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Archive for the ‘mere empirics’ Category

high status policy research is often not the best policy research

At Overcoming Bias, Robin Hanson observes that his fellow economists don’t always focus on the policies that have broad consensus, are easy to understand, and easy to implement. He uses the example of road pricing:

Heavy traffic is a problem every economist in the world knows how to solve: price road access, and charge high prices during rush hour. With technologies like E-ZPass and mobile apps, it’s easier than ever. That we don’t pick this low-hanging fruit is a pretty serious indictment of public policy. If we can’t address what is literally a principles-level textbook example of a negative spillover with a fairly easy fix, what hope do we have for effective public policy on other margins?

 

I agree. Think about status in economics – what sorts of work gets you the rewards? For a while, it was really, really hard math. Also, macro-economics, which is a notoriously hard field. Recently, insanely clever identification work. What do these have in common? They are hard. In contrast, how many Bates or Nobel prizes have been awarded for simple, high impact work, like road pricing? Nearly zero is my guess.

The same is true in sociology. Sociologists often imagine themselves coming up with marvelous approaches to solving deeply rooted social inequalities. For example, a few months ago, we discussed research on gender inequality and how it might be explained, partially, by the relative over- or under-confidence of men and women. In other words, it might be that women are overly cautious in terms of promotions.

One simple solution would be to require all eligible people to apply for promotions (e.g., require that all associate profs apply for full professorship after a few years). It is a simple rule and would almost certainly help. The response in the comments? The solution doesn’t remedy gender prejudice. Well, of course not, but that wasn’t the point. The point was to fix a specific issue – under representation of women in applicant pools. I have no idea how to eliminate the bias against women, but I can make sure they get promoted at work often – and it’s easy!

Bottom line: Social scientists have their priorities reversed. They get rewarded for trying to solve insanely hard problems, while leaving a lot of simple problems alone. That’s leaving cash on the table.

50+ chapters of grad skool advice goodness: Grad Skool Rulz/From Black Power

Written by fabiorojas

September 16, 2014 at 12:01 am

new computational sociology opportunity at facebook

Facebook has a new fellowship for PhD students. $37k, tuition/fee support, and visits to FB HQ. It’s awesome. Check it out.

Thanks, Mark.

50+ chapters of grad skool advice goodness: Grad Skool Rulz/From Black Power

Written by fabiorojas

September 10, 2014 at 12:01 am

urban police puzzle and ethnographic method

A few days ago, we discussed an empirical issue around Goffman’s On the Run ethnography. That work focuses on how police intervention cripples poor Black men. The issue is that other ethnography reports an under policing of poor Black neighborhoods. Earlier, I suggested a voter driven explanation – voters like to see young Black men arrested on drug charges and reward police for it.

Here, I’d like to raise a methodological issue. Goffman’s ethnography is not typical in the sense of studying a field site like a firm or a neighborhood. Rather, the ethnography is a study of a cohort of people. You follow them around. That is different than field site ethnography where you choose a location and focus on the action happening in a space. People come in and out. So it is not surprising that if you stand on a modal street corner in Philly, you won’t see many cops walk by. In contrast, if you follow people who are the target of police, then you will, not surprisingly, see a lot of police.

50+ chapters of grad skool advice goodness: Grad Skool Rulz/From Black Power 

Written by fabiorojas

September 4, 2014 at 12:01 am

how to hang out with computer scientists

I’ve recently argued that sociology has an amazing opportunity. The emergence of data science means that you should have people who really understand research design and social behavior. It doesn’t mean that sociology will automatically reap the benefits. Rather, we’ll have to work at it. My suggestions:

  • Sociology programs should now make basic programming a standard feature of the undergrad and graduate degree.
  • We have to have an internal discussion about the strengths and weaknesses of Internet generated data, much in the same way that there is a literature on the pros and cons of surveys, experiments, and ethnography.

We should also reach out to our colleagues:

  • Start cross-over workshops.
  • Reach out to faculty who already work with behavioral data by offering to help with grants
  • Personally, I’ve found it hard to work with CS graduate students. They have the normal level of grad student instability + six figure paychecks waiting for them outside of academia. But still, some are very curious, super smart, and willing to think about behavioral science.

The major barrier, in my view, is the differing publication style. CS happens very, very quickly – sometimes in a manner of weeks, while sociology is slow. We have to stop that.

