In standard microeconomics, the long-run demand is unimportant for the market price of a good. Firm competition, entry, and exit causes economic profits to be zero and the price to be equal to firms’ identical minimum average cost. This unreasonably assumes that they have constant technology. That is, they have a constant mix of productive inputs and practices.
Just so we’re clear: time is passing such that firms can enter, exit, and adjust the price – but no productive innovation occurs. For the modeling, we freeze time for technology, but not for other variables. The model ceases to reflect reality on the margin of scale-induced innovation. The standard model assumes an optimal quantity of production for each firm and the only way for total output to change is for there to be more or fewer firms. The model precludes adopting any different technology because firms are already producing at the minimum average cost – if they could produce more cheaply, then they would.
Enter Scale
One of my favorite details about production was taught to me by Robin Hanson.* Namely, that the scale of production isn’t merely with the aid of more raw materials, labor, and capital. There are perfectly well-known existing technologies and methods that reduce the average cost – if the firm could produce a large enough quantity. This helps to illustrate what counts are technology. A firm can achieve lower average costs without inventing anything, and merely by adopting a superficially different production method.
For-profit firms are well-oriented. The managers within firms may not make profit their only explicit priority, but it is pre-requisite to their other concerns. Without profits, firms eventually cease to exist. Non-profits are different. They might have revenues due to sales and operate much like a for-profit firm. But, they many times operate on revenue from donations and endowments. Because the success of non-profits is harder to measure, the signals of triumph and defeat do not orient the employees as clearly. The result can be that there is a lot of ruin in a non-profit. Plenty of tasks are done inefficiently, poorly, or not at all.
Mission-driven non-profits are able to attract enthusiastic, dedicated employees given the pay that they offer. But, supporting the mission of such an organization often acts as an implicit “belief test”, filtering out other would-be job applicants who self-select out of applying to open positions for which they are otherwise qualified. Indeed, part of the purpose of mission statements is to filter for the kind of employees that the organization managers or donors desire. While the employees may be enthusiastic and dedicated to the mission, that is mostly separate from whether they have the technical skills to flourish in their position and to effectively serve the organization.
I like a good lump sum tax. People *must* pay the tax without exception and the advantage over current progressive marginal income taxes is that the marginal wage received doesn’t fall with greater earnings. Employment rises and output rises. To the extent that college students fail to understand their student loans, the indebted graduates essentially pay a lump sum tax each period.
Of course, the exception is income based repayment (IBR) – especially with forgiveness after X years. IBR adjusts the incentives substantially. Under the standard system, your wages are garnished if you fail to make loan payments. Under IBR, lower earnings trigger lower monthly payments. Clearly, in contrast to the standard method, IBR incentivizes more leisure, less income, more black market activity, and higher loan balances. Indeed, all the more so if there is a forgiveness horizon. Someone just has to have low enough income for say 15 years, and their past debt is forgiven (with caveats & conditions).
My principal objection to IBR policy is the resulting malinvestment in human capital. Defaulting on loans is a sign that some investment was inadequately productive to repay the resources consumed by its endeavor. We call that a loss. Real resources of time, attention, and goods and services were consumed in order to produce capital that failed to serve others more than the opportunity cost of those resources.
Incentives matter. I’ve taught at both public and private universities, and students have given me both great course evaluations and less great student evaluations. The private university cared a lot more about them. Obviously, some parts of student evaluations of their instructors are beyond the instructor’s control. The instructor can’t control inalienables and may not be able to change their charisma. But what about the things that instructors can control? Regardless of your current evals, here are 5 policies that are guaranteed to improve your course evaluations.
1: Very Clear Expectations/Schedule
Have all deadlines determined by the time that the semester starts. Students are busy people and they appreciate the ability to optimally plan their time. Relatedly, students desire respect from their instructor. Having clear rubrics and deadlines helps students know your expectations and how to meet them – or at least understand how they failed to meet them. Students want to feel like they were told the rules of the game ahead of time. This means no arbitrary deductions or deadlines. The syllabus is a contract if you treat it like one.
