Archive for the ‘fabio’ Category
Remember when everybody said that the polls completely got the 2016 presidential election wrong? Now we have final numbers on the popular vote count, and guess what? The national polls were on target:
- In the Real Clear Politics rolling average, the final estimate was HRC up by 3.3%. In the final popular vote count, the Cook Report found that the final difference was 2.1%. Being 1.2% off on the margin is pretty flipping good.
- In terms of the percent per candidate, the polls did worse because people over reported support for 3rd parties. Stein and Johnson together got 3% more in the polls than the results. This is evidence for the “parking lot theory of third parties.”
However, the state polls sucked. Not too hard, but they did suck a little bit, except Wisconsin and Minnesota, which totally sucked:
- Wisconsin – off by over 7%.
- Michigan – off by 3.4%
- Ohio – off by 4.6%
- Pennsylvania – off by 2.6%, which is not bad. HRC losing Pennsylvania was definitely within the margin of error here.
- Minnesota – off by 4.7% (My average, 6.2% vs. 1.5% final)
This is consistent with conventional wisdom about state polls, which is that they are less reliable because it is hard to pinpoint people in states, hard to identify likely voters, and have smaller electorates that can fluctuate (e.g., voter registration laws or bad weather).
Still, in retrospect, looking at state polls did suggest that a popular vote/electoral vote split was possible. A Trump victory was within the margin of error of the polling average in a number of states such as New Hampshire and Pennsylvania. This observation about state polls is also consistent with the finding that the HRC lead was due to urban centers.
Bottom line: The conventional social science about polls held up. National polls do decently, states polls a bit worse and in some cases badly. However, they was plenty of evidence that Trump might get an electoral college victory, but you had to really read the state polls carefully.
will trade associations exacerbate growing economic inequality in the united states? a guest post by howard aldrich
Howard Aldrich is the Kenan Professor of Sociology at UNC-Chapel Hill. This post examines an important question at the intersection of economic and political sociology, the role that trade groups have in American politics. This post originally appeared on Howard Aldrich’s blog and is reposted with permission.
An essay prepared for a special section of the Journal of Management Inquiry gave me an opportunity to reflect on potential social changes in the US resulting from major political changes over the past three decades. I believe a long-term decline in class consensus within the American business elite (Mizruchi, 2013) has raised the relative power of trade associations, compared to the powerful peak business associations of a bygone era, paving the way for more narrow self-interested actions and diminishing the influence of other kinds of interest associations. The worldview of the incoming president and his cabinet officials will facilitate this development, I believe.
Historically, business managers and owners could attempt to exert influence at four different levels in the system. First, they could get involved as individual executives, contributing money, lobbying officials and agencies, and so forth. Second, representatives of their organizations could do the same, especially through board interlocks with other firms in different industries, through which could diffuse general business practices as well as practices aimed at producing public goods (Davis & Greve, 1997; Galaskiewicz, 1985). Third, firms could participate in specific industries’ trade associations that favored policies and practices they favored (Ozer & Lee, 2009). Fourth, and perhaps most important, a handful of peak associations sat above the previous three levels, cutting across firms and industries, and claiming to speak for the business community as a whole. For example, the now-defunct CED (Committee for Economic Development) advertised itself as offering “reasoned solutions from business in the nation’s interests.”
Over at Pacific Standard, Seth Masket expresses surprise at the fact that many in the Republican party have abandoned traditional GOP policy goals and ideological beliefs:
Most recently, this has been apparent in Trump’s responses to reports by American intelligence agencies that Russia and WikiLeaks hacked Democratic National Committee servers and worked to undermine Hillary Clinton’s presidential