AI Summary. The authors estimate that students assigned teachers who inflate average grades have lower future test scores, high school graduation, college enrollment, and earnings, with a one standard deviation increase in a teacher's average grade inflation reducing the present value of students' lifetime earnings by $213,872. Inflating passing grades instead raises graduation with limited long-run effects.

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Data from Los Angeles and Maryland linking high school, postsecondary, and earnings records suggest that one class-year with a teacher with 1 SD higher mean grade inflation reduces the PDV of their students’ lifetime earnings by ~$213,872.

How does teacher grade inflation affect students' long-term outcomes and earnings?

Core argument: The authors estimate that assignment to a teacher with one standard deviation higher average grade inflation reduces the present discounted value of students' lifetime earnings by $213,872 per year.

We develop two teacher-level measures of grade inflation: one measuring average grade inflation (the year-specific teacher fixed effect showing the teacher’s average contribution to grades after controlling for the student's contemporaneous performance in the focal subject as measured by the corresponding subject test score as well as prior test scores, prior grades, and other background characteristics), and another measuring a teacher's propensity to give a passing grade [which affects primarily students near the bottom of the distribution]. A [separate] cognitive value-added measure [included in the regressions] is a teacher fixed effect capturing how much a teacher raises students' standardized test scores relative to what would be predicted from the students' prior test scores and background characteristics. Grade-inflating teachers have moderately lower cognitive value-added and slightly higher noncognitive value-added. The two [grade-inflation] measures differentially impact students' long-term outcomes. Being assigned a higher average grade inflating teacher reduces a student's future test scores, the likelihood of graduating from high school, college enrollment, and ultimately earnings. A teacher with one standard deviation higher average grade inflation reduces the present discounted value [PDV] of lifetime earnings of their students by $213,872 per year.  In contrast, passing grade inflation reduces the likelihood of being held back and increases high school graduation, with limited long-run effects. [Figure 7 in the gallery].

Takeaways by Macro Roundup® AI

  1. The authors estimate that assignment to a teacher with one standard deviation higher average grade inflation reduces the present discounted value of students' lifetime earnings by $213,872 per year.
  2. Higher average grade inflation by teachers lowers students' future test scores, high school graduation likelihood, college enrollment, and earnings, while passing-grade inflation instead reduces grade retention and raises high school graduation with limited long-run effects.
  3. Grade-inflating teachers have moderately lower cognitive value-added, measured as a teacher's contribution to standardized test scores beyond what students' prior scores and background characteristics predict, and slightly higher noncognitive value-added.

AI Summary. Adjusting for time spent, tuition costs, and completion probability drastically reduces the estimated return on a master's degree, leaving master's programs with the lowest adjusted return among degree types. Averages mask wide variation: business master's degrees show a negative return on average, while top programs' graduates can earn millions more.

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Mark Schneider @AEIecon reviews the ROI of master’s degree programs and finds “vast differences among fields of study and among programs in the same field of study.” He argues that policymakers need to provide accurate ROI metrics to applicants.

Do master's degrees pay off for students after accounting for costs?

Core argument: Adjusting lifetime earnings gains for time spent in school, the cost of the degree, and the probability of completion drastically reduces the return to master's students and places master's degrees last on adjusted ROI.

The absolute increase in lifetime earnings is the average gain in income between students completing the degree and the counterfactual earnings of similarly situated students without the degree. Incorporating the time spent getting a master’s degree, the cost of obtaining the degree, and the probability of completing a program generates an “adjusted ROI.” Taking these costs into account drastically reduces the return to the student—and puts the return for master’s degrees dead last. [But] Averages Hide Lots of Information. Business is the single largest field of study for master’s students; but, on average, business master’s degrees have a negative ROI. But graduates from the top performing programs—including Dartmouth, the Massachusetts Institute of Technology, and the University of Pennsylvania— can earn millions more than graduates from other business programs. Students who choose badly could experience a negative ROI of over $1 million. Even in computer science, the field with the highest overall ROI, graduates from some programs experienced negative ROIs.

