums to tasks are relatively small and roughly constant over time. Most of the racial gaps in the average task returns are slightly negative suggesting that the task return to Black men are systematically smaller than White men of similar age and education”. ED: If black people are paid more than whites on average in analytics jobs, how do they measure discrimination in analytical jobs? "New to our model is differential returns to performing each type of tasks across different race groups. We allow for three forces that can cause the returns to tasks to differ across races. First, the average skill level may differ across race groups stemming from current or past discrimination.16 We capture this by writing the skill endowment vector as Φgi = {ηgk +φi1.., ηgK +φiK }, where ηgk represents the skill (or human capital) level of group g associated with task k. Second, workers of a particular race group may face taste-based discrimination in performing a task. Taste-based discrimination may exist if for example customers do not like interacting with Black employees or White workers do not like interacting with their Black co-workers. In the presence of taste-based discrimination, we assume that employers perceive the efficiency of the discriminated workers to be δtaste+ηgk+φik rather than ηgk+φik, gk where δtaste is the race-specific taste-based discrimination coefficient in task k. Thus, the gk potential log wages of a worker belonging to race g becomes:

Lastly, workers may face statistical (rather than taste-based) discrimination if their employers do not perfectly observe individual workers’ skills. If skills are observed with noise, employers form expectations about a worker’s marginal product by using information about the individual’s group. The statistical discrimination coefficient, δstat, will therefore differ gk by task based on underlying gaps in group mean of skills (ηgkt’s) used to perform the task. We formally incorporate the notion of statistical discrimination into the model by intro- ducing noise to skill measurement. Suppose employers cannot observe worker’s true efficiency, ηgk + φik, and instead only observe a noisy skill measure given by where the noise εik is drawn from a normal distribution with mean zero and variance σ2 (common to all race groups). Employers, however, observe a worker’s group affiliation and know the underlying distributions of ηgk + φik and εik. In this environment, employers set the wage of each worker at the worker’s expected marginal revenue product conditional on observed skills (sˆi1..,sˆik) and the worker’s group affiliation… In sum, our task-based model of discrimination predicts differential sorting patterns across race groups as group-specific forces such as discrimination and racial skill differences make the task returns differ by group.”

“…Contact: measures the extent that the job requires the worker to interact and communicate with others (i) within the organization or (ii) with external customers/clients or potential customers/clients. To create our measure of Contact tasks we use two 1998 O*NET work activity variables taken from Deming (2017). Specifically, we use the variables Job-Required Social Interaction (Interact) and Deal With External Customers (Customer). 4 Interact measures how much workers are required to be in contact with others in order to perform the job. Customer measures how much workers have to deal with either external customers (e.g., retail sales) or the public in general (e.g., police work). To make our measure of the Contact task content of an occupation, we take the simple average of Interact and Customer for each occupation. Occupations with high measures of Contact tasks include various health care workers, waiter/waitress, sales clerks, lawyers, various teachers, and various managers….”

"... Figure 1 shows the difference in log wages between Black and White workers conditional on age and education using our sample of Census/ACS individuals. In particular, for each year, we regress an individual’s log hourly wage on a race dummy and controls of age (fiveyear age dummies) and series of dummies indicating the individual’s accumulated level of education. Consistent with other findings in the literature, the demographically adjusted racial wage gap narrowed substantially between 1960 and 1980 but has been constant at a gap of roughly 20 log points since 1980.5 Our goal is to explain both the wage convergence in Figure 1 between 1960 and 1980 as well as its stagnation post-1980..."

Task-Based Discrimination: Comments Image 3

Skills measure

“..Cognitive Skills (COG): We follow the literature and use the respondent’s standardized scores on the Armed Forces Qualifying Test (AFQT) as our measure of cognitive skills. The AFQT is a standardized test which is designed to measure an individual’s math, verbal and analytical aptitude. The test score was collected from all respondents in their initial year of the survey and was measured in both the 1979 and 1997 waves….”

