"It is true that the answers to many “big picture” macroeconomic questions — like the causes of recessions or the determinants of growth — remain elusive. But in this respect, the challenges faced by economists are no different from those encountered in medicine and public health. Health researchers have worked for more than a century to understand the “big picture” questions of how diet and lifestyle affect health and aging, yet they still do not have a full scientific understanding of these connections. Some studies tell us to consume more coffee, wine and chocolate; others recommend the opposite. But few people would argue that medicine should not be approached as a science or that doctors should not make decisions based on the best available evidence."
Chetty basically gets it right in my opinion. Economics should be considered a science. However, Chetty's own article provides an example of why so many find it hard to view economics in this way.
Chetty brings up three examples of what a "scientific" approach to economics has supposedly taught us:
"Consider the politically charged question of whether extending unemployment benefits increases unemployment rates by reducing workers’ incentives to return to work. Nearly a dozen economic studies have analyzed this question by comparing unemployment rates in states that have extended unemployment benefits with those in states that do not. These studies approximate medical experiments in which some groups receive a treatment — in this case, exten ded unemployment benefits — while “control” groups don’t.
These studies have uniformly found that a 10-week extension in unemployment benefits raises the average amount of time people spend out of work by at most one week. This simple, unassailable finding implies that policy makers can extend unemployment benefits to provide assistance to those out of work without substantially increasing unemployment rates.
Other economic studies have taken advantage of the constraints inherent in a particular policy to obtain scientific evidence. An excellent recent example concerned health insurance in Oregon. In 2008, the state of Oregon decided to expand its state health insurance program to cover additional low-income individuals, but it had funding to cover only a small fraction of the eligible families. In collaboration with economics researchers, the state designed a lottery procedure by which individuals who received the insurance could be compared with those who did not, creating in effect a first-rate randomized experiment.
The study found that getting insurance coverage increased the use of health care, reduced financial strain and improved well-being — results that now provide invaluable guidance in understanding what we should expect from the Affordable Care Act.
Even when such experiments are unfeasible, there are ways to use “big data” to help answer policy questions. In a study that I conducted with two colleagues, we analyzed the impacts of high-quality elementary school teachers on their students’ outcomes as adults. You might think that it would be nearly impossible to isolate the causal effect of a third-grade teacher while accounting for all the other factors that affect a child’s life outcomes. Yet we were able to develop methods to identify the causal effect of teachers by comparing students in consecutive cohorts within a school. Suppose, for example, that an excellent teacher taught third grade in a given school in 1995 but then went on maternity leave in 1996. Since the teacher’s maternity leave is essentially a random event, by comparing the outcomes of students who happened to reach third grade in 1995 versus 1996, we are able to isolate the causal effect of teacher quality on students’ outcomes.
Using a data set with anonymous records on 2.5 million students, we found that high-quality teachers significantly improved their students’ performance on standardized tests and, more important, increased their earnings and college attendance rates, and reduced their risk of teenage pregnancy. These findings — which have since been replicated in other school districts — provide policy makers with guidance on how to measure and improve teacher quality."
Chetty's presentation of these findings is a perfect example of why it is hard to see economics as a traditional science. Take his analysis of the literature on unemployment benefits. As far as I can tell, his first claim, that a 10 week extension only modestly raises the time workers choose to stay out of work, is correct.
However, let us look at a very new NBER paper on the effect of unemployment benefits that also uses what Chetty would identify as a scientific approach. Here is the abstract:
"We exploit a policy discontinuity at U.S. state borders to identify the effects of
unemployment insurance policies on unemployment. Our estimates imply that most of
the persistent increase in unemployment during the Great Recession can be accounted
for by the unprecedented extensions of unemployment benefit eligibility. In contrast to
the existing recent literature that mainly focused on estimating the effects of benefit
duration on job search and acceptance strategies of the unemployed – the micro effect
– we focus on measuring the general equilibrium macro effect that operates primarily
through the response of job creation to unemployment benefit extensions. We find that
it is the latter effect that is very important quantitatively." (emphasis added)
This is very important because this paper actually does not dispute the empirical claim that Chetty made, but it does dispute the policy conclusion he came to based on that empirical claim. The fact that unemployment benefits only modestly increases the time workers want to stay out of the market does not automatically imply that dramatic increases in the duration of unemployment benefits will not have significant negative effects on employment. Indeed, as this paper shows using just the kind of method Chetty recommends, looking at other channels through which unemployment benefits can affect employment yields a much less positive result for advocates of expanding these benefits.
With regards to Medicaid, Chetty also paints a surprisingly incomplete picture of the Oregon Medicaid experiment. As you will recall, Chetty is correct in pointing out that expanding Medicaid seems to have increased usage of health care, decreased financial strain, improved mental health, and improved self reported well being, but he, quite surprisingly given the caliber economist Chetty is, leaves out the less flattering (for supporters of the ACA) part of the study that found no statistically significant increase in objective measures of physical health for patients who received Medicaid.
At best, the Medicaid study was a mixed result for supporters of expanding the Medicaid program (which the ACA does quite dramatically). At worst, the study is a sad demonstration of how bad Medicaid (and perhaps insurance in general) is at improving objective physical health. Why Chetty presented this study as an unambiguous victory for the pro Medicaid crowd is a mystery to me (although I suspect support of ACA has something to do with it)?