Takeaways by Macro Roundup® AI

  1. Adjusting lifetime earnings gains for time spent in school, the cost of the degree, and the probability of completion drastically reduces the return to master's students and places master's degrees last on adjusted ROI.
  2. Business, the single largest field of study for master's students, has a negative ROI on average, while graduates of top-performing programs at Dartmouth, MIT, and the University of Pennsylvania can earn millions more than graduates of other business programs.
  3. Students who choose badly could experience a negative ROI of over $1 million, and even in computer science, the field with the highest overall ROI, graduates of some programs experienced negative ROIs.

AI Summary. Children from top-1% families are more than 2x as likely as middle-class students with comparable SAT/ACT scores to attend an Ivy-Plus college, with two-thirds of the gap attributable to higher admissions rates. The authors attribute this advantage to legacy preferences, weight on non-academic credentials, and athlete recruitment.

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.@OppInsights finds that the “Ivy-Plus” (Ivy League, plus UChicago, Duke, MIT, Stanford) admit students from the highest income families scoring in the top 1% of SAT/ACT at far greater rates than those from lower-income families.

Why are high-income students likelier to attend highly selective private colleges?

Core argument: Children from top-1% families are more than twice as likely to attend an Ivy-Plus college (Ivy League, Stanford, MIT, Duke, Chicago) as middle-class students with comparable SAT/ACT scores.

Children from families in the top 1% are more than twice as likely to attend an Ivy-Plus college (Ivy League, Stanford, MIT, Duke, and Chicago) as those from middle-class families with comparable SAT/ACT scores. Two-thirds of this gap is due to higher admissions rates for students with comparable test scores from high-income families; the remaining third is due to differences in rates of application and matriculation. The high-income admissions advantage at private colleges is driven by three factors: (1) preferences for children of alumni, (2) weight placed on non-academic credentials, which tend to be stronger for students applying from private high schools that have affluent student bodies, and (3) recruitment of athletes, who tend to come from higher-income families. Highly selective public colleges that follow more standardized processes to evaluate applications exhibit smaller disparities in admissions rates by parental income than private colleges that use more holistic evaluations.

Takeaways by Macro Roundup® AI

  1. Children from top-1% families are more than twice as likely to attend an Ivy-Plus college (Ivy League, Stanford, MIT, Duke, Chicago) as middle-class students with comparable SAT/ACT scores.
  2. Two-thirds of that attendance gap comes from higher admissions rates for high-income students with comparable test scores, and the remaining third from differences in application and matriculation rates.
  3. The high-income admissions advantage at private colleges reflects preferences for alumni children, weight on non-academic credentials that tend to be stronger among applicants from affluent private high schools, and recruitment of athletes, while highly selective public colleges using more standardized evaluation show smaller disparities by parental income.

AI Summary. After accounting for housing costs, non-college workers now face an urban wage penalty rather than the wage premium that historically offset higher living expenses in dense cities. Housing costs in dense urban centers have risen disproportionately relative to wage growth, eroding the financial benefit of urban employment for these workers.

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Non-college workers now face an urban wage penalty after accounting for housing costs, reversing a historical trend where urban areas offered wage premia that offset higher living expenses.

Do housing costs erase the urban wage premium for non-college workers?

Core argument: Non-college workers face an urban wage penalty once housing costs are accounted for, reversing the historical pattern in which urban wage premia offset higher living expenses.

Recent data indicates a shift in economic dynamics for non-college workers, who now face an urban wage penalty after accounting for housing costs. Historically, urban areas offered wage premia that offset higher living expenses, but this trend has reversed. The cost of housing in dense urban centers has surged disproportionately compared to wage growth, eroding the financial benefits of urban employment for non-college workers. This shift suggests a reevaluation of the economic advantages traditionally associated with urban living, as the rising cost of housing outpaces wage increases, leading to a net financial disadvantage for this demographic.

Takeaways by Macro Roundup® AI

  1. Non-college workers face an urban wage penalty once housing costs are accounted for, reversing the historical pattern in which urban wage premia offset higher living expenses.
  2. Housing costs in dense urban centers have risen disproportionately relative to wage growth, eroding the financial benefit of urban employment for non-college workers.

