See me at the New York Times

 I have taken up blogging at the New York Times and so am updating this blog infrequently.  Please follow my Times posts here.

It's a new year. Time for a dubious causal claim!

A new study shows that richer neighborhoods in New York City are closer to public transportation and economic activity. It then makes a big leap, asserting that the causal arrow runs from transportation to income. This is a plausible hypothesis, but the study provides no empirical support for this interpretation. 

The study definitely does not support the sensationalist assertion that a college education is less important for financial well-being than access to public transportation:

“In New York, mass transit is the path to economic mobility, not education,” said Mitchell Moss, the center’s director. “It’s far more important to have a MetroCard than a college degree.”

The quote is from the Wall Street Journal

http://www.wsj.com/articles/report-connects-jobs-transportation-web-1420246652

Google "Report Connects Jobs, Transportation Web" if the WSJ link hits a paywall. 

The full study is here:

http://wagner.nyu.edu/rudincenter/wp-content/uploads/2014/12/NYURudinJobAccessReport.pdf

 

Feds explore eliminating private debt collectors for student loans

Feds explore eliminating private debt collectors from student loan system. Terrific move. 

On The Perils of Being a Policy-Engaged Academic

Jon Gruber is getting a ton of heat for his comments on Obamacare, which were recorded in seminars. Jon was the intellectual architect of Obamacare, which has fundamentally changed how health care is funded in this country. Millions who did not have health insurance are now covered. Few academics have had the policy impact that Gruber has.

When you have influence, out come the knives. Opponents of Obamacare have scoured the internet for "gotcha" Gruber moments.  And because Jon is a forthright academic, there are lots of examples of him saying what's true rather than what is politically expedient.  There are probably also cases of him being wrong, which happens even to MIT economists. A video of me being impolitic and/or wrong would be a breeze to compile.

I am proud to say that Gruber was my doctoral adviser at MIT. He taught me how to be a rigorous and relevant researcher, and for that I am eternally grateful.

Is Revenue at Public Colleges Really Stagnant?

I wrote in a recent column in the New York Times that tuition at public colleges has doubled over the past 25 years but that, paradoxically, per-student revenue has been flat. The explanation? State support for public colleges has plummeted, leaving colleges increasingly dependent on tuition to fill the revenue gap. 

All of the figures discussed in the column were adjusted for inflation. The most common statistic used to adjust for inflation is the Consumer Price Index (CPI), calculated by the Bureau of Labor Statistics. As noted by a commenter at the Upshot, the report I cited for trends in college revenues did not use the CPI but rather an index developed by the State Higher Education Executive Officers (SHEEO), an association of public colleges. Is this price index a biased measure that inaccurately portrays revenue trends for public colleges?  

To cut to the chase: the trends are unchanged if we use CPI to adjust for changing prices between 1988 and 2013. Revenues at public colleges have risen little, their net tuition revenue has more than doubled, and state support has sunk.

Why does the association of public colleges use a specialized index? The Higher Education Cost Adjustment, or HECA, is intended to track changes in the costs of inputs purchased by colleges, while the CPI tracks changes in the costs of items purchased by households. Since colleges and consumers buy different things, the CPI and HECA weight their prices differently.  

Colleges are more labor intensive than other industries, so the HECA puts a heavier weight on employment costs than the CPI, with three-quarters of the HECA index going to the Employment Cost Index and a quarter to the Gross Domestic Product Implicit Price Deflator. Both of these indices are calculated by federal agencies - the Bureau of Labor Statistics and Bureau of Economic Analysis, respectively. This was a good choice on SHEEO’s part – it’s better to have a third party calculating the index so as to avoid the temptation of manipulating the statistics for self-serving purposes.

When wages go up faster than inflation, the HECA goes up faster than the CPI, since it weighs labor costs more heavily than the CPI. But since wages for most workers have been flat for decades, differences between the CPI and the HECA are small. The online CPI calculator tells us that a consumer in 2013 needs $1.97 to buy what she could have bought for a dollar in 1988. The HECA index says that a college in 2013 needs $2.12 to buy the inputs it could have bought for a dollar in 1988.   

How do the different price indices affect our conclusions about stagnant college revenue and spiraling tuition prices? Not much. According to the HECA, public colleges are getting just about the same revenue per student in 2013 ($11,500) as in 1998 ($11,300). According to the CPI, colleges were only getting $10,500 in 1988, so it’s a bigger jump to $11,500. Put differently, the CPI says the revenue of public colleges has increased by 8% over the last quarter century, while the HECA says it’s 2%. And the two indices agree that state support for public colleges has plummeted, while tuition revenue has doubled.  The index might differ, but the story remains the same. 

