Michael Sykuta

NSF Funding for Economics Research: Good or Bad?

The latest Journal of Economic Perspectives includes a pair of papers debating the social value of economics research funding from the National Science Foundation, featuring Robert Moffitt from Johns Hopkins and Tyler Cowen and Alex Tabarrock from George Mason. The abstracts of their respective viewpoints follow:

Robert Moffitt: “In Defense of the NSF Economics Program“
The NSF Economics program funds basic research in economics across all its disparate fields. Its budget has experienced a long period of stagnation and decline, with its real value in 2013 below that in 1980 and having declined by 50 percent as a percent of the total NSF budget. The number of grants made by the program has also declined over time, and its current budget is very small compared to that of many other funders of economic research. Over the years, NSF-supported research has supported many of the major intellectual developments in the discipline that have made important contributions to the study of public policy. The public goods argument for government support of basic economic research is strong. Neither private firms, foundations, nor private donors are likely to engage in the comprehensive support of all forms of economic research if NSF were not to exist. Select universities with large endowments are more likely to have the ability to support general economic research in the absence of NSF, but most universities do not have endowments sufficiently large to do so. Support for large-scale general purpose dataset collection is particularly unlikely to receive support from any nongovernment agency. On a priori grounds, it is likely that most NSF-funded research represents a net increase in research effort rather than displacing already-occurring effort by academic economists. Unfortunately, the empirical literature on the net aggregate impact of NSF economics funding is virtually nonexistent.

Tyler Cowen & Alex Tabarrock: “A Skeptical View of the National Science Foundation’s Role in Economic Research”
We can imagine a plausible case for government support of science based on traditional economic reasons of externalities and public goods. Yet when it comes to government support of grants from the National Science Foundation (NSF) for economic research, our sense is that many economists avoid critical questions, skimp on analysis, and move straight to advocacy. In this essay, we take a more skeptical attitude toward the efforts of the NSF to subsidize economic research. We offer two main sets of arguments. First, a key question is not whether NSF funding is justified relative to laissez-faire, but rather, what is the marginal value of NSF funding given already existing government and nongovernment support for economic research? Second, we consider whether NSF funding might more productively be shifted in various directions that remain within the legal and traditional purview of the NSF. Such alternative focuses might include data availability, prizes rather than grants, broader dissemination of economic insights, and more. Given these critiques, we suggest some possible ways in which the pattern of NSF funding, and the arguments for such funding, might be improved.

11th Annual Conference on Empirical Legal Studies

11th Annual Conference on Empirical Legal Studies (CELS)
Duke Law School, Durham, North Carolina
Friday, November 18 and Saturday, November 19, 2016

Duke Law School is pleased to host the 11th Annual Conference on Empirical Legal Studies (CELS) on November 18-19, 2016. CELS is a highly regarded interdisciplinary gathering that draws scholars from across the country and internationally and is sponsored by the Society for Empirical Legal Studies. The conference brings together hundreds of scholars from law, economics, political science, psychology, policy analysis, and other fields who are interested in the empirical analysis of law and legal institutions. Papers are selected through a peer review process and discussion at the conference includes assigned commentators and audience questions.

Paper submissions are due by July 31, 2016.

For more information about the conference click here (https://law.duke.edu/cels2016/).

Three Simple Rules

If you think economics is too complicated, too mathematical, or just plain stupid, I hope I can convince you otherwise—and that you, too, are capable of wielding the sword of economics to cut through much of the muck and mire that muddles public discourse.

Economics, at its foundation, is simply a framework for understanding how people choose to use the resources available to them; whether money, raw physical goods, knowledge, talents or time. Economists can make it very complicated–to the point of losing the economic intuition in the mathematics of the models they use. But at its foundation economics is based on some very simple premises that don’t take a PhD in economics–or mathematics–to understand and apply to real life. Sadly, too few people understand that–and fewer still use that understanding.

There are three basic assumptions I propose at the beginning of every course I teach. I believe they are sufficient to understand the vast majority of human behavior. And they involve no math:

1) People aren’t stupid. Okay, I know that sounds like a stretch. But let’s start by at least giving them the benefit of the doubt. What I mean here is simply that people behave in ways they think are going to make them happier. Leave it to the econ nerds to debate hyper-rationality, bounded rationality, behavioral biases and such. And people are not always right and what makes them happy may not be things we (meaning society–or your particular opinion) think are appropriate. But as a general rule, people behave the way they do intentionally with the objective of making themselves happier–even if that’s by making someone else happier.

2) More is better. Early in my career I had the opportunity to work with a couple Nobel prize-winning economists. I remember Ronald Coase once saying, “You can explain 95% of human behavior with the assumption that people prefer more money to less.” I’d argue it might be higher. And if you allow for things other than money, you get 100%. Yes, there are things that people don’t like, and more of that is not better. But whatever thing a person might value, you can safely assume that they believe more is better than less.

3) More more is less better. (Thank you, David Rose, for your quirky sense of humor.) People generally prefer more of something (good) to less; but the more of it they have, the less valuable it becomes at the margin. It really is possible to have too much of a good thing. So while more might be better, we have to allow for the fact that once they have some more, they may not want as much more–especially if it comes at a cost of having less of something else.

Put those three simple “rules of economics” together and you have a pretty powerful toolkit for understanding incentives–and if you understand incentives, the rest of economics pretty much falls into place.

That’s the purpose of this blog; to highlight how economic principles can help inform a variety of everyday issues–from industry structure and regulation to daily life decisions. Consequently, I’m likely to post on a wide array of topics. And if there’s one in particular you’re interested in, I’d love to hear from you. My contact info is just to the right.

7th International Conference on Public-Private Partnerships

Registration is open for the seventh international conference on “Contracts, Procurement, and Public-Private Arrangements” on 14-15 June at Université Paris – Panthéon-Sorbonne.

This conference focuses on the recent developments in contract theories. Papers are invited on all topics of contract theories including: Relational contracting, transaction costs, renegotiations, incentives, award mechanisms, incomplete contracting, contract design, benchmarking, privatization, corruption, institutions.

See here for more information

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 important 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” research 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.