2: Mid-Semester Evaluations
One of the absolute best ways to improve your evaluation is to ask your evaluators for a performance update. Make a copy of your end-of-semester course evaluation and issue it about halfway through the semester. Then, summarize the feedback and review it with your class. This achieves three goals. (1) It is an opportunity to clarify policy if there are misplaced complaints. You may also wish to explain why policy is what it is. Knowing a good reason makes students more amenable to policies that they otherwise don’t prefer. (2) It provides voice to students who have things to say. Often, students want to be heard and acknowledged. It’s better that a student vents during the informal mid-semester survey than on the important one at the conclusion of the course. (3) If there are widespread issues with your course, then make changes. If you’re on the fence about something, then take a poll. And if you decide to make changes, then be graciously upfront about it. Unexplained or covert changes violate policy #1.
Lot’s of economists use FRED – that’s Federal Reserve Economic Data for the uninitiated. It’s super easy to use for basic queries, data transformations, graphs, and even maps. Downloading a single data series or even the same series for multiple geographic locations is also easy. But downloading distinct data series can be a hassle.
I’ve written previously about how the Excel add-on makes getting data more convenient. One of the problems with the Excel add-on is that locating the appropriate series can be difficult – I recommend using the FRED website to query data and then use the Excel add-on to obtain it. One major flaw is how the data is formatted in excel. A separate column of dates is downloaded for each series and the same dates aren’t aligned with one another. Further, re-downloading the data with small changes is almost impossible.
Only recently have I realized that there is an alternative that is better still! Stata has access to the FRED API and can import data sets directly in to its memory. There are no redundant date variables and the observations are all aligned by date.
At a Chinese restaurant, I got a fortune that said, “Success is in starting a new project at work.” It struck me as very funny, and it resonates with other people on Twitter.
Starting a new project at work does not translate to success in academia. The danger is usually in starting too many projects and finishing too few.
Starting a new research project, whether alone or with coauthors, is exciting. You fall in love with a new idea.
The hard part is sticking with that idea until the very end of the publication process. This is more comparable to staying married. The project will see you at your worst, and you will discover that the project is not as wonderful as it seemed initially. You might end up re-writing the manuscript several times, years after the initial infatuation has worn off.
Academics do need to start projects. It is important to start the right projects. A reason to not start too many projects is to preserve time for the best work. A downside to being overloaded is that you might have to say no to a new project when an actual good opportunity comes along.
In my post on the Beatles documentary Get Back, I observed the way that the bandmates start new songs together. It reminded me of coauthors convincing each other to start a new project.
Their creative process resembles co-authoring a research paper. When Paul is working out a song and humming through places he hasn’t worked the lyrics out yet, that reminds me of the early drafts of a paper. You don’t have to have the whole Introduction written. The hook of a song is a bit like the main result of a research paper. Persuading yourself and your coauthor that you have a project worth finishing is the first step. Coauthors have unspoken agreements on how the project is going to proceed. The tacit knowledge of the collaborative process is one of the most important things you can learn in graduate school.
This quote from Rules of Thumb was surprising: “None of this is part of a grand plan. At any moment, I work on whatever then interests me most. Coming up with ideas is the hardest and least controllable part of the research process. It is somewhat easier if you have broad interests.” He goes on to say: “I sometimes fear that because I work in so many different areas, each line of work is more superficial than it otherwise would be. Careful choice of co-authors can solve this problem to some extent, but not completely.”
He really refutes my fortune cookie with this line, “Deciding which research projects to pursue is the most difficult problem I face in allocating my time.” Success is about starting the right projects and no others.
I just found out I’ll be receiving a Course Buyout Grant from the Institute for Humane Studies. It will allow me to teach less next year in order to focus on my research on how Certificate of Need laws affect health care workers.
I’m happy about this because I think this research is valuable and time is my main constraint on doing it (especially doing it quickly enough to inform ongoing policy debates in several states). But I’m also happy because I finally got what I consider to be a “true” grant after many rejections.
I’ve received research funding many times before (e.g. Center for Open Science funding for replications), but it was always relatively small amounts that went directly to me. True grants tend to be larger and to be paid directly to the university. That’s the case with the course buyout grant, which essentially pays the university enough that they can hire someone else to teach my class.