“…Non-cognitive Skills (NCOG): We use the measures of non-cognitive skills created by Deming (2017). Deming (2017) uses questions pertaining to the Rotter Locus of Control Scale and the Rosenberg Self-Esteem Scale for the NLSY79 cohort to make a measure of noncognitive skills.8 Likewise, for the NLSY97 cohort Deming (2017) uses respondent answers (provided prior to entering the labor market) to the question “How much do you feel that conscientious describes you as a person?” to approximate respondents’ non-cognitive skill. Deming (2017)’s non-cognitive skill measures are expressed in z-score units….”

“…Social Skills (SOC): We again follow Deming (2017) to generate a unified measure of social skills using a standardized composite of two variables that measure extroversion in both waves. Specifically, for the NLSY79, we use self-reported measures of sociability in childhood and sociability in adulthood. Individuals were asked to assess their current sociability (extremely shy, somewhat shy, somewhat outgoing, or extremely outgoing) and to retrospectively report their sociability when they were age 6. For the NLSY97, we proxy for social skills using the two questions that were asked to capture the extroversion factor from the commonly-used Big 5 personality inventory. For each wave, we normalize the two questions so they have the same scale and then average them together. We then convert the measures into z-score units. Deming (2017) shows that these measures of social skills positively predict individual wages when they are adults even conditional on controlling for individual measures of cognitive skills (AFQT)….”

“…in Figure 2. Panels A and B, respectively, show the patterns excluding and including the vector… of demographic controls. The main take-away from the figure is that both the level difference in racial task gaps in 1960 and the subsequent time series trend differ markedly by task. The differences are especially pronounced when we compare the racial gaps in Abstract and Contact tasks.In the early 1960s, Black workers were systematically underrepresented both in occupations that required a high intensity of Abstract tasks and in occupations that required a high intensity of Contact tasks. In terms of magnitudes, in 1960 a one-standard deviation increase in the Abstract task contents of an occupation reduced the probability that an individual working in that occupation was Black by about 3 percentage points, and a one-standard deviation increase in the Contact task contents reduced the probability that the individual was Black by about 4 percentage points, both conditional on education. Over the last half a century, however, Black men have made significant progress relative to White men with respect to sorting into occupations that require Contact tasks, while they made no progress relative to White men in the extent to which they sort into occupations that require Abstract tasks.Whereas the racial gap in Abstract tasks remained essentially constant through 2000 and widened slightly after 2000, the large racial gap in Contact tasks that existed in 1960 has all but disappeared by 2018. This finding persists whether or not we control for individual age and education (Panel A vs. Panel B), although the level of the Abstract task gap narrows once we control for them. If discrimination took the form of co-workers and customers not wanting to interact with Black workers in 1960, the patterns in Figure 2 are consistent with that form of discrimination abating over time.10 Although we focus on such differences to a lesser extent, there were only small racial differences in the propensity for Black and White men to work in occupations that require Manual tasks. Likewise, there was some convergence in the propensity to work in occupations that require Routine tasks between 1960 and 1980, but large racial gaps still remain post-1980….”

Task-Based Discrimination: Comments Image 4

“…Figure 4 reports estimates of the raw wage premium by task requirement for White men (Panel A) and the demographically adjusted Black-White gaps in the wage premium by task requirement (Panel B). Three main findings emerge from this figure. First, the average wage premium of Abstract tasks for White men was between 10 and 15 percent higher than the return to the other tasks in 1960. Moreover, the relative return of Abstract tasks has been increasing since 1980. This increase in the return to Abstract tasks has received lots of attention in the literature (Autor and Dorn (2013), Deming (2017)). Second, in contrast, the wage premium associated with Contact tasks was notably lower in the early 1960s and has not changed much since then. Finally, the racial gaps in the wage premiums to tasks are relatively small and roughly constant over time. Most of the racial gaps in the average task returns are slightly negative suggesting that the task return to Black men are systematically smaller than White men of similar age and education….”

