The Old College ROI

Today I ran across a graphic from The Economist in March 2015 that shows the return on investment (ROI) to different college majors by level of selectivity of the college the student attended. The charts show that while college pays, it does not pay the same for everyone. More specifically, it does not pay the same for every major. Engineering and math majors have high ROIs, followed by business and economics majors. Humanities and arts majors have lower ROIs on average.

If you’re underwhelmed by the realization, you should be. After all, it’s really common sense and something I’ve written about before here. But it’s a fact that seems incomprehensible to so many (for starters, count the number of votes Bernie Sanders has received). This is imCollege ROIportant because college education is subsidized not by degree, but by the expense of the school the student chooses. An arts major at Stanford is paying the same tuition as the engineering major–and likely borrowing just as much money–but their returns on investment for those educations are vastly different. Put another way, the value of those degrees are very different, even if the price of the degrees is the same.

Interestingly, though, the ROI by degree does not change much based on the selectivity of the school (typically a measure of quality). Looking at each of the degree types, there is very little obvious correlation between selectivity and ROI (taking into account financial aid; i.e., based on net-cost not listed tuition). While students from more selective schools may earn higher starting salaries, the higher cost of their education means they are getting no better return on their financial investment than students of similar majors at much less selective schools.

This suggests that the market for college graduates is actually working pretty darn well when you take into account students’ degrees (i.e., the value of the human capital they develop in college).

It also suggests we should reconsider federal policy for student loans. If we insist on continuing to subsidize higher education (and all the ills that creates), at least we could do it more intelligently by tying loan amounts to degree programs rather than tuition levels.

Death, Taxes, and Opportunity Costs

They say two things are unavoidable in life: death and taxes. I’d like to propose adding opportunity costs to that list.

In his State of the Union address in January, President Obama announced his support for a “moonshot” researchsotu initiative to cure cancer. “For the loved ones we’ve all lost, for the family we can still save, let’s make America the country that cures cancer once and for all,” the President announced to a hearty round of applause. And deservedly so. I suspect there are few, if any, people whose lives have not been touched by cancer, either suffering it directly or with loved ones.

Since then, I’ve had several friends on Facebook post their support of the President’s proposal and their personal desire to eradicate cancer. Some even arguing we should spend “whatever it takes” to rid ourselves of this horrible disease. But while I empathize with their heart-felt conviction, I can’t help but ask, “at what cost?” And I don’t mean (just) the dollars and cents. Okay, the billions of dollars. I mean the opportunity cost of focusing so many resources on the goal of “curing” cancer.

As an economist, one (should) necessarily asks the question: what is the marginal benefit versus the marginal cost of eliminating cancer. Sounds cold and heartless? Bear with me a minute.

According to the US Dept of Health & Human Services, Continue reading “Death, Taxes, and Opportunity Costs”

Database of Federal Regulations

Omar Al-Ubaydli and Patrick McLaughlin (both at George Mason University) have an article in the most recent issue of Regulation & Governance documenting their RegData database, which “measures [federal] regulation for industries at the two, three, and four-digit levels of the North American Industry Classification System.” While any attempt to quantify regulations is fraught with problems, as the authors note in their paper, their text-based approach would seem as good a method as any (and superior to some) for providing a numerical measure of regulation that could be used for empirical research. And what’s even better, the data are freely available here. The abstract of the paper reads:

We introduce RegData, formerly known as the Industry-specific Regulatory Constraint Database. RegData annually quantifies federal regulations by industry and regulatory agency for all federal regulations from 1997–2012. The quantification of regulations at the industry level for all industries is without precedent. RegData measures regulation for industries at the two, three, and four-digit levels of the North American Industry Classification System. We created this database using text analysis to count binding constraints in the wording of regulations, as codified in the Code of Federal Regulations, and to measure the applicability of regulatory text to different industries. We validate our measures of regulation by examining known episodes of regulatory growth and deregulation, as well as by comparing our measures to an existing, cross-sectional measure of regulation. Researchers can use this database to study the determinants of industry regulations and to study regulations’ effects on a massive array of dependent variables, both across industries and time.

Now, if only there was such a database of State-level regulations.