Robert Armstrong, "Did QE Cause Inflation,"Financial Times, July 19, 2022, https://www.ft.com/content/2c3fc086-b543-4efe-b696-8c84aa919b69

Did QE cause inflation?

QE, bank lending and inflation Did quantitative easing contribute to inflation, and how much? Will quantitative tightening have the opposite effect?

Important questions: it is generally thought that low rates did contribute to inflation, and the Federal Reserve is hoping like hell that higher rates will slow it. The role of QE and QT, as we’ve noted before, is trickier. There is little consensus among practitioners, academics and Fed officials about what central bank bond buying does to the economy and how it does it. It would be a lot better if we had a clear idea of what was going on.

Benn Steil and Benjamin Della Rocca, economists at the Council on Foreign Relations, have argued in a recent post that QE had a very important, perhaps even determining, role in creating inflation. I suspect they’re wrong, but it’s an important argument to consider.

Here’s how the argument goes (picky financial details incoming!).

When, in the course of QE, the Fed buys a Treasury bond from investors, the transaction is completed through an intermediary — a bank. The proceeds from the sale become a customer deposit at that bank, a liability. At the same time, the bank is credited with a reserve deposit at the Fed for the same amount, an asset.

Because a bank will always have fewer reserve assets at the Fed than deposit liabilities, the addition of the same amount to each will push the bank’s reserve/deposit ratio up. QE improved the bank’s liquidity in that sense.

A bank that has more liquidity has an incentive to lend. They don’t have to lend more, but they have reason to, in order to optimise their balance sheet. This is what Steil and Della Roca call the “credit channel” of QE.

In 2020, the Fed bought many billions in Treasuries, but banks’ reserves fell (they call them “excess reserves”; more on this shortly). This, Steil and Della Rocca say, is evidence that “QE was working as hoped” — that is, it was encouraging lending. When lending goes up, they argue, excess reserves fall.

But the gap between the Fed’s holding of Treasuries and reserves grew wider and wider, allowing Steil and Della Rocca to predict that inflation was coming. Here is their chart:

Moving to density: Half a century of housing costs and wage premia from Queens to King Salmon: Extended Excerpt Image 1


“As that gap began rising again in May 2021], with Core PCE inflation running at 3.5 per cent, the Fed should clearly have declared victory and ended its bond buying. Instead, it continued the binge for another 10 months. By that time, March 2022, Core PCE inflation was up to 5.2 per cent, and the Fed should have been well into hiking rates.”

I suspect that this argument is wrong for two reasons, one involving how banks behave, and one about financial plumbing.

I’ve been talking to bankers for several years, as an analyst and a reporter, and I have never heard of talk about the decision to lend or not in terms of having ample liquidity or being liquidity constrained. Instead, the effective constraint is loan demand — the availability of creditworthy borrowers who want money.

I asked my favourite bank analyst, Brian Foran of Autonomous Research, about this, and he confirmed my suspicions. “Ninety per cent of bank lending decisions are, do my customers want to borrow and are they in good shape to do so,” he told me. “I’ve never sat in a meeting with a bank CEO who said ‘I’ve got all these deposits and I have to figure out a way to lend them out’. They might say — I have a high loan to deposit ratio and I have to work that out over the long run,” for example by retaining more of the loans the bank makes rather than selling them on to the secondary market.

It could be that bankers either are unaware or hesitant to admit the role that liquidity plays in lending decisions. But there is another point, made to me by former Fed trader and the “Fed Guy” blogger Joseph Wang. Banks’ lending isn’t constrained by the volume of liquidity, but rather by its cost. If banks need cash they can always borrow it at some price. Reserves at the Fed, if they ever did matter, don’t matter now, because as Steil and Della Rocca point out in a footnote, the Fed eliminated all reserve requirements in March of 2020.

Now the financial plumbing point. Here is a version of the Steil and Della Rocca chart, with two other series added: total bank credit creation, and the balances in the Fed’s reverse repo programme (more on what that is momentarily).