Task-Based Discrimination: Comments Image 5

“…The results are shown in Table 1. Each column comes from a separate regression projecting an individual’s cognitive, non-cognitive or social skills on the relative task content of the occupation in which they work...Table 1 highlights that individual skill supplies respond differentially to relative task demands. Workers with higher cognitive skills are more likely to match with jobs that require higher Abstract tasks and workers with higher social skills are more likely to match with jobs that require higher Contact tasks. For example, occupations where their relative Abstract task requirement is one-standard deviation higher attract workers who score approximately 0.2 standard deviations higher on cognitive tests. Occupations where their Contact tasks requirement is one-standard deviation higher attract workers whose social score is 0.08 standard deviation higher. The results in this table highlight that the NLSY skill measures are informative about the types of jobs into which individuals sort. Having shown that the occupational task measures are associated with particular NLSY skill measures within the sample of White men, we now explore differences in pre-labor market skills between Black and White men within the NLSY cohorts.13 Table 2 reports the racial gap in cognitive, non-cognitive, and social skills with various controls for the two separate NLSY samples. The first column for each sample includes all NLSY respondents in the sample without conditioning on employment; each of these samples has only one NLSY respondent per regression. The remaining columns pool over all years and only include individuals that were working. The second column within each sample adds no further controls, while the third column controls for the individual’s maximum level of education and the last column controls also for their occupation The main takeaway from this table is that the racial gap in cognitive skills is large and narrows over time (especially for working men), whereas the gaps in non-cognitive and social skills are relatively small and constant over time. As seen from Table 2, Black men from the NLSY79 have AFQT scores that are 1.20 standard deviations lower than White men from the NLSY79 cohort. The gap declines to 0.93 when we additionally control for racial education and occupation differences. The race gap in AFQT scores narrowed somewhat between the NLSY79 and NLSY97 cohorts but was still large at -0.58 standard deviations conditional on education and occupation in the later period. On the other hand, the racial skill gaps in non-cognitive and social skills conditional on education and occupation were close to zero for both NLSY cohorts….”

"... Once we control for the rising return to cognitive skills over time, however, we find a strong convergence in racial wage gaps post 1980. Specifically, in column 3, we control for time-varying return to just cognitive skills. In this column, we find a narrowing of the racial wage gap relative to the 1980s of about 4 log points in the 1990s, about 9 log points in the 2000s, and about 10 log points in the 2010s. The results are nearly identical when we additionally control for time-varying returns to non-cognitive and social skills (column 4).27 As suggested by our model, conditioning out the effects of time-varying skill returns - the rising return to cognitive skills in particular - unveils the convergence in the racial wage gap due to changing race-specific factors. Strikingly, the magnitude of the convergence we estimate in the NLSY between 1980 and 2018 once properly controlling for the changing returns to skills (column 4 of Table 4) is close to the magnitude we estimate from our structural model(Panel B of Figure 7)..."

Good factoid in table four, "... the wage return to cognitive skills is higher for Black men than for White men with the same occupation and education...." implies higher skilled black men see a wage premium.

"... First, in Table 4 above, we exploit the panel structure of the NLSY and show that controlling for unmeasured traits by including individual fixed effects hardly affects the estimated changes in the racial wage gap over time (compare columns 1 and 2 of Table 4). This suggests that omitted skills play little role in the evolution of the racial wage gap over the last forty years. Second, in the appendix, we examine whether the labor market returns to skills differ between Black and White men in the NLSY. We find that the labor market returns to social skills are similar between Black and White men. This finding is consistent with there being no differential bias between Black and White men with respect to predicting Contact task efficiency from measured traits. On the other hand, consistent with the findings in Neal (2006), the wage return to cognitive skills is higher for Black men than for White men with the same occupation and education. This is suggestive of the possibility that missing traits associated with Abstract tasks differ systematically between Black and White men...."