Craft Beer in the US: History, Stats and Geography

Ken Elzinga (Virginia) and Carol and Victor Tremblay (Oregon State) have a paper in the latest Journal of Wine Economics titled “Craft Beer in the United States: History, Statistics and Geography.” The paper provides a great overview of the history of the craft brew industry as well as some interesting analysis on the geographic development of the industry. The history section seems to draw heavily on Tom Aticelli’s 2013 book The Audacity of Hops: The History of America’s Craft Beer Revolution, but provides a much more concise summary. And paired with the statistical overview of the beer industry in general and the empirical analysis of the craft brew industry that follows, this paper offers a nice, short primer for anyone interested in the history (and economics) of the craft brew industry in the US. The paper’s abstract follows:

We provide a mini-history of the craft beer segment of the U.S. brewing industry with particular emphasis on producer-entrepreneurs but also other pioneers involved in the promotion and marketing of craft beer who made contributions to brewing it. In contrast to the more commodity-like lager beer produced by the macrobrewers in the United States, the output of the craft segment more closely resembles the product differentiation and fragmentation in the wine industry. We develop a database that tracks the rise of craft brewing using various statistical measures of output, number of producers, concentration within the segment, and compares output with that of the macro and import segment of the industry. Integrating our database into Geographic Information Systems software enables us to map the spread of the craft beer segment from its taproot in San Francisco across the United States. Finally, we use regression analysis to explore variables influencing the entrants and craft beer production at the state level from 1980 to 2012. We use Tobit estimation for production and negative binomial estimation for the number of brewers. We also analyze whether strategic effects (e.g., locating near competing beer producers) explain the location choices of craft beer producers.

Douglass C. North, 1920-2015

I received word today that Douglass North passed away yesterday at the age of 95 (obit here). Professor North shared the Nobel Prize in Economic with Robert Fogel in 1993 for his work in economic history on the role of institutions in shaping economic development and performance.DoughNorth_color_300-doc

Doug was one of my first professors in graduate school at Washington University. Many of us in our first year crammed into Doug’s economic history class for fear that he might retire and we not get the chance to study under him. Little did we expect that he would continue teaching into his 80s. The text for our class was the pre-publication manuscript of his book, Institutions, Institutional Change and Economic Performance. Doug’s course offered an interesting juxtaposition to the traditional neoclassical microeconomics course for first-year PhD students. His work challenged the simplifying assumptions of the neoclassical system and shed a whole new light on understanding economic history, development and performance. I still remember that day in October 1993 when the department was abuzz with the announcement that Doug had received the Nobel Prize. It was affirming and inspiring.

As I started work on my dissertation, I had hoped to incorporate a historical component on the early development of crude oil futures trading in the 1930s so I could get Doug involved on my committee. Unfortunately, there was not enough information still available to provide any analysis (there was one news reference to a new crude futures exchange, but nothing more–and the historical records of the NY Mercantile Exchange had been lost in a fire).and I had to focus solely on the deregulatory period of the late 1970s and early 1980s. I remember joking at one of our economic history workshops that I wasn’t sure if it counted as economic history since it happened during Doug’s lifetime.

Doug was one of the founding conspirators for the International Society for New Institutional Economics (now the Society for Institutional & Organizational Economics) in 1997, along with Ronald Coase and Oliver Williamson. Although the three had strong differences of opinions concerning certain aspects of their respective theoretical approaches, they understood the generally complementary nature of their work and its importance not just for the economic profession, but for understanding how societies and organizations perform and evolve and the role institutions play in that process.

The opportunity to work around these individuals, particularly with North and Coase, strongly shaped and influenced my understanding not only of economics, but of why a broader perspective of economics is so important for understanding the world around us. That experience profoundly affected my own research interests and my teaching of economics. Some of Doug’s papers continue to play an important role in courses I teach on economic policy. Students, especially international students, continue to be inspired by his explanation of the roles of institutions, how they affect markets and societies, and the forces that lead to institutional change.

As we prepare to celebrate Thanksgiving in the States, Doug’s passing is a reminder of how much I have to be thankful for over my career. I’m grateful for having had the opportunity to know and to work with Doug. I’m grateful that we had an opportunity to bring him to Mizzou in 2003 for our CORI Seminar series, at which he spoke on Understanding the Process of Economic Change (the title of his next book at the time). And I’m especially thankful for the influence he had on my understanding of economics and that his ideas will continue to shape economic thinking and economic policy for years to come.

Do Medical Marijuana Laws Increase Hard-Drug Use?