50+ chapters of grad skool advice goodness: Grad Skool Rulz/From Black Power

Written by fabiorojas

August 8, 2014 at 12:01 am

Posted in fabio, mere empirics

data bleg: categorical data

Please put in the comments, or link to, a data set that has the following properties:

  1. A few hundred cases, but not too many ( 300 < N < 1000).
  2. Longitudinal categorical variable X with the following properties
  3. Categorical variable should NOT be ordered. States should be like {chocolate,vanilla, strawberry}, not {strong agree, neutral, strong disagree}.
  4. About 4-7 time periods.
  5. About 4-7 states that X can be in (e.g., five political parties, five ice cream flavors).
  6. “Legitimate data” – no one will bug me about using this data set. Decent response rate, nice set of covariates for X, data collected for a legitimate research project, etc.

This is for a methods project I’ve been working on. So I don’t need something fancy, just something that that has these specific properties to highlight the strengths of the method. Feel free to email me as well.

50+ chapters of grad skool advice goodness: Grad Skool Rulz/From Black Power

Written by fabiorojas

July 25, 2014 at 12:01 am

Posted in fabio, mere empirics

computer science “brain drain”

has an interesting post on the perceived “brain drain” in computer science. From a recent post at the Committee on the Anthropology of Science, Technology, and Computing blog:

But what do scientists think of big data? Last year, in a widely circulated blog post titled “The Big Data Brain Drain: Why Science is in Trouble,” physicist Jake VanderPlas made the argument that the real reason big data is dangerous is because it moves scientists from the academy to corporations.

“…But where scientific research is concerned, this recently accelerated shift to data-centric science has a dark side, which boils down to this: the skills required to be a successful scientific researcher are increasingly indistinguishable from the skills required to be successful in industry. While academia, with typical inertia, gradually shifts to accommodate this, the rest of the world has already begun to embrace and reward these skills to a much greater degree. The unfortunate result is that some of the most promising upcoming researchers are finding no place for themselves in the academic community, while the for-profit world of industry stands by with deep pockets and open arms.  [all emphasis in the original]“

His argument proceeds in four steps: first, he argues that yes, new data is indeed being produced, and in stupendously large quantities. Second, processing this data (whether it’s biology or physics) requires a certain kind of scientist who is skilled at both statistics and software-building. Third, that because of this shift, “scientific software” to clean, process, and visualize data has become a key part of the research process. And finally, because this scientific software needs to be built and maintained, and because the academy evaluates its scientists not for the software they build but for the papers they publish,  all of these talented scientists who would have spent a lot of their time building software are now moving to corporate research jobs, where this work is better rewarded and appreciated. All of this, he argues, does not bode well for science.

We’ve discussed this point on the blog before. We aren’t keeping good people in the academy. Aside from the financial incentives, we are really bad in terms of career development, job security, and gender equity. No wonder why we can’t keep people. We have to seriously reconsider the model where the only people who get good rewards are those who spend a decade getting their PhD dissertation published.

50+ chapters of grad skool advice goodness: Grad Skool Rulz/From Black Power

Written by fabiorojas

July 24, 2014 at 12:01 am

Posted in fabio, mere empirics

go to big cities, big data!

This August 15, Alex Hanna, a computational sociologist at Wisconsin, will host “Big Cities, Big Data” at the campus of UC Berekeley. BC/BD is a “hackathon” – a meeting of people who program all night long to develop new projects. The next day, the results will be presented at a workshop at ASA. From the announcement:

The theme is “big cities, big data: big opportunity for computational social science,” the idea being looking at contemporary urban issues — especially housing challenges — using data gathered and made publicly available by cities including San Francisco, New York, Chicago, Austin, Boston, Somerville, Seattle, etc.

The hacking will start at noon on August 15 and go until the next day. Sleeping is optional. We’ll have a presentation and judging session in the evening of August 16 in San Francisco, exact location TBD.

We’re working with several academic and industry partners to bring together tools and datasets which social scientists can use at the event. So stay tuned as that develops.

Check it out! It’s the place to meet the next generation of sociology hackers!

50+ chapters of grad skool advice goodness: From Black Power/Grad Skool Rulz

Written by fabiorojas

July 8, 2014 at 12:01 am

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