Moving to density: Half a century of housing costs and wage premia from Queens to King Salmon: Extended Excerpt Image 2


Now, one thing in this chart fits very nicely with the Steil/Della Rocca account. The total amount of Treasuries the Fed has added to its balance sheet in the Covid era, $3.3tn (light blue line), at the moment very closely matches the new lending created by US banks (fuchsia line).

Here’s the problem, though. Lending does not track reserves (blue line) at all. Now, as argued above, I don’t think there is much reason that it should. And if those two don’t track, the Steil/Della Rocca argument does not work, because it depends on the idea that higher reserves, driven up by QE, incentivise lending.

And there is another explanation, other than higher lending, for why banks’ reserves have come down. It was, again, explained to me by Joseph Wang. The reserves are being funnelled, somewhat circuitously, into the Fed’s growing reverse repo programme (yellow line).

Here is how that funnelling would take place (now we are getting really technical, so feel free to skip the next two paragraphs). The Fed uses the reverse repo programme to sop up excess liquidity in the banking system that would otherwise force the overnight rate below the Fed’s target. Market participants, mostly money-market funds, can give the Fed their cash and receive an interest-paying Treasury security in return. It’s a collateralised overnight loan to the Fed.

In recent years banks have had more deposits than they wanted, which caused problems with their capital requirements. So they have pushed clients towards money market funds. The money market funds have, in turn, put more and more money into the RRP. The way that transaction takes place is that the money market fund that wants to participate in the RRP makes (another) deposit at a bank, and then that same sum is taken out of the bank’s reserve account at the Fed, and placed in a Fed RRP account. The bank’s reserves at the Fed fall.

I’m not totally confident in any of this, and look forward to hearing what Steil and Della Rocca have to say in response. But it seems to me that if QE encourages lending, the mechanism is much more indirect than the one they suggest.

Zooming back out to why all this matters. Unhedged’s best guess is that QE works primarily by injecting/sopping up liquidity in financial markets, not by encouraging lending. So QT will have its effect by withdrawing liquidity from markets, making them more volatile, diminishing investor risk appetites and increasing demand for cash and risk-free assets. That, rather than weaker lending, is what we should prepare for as QT proceeds.

Ed Comment:PS Foreign-born in NYC more than doubled from 18% in 1970 to 38% in 2010 (probably close to 40% today). 60% of that was Hispanic. Most of the rest was Asian, which is probably skewed low-skilled in the first generation. The share of non-Hispanic whites dropped from 63% to 31%—arguably a proxy for skill. The changes are not insignificant. My cursory take is that the share of lesser-skilled grew significantly. My guess is that this shift probably put downward pressure on lesser-skilled wages, upward pressure on the rents for unskilled workers, and shifted the composition of lesser-skilled jobs toward even lesser-skilled jobs at the margin as supply expanded relatively.

Ed Comment:See 2 graphs below and attached. Management-related/decision-related jobs have nearly doubled since the 1970s. It’s hard to believe that cities, of all places, ran counter to that trend. Perhaps the marginal worker in those additional positions previously didn’t go to college and now they do, but that would make the result a data comparability issue, not an actual change in the economics. What we care about are the economics for a person of a given inherent capability. Given how much your analysis shows the economics have changed, I find it hard to believe the economics haven’t changed, even if data capability perhaps makes the change less than it appears. But by the same token, I also believe that a shift away from middle management/middle capability (i.e. a hollowing out of the middle management in cities) as the explanation seems unlikely too given there was a large nationwide expansion in middle management.