According to a recent study by Yu-Wei Luke Chu in the Journal of Law & Economics, the answer is not just “No,” but that medical marijuana laws may actually decrease heroin use as consumers substitute the legal marijuana for heroin. Below is the abstract:

Medical marijuana laws generate significant debate regarding drug policy. For instance, if marijuana is a complement to hard drugs, then these laws would increase the usage not only of marijuana but also of hard drugs. In this paper I study empirically the effects of medical marijuana laws by analyzing data on drug arrests and treatment admissions. I find that medical marijuana laws increase these proxies for marijuana consumption by around 10–15 percent. However, there is no evidence that cocaine and heroin usage increases. From the arrest data, the estimates indicate a 0–15 percent decrease in possession arrests for cocaine and heroin combined. From the treatment data, the estimates show a 20 percent decrease in admissions for heroin-related treatment, although there is no significant effect for cocaine-related treatment. These results suggest that marijuana may be a substitute for heroin, but it is not strongly correlated with cocaine.

Fun (Facts & Fiction) With Numbers: Health Care Edition

The graph below, courtesy of the Kaiser Family Foundation, is featured in a VOX post purporting to explain why your health bills are gettng larger (all in one chart!).

kff deductiblesThe article focuses on the fact that deductibles have risen so dramatically as a major explanation for why it seems like we’re spending so much more on health care, even as health care expenses have been growing more slowly. There is some truth in the claim, and especially to the argument that people are more careful spending on health care when they have to pay for more of it up front, but there are some serious problems with this chart that can lead one to some pretty wrong conclusions.

First, what the graph doesn’t reflect is that the increase in premiums and the increase in deductibles are not, as the picture would appear, necessarily moving together for the people paying them. These are averages, and averages hide lots of information. Moreover, the graph makes it look like the two are increasing are independent of one another; i.e., that people are paying both 24% more in premiums and 67% more in deductibles since 2010. But that’s not the case. Since the ACA, many employers have moved to high-deductible plans that have lower premiums than the low-deductible plans that were popular pre-2010 (see below). What the graph hides is that people with low-deductible plans have seen higher than 24% increases in premiums while people with high-deductible plans have seen much lower increases in premiums–if not actual reductions in their premiums. What has changed is not necessarily how much people are paying for healthcare, but how they are paying it: in premiums or in deductibles. The graph above fails to show that.

Second, looking more closely at the news release on the Kaiser website, the 67% increase in deductibles is an increase in total deductibles paid–not the increase in the average deductible per employee. It reflects not only any increase in deductibles, but the increase in the number of people who have (higher) deductibles. That’s a pretty sneaky way to inflate the numbers on the graph to make it look like the average person is actually paying that much more. Consider the following two graphs, also from the Kaiser Family Foundation 2015 survey. kff-mkt-share-type kff-premiums The table on the left shows that premiums for high deductible plans (HDHP/SOs) are significantly lower than premiums for other types of policies. The table on the right shows that the market share of HDHP/SO plans has increased tremendously since gaining ground in 2006. In fact, to relate this to the first graph above, participation in HDHP/SO plans almost doubled from 2010 to 2015, meaning that 50 points of the 67% increase in deductibles could be attributable solely to more people choosing high deductible plans, specifically because the premiums are so much lower. And what the Kaiser report doesn’t say is how much employers contribute to the HSA plans that often accompany HDHP/SO plans. For some individuals, switching to the HDHP/SO plan may actually reduce their total out-of-pocket expense for health care. So while the original graph makes it look like everyone is paying more, that is likely not true for many people–and certainly not at the rate the original graph might suggest.

Finally, because the first graph is in percentages, it hides even more information that changes the story. Suppose deductibles had been $500 and increased to $1,000 or even $2,000. That’s would be a 100-300% increase! 300%! But that’s only $1,500. Not that $1,500 is chump change, but compared to the average annual premium of $6,251 (see the left-hand table above), that’s just 24%–ironically, about the total increase in premiums over the past five years. Even if that $500 deductible grew at the 67% shown in the first graph (which we know from #2 that it didn’t), the increase in actual out-of-pocket health care costs would have been $335–not quite the cost of two lattes a week.

Mark Twain is famously quoted as saying (and actually quoting Disraeli), “There are three kinds of lies: lies, damned lies and statistics..”  I’m not saying VOX (or Kaiser) are lying. But be careful when you see things like VOX’s report about some “fantastic new chart.” It’s far too easy to be misled if you don’t think carefully about the numbers being thrown about.

Bonus: If you’re interested in what the research says about the effects of high deductible plans, RAND has a nice summary site with links to additional resources.