Kids' Education and Parents' Longevity - Authors' Reply and Mine

I posted a piece to the New York Times Upshot this week discussing  a paper by Esther Friedman of RAND and Rob Mare of UCLA.  The authors' reply is now posted at the end of my Upshot article. Go read it and then come back for my take, below.

Back? Great. 

The heart of my post was that the paper did not convincingly demonstrate a causal relationship. If the paper did not claim causality, this would be a hollow criticism. In their reply the authors say "Nowhere in our article do we assert that the relationship we find is causal." I went back and reread the paper. It uses causal language.  It is especially prevalent in the abstract and discussion, where editors and readers expect the punchline. Many a reader will read only these sections. 

This may seem nitpicky. But language matters. While academics might read between the lines to discern that a paper isn't really claiming causality, the lay person and policymaker almost certainly won't. 

Language in the paper that describes a causal relationship, with emphases added, is below. Note you don't have to use the word "cause" to imply causality. "Guns kill" is a causal statement.  So is "Guns have independent effects on mortality." 

Abstract:

"We show that adult offspring’s educational attainments have independent effects on their parents’ mortality, even after controlling for parents’ own socioeconomic resources."

"These findings suggest that one way to influence the health of the elderly is through their offspring."

Introduction:

"This article investigates whether the educational attainments of individuals should be viewed as a family resource, benefiting not only the individuals themselves but also their parents."

Discussion:

"This research isolates a mechanism through which differences in health and mortality come about, to wit: the differential educational attainments of offspring."

"Our results suggest that in the United States, parents benefit from having more-educated offspring — a benefit that extends beyond the effects of parents’ own SES."

"Although health policy research typically emphasizes individual interventions with immediate outcomes, this work shows that another way to influence the health of the elderly is through their offspring."

"Policies targeting one generation of the family may set in motion a series of reactions that lead to improved health for others in previous generations, subsequent generations, and the broader family unit."

"This article shows that generations of families are interdependent, and the well-being of one generation does not necessarily come at the expense of the well-being of other generations. Improving offspring’s lives may benefit not only the offspring themselves over their lifetimes but their parents as well."

Dosage Matters. If It Doesn't, Your Study is LIkely Wrong

A warning sign of a biased estimate of a treatment effect: no relationship between dosage and the outcome.

In this study highlighted in the New York Times, the authors conclude that five minutes of regular running has the same effect on health as 150 minutes.

This statistical red flag signals that runners, as a group, are different from non-runners in ways that have their own effect on health. In stats parlance: omitted variables bias. 

H/T to Professor Ariel Kalil.

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PLUS loans are not student aid

This article (link at end of post) does an amazing job of highlighting the problems with parental PLUS loans. Kudos to Rachel Fishman at New America Foundation for nailing this topic so well. 

This vignette, which Fishman draws from a piece advocating the loosening of credit standards on PLUS loans, is supposed to create outrage that this parent was denied a PLUS loan:

"Kristina, a senior English major at Claflin University, needed a $10,770 Parent PLUS Loan to finish her senior year; her request was denied. Her single father is doing his best, but he has only a high school education and seven other children to support. Like so many HBCU students, Kristina is looking toward a career of service after graduation, as an officer in the U.S. Air Force."

A single father with only a HS education and 7 kids should take out a loan that is not dischargeable in bankruptcy so his kid can finish college? That's insane. 

The solution here is an income-contingent loan for the student whose prospects are so bright - not to place  Kristina's dad and six siblings in economic peril. 

http://www.edcentral.org/plus-loans-grants/

The (Fixable) Problem with Pay It Forward

Cross-posted from the Hamilton Project blog:

Michigan has now joined Oregon in proposing a “Pay It Forward” student lending system in which students pay no tuition up front and pay back a fixed percentage of their income after college. This sounds very similar to an income-contingent repayment (ICR) system, which I advocate in a recent Hamilton Project proposal. But there is a key difference between Pay It Forward and ICR, and it’s one that makes Pay it Forward unworkable as proposed.

In both the Michigan and Oregon versions of Pay It Forward a borrower in repayment contributes a fixed percentage of income for a fixed number of years. Her liability is not denominated in dollars, as in a standard loan, but as a fixed number of payments. 

