Category: Economics Profession

Why study social sciences?

Monkey Cage Blog has a great post up in response to criticism of his exhortations to study social sciences. He makes a broad argument about the validity of social science research because it has effects on the way that people live their lives. To be selfish for a moment, he highlights some important questions that I examine every day:

Families.  What makes families more or less successful?   What makes marriages more successful?  What makes them fail?  What are the effects of divorce?  Does it hurt the children of divorce?  How much, in what ways, and for how long?  A medical doctor can treat the effects of family dysfunction and divorce—say, with anti-depressants or therapy and so on—but we can learn and know more about how to prevent some of this dysfunction from doing social science.

The post is really about funding for social science research rather that defending my everyday work. It’s also not really about teaching undergrads social sciences, but clearly, we have train undergrads in social sciences if we eventually want some of them to do research in the social sciences. I think there’s a point to be made about how learning about these wide-reaching social phenomena—families, schools, economies, politics, attitudes, networks and norms—forces students to think about cause and effect in a nuanced way. When it’s not clear how X might affect Y or how Z has effects on X that in turn effect Y, it takes creativity and imagination and critical thinking to sort it out. It’s not that social sciences can do this exclusively, but the nature of the topics student lends itself to varied analysis and the development of skills that are useful in many careers.

The Beltway Deficit Feedback Loop. Or, why we should all commit to reading various news sources

Last week, I was surprised by my students’ apparent belief that debt and deficit spending was high during the first Obama administration and that it was the first thing they thought of when asked about the effects of government spending.

By way of explanation, Greg Sargent of The Plum Line takes on the current Joe Scarborough vs. Krugman (and the world of economists) debate via the Beltway Deficit Feedback Loop.

The relentless bipartisan focus on the deficit convinces voters to be worried about it, which in turn leads lawmakers to spend still more time talking about it and less time talking about the economy, a phenomenon that is self-reinforcing. This is exacerbated by some commentators and news orgs, who continue to treat the deficit scolds with a great deal of deference, while marginalizing the opinion that we should prioritize boosting the economy and job creation as a means of getting the country’s fiscal problems under control over time without savage spending cuts that will hurt a lot of people. Back in 2011 one study actually confirmed that newspapers were spending far more time talking about the deficit than the economy — at a time when the recovery was in serious peril.

h/t @EJDionne

Off to San Diego

This space has been pretty quiet lately as I’ve been preparing for interviews and writing applications and trying to get all that work done that I didn’t do during the semester. It’s not likely to pick up again as I’m off to San Diego today for the meetings of the American Economics Association. It’s a conference also known as the Allied Social Sciences Association Meetings, which makes my sociologist and anthropologist friends laugh out loud (because they’re not invited…shhhh!) It’s weird; it’s true. I don’t pretend to understand.

At any rate, if you’re interested in following along, there is a twitter handle for the meetings @ASSAmeeting, and a hashtag #ASSA2013. There’s even an app for that.

Sorry. I had to. But really, the app is super helpful. So much easier trying to put in meetings  and interviews on Pacific Time than subtracting hours from wherever I happened to be when the meeting was scheduled. It also has the full conference schedule.

If you’re around and want to grab coffee or a drink or a meal, drop me a line, tweet me, or call. I’m here all week, folks.

Oh, the wiki

When I went on the job market for the first time two years ago, I was advised not to consult the economics job market rumors forum. Given that I had no idea what it was, I immediately went and consulted it, only to have my spirit broken by the rank misogyny, stress, and trolling that dominated the forum. EJMR is still full of a lot of that crap, but it’s growing up in a way that I think has the potential to be beneficial to economists and the economics profession.

In particular, EJMR this year redid “the wiki”, or the crowd-sourced table of calls made to applicants on the job market each November and December. The redesign, and incorporation into the EJMR framework, has actually been incredibly user-friendly and informative. Yes, it sucks to hear that Dream University XYZ called someone and didn’t call you, but it’s really nice not to be waiting for them to call anymore. It’s anonymous, but usually updated incredibly rapidly. I’ve received emails or phone calls and went to check the wiki within minutes and seen it updated already.