Stan Comment:What David Autor would say on the wages side is that non-college urban adults disproportionately used to hold middle-skill, blue-collar production and white-collar office, administrative, and clerical jobs. This was the case when those jobs were overrepresented in the densest areas, but that is no longer the case -- probably because there's no real productivity advantage to doing them in dense areas anymore. So that puts downward pressure on urban wages for non-college workers. If urban housing prices go up in addition (because of the opposite trend on the college worker side of the economy), that makes it even less attractive to locate that kind of employment in dense areas (and for the people who used to hold those jobs to live there). The equilibration process is then a straightforward spatial one, with call centers and the people who work there ending up in low-density CZs. The non-college urban employment that remains there is then all sorts of relatively poorly paid services work that you can't do from afar -- leisure and hospitality, transportation, personal services, childcare, etc. The educational make-up of the immigrant population is more polarized (more graduate degrees and more folks with less than a 9th-grade education) than that of the native population, so you'd expect to see more immigrants (of both types) in the densest areas even if they didn't cause any of the labor or housing market developments. But I'd be curious to see cause and effect more disentangled for sure. (On a side note, this polarized composition of the immigrant population has really escalated. Immigrants since 2014 are about 50% college-educated versus 33% for native-born, and 10% 9th grade or less versus 2% for natives.)

Ed Comment:I agree with your thought about high-skilled workers driving up the price of real estate for low-skilled workers. But then why doesn't both low-skilled wages rise to compensate for the higher rents and/or the composition of low-skilled workers shift (upward) to equilibrate? Otherwise you have to believe that low-skilled workers would have accepted less in the past, which I find hard to believe. A supply shocks seems easier to reconcile, especially with 45 million foreign-born adults and their 20 million native-born adult children. That's not an insignificant amount, quite the contrary.

Stan Comment:“Ed, Thanks for these comments. We definitely have data on foreign-born migrants in the ACS. The reason why we look at native-born migrants is because that migration is much more driven by market forces than international migration, which is of course to a large extent the result of decisions by politicians. And in the context of looking at how wages and housing costs affect migration, we thought it might be more reasonable just to look at natives. But the data is definitely there and this may be something worth exploring. My intuition is as follows -- On the wages side: you're surely right if we think of one labor market -- high-skilled immigrants raise native wages on net, low-skilled immigrants probably do not and certainly not for the natives they compete with directly. On the housing side: I agree with you that home price variation in this period is basically all driven by the price of land, not by construction costs. My intuition is different from yours here though. I don't think there's a ton of segmentation between areas where college workers and non-college workers live, at least at the margin in the places where supply is constrained and on the time horizon we're looking at. Think of Brooklyn, Somerville (or Cambridge, to be honest), and Arlington, Va.(My favorite movie illustration of this is Remember the Titans, set in 1971, which presents Alexandria, Va, as practically rural Georgia and certainly blue-collar.)As a result, if housing is not segmented -- at least not at the margin -- high-skilled immigrants (or natives) will simply drive up housing prices more than low-skilled immigrants because they are richer. But I should think more about both issues!”

AI Summary. The authors estimate that the Post-9/11 GI Bill raised veterans' bachelor's degree attainment by 2 percentage points and associate's degree attainment by 0.4 percentage points, at a cost of about $313,000 per additional bachelor's degree. They find it reduced wages by 1.8% seven years after separation, attributing this to low-value educational choices and foregone labor market experience.

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Research by @AndrewBarr suggests the Post-9/11 GI Bill led to a 1.8% reduction in wages 7 years post-separation, indicating diminishing returns in a saturated education market.

Does subsidizing veterans' college tuition raise their degrees and earnings?

Core argument: The Post-9/11 GI Bill raised veterans' B.A. attainment by 2 percentage points and associate's degree attainment by 0.4 percentage points, gains modest relative to earlier GI Bills, at a cost of roughly $313,000 per additional bachelor's degree.

The Post-9/11 GI Bill (PGIB) increased educational attainment among veterans, raising B.A. attainment by 2 percentage points and associate's degree attainment by 0.4 percentage points. However, these gains are modest compared to earlier GI Bills, with a cost per additional bachelor's degree of ~$313,000. Despite increased enrollment, particularly in two-year and for-profit institutions, the PGIB led to a 1.8% reduction in wages seven years post-separation. This suggests that the labor market experience lost during education outweighs the benefits of additional schooling, indicating diminishing returns in a saturated education market. The negative earnings impact is attributed to low-value educational choices and the opportunity cost of foregone labor market experience.