An aside on vocabulary: economists call this a graduate tax –a tax on earnings for those who have gone to college.  It’s called a tax, rather than a payment, because a borrower can’t buy her way out of the liability. The borrower is taxed for 25 years, even if she has repaid the principal (plus interest) after a few years.

In the proposed Pay It Forward systems, a graduate who does extremely well in the labor market will end up repaying many times over the cost of her education, while one who does poorly will pay much less. There is therefore cross-subsidization in this system, with the “winners” paying some of the college costs of the “losers.”

Economic theory – and history – shows that loans funded by a graduate tax won’t work because those expecting high earnings won’t participate. Yale famously attempted a graduate tax in the 1970s, lending money to its undergraduates and then having them pay back a fixed percentage of their income for a fixed number of years. What happened? Yale students who expected high earnings (e.g., aspiring investment bankers) shunned the program, while those who expected low earnings (e.g., aspiring artists) embraced it. Yale’s program spiraled into insolvency.

This is a classic case of adverse selection – borrowers who would be subsidized participate while those who would subsidize stay away. This is unsustainable, as without the high earners the system does not get enough payments to cover tuition costs. Because of adverse selection graduate tax can work only if participation is mandatory, with everyone forced into the borrowing pool.  

Another interesting aside: this is similar to the dynamic in insurance markets, which collapse if sick people buy coverage and healthy ones go without. Adverse selection is why coverage is mandatory under the Affordable Care Act, and it’s why health policy wonks have so carefully tracked the enrollment of young, healthy people in the new insurance exchanges. The young, healthy participants cross-subsidize the older, sicker participants, just as high earners subsidize low earners in a (mandatory) graduate tax.

Neither the Oregon nor Michigan plan requires all borrowers to participate in their programs. I (and other economists) therefore predict that the programs, as currently proposed, will be brought down by adverse selection.

A minor tweak to Pay It Forward, as outlined in my Hamilton proposal, will maintain its positives (simplicity, insurance against bad draws in the labor market) while eliminating the negative (unsustainability). The change is this: denominate debt in dollars, and let borrowers pay their debt. If a student borrows $25,000 and (due to pluck and luck) earns enough that she has paid back the principal plus interest after just ten years, she will stop paying into the program. 

If a borrower instead runs into hard times and still owes money after 25 years, the balance will be forgiven. In this way, Pay It Forward and my income-contingent repayment proposal both subsidize low earners.  The key is that my modification keeps high earners from fleeing the program, transforming Pay It Forward it into a universally attractive program rather than one that appeals primarily to low earners. 

As pointed out by Theda Skocpol, universal programs are politically more resilient than those that primarily benefit poor people. Keeping high earners in Pay It Forward will give it the broad-based political support it needs to survive. 

My tweak also makes transparent the cost of Pay It Forward. Some borrowers cannot, given their low earnings, pay off their loans. Their forgiven debt is the cost of the program, and is borne by all taxpayers.  Without my tweak, the costs of Pay It Forward are disguised by the fairytale that it is self-funding. When high earners shun the program and it collapses, the taxpayers will be on the hook to bail it out. The financial cost, in the end, is the same, but the political cost is much higher, since the program will be deemed an expensive failure.  

With the political momentum behind Pay It Forward, we have a rare opportunity to restructure and reimagine how we pay for college. Get the design right, and we will have a financially and politically sustainable system for funding college that works for students and taxpayers. Get the design wrong, and we will have a spectacular flameout that sets back reform for another generation.

Girls' Growing Advantage on PISA Tests

There is a long history of academic articles and social commentary on boys' advantage on tests in math and science. Two low points: the Barbie doll who chirped "Math class is tough!" and Larry Summer's off-the-cuff, uninformed opinions on why there are not more women in science.

Girls have now caught up with boys on math and science in many tests, including the PISA, which was released last week. At least one article pointed out that, in the US, there is no longer a gender gap in math and science scores.

I saw no articles on a much more striking result: girls' advantage on reading scores is absolutely enormous. This gender gap is much, much larger than the math gap was ten years ago.

When do we get a Ken doll whining "Literacy is hard?" Will pundits soon enlighten us as to why boys are intrinsically unmotivated to read?