More proof that EJMR has grown up a bit comes in the form of the recently added journal wiki, which I think is absolutely brilliant. Economics, from what I know, suffers from one of the longest (and most excruciating) publishing cycles in academia. My astrophysicist friends complain that their papers take eight months to get out and my eyes pop of my head. Try two years. Or three. The wiki itself is still kind of a jumble of information and lacks a good way to aggregate data. For instance, it would be useful to be able to find mean and median response times and see the number of entries for a given journal. The data is easily copied and pasted into Excel, so one could feasibly take all the information for a given journal and perform those quick data summaries oneself. Though it would strip away some of the anonymity, it would also be nice to know where those papers were eventually published. But perhaps I’m asking too much.

The journal wiki is similar to the jobs wiki in that it’s anonymous, crowd-sourced, and voluntary. The big difference is that while one school made 20-30 phone calls and only one person had to post the outcome, each journal submission and rejection is separate. You can’t rely on another person’s entering your rejection. The journal wiki poses a larger free-rider problem because each of piece of information is only controlled by a single individual (or author group). I imagine that despite the collective action problem, it will still gets high levels of participation. In fact, it’s already quite filled out and has only been up a few days.

I’m all for more information. I’m all for making publishers and referees more accountable. I also wonder if it won’t push some better papers to lesser known journals. With a clear time-to-publication advantage, lower-ranked journals could attract better papers and upset the hegemonic closed circle that tends to dominate the highly ranked, very slow to publish journals. It could also damn those papers to obscurity, but it will be interesting to see if it has any effect on overall response times and time-to-publication.

Still learning–experimental economics edition

Until this year, I didn’t really know what experimental economists did. Despite having two experimentalists in my department, one of whom I spend a lot of time with, the whole concept of observing people’s decisions in a controlled environment but not controlling for anything about the individuals seemed totally silly to me. Aren’t you people worried about unobserved heterogeneity? I think my initial outburst was probably a bit oversimplified and I’m getting a better idea of what it means to do economics experiments now, but I’m still a bit skeptical.

After I excitedly read and recounted a paper on experiments and corruption the other day, a colleague of mine at Gettysburg allowed me to sit in on his energy economics class and experiment. Rim runs an experiment in every class after students present papers they’ve read and he presents the relevant theory on some aspect of energy economics. The day I visited, we talked about gasoline markets, differential pricing for differently located gas stations, and profit margins for owners of capital at various stages of the crude-to-consumer pipeline.

Then we got to play video games for the rest of class. I mean, we did an experiment. Half of us were set up in the experiment as gasoline suppliers and half as gas station owners. Each owner had two stations and a dedicated supplier who would set the price for each station. Importantly, the suppliers could dictate different prices for each station. When the game started, cars would enter the matrix and choose where to buy gasoline, usually closer to where they entered, but some were willing to drive if the price difference was great enough. Both suppliers and station owners could change the price as often as they wanted and consumer behavior was dictated by a random draw.

It took me a minute to get the hang of things. For instance, I didn’t realize for the first few rounds that the cost of crude (I was a supplier) was changing because I was so busy trying to figure out what the equilibrium price was for the two different gas stations. It was fun, though, to see how prices evened out and occasionally got out of control. It turned out that the inside-town gas stations had about 30-cent lower prices than the outside-town gas stations. This seems to me like a function of the parameters of the experiment, not really predictable behavior, but perhaps I’m wrong and it’s a function of the interaction of the parameters with behavior.

I think it’s asking a lot to assume that random college kids who know they’re getting paid a few bucks are going to act like gasoline station owners or suppliers. I know that experiments in other fields work like this too, in some way. The idea is to try to control everything you can in order to isolate the behavior or reaction or change. It’s what I try to do with econometrics; it’s what others try to do when they create models as well.

Perhaps it’s just indicative of my general skepticism of my own field, but I’m excited to learn more about it, too.

