Archive for the ‘research’ Category
Last year, Nicholas Christakis argued that the social sciences were stuck. Rather that fully embrace the massive tidal wave of theory and data from the biological and physical sciences, the social sciences are content to just redo the same analysis over and over. Christakis’ used the example of racial bias. How many social scientists would be truly shocked to find that people have racial biases? If we already know that (and we do, by the way), then why not move on to new problems?
Christakis’ was recently covered in the media for his views and for attending a conference that tries to push this idea. To further promote this view, I would like to introduce Christakis’ Query, which every researcher should ask:
Think about the major question that you are working on and what you think the answer is. Estimate the confidence in your answer. If you already know the answer with more than 50% confidence, then why are you working on it? Why not move on?
Try it out.
Before the holiday, we asked – what should computational sociologists know? In this post, I’ll discuss what sociology programs can do:
- Hire computational sociologists. Except for one or two cases, computational sociologists have had a very tough time finding jobs in soc programs, especially the PhD programs. That has to change, or else this will be quickly absorbed by CS/informatics. We should have an army of junior level computational faculty but instead the center of gravity is around senior faculty.
- Offer courses: This is a bit easier to do, but sociology lags behind. Every single sociology program at a serious research university, especially those with enginerring programs should offer undergrad and grad courses.
- Certificates and minors: Aside from paperwork, this is easy. Hand out credentials for a bundle of soc and CS courses.
- Hang out: I have learned so much from hanging out with the CS people. It’s amazing.
- Industry: This deserves its own post, but we need to develop a model for interacting with industry. Right now, sociology’s model is: ignore it if we can, lose good people to industry, and repeat. I’ll offer my own ideas next week about how sociology can fruitfully interact with the for profit sector.
Add your own ideas in the comments.
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.
A common, and important, critique of journals is that they don’t want to publish null results. So when I saw a new piece in Socio-Economic Review yesterday reporting essentially null findings, I thought it was worth a shout-out. The article, by economist Stefan Thewissen, is titled, “Is It the Income Distribution or Redistribution That Affects Growth?” (paywalled; email me for a copy). Here’s the abstract:
This study addresses the central question in political economy how the objectives of attaining economic growth and restricting income inequality are related. Thus far few studies explicitly distinguish between effects of income inequality as such and effects of redistributing public interventions to equalize incomes on economic growth. In fact, most studies rely on data that do not make this distinction properly and in which top-coding is applied so that enrichment at the top end of the distribution is not adequately captured. This study aims to contribute using a pooled time-series cross-section design covering 29 countries, using OECD, LIS, and World Top Income data. No robust association between inequality and growth or redistribution and growth is found. Yet there are signs for a positive association between top incomes and growth, although the coefficient is small and a causal interpretation does not seem to be warranted.
Okay, so there’s the “signs for a positive association” caveat. But “the coefficient is small and a causal interpretation does not seem to be warranted” seems pretty close to null to me.
In light of the attention this report from S&P has been getting — e.g. from Krugman today (h/t Dan H.) — all solid findings, null and otherwise, on the inequality-growth relationship warrant publication. Hats off to SER for publishing Thewissen’s.