Task-Based Discrimination: Comments Image 8

Bottomline"...There are two important results highlighted in the paper. First, our paper provides an explanation for both why the Black-White wage gap narrowed between the 1960s and 1970s and why the racial wage gap has remained constant thereafter. We find that the constant wage gap between Black and White men post-1980 is due to two offsetting effects. Both the racial skill gap narrowed and taste-based discrimination fell post-1980 resulting in the wages of Black men converging to those of White men, all else equal. However, during the same period, the return to Abstract skills rose disadvantaging Blacks relative to Whites. This latter effect resulted in increasing racial wage gaps during the 1980-2018 period. The magnitude of these two effects were roughly similar resulting in a relatively constant racial wage gap post-1980. On the other hand, we show that the relative wage gains of Black men relative to White men during the 1960 to 1980 period stemmed solely from declining discrimination and a narrowing of racial skill gaps; changing task prices did not undermine any of these gains during this earlier period. We also provide a road map to empirical researchers looking to uncover changing race specific factors in micro data. Second, our paper establishes that the declining racial gap in Contact tasks between 1960 and 2018 is a good proxy for declining taste-based discrimination during this period.We motivated the introduction of this novel task measure by conjecturing ex-ante that occupations which require many interactions with others are more likely to be susceptible to taste-based discrimination; our model and data work confirm this conjecture ex-post. Specifically, the fact that there are very small racial gaps in social skills - combined with the fact that measures of pre-labor market social skills in the NLSY are highly predictive of subsequent entry into occupations that require Contact tasks - implies that racial gaps in Contact tasks must stem almost entirely from taste-based discrimination and very little from racial skill differences or statistical discrimination. Our model thus implies that the changes in racial gaps in Contact tasks over time is a good proxy for taste-based discrimination. To further provide evidence for this conclusion, we document that state-level racial gaps in Contact tasks correlate strongly with state-level survey measures of taste-based discrimination, while state-level racial gaps in Abstract tasks correlate with them only weakly….”

Ed Comment:I read the paper, so I read the definitions. I was looking for your explanation of what they were measuring after you read the paper, not theirs. after reading the paper, (and these snippets) it was unclear to me, and I was uncertain about what data they were truly using/measuring. Experience has taught me that people often say they did (or measured) X when they did something other than what they said they did. Sometimes they exaggerate. Sometimes they don’t fully understand what they did. Often, they assume they did X because they believe they did X or hoped to do X. In their equation, which variable is the AFQT test score? …or years of schooling? Is years of schooling their measure of analytical capability? I still don’t understand how blacks are discriminated against in analytical tasks when they are paid more on average of analytical tasks. (Are they paid more on average? That was unclear.) Blacks would have to have higher scores/more years of schooling or perform a mix of more analytical (more higher paid tasks). Which is it? My life experience is that the scores are lower on average. They are paid more for lower scores because high scoring blacks are highly sought after. But they may be paid less than higher scorers performing the same job. So I wouldn’t think the above would not be the case, i.e., blacks do not have higher scores on average or more analytical jobs. So I’m looking for your description for what the math in the study says in this regard. How does the study account for this?