Of course, the benefits that Medicaid did bring were quite large, but one would imagine a smaller Medicaid program that relied more on cash transfers would still have those benefits while perhaps leading to more efficient use of health resources, but that is a topic that deserves it's own post.
As for the study on the benefits of good teachers, I have not looked into this particular issue and thus have no comment on this.
My goal here is not to "take down" Chetty's arguments. Instead, I would like to offer an explanation for why economics is not often thought of as a traditional science: it is too political. Chetty is among the most respected economists in the world (as he should be). If any economist could be politically objective, Chetty would fit the bill, but we still see political bias seeping through in this article.
Economics is a science, but it is a very politicized science. The Medicaid study, with its ambiguous results, offered justification for the policy proposals of both supporters and opponents of ACA, for example. Both sides were offering an incomplete picture of the study in this debate, but both sides were also correct the claims they made even if they strategically left out inconvenient findings.
Economics certainly can be seen as a science when it comes to making observations about the world, but when it comes to recommending certain policies, economics is only as scientific as the biases of the economists allow it to be.
6 comments:
"(Chetty) leaves out the...part of the study that found no statistically significant increase in objective measures of physical health for patients who received Medicaid."
Note: you're confusing "no statistically significant result" with "no result". There were positive results, but somewhat below the .95 level of significance. It's also worth noting that the size of the unintended experiment was small enough that to get to the .95 level would have required a huge difference in health, more than is reasonably expected in the first year of care.
And directly to your main point, I've seen this mistake often from people repeating it from a small number of primary sources, all on the anti-ACA side. Economics (macro, at least) is indeed a very political science, and you're soaking in it too.
At the root, macro decisions move huge amounts of money, which is of extreme interest to those with huge amounts of money to be moved, and with huge amounts of money comes the ability to fund think tanks. And we know Upton Sinclair's quote, that "It is difficult to get a man to understand something, when his salary depends upon his not understanding it." With rich men on both sides of most questions, it is unsurprising that macro will remain a contentious field.
"There were positive results, but somewhat below the .95 level of significance..."
First, It's generally a bad idea to disregard established scientific metrics for significance, especially in support of an author who argues that economics is super scientific. Second, the study results were based on a two-year (not one) time horizon. Third, the study documented many negative health outcomes that didn't reach the level of statistical significance, like reduced cardiovascular health. So if we're disregarding statistical science, let's take the bad with the good.
Great post. I am Raj Chetty's biggest fan, and this NYT op-ed kind of depressed me. Not at all the quality of thought I've come to expect from him.
Erik,
The commenter below you did a good job responding to your points on the Medicaid study. I'll add that it is likely that the Oregon study actually overestimated the effect of Medicaid given that the population was somewhat self selected. Not everybody who became eligible for Medicaid as a result of the lottery actually signed up. The researchers only looked at those who signed up. Poor individuals who could receive free insurance but decided not to sign up are likely not a very healthy group, given that they either didn't care enough about their health to sign up or were not capable for some reason. This skews the results and perhaps gives Medicaid a bit of credit where credit is not due.
As to your second point, I think you are right, but we also must not ignore the role that other interest groups (unions, environmental groups, etc) and, to be frank, the ideologies and beliefs of policymakers play in macro policy as well.
Anonymous,
I appreciate it, and I also appreciate your helpful comments on the Oregon Medicaid study.
A general point I want to make is that instead of comparing Medicaid to no Medicaid we should compare Medicaid to a smaller Medicaid based more on cash transfers. The Oregon study does show that spending money on health care for the poor helps the poor. It also shows that Medicaid doesn't do it very well. Obviously, the money is still being spent so we still see less financial strain and better mental health.
The Oregon results show that Medicaid is superior to no Medicaid, but not by as much as it should be given how much money we spend on it. This study bolsters the case for reforming Medicaid before expanding it, and I think that is a point often lost on those who read into this study as a vindication of Medicaid.
Really interesting follow up on Chetty's post !
It seems your conclusion is that "increased usage of health care, decreased financial strain, improved mental health, and improved self reported well being" are objective measures that need to be set against "no statistically significant increase in objective measures of physical health". Both of these are results from the study accepted by pro and anti ACA proponents.
Such differing interpretations of the same study do happen in "proper sciences" (look up interpretations of the unconfirmed Higgs boson signals after the first run) so I am surprised that you conclude Chetty's argument as weak on the basis of differing interpretation of study results.
Prasad,
I'm not arguing that Chetty is wrong that economics is a science. Indeed, I even said that I thought Chetty was correct about that.
I was simply pointing out that Chetty painted a misleading picture of the Oregon Medicaid study, and that his policy conclusion on unemployment insurance did not necessarily follow from the evidence he put forward. These aren't reasons that economics should not be seen as a science, but they are reasons some people might perceive it as being too political to be considered a science.
I appreciate the thoughts though.
I thought the bigger problem with the Oregon study is that a lot of people selected by the lottery did not actually take the opportunity. So what you were left with is a study that compared the lottery winners who were conscientious enough to take the opportunity to *all* of those who did not win the lottery, without such a self-selection mechanism.
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