Takeaways by Macro Roundup® AI

  1. The Post-9/11 GI Bill raised veterans' B.A. attainment by 2 percentage points and associate's degree attainment by 0.4 percentage points, gains modest relative to earlier GI Bills, at a cost of roughly $313,000 per additional bachelor's degree.
  2. Despite increased enrollment concentrated in two-year and for-profit institutions, the Post-9/11 GI Bill reduced veterans' wages by 1.8% seven years after separation, attributed to low-value educational choices and foregone labor market experience.
  3. The labor market experience lost during schooling appears to outweigh the benefits of additional education for Post-9/11 GI Bill beneficiaries, suggesting diminishing returns in a saturated education market.

Evidence from the GI Bill passed after the 2001 attacks finds that the money did expanded both college enrollment and BA completion, but reduced average annual earnings particularly for individuals with lower test scores and fewer occupational skills who the opportunity costs of enrolling in college as opposed to working is high. Results suggest talent is the limiting constraint, "...For the full set of Army veterans separating during this period,we find positive impacts on educational attainment but negative impacts to earnings. This result suggests that the housing subsidies are so generous as to bedrawing veterans into education and out of the labor market - even for veterans with low marginal returns to education.We explore whether this negative impact is driven by changes in college type, heterogeneity in the returns to college, or reductions in labor force experience. Our results suggest that some combination of lost experience and enrollment choices of veterans from military occupations that have lower predicted returns explains some of the effect. Veterans who were already enrolled when the new policy arrives see very modest degree attainment gains and suggestive evidence of a negative effect on earnings seven years after initial enrollment....Overall, our results suggest caution in providing education subsidies of this generosity to non-traditional students. While educational attainment increased modestly as a result of the benefit expansion, subsequent earnings did not. Furthermore, our analyses suggest that it is unlikely that earnings will ever grow enough to recover the earnings lost during the first seven years following separation….”

Note the negative earnings could be driven by the low value add of many of the schools (the for profit education sector)

Bottomline:“…We find that the PGIB raises the educational attainment of veterans at both the B.A. and A.A. levels. Considering the “overall” impacts of PGIB, the policy raised B.A. attainment by 2 percentage points and associate’s degree attainment by 0.4 percentage points. The impacts for already enrolled veterans are smaller in magnitude and may be zero. These impacts are not large given the generosity of the program and the responses found in studies of earlier GI Bills. Angrist (1993) found that veteran’s benefits on average raised schooling by 1.4 years. We are finding average increases of 2 months!Angrist and Chen (2011) find that veteran status (appropriately instrumented) raises average years of schooling by 0.24 to 0.27 years, raises B.A. attainment by 5 percentage points and associate’s attainment by 6 to 9 percentage points. Despite the large benefit amounts, PGIB delivers smaller educational gains than earlier GI Bills. Given the increase in bachelors attainment of 2.75 percentage points and an average increase in benefits of $8600, this implies a cost per additional bachelor’s degree of around $313,000. This calculation uses the differences in differences estimates. If we instead use the geographic variation to estimate impacts of total benefits used and increased bachelors attainment from a $1000 increase in annual benefits offered, we obtain $1460/.0046 = $317,000 per additional bachelor’s degree. These estimates are on the higher end of the range of estimates summarized by Dynarski (2003) and Dynarksi Hyman Schazenbach (2013). Our most remarkable findings are the negative impacts on labor income for the sample of veterans exposed to the program. Seven years after their separation from the Army, the PGIB lowered wages by about 1.8 percentage points. There are numerous possible explanations for this finding. One possibility is that this estimate constitutes a medium-term effect caused by the reduced labor market experience reflective of the schooling gains that we find. This is not our preferred explanation because the negative impacts survive all specification checks in which we control for years of experience, and the additional months of schooling are small relative to the average reduction in earnings. Our hypothesis is that the negative earnings returns stem from (a) the low value-added of many of the schools chosen by veterans under the PGIB combined with (b) the generosity of the BAH, which may be inducing veterans to forego valuable labor market human capital accumulation in favor of very marginal school enrollments.By pursuing schooling opportunities of marginal value, veterans may be missing out on opportunities to build occupation- or firm-specific human capital or to immediately put their Army-taught skills to work in the labor force. Admittedly, this is a more complex version of the experience story, rather than a rejection of the experience hypothesis. Our findings motivate ongoing work such as Barr et al, et al. (2019), who are providing additional information to separating veterans about the earnings returns to various frequently attended colleges and chosen majors. The contrast between PGIB impacts and that of earlier GI Bills may be driven by the changing composition of the Army. The Korea and Vietnam era GI Bills studied by Angrist and Chen (2011) involved conscription, which likely included large groups of young men who were well suited to college-going and further education. The PGIB was implemented in an all-volunteer Army, and so is offered to a group of young women and men who have already chosen the military over college after leaving high school…”