Job lising of the month

I’m wrapping up my job-applying, at least for the big pre-December 1 deadline push, and am now mostly in the process of looking back at jobs I didn’t apply for in places I’d really like to live. Unsurprisingly, Denver is one of the places, and despite an apparent hiring spree by Colorado schools this year, I’m not a particularly good fit for the faculty positions that are open.

I’m curious, though, if there’s actually anyone who fits this University of Denver opening for an assistant professor of Economics: “Must show promise of distinction in research and publications in the fields of the Chinese economy, environmental economics, and feminist economics.” (emphasis mine.)

Not just heterodox, but feminist, examining questions of environment, and concentrated in an area where those that run the economy are largely indifferent to both feminist and environmental concerns. It kind of boggles the mind. I’m really curious to see who they end up hiring. In fact, I’d like to meet her; she sounds like a rockstar.

Who wants to work in Silicon Valley: Economist’s edition

I’m not sure if this is an entirely new phenomenon or I’m just more aware of it, but I’m incredibly intrigued by the proliferation of tech firms–start-ups, established big guys, gaming companies, and more–that are seeking to hire economists this year in various research roles. Maybe it’s not new, but rather what’s new is their move to advertise in the two established aggregated job listings that economics candidates are likely check these days.

Today’s job listing comes from Google, who is looking for someone to work in the are of Knowledge. With a capital K.

The area: Knowledge

There is always more information out there, and the Knowledge team has a never-ending quest to find it and make it accessible. We’re constantly refining our signature search engine to provide better results, and developing offerings like Google Instant, Google Voice Search and Google Image Search to make it faster and more engaging. We’re providing users around the world with great search results every day, but at Google, great just isn’t good enough. We’re just getting started.

It just sounds so much sexier than the academic jobs, no? Who doesn’t want to further knowledge?!

CSWEP Mentoring Breakfast at ASSA/AEA

This arrived in my mailbox this morning from my advisor. This is a great opportunity for junior economists to network and ask questions of senior economists at a variety of institutions.

The Committee on the Status of Women in the Economics Profession (CSWEP) is hosting its inaugural mentoring/networking breakfast for junior economists at the ASSA meetings on Sat., January 5 from 7-10 am. Senior economists (predominately senior women) will be on hand to provide mentoring and networking opportunities in an informal setting. A light continental breakfast will be provided.

Junior economists who have completed their PhD in the past 6 years or graduate students who are on the job market are encouraged to attend this event.

The event is an informal meet and greet affair in which junior participants are encouraged to drop in with questions on topics such as publishing, teaching, grant writing, networking, job search, career paths, and the tenure process. Senior economists who have committed to attend at least one hour of the breakfast are affiliated with institutions such as Duke, Texas, UCLA, Cornell, NY Federal Reserve, NSF, Lafayette College, UC-Santa Barbara, UC-San Diego, Iowa State, Maryland, Kentucky, Kansas, Agnes Scott College, Virginia Commonwealth University, Georgia Tech, Rutgers, Tufts, UT-Dallas, Missouri-St Louis, Indiana, and Colorado.

We are now accepting registration for junior participants.  To  pre-register, send an email to terra.mckinnish@colorado.edu with the subject heading “CSWEP breakfast” containing your name, current institution and position title, year and institution of PhD.

Sincerely,

The Committee for the Status of Women in the Economics Profession

Signaling and subconscious bias in Economics papers

I saw down this afternoon to read a paper on inheritance laws and gender for a paper on societal discrimination against adolescent girls. I found it through another paper that describes it as identifying a causal effect of allowing women to inherit land on educational attainment and age of marriage and so of course my econometric feelers went up. I was going to read it anyway, but you all know I’m glutton for seeking out statistical causal identification strategies. I’ve been putting off the paper because though the paper comes out of the World Bank’s Working Paper series, the cover page really wasn’t doing it for me.

I finally scrolled to the next page this afternoon, and it’s typed using LaTeX, a scientific word processing program. Wouldn’t you know that just seeing that font makes me that much more excited to read it? And, though I hate to admit it, maybe even a little more trusting of what’s coming, even though I haven’t read it yet?

