Selectivity and Selection Bias: Are Selective Colleges Better?

If you have ever been through the process of applying to colleges, you have almost certainly heard the term “selective colleges.” If you haven’t the basic idea is that some colleges are harder to get into, for example as measured by what percentage of applicants are accepted to the school. The assumption of both applicants and schools is that a more selective college is “better” in some sense than a less selective college. But is it?

In a new working paper, Mountjoy and Hickman explore this question in great detail. The short version of their answer: selective colleges don’t seem to matter much, as measured by either completion rates or earnings in the labor market. That’s an interesting result in itself, but understanding how they get to this result is also interesting and an excellent example of how to do social science correctly.

Here’s the problem: when you just look at outcomes such as graduation rates or earnings, selective colleges seem to do better. But most college freshmen could immediately identify the problem with this result: that’s correlation, not causation (and importantly, they probably knew this before stepping onto a college campus). Students that go to more selective colleges have higher abilities, whether as measured by SAT scores or by other traits such as perseverance. It’s a classic selection bias problem. How much value is the college really adding?

Here’s how this paper addresses the problem: by only looking at students that apply to and are accepted to colleges with different selectivity levels, but some choose to go to the less selective colleges. What if we only compare this students (and of course, control for measurable differences in ability)?

Now this approach is not a perfect experiment. Students are not randomly assigned to different colleges. There is still some choice going on. But are the students who choose to attend a less selective college different in some way? The authors try to convince us in a number of ways that they are not really that different. Here’s one thing they point out: “nearly half of the students in our identifying sample choose a less selective college than their most selective option, suggesting this identifying variation is not merely an odd choice confined to a small faction of quirky students.”

Perhaps that alone doesn’t convince you, but let’s proceed for now to the results. This chart on post-college earnings nicely summarizes the results (see Figure 3 in the paper, which also has a very similar chart for completion rates)

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Have you heard about Human Capital?

While writing a paper recently, I was reminded of the importance of economic modelling.

Macroeconomic models are fun to rag on – everybody does it. But all economic models help us to express our understanding of the world clearly and help us to be specific when the temptation to hand-wave is strong. After all, a model is just a fancy way of saying “a system of logic”.

The paper linked above is several revisions in. What you don’t see are the mistakes that my co-author and I made along the way and the vagueness that we had to resolve. An earlier version of the paper simply stated that deaf people were endowed with less human capital than people who could hear. So far so good. But then we said that it was ambiguous who, the deaf or the hearing, would ultimately have more human capital after making additional human capital investments.

But this is not the case!

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You Shouldn’t Be Writing (All the Time)

Many people get the idea that they should be working all the time. Certainly many academics do, which for us means a continuous internal reminder that “you should be writing”.

I thought this way in grad school but I don’t anymore. I now almost never work on nights & weekends, and often not on afternoons. Yet I get just as much work done, maybe more, and I’m much happier about it. How can this be?

This post from Ava provides a great explanation. Its very short and you should read it, but I think it illustrates best through its literal illustrations:

Today is a good example. I’m writing this at noon, having just finished the revisions requested by a journal after 3 hours of solid work. Now, rather than start revising the next article & doing a bad job of it, I’ll take the rest of the day off. Real original thought is hard- I know I can do it for about 3 hours on a typical day, I have no one to impress by pretending to work longer, and one way or another the output will speak for itself. As remote work grows, this ability to do the real work and then stop rather than fill time “working” should be available to more people outside of academics.

The Research Process: Building and Utilizing a Research Network

This summer I’m writing a series of posts about the curriculum of the research process, from the initial idea to the development of a complete draft. This week, I’m focusing on how to build and utilize a research network to support the development of your project from the initial idea to the data scaffolding of the first draft.

Why do you need a network? Why can’t you just lone wolf this research thing? For starters, going solo necessarily means you’re going to try reinventing the wheel at some point. Beyond that, your network can help you avoid common pitfalls in finding and using specific datasets, alert you to working papers in your general field, expose you to new methods that are being piloted in your discipline, and provide support when the going gets rough (it’s going to at some point). Your network also includes people who could be potential readers for your paper before you submit it to a journal and people who use their platform to boost junior scholars by inviting them to present in conference sessions, seminars, and workshops.

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The Research Process: Identifying the Ideas that Motivate You

Hello to all the EWED readers! I’m Dr. Darwyyn Deyo, an Assistant Professor of Economics at San José State University and a Visiting Scholar at the Knee Center for the Study of Occupational Regulation. I research law and economics, occupational licensing, and the economics of crime. I would also like to thank Joy for inviting me to write some blog posts this summer! I’ll be writing a series of posts about the curriculum of the research process, from the initial idea to the development of a complete draft. This week, I’m focusing on the mechanics behind choosing that initial idea.

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Working Hard for the Money

40 hours. That’s what we think of as a typical workweek. 8 hours per day. 5 days per week. Perhaps the widespread practice of working from home during the pandemic (as well as the abnormal schedule changes for those unable to work from home), has led some to rethink the nature of the workweek. But the truth is that the workweek has always been evolving.

Take this chart, for example. It comes from Our World in Data (be sure to read their excellent related essay as well), and the historical data comes from a paper by Huberman and Minns. I’ve singled out 4 countries, but you can add others at the OWiD link.