Proposal for Reforming Student Loan Repayment

Last week I released, through the Hamilton Project of the Brookings Institution, a proposal for reforming the repayment of student loans. This news item from my school's website sums up the event and press coverage:

On October 21, The Hamilton Project hosted a forum on the evolving role of higher education in American society.  At the forum, Susan Dynarski presented a paper titled "Loans for Educational Opportunity: Making Borrowing Work for Today's Students," which served as the focus of a roundtable discussion. The paper was co-authored by Education Policy Initiative postdoctoral research fellow Daniel Kreisman.

Dynarski and Kreisman propose a single, income-based loan repayment system that automatically deducts payments from borrowers' paychecks to replace the current array of repayment options. Dynarski emphasized that the U.S. currently has "not a debt crisis, but a repayment crisis," stating: 

"We have a repayment crisis because student loans are due when borrowers have the least capacity to pay. It often takes years for college graduates to settle into a steady, high-paying job that reflects the value of their education."

Dynarski and Kreisman's proposal was one of three papers released by The Hamilton Project suggesting major changes to the financial aid system. Their work received coverage in The New York Times, The Chronicle of Higher Education and Inside Higher Education.

Postdoc Position in Education Research at Univ of Michigan

We are looking for a great postdoc to work on our team here at University of Michigan. We have a tremendous set of resources for a recently-minted social scientist (econ, sociology, poli sci, education, psych...) who wants to broaden and deepen skills in experiments, quasi-experimental analysis, and working with state and district partners in evaluation.

http://www.edpolicy.umich.edu/training/epi-postdoc.pdf

The Missing Manual: Using National Student Clearinghouse Data to Track Postsecondary Outcomes

I have a new paper with two great colleagues, Steve Hemelt (former Michigan post-doc, now a prof at UNC-Chapel Hill) and Josh Hyman (current post-doc, newly-minted PhD in economics and public policy from Michigan). The three of us have worked extensively with data from  the National Student Clearinghouse, and in this paper we share insights and advice about this relatively new data rource.

"This paper explores the promises and pitfalls of using National Student Clearinghouse (NSC) data to measure a variety of postsecondary outcomes. We first describe the history of the NSC, the basic structure of its data, and recent research interest in using NSC data. Second, using information from the Integrated Postsecondary Education Data System (IPEDS), we calculate enrollment coverage rates for NSC data over time, by state, institution type, and demographic student subgroups. We find that coverage is highest among public institutions and lowest (but growing) among for-profit colleges. Across students, enrollment coverage is lower for minorities but similar for males and females. We also explore two potentially less salient sources of non-coverage: suppressed student records due to privacy laws and matching errors due to typographic inaccuracies in student names. To illustrate how this collection of measurement errors may affect estimates of the levels and gaps in postsecondary attendance and persistence, we perform several case-study analyses using administrative transcript data from Michigan public colleges. We close with a discussion of practical issues for program evaluators using NSC data."

Government Jobs and "Miserly" Pay

This article lacks perspective on what low-wage workers earn in the US:

"And while pay for senior civil servants can be generous, other salaries can be equally miserly. Wages can vary depending on the location, but in Philadelphia, jobs at salary level GS-2 — a post that typically goes to someone with a high school diploma and no experience — pay as little as $24,379 annually."

 My quick tabulation of the March 2012 Current Population Survey (I have it on DropBox, if you are stymied by the BLS blackout!) shows that, among full-time workers in their early twenties with only a high school degree, median earnings are $18,000 and the 75th percentile is $28,000. Government work has always been a safe haven for those with low skills, and $25,000 a year does not look miserly for those competing for jobs in this particular market. A G-2 is a great job for someone with no experience and little education.

Sex! Money! Religion!

I spend a lot of time bashing epidemiological papers, because they are such easy targets and get such breathless headlines. I am pleased to have the opportunity to bash an economics paper, courtesy of a tip from esteemed colleague David Figlio. David pointed me to a paper that concludes that more sex produces higher wages. It's forthcoming in the International Journal of Manpower (no, I am not making this up).

When we compare the sexual activity of those with high and low wages, we might worry that there are confounding factors that lead a person to succeed in both the mating market and labor market. For example, if you are persuasive enough to talk someone out of their pants, you may well be persuasive at talking yourself into a raise.  If you have the health and energy to be productive at work, those traits help you be more reproductive, too. Simultaneity bias is also plausible: the relationship could run the other way, with higher wages leading to more success in the sex market.