Dataset: "...We use detailed administrative data for a large set of individuals to examine the effect of increased benefits for higher education. We merge the full set of U.S. Army veterans separating between 1994 and 2016 with National Student Clearinghouse (NSC) data and population-based administrative tax return data. The NSC provides measures of college-going and degree attainment. Information from the 1098-T tax form provides supplementary information on college-going. The 1098-Ts are particularly useful because they have complete coverage of forprofit institutions while the NSC does not. Tax return data also contain information on the utilization of federal tax benefits for higher education, labor income, total income, and demographic characteristics such as marital status and family size. We supplement these with third-party reported information on labor income from Forms W-2 and 1099-MISC. These information returns provide individual-level measures of earnings that are available even if an individual does not file a tax return. Because these administrative tax records are available through 2017, we are able to track income and demographic outcomes for eight or more years after separation from the Army and initial enrollment in college..."
Evidence Related To Enrolment:"...Figure 6 uses “ever enrolled” as the outcome, as measured using NSC data, and plots the coefficients on the interactions between eligibility status and separation year. The estimate for the 2009 cohort is that PGIB raised enrollment by 5 percentage points relative to the 2002 cohort. In Figure 7, we perform a similar exercise using data from Form 1098-T to measure enrollment across various windows post-separation. Using the largest window (within 6 years of separation), we again find enrollment impacts of about 5 percentage points for the 2009 cohort. In addition to confirming the estimates from the NSC data, this figure provides further support for treating the 2007 and prior cohorts as being in the pre-period. The vertical line in each panel indicate the first cohort whose enrollment could have been affected by the higher benefit levels. For example, in panel (a), enrollment within a year of separation could have been affected for those separating in 2008 (as the benefits were available in fall 2009), but would not have been affected for those separating in 2007. The figure illustrates that even though the 2007 and prior cohorts were eligible for the PGIB benefits when they became available in 2009, there was no effect on their decision to enroll. This is consistent with the general pattern of post-separation enrollment and benefit use which tends to occur quickly after separation or not at all…”

You Can't Handle The Truth: The Effects Of The GI Bill On Higher Education And Earnings: Extended Excerpt Image 1


You Can't Handle The Truth: The Effects Of The GI Bill On Higher Education And Earnings: Extended Excerpt Image 2


“…The observed increases in enrollment and months of enrollment translate into modest increases in degree attainment. Figure 9 illustrates the effect of GI bill eligibility on earning a bachelor’s degree within six years of separating from the Army, suggesting B.A. completion impacts of about 2 percentage points for the 2008 and 2009 cohorts. The implied effects for the 2010 cohort (which include potentially endogenous separation in response to PGIB) are larger at 3-4 percentage points. Figure 10 suggests Associates degree completion impacts of 0.5 to 1 percentage point….”

