Archive for the ‘research’ Category
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.
In one of my graduate courses, I taught the Rand health insurance experiment. It’s a famous study where some people were randomly given health insurance coverage to see how it affected access and health. The bottom line is that using insurance to decrease the costs of health via low co-payment helps with access, but not with health. In the discussion, I mentioned how this result surprises people. Then, one of my BGS* said the following, paraphrased by me:
The reason this might be surprising from an economic perspective is that social behavior is a question of relative prices. Obviously, purchasing health care would become more common if it were made easier. However, health is often beyond the ability of individuals to directly influence. Health might be due to genetic factors, social class, occupation, and other processes that are not easily countered by a visit to a doctor. Health is the result of a long chain of events. These policy interventions only happen at the end, so the modest effects shouldn’t be surprising.
Now, we did discuss the famous finding that the intervention helped with low-income individuals. But this supports the “end of the chain” view of health. For most people, they already have the resources and environment that will help with prevention of chronic health problems (e.g., malnutrition in youth) or managing short term issues that could become long term issues (e.g., avoiding jobs that might lead to injury). But low income individuals don’t have the resources for basic health self-management and even simple interventions might have a big impact. My take home? Think about the chain and the closer you are to the end, the more focused the policy effects will be, if it exists at all.
* Brilliant Graduate Student