Kind of scary. I would love to see a study of this over time. How is a paper typed in Word Perfect received versus a paper typed using a scientific editor?

Cited:  Deininger, K., A. Goyal, and H. Nagarajan (2010) “Inheritance Law Reform and Women’s Access to Capital: Evidence from India’s Hindu Succession Act.” Policy Research Working Paper 5338. Washington, DC: World Bank.

Big data and what it means for economists

Over the past few days, a couple of pieces have come out about Big Data, or rather how economists and other social scientists are incorporating the extremely large datasets that are being collected on every one of us at every minute. Justin Wolfers, at the Big Think, says “whatever question you are interested in answering, the data to analyze it exists on someone’s hard drive, somewhere.” Expanding on Wolfers, Brett Keller speculates as to whether economists will “win” the quant race and “become more empirical.” Marc Bellemare thinks (in a piece that’s older, but still relevant) that the social sciences will start to converge in their methods, with more qualitative fields adopting mathematical formalism to take advantage of how much we know about people’s lives. Justin Wolfers and Betsey Stevenson go on in a related piece at Bloomberg about the boon that big data is for economics.

Not withstanding the significant hurdles to storing and using large datasets over time (ask a data librarian today about information that’s on floppy disks or best read by a Windows XP machine. Heck, look at your own files over the past ten years: can you get all the data you want from them? What would it take to get it all in a place and format you could formally analyze it?), I find the focus on data a little short sighted. And don’t get me wrong; I love data.

Wolfers and Stevenson think that the mere existence of data should change our models, that the purpose of theory nowadays should be “to make sense of the vast, sprawling and unstructured terabytes on our hard drives.” We do have the capability to leverage big data to gain a more accurate picture of the world in which we live, but there is also the very real possibility of getting bogged down in minutiae that comes from knowing every decision a person ever makes and extrapolating its effect on the rest of their lives. It’s the butterfly flaps its wings effect, for every bite of cereal you take, for every cross word your mother said to you, for every time you considered buying those purple suede shoes and stopped yourself–or didn’t. I’m being a bit melodramatic, of course, but it’s very easy, as an economist, as a graduate student, as a pre-tenure professor short on time, to let the data drive the questions you ask. It’s also often useful, I’m not saying that finding answerable questions using existing data is universally bad, by any means. But if we have tons of information on minutiae, we’ll probably ask tons of questions on minutiae, which I don’t think brings us any closer to understanding much of anything about human behavior.

On the convergence side, I worry about losing things like the ethnography. It may not be my strong point, but it’s useful, its methods and ouput informed my own work, and if convergence and big data mean anthropologists start relying solely on econometrics and statistics and formal mathematics, we’ll lose a lot of richness in our history and academics. I’m all for interdisciplinary work, for applying an economic lens to all facets of human interaction and decisions, but I don’t think our way of thinking should supplant another field’s. Rather, it should complement it.

Finally, incorporating big data into models that already exist will mediate some problems (unobserved heterogeneity that can now be observed, for example), but not all. Controlling linearly for now observable characteristics in a regression model has plenty of downsides, which I won’t enumerate, but can be found in any basic explanation of econometrics or simple linear regression.

Similarly, our tools for causal identification keep getting knocked down. At one time, regression discontinuity design was hot, and smacked down. Propensity score matching was genius and then, not so much. Instrumental variables still has this rather pesky problem that we can’t actually prove one of its key components. It’s not to say these tools don’t have value. When implemented correctly, they can indeed point us to novel and interesting insights about human behavior. And we certainly should continue to use the tools we have and find better ways to implement them, but the existence of big data shouldn’t mean we throw more data at these same models, which we know to be flawed, and hope that we can figure out the world. If we’re indeed moving towards more empirical economics (which is truthfully the part I practice and am most familiar with), we still need better tools. The models, the theory, the strategies for identification have to keep evolving.

Big data is part of the solution, but it can’t be the only solution.