You Can't Handle The Truth: The Effects Of The GI Bill On Higher Education And Earnings: Extended Excerpt Image 3


“…Table 5 presents analogous estimates using the 1098-T data to create our measures of enrollment; here we focus on enrollment occurring within two years of separation (overall and by sector). Panel A presents the difference-in-differences estimates, confirming the modest overall effect on enrollment in the NSC data. Columns (2)-(5) shift the outcome to any enrollment in a four year public, four year private, two year public, or for profit. Since a veteran may enroll in multiple school types, the institution type PGIB effects add up to more than the overall “any enrollment” effect. The PGIB-induced enrollments are tilted toward two-year and for-profit institutions, but there are also gains in enrollment at four-year private schools. Enrollment in a for-profit rises by 2.7 percentage points and enrollment in a two-year public college rises by 2.0 percentage points. In contrast, enrollment in a four-year private institution rises by.6 percentage points, while four year public enrollment falls modestly.We are somewhat cautious in interpreting these difference-in-differences estimates given differences in the composition of eligible and ineligible veteran enrollment and weaker evidence of parallel trends for these outcomes. The corresponding estimates from our geographic variation strategy are in Panels B-D. These estimates are largely uninformative, with the exception of a consistent and large effect of benefits available on four-year private enrollment; an increase in annual benefits of $10,000 corresponds to an increase in four-year private enrollment of 2 percentage points (or 34 percent)….”

You Can't Handle The Truth: The Effects Of The GI Bill On Higher Education And Earnings: Extended Excerpt Image 4


Subsequent Labor Market Outcomes

“…Figure 19 presents our standard event study for labor income measured seven years after separation, illustrating a reduction in labor income of about $1,000 for cohorts separating in 2008 and after. In Table 7, we show PGIB’s impacts on wages, log wages, labor income, log labor income, and indicator variables for positive earnings and labor income, each measured seven years after separation from the Army. The PGIB has consistently negative impacts on wages and labor income for veterans. In Panel A, the PBIG reduces annual earnings seven years after separation by about $900. In the log wages specification (which exclude zeroes), the negative impact is roughly 2 percentage points. The impacts of the PGIB on whether a veteran “has W-2 wages” is small, negative and not statistically significant…”

You Can't Handle The Truth: The Effects Of The GI Bill On Higher Education And Earnings: Extended Excerpt Image 5


“….In Figure 20, we plot the difference in earnings between eligible and ineligible veterans at each year after separation, separately in the pre (2003/04) and post (2008/09) period. In other words, the vertical difference between each dot represents a difference-in-differences estimate at a different year after separation, with the difference at 7 corresponding to our estimate in Table 7. The figure shows that in both periods the earnings of eligible veterans are lower than that of ineligible veterans one year after separation, consistent with the higher post-separation enrollment of eligible veterans. In the pre-period, this gap closes and flips as eligible veterans transition into the labor market; by three years after separation eligible veterans earn between around $1,500 more per year than ineligible veterans. By six to seven years after separation, the difference flattens at around $5,000 to $6,000. While the post-period eligible veterans are also earning more than ineligible veterans by the second year after separation, the difference between their earnings is consistently smaller than in the pre-period. In other words, consistent with the additional enrollment generated by the PGIB, eligible veteran earnings are relatively lower during the years immediately after separation. However, this additional education does not appear to generate subsequent labor market returns. Even seven years after separation, the negative effect of the PGIB persists…”

You Can't Handle The Truth: The Effects Of The GI Bill On Higher Education And Earnings: Extended Excerpt Image 6


“…Figures 28 and 29 repeat this exercise using civilians enrolled at the same school as controls. While perhaps less similar to eligible veterans than ineligible veterans, using civilians allows us to form a significantly larger (and equally distributed across colleges) set of control group individuals. These figures again suggest that the PGIB resulted in modest increases in educational attainment. However, they also indicate a large negative effect of the PGIB on labor income. The analogous estimates are presented in Table 13. The estimates suggest large negative effects on eligible veteran earnings, driven largely by a much higher likelihood of reporting no labor income….”
You Can't Handle The Truth: The Effects Of The GI Bill On Higher Education And Earnings: Extended Excerpt Image 7

Andrew Barr, Laura Kawano, Bruce Sacercote, WIlliam Skimmyhorn and Michael Stevens, "You Can't Handle The Truth: The Effects Of The GI Bill On Higher Education And Earnings" National Bureau Of Economic Research, July 2021, https://www.nber.org/papers/w29024

Ed Comment:I believe sending lower-scorers to college is a poor investment for both the students and society. Worse, they surely took useless courses