Ben Comment:Ok, let me try again: ED: How do they measure the extent to which the job is analytical? First they take an occupation’s title (economist, researcher, scientist, burger flipper, etc.) and look up that occupation in DOT/O*NET. DOT/ONET matches an occupation’s title with the types of tasks that occupation performs and what skills are needed to perform them Firms are surveyed to see how much of a particular task is required of a particular occupation (the results have different scales so they’re harmonized). Then these tasks are classified into the 4 categories - by Autor & Dorn in the cases of Abstract, Routine, Manual and by Demings in the case of Contact. They determine which jobs are analytical by asking businesses which jobs require analytical tasks. NOTE - occupation is a control. The paper is about *tasks* and *skills*. So they merge DOT/ONET with ACS on occupation and regress wages onto the tasks/skills necessary. ED: What measures do they use for the pedigree of their analytical credentials/What measure of analytical prowess do they use? In the sections that examine the ACS, they’re using years of schooling. That’s section 3.1. In equation (1) on pg 12, the years of schooling is in X_(it) and is a control variable. They then move onto the NLSY data in section 3.2 (pg 16). These data are more robust in terms of demographic detail but also have a smaller sample. There are 2 waves of the NLSY - 79 and 97. In Appendix A.2, they explain both waves include the AFQT score. In general, you’re right, highly analytical people are unlikely to have taken this. In this particular case, however, everyone in the survey took it. Participants are chosen at random to participate in the survey. As far as I understand, once in the survey, everyone is given this test. Now, it’s true, surveys are likely to miss top top performers but this methodology does not suffer from the same selection bias using just military enrollment would. They also exclude observations with missing scores and reweight the entries. Section 6.3 is where they start using the NYSL for wage regressions: Equation 9 on pg 34. In this equation, one of the S^(NYLS)_(ki)’s is cognitive ability, and one of the variables in X_(it) is education. This regression specification does NOT interact race with cognitive ability so we are looking at the difference in wages of blacks/whites controlling for cognitive ability. Table A1 in appendix E shows the coefficient for black * Cognitive - and it’s positive. They also show the model imputed skills black*abstract (panel B) and that’s positive as well. The abstract skill variable, however, is a model implied variable constructed in section 7. To construct the variable, they take the average of the tasks done by each race. This is a complicated procedure to describe in words but the equations are on page 36+37. Essentially, they regress the occupational-average of observed task specific skills for whites (model generated) onto the average skill levels for whites observed in the NYSL. They then use the estimated white-black skills gap (table 1 pg 17) to impute the racial gaps in the task-specific skills in each occupation. Note: They do not show an explicit regression equation for this table but do explain the controls in the footnotes. I think I’ve covered the different measures of skills and how they’re used. ED: are they making some adjustment for the extent to which the job is analytical and for the pedigree of the person’s analytical credentials? They are NOT making adjustments for pedigree of institutions. They do control for years of education. However - they’re using actual pre-college test scores in their regressions or using those test scores to impute skills, so it’s unclear what the pedigree of the institution adds - especially if you believe those test scores are highly correlated with the pedigree of the institutions. ED: If black people are paid more than whites on average in analytics jobs, how do they measure discrimination in analytical jobs? They are interested in 2 types of discrimination, taste based and statistical. Taste based means - we don’t like black people. Statistical means - if a boss cannot perfectly observe a person’s analytical ability, they default to that person’s group’s mean of ability. This means, for smart blacks, if ability is noisy, they are assumed to have the average level of black intelligence and therefore earn lower wages. This type of discrimination is laid out in section 4, pg 22, the paragraph towards the bottom starting with “Conceptually, it would be useful to see the statical discrimination term ….” Essentially, if there is noise in determining someone’s analytical skill, they’re assumed to have the average and this discriminates against smart blacks. ED: In their equation, which variable is the AFQT test score? …or years of schooling? Is years of schooling their measure of analytical capability? I think I answered which variables are which above, but let me know if it’s still unclear. Their measure of analytical capabilities is the actual AFQT test score in the data. They ALSO include years of schooling as a control. ED: I still don’t understand how blacks are discriminated against in analytical tasks when they are paid more on average of analytical tasks. (Are they paid more on average? That was unclear.) Blacks would have to have higher scores/more years of schooling or perform a mix of more analytical (more higher paid tasks). Which is it? Blacks, on average, are paid less than whites (figure 1). Figure 1 was created with the ACS data so does NOT have the intelligence controls. Figure 4 breaks out the racial wage gap in terms of tasks. This graph, again, is created using the ACS data and does not include controls for tests scores. Figure 5 Panel A shows the model predicted racial wage gap. It’s model generated (not data) but also shows blacks, even smart ones, earning less. They do not have a break out of wages vs test scores from the NYSL like you’re describing. (This is where editorializing has to come in, sorry): As for the discrimination - remember regressions are holding all else equal. So what those positive coefficients mean is: Holding all else equal (occupation, wage, education, and ALL OTHER SKILLS), and increase in intelligence for a black guy will raise his wages more than the same sized increase in intelligence for a white guy. But the discrimination can come from all sorts of other things - like the occupations they have access to or the schooling they have access to or any number of other things. Additionally, if a white and a black guy have similar wages, conclusion of the paper says it’s likely the black guy is more skillful in Contact/Routine (tasks) or in Social/non-cognitive skills than the white guy; so raising his intelligence makes him *more* skillful than his white counterpart.

@JordanWeissmann Masters degrees have become a lucrative venture for universities, with students accumulating unsustainable debt. Stronger regulation is needed, including a cap on borrowing & loan forgiveness, to prevent excessive student debt.

Do uncapped graduate loans let universities profit from student debt?

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@WSJ highlights the economic implications of pursuing a graduate degree by examining the debt-to-income ratio across various programs and degree levels. Data reveals that graduates from prestigious institutions often incur debt exceeding their post-graduation earnings capacity.

Does graduate program debt relative to earnings vary widely by school?

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Research by @ErikHurst highlights task-based discrimination, where Black workers are underrepresented in high-paying Abstract tasks, widening the racial wage gap since 1980.

Does rising demand for abstract tasks offset declining racial discrimination in wages?

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