If we wanted to apply gold-standard research methods to this question, we would gather up volunteers, randomize them into treatment and control groups, and expose members of the the treatment group to an intervention that increases their sexual activity. I will leave this intervention to your imagination. We would then compare the wages of the treatment and control group to get the effect of the intervention. A bit more statistical fiddling would get us the causal impact on sex on wages, at least for those who were stimulated to action by the intervention.

In this paper, the author does not run a randomized trial, but instead uses observational data to draw his conclusions - specifically, an instrumental-variables strategy.   The idea here is to find a variable (the "instrument") that is correlated with the "treatment" of interest (sex) but is correlated with the outcome (wages) only through that treatment.

In this particular context, an instrument is valid if it 1) is correlated with sexual activity and 2) is not correlated with wages through any channel except sexual activity. The first condition is called the relevance condition and the second the exclusion restriction. 

The relevance condition is testable. A strong instrument (must ... resist... salacious ... pun) scores an F-test of 10 or above in the equation that estimates the relationship between sex and the instrument. Instruments that pass this test are a dime a dozen. Don't let a strong instrument turn your head - hold out for a plausible exclusion restriction.  The exclusion restriction is not formally testable - it's an identifying assumption. A good instrumental-variable paper will kick the tires hard on the instrument, using both data and knowledge of the world to make the case that there is no possible channel through which the instrument affects the outcome of interest.

In this paper, the author's identifying assumption is that religiosity has no relationship with economic activity except through its (negative) correlation with sexual activity.

Could religion have a relationship with wages, other than through its "effect" on sex? Are these results believable?

I now channel Max Weber (who penned The Protestant Ethic and the Spirit of Capitalism):

Max says "Ja," and "Nein."

Cranky Haiku to Institute of Education Sciences, written on a Sunday in August

grants due early fall
cast shadow on last weekends
of fading summer

Caffeine Drives the Finnish Education Miracle

I have discovered the driver of the Finnish education miracle! It's not the lack of testing, or the well-paid teachers, or the late school-starting age. It's that Finns are the top coffee drinkers in the world, by a long shot. Per-capita consumption is 12 kilos a year, three times that in the the US. Norway is a distant second at 10 kilos.

Since each kilo produces about 120 shots of espresso, this implies that Finns (including kids!) average four cups coffee a day. Take the kids out of this denominator (17% of the population is under 15) and you get closer to five cups a day per adult.  According to recent, crappy research on the health effects of coffee, everyone in Finland should now be dead.

Causal Inference: Coffee Edition

Aigh! Coffee won't kill me, but bad science (and science reporting) might. 

"In a study of more than 40,000 individuals, researchers found that people who drink more than 28 cups per week (that's about four a day) have a 21 percent increased mortality risk and a more than 50 percent increased risk if under 55."

Kudos to the reporter for helpfully dividing 28 by 7 for the reader! I might have severely misinterpreted this research otherwise.

Now, how about we give the magnitudes of our estimates a sniff test before publishing? Four cups of coffee a day increases mortality by 50%? This is enormous. Note that this is an increase in mortality from all causes - not just mortality from conditions that might have a plausible, theoretical link to coffee consumption, such as bladder cancer, uncontrolled tremors, and concussion from bouncing off walls. 

Here is a sniff test of the magnitude of this estimate: a similar, correlational analysis showed that light smoking (less than half a pack of day) is associated with an increase in all-cause mortality of 30%. Heavy smoking (more than half a pack a day), an increase of 80%.  These magnitudes are in the same ballpark as the coffee study, which immediately suggests to me that the coffee estimates are absurd. 

I would hazard that maybe, just maybe, there are confounding factors that the coffee study did not pick up. For example, the authors did not control for physical activity, education or marital status. If inactive, single, high school dropouts drink more coffee, we would get the inflated estimates we see in this paper, since this population dies younger than those who are active, married and better educated.

Now, the smoking estimates have the same weakness as the coffee estimates: they are conditional correlations that do not imply cause and effect. The critical difference is that in the case of cigarettes we have plausible theoretical links between exposure and mortality (lung cancer, emphysema) and decades of clinical and lab science that shows convincingly that cigarettes kill people. Coffee? Nada, though the bluestockings have been trying for a long time to show that it must be harmful. With this lack of theory and evidence linking coffee and mortality, the coffee researchers should be especially cautious in interpreting their correlations as causal.

So, file this one under lousy research as well as lousy reporting. This study gives the cocoa case a run for its money. We are fast building a hall of shame of hot-beverage research and reporting.