The historical declines are dramatic. This is especially true in Sweden. The average Swedish worker labored for over 3,400 hours per year in 1870. Today, that’s down to 1,600 hours. In other words, the typical Swede works less than half as many hours as her historical counterpart. Wow! The decline for the US is not quite as dramatic, but still astonishing: a US worker today labors for only about 57% of the hours of his 1870 predecessor.

It’s tempting to focus on the differences across countries today: the average worker in the US works about 250 hours more than the average French worker. That’s 6 weeks of vacation! And as recently as 1980, the US and France were roughly equal on this measure. We might also wonder why these historical changes happened. For a very brief introduction to the research, I recommend the last section of this essay by Robert Whaples.

But still, the historical declines are dramatic, even if we in the US haven’t seen much improvement in the past generation (and those poor Swedes, working 100 hours per year more than 40 years ago).

I think another natural question to ask is whether GDP data is distorted, at least as a measure of well being, given these differences in working hours. The answer is partially. Let’s look at the data!

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Power Drill Versus Python 3.9

I wrote this Complaint on Wednesday:

Consider the power drill. I learned how to use one when I was a child. I have one today that I occasionally bring out for home repair projects. It works just the way it did when I was 8 years old, and I expect nothing to change. There is something nice about tools that function the way they worked the last time you pulled them out of the basement.

Consider programming a web app. Two years ago, I created a web app that I will refer to as Buchanan2. This week I wanted to create a new web app, called Buchanan3. I thought, if I’m lucky, I can use Buchanan2 code as a scaffold upon which to build Buchanan3 in a matter of hours.

To build Buchanan2, I used ToolA and ToolB and Python-based coding. When I opened up ToolA, I got a message that Old ToolA would be deleting next month, so I better upgrade to New ToolA. Ok. I upgraded, hoping it wouldn’t break Buchanan2. Then I opened up ToolB, and it was worse in the sense that more had changed. Also, Buchanan2 had been build using Old Python. New ToolA will not tolerate Old Python. I must figure out how to upgrade to New Python. I fear that Buchanan2 will break if I make all these changes.

While navigating all the changes and upgrades, I fight to stay on the free tier of ToolB, since I already pay for ToolA.

I spent hours searching through documentation. Maybe I could have worked faster. The flesh rebels against this labyrinth, as you would flee a room when the fire alarm sounds. Suddenly, I was on Twitter, and then I was in the kitchen getting snacks.

These situations make me very frustrated, but not hopeless. I have faith that if I bang my head against the desk enough times, and read one more message board reply, that Buchanan3 will work. It has to work. It will work, eventually. I hate New Python, and New ToolB, and anyone who would force me to learn new things all over again. Yet, in this fashion, I somehow got Buchanan2 to work, years ago.

I will keep at it, as a reluctant irritable self-taught programmer.

Today is Sunday. Since writing that, I have progressed and I feel much better. One thing that helped was getting on the lowest paid tier of Tool B and writing an email directly to the creator of Tool B. He wrote back quickly and helped me see my user error. If I’m lucky, Buchanan3 will be working within 2 weeks.

This situation reminds of research by David Deming and Kadeem Noray of Harvard. They find that recent STEM graduates make more money than their peers who picked softer subjects to study in college. Demin and Noray suggest that technical skills become obsolete in a matter of years and thus the wage premium for studying STEM in college declines over the first decade of working life.

My experience is just one anecdote, but there is no way that my college education a decade ago could have exactly prepared me for New Python, New ToolA and New ToolB. Those tools didn’t exist back then.

Gen Z on Deep Work

I asked students to read an excerpt of the first chapter of Cal Newport’s book Deep Work and comment in a discussion board. The prompt asked whether deep work goes on in college and what are the barriers to deep work. I think it’s important for society that some people engage in deep work on our problems. I’m interested in how 20-year-olds perceive Newport’s ideas on focus and what barriers they identify to deep work.

Replies ranged from “I do believe that deep work is happening at college, but I think that it is hard to find students using this strategy regularly.” to “I know multiple people who do not practice deep work….” They each have a different subjective view of “deep work” and their replies are anecdotal. It’s possible that some students are too hard on themselves, considering that I biased them to be negative with the discussion prompt. Some of them might have thought that “deep work” requires many consecutive hours of focus, which is not actually what I expect of undergraduates. Still, the discussion could be helpful to others who aspire to deep work.

The following barriers to deep work were identified:

“The barriers that we experience include social media, roommates, friends, significant others, going to classes, having to work, and any number of other things that cause our day to become disjointed …  We are the first generation that has spent the majority of our life utilizing social media… and in general, are used to taking in information from a large number of sources over a short period of time.

“Most students cannot spend a large amount of hours just focused on the one task at hand and that is required for deep work. For most college students it will be nearly impossible to practice deep work because of a job, outside social life, or a heavy class workload …

“I believe that deep work happens in college a lot.  Students often times must prepare/study for tests for a long time and that is when it happens the most.  When someone has to study for hours they are intensely focused if they put themselves in a good studying environment…

“This can be achieved when you are able to clear your mind of external things and place yourself in a non-distracting environment. As a college student, this can be difficult especially because we are constantly thinking about our to-do list, when will we hang out with friends, or what’s for dinner.

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