I may have lost count but I’m pretty sure this was the 13th “true grant” I have applied for, and the 1st I will actually receive. Academics have to get used to rejection, since we need to publish and decent journals tend to reject 80%+ of the articles they receive. But for some reason I’ve found grants much harder even than that. From some combination of skill, luck, and targeting lower-tier journals than perhaps I could/should, my acceptance rate for journal articles is probably nearing 50%. I expected this to translate over to grants but it absolutely did not, they seem to be a much different ballgame, one I’m still figuring out.
I’d like to share some of those past misses, both to let junior people see the bumpy road behind success (like a CV of failures), and to try to extract lessons from an admittedly small sample. These proposals were not funded, and probably weren’t even close:
Peterson Foundation US 2050
MacArthur Foundation 100 & Change
RI INBRE (2x)
National Institute for Health Care Management (1x, waiting to hear but probably about to be 2x)
What did these failures of mine all have in common? Me, of course. This is not just a truism; in most of these cases I was applying for major grants solo as an assistant professor without previous funding. The usual advice is to work your way up with smaller grants or, preferably, as the collaborator of a senior professor with lots of previous funding who knows how things work. I knew that would be smart but I’ve tended to be at institutions without senior people in similar fields; almost all my research has either been solo or coauthored with students or assistant professors. Even my PhD advisor was a brand-new assistant professor when we started working together. I had good reasons for ignoring the usual advice to work with well-known seniors, and it has mostly served me well, but grants seem to be the exception.
Twice, I think I did come close on grant proposals, and both times it involved help from seniors at other institutions who had lots of previous funding. At one foundation that funds a lot of social science, my senior coauthor and I got glowing external reviews, but the internal committee rejected us on the grounds that we could do the project without their funding. They were right in the sense that we did do project anyway with no funding; it got published and even won a best paper award. But with their funding we would have done it faster and better and they would have gotten credit for it.
I do think it is smart for funders to consider whether the research would happen anyway without them, or whether their funding really improves things. But I think it is rare for funders to actually do this, and taking this rejection as advice probably led me to more rejections. I tried to propose bigger, more ambitious projects that needed expensive data so it was clear that I really needed the funding; but for most funders this probably made things worse. I have since heard several times that people who get lots of funding from major funders like NIH tend to submit proposals for research they have essentially already finished; that is why their proposals can look so thorough, credible, and polished. They then use the funding to work on their next project (and next proposal) instead of what they said it was for. That seems sketchy to me, but it’s certainly ethical to turn the proposal dial back somewhat toward “obviously achievable for me” from “ambitious and expensive”, and that’s what I’ve done more recently.
The other time I came close was with an R03 proposal to the Agency for Healthcare Research and Quality. First I got a not-close rejection, as I mentioned in the big list, where my proposal was “not discussed”. But AHRQ allows resubmission. At the prompting of my (excellent) grants office, I got feedback on the proposal from two kind seniors at other schools who get lots of funding. I rewrote the proposal based on their comments plus the rejection comments (which were actually quite detailed despite it being “not discussed”) and resubmitted it. This went way better- the resubmission got discussed with an impact score of 30 and a percentile of 17. Lower scores are better for AHRQ/NIH so this was pretty good, good enough that it might have been funded in a normal year, but 2019 was a bad year for government funding (though through some weird quirk I never actually got rejected; 4 years later their system still says “pending council review”). Again, the key to getting close was getting detailed feedback from people who know what they are talking about.
Of course, it also helps to get to know people at the funders and to become more senior yourself. It’s not surprising that my first major grant is coming from IHS given that I’ve been involved with them in all sorts of ways since going to a Liberty & Society seminar way back in 2009. Most funding goes to more senior people who have more connections, knowledge, and proven experience. This is extreme at perhaps the largest funder of research, the National Institutes of Health, where less than 2% of funded principal researchers are under age 36.
This may be the real secret for winning grants- just get older. My 12 rejections all came when I was younger than 36, while my first acceptance came less than a month after my 36th birthday.
In all seriousness, thanks to the Institute for Humane Studies, and I hope that a year from now I’ll be writing here about the great work that came out of this. For everyone with a growing pile of rejections, maybe the 13th time will be the charm for you too.
Economists overwhelmingly favor looser immigration controls. Allowing people to immigrate would improve the allocation of scarce labor and capital and it is a far cheaper way to aid poorer families than sending direct payments or trying to develop an entire country. Let’s cover some static analysis basics for migrating workers and their dependents.
Workers, Labor Markets, & Output Markets
There are two markets to consider: The new home country and the old home country. If workers leave the old country in search of the higher wages in the new country, then world employment remains unchanged. Employment obviously rises in the new country and falls in the old country. With identical laborers (a terrible assumption that’s the least charitable to immigration), wages in the new country fall and wages in the old country rise. This logic illustrates the cheap aid of which economists are fond.
One of the oldest theories in economics is the idea of compensating differentials. A job represents not just a certain amount of money per hour, but a whole package of positive and negative things. Jobs have more or less stability, flexibility, fun, room to grow, danger… and non-cash benefits like health insurance. The idea of compensating differentials is that, all else equal, jobs that are good on these other margins can pay lower cash wages and still attract workers (thus, the danger of doing what you love). On the other hand, jobs that are bad on these other margins need high wages if they want to hire anyone (thus, the deadliest catch)
I think this theory makes perfect sense, and we see evidence for it in many places. But when it comes to health insurance, everything looks backwards. A job that offers employer-provided health insurance is better to most employees than one that doesn’t, so by compensating differentials it should be able to offer lower wages. There’s just one problem: US data shows that jobs offering health insurance also offer significantly higher wages. The 2018 Current Population Survey shows that workers with employer-provided health insurance had average wages of $33/hr, compared to $24/hr for those without employer insurance.
All the economists are thinking now: that’s not a problem, compensating differentials is an “all else equal” claim, but not all else is equal here. The jobs with health insurance pay higher wages because they are trying to attract higher-skilled workers than the jobs that don’t offer insurance.
That’s what I thought too. It is true that jobs with insurance hire quite different workers on average:
The problem is, once we control for all the observable ways that insured workers differ, we still find that their wages are significantly higher than workers who don’t get employer-provided insurance. Like, 10-20% higher. That’s after controlling for: year, sex, education, age, race, marital status, state of residence, health, union membership, firm size, whether the firm offers a pension, whether the employee is paid hourly, and usual hours worked. I’ve thrown in every possibly-relevant control variable I can think of and employer-provided health insurance always still predicts significantly higher wages. Of course, there are limits to what we get to observe about people using surveys; I don’t get any direct measures of worker productivity. Possibly the workers who get insurance are more skilled in ways I don’t observe.
We can try to account for these unobserved differences by following the same person from one job to another. When someone switches jobs, they could have health insurance in both jobs, neither, only the new, or only the old. What happens to the wages of people in each of these situations? It turns out that gaining health insurance in a new job on average brings the biggest increase in wages:
What could be going on here? One possibility is that health insurance makes people healthier, which improves their productivity, which improves their wages. But we control for health status and still find this effect. The real mystery is that papers that study mandatory expansions of health insurance (like the ACA employer mandate and prior state-level mandates) tend to find that they lower wages. Why would employer-provided health insurance lower wages when it is broadly mandated, but raise wages for individuals who choose to switch to a job that offers it?
My current theory is that “efficiency benefits” are offered alongside “efficiency wages”. The idea of efficiency wages is that some firms pay above-market wages as a way of reducing turnover. Workers won’t want to leave if they know their current job pays above-market, and so the company saves money on hiring and training. But this only works if other firms aren’t doing it. The positive correlation of wages and insurance could be because the same firms that pay “efficiency wages” are more likely to pay “efficiency benefits”- offering unusually good benefits as a way to hold on to employees.
I still feel like these results are puzzling and that I haven’t fully solved the puzzle. This post summarizes a currently-unpublished paper that Anna Chorniy and I have been working on for a long time and that I’ll be presenting at WVU tomorrow. We welcome comments that could help solve this puzzle either on the empirical side (“just control for X”) or the theoretical side (“compensating differentials are being overwhelmed here by X”).
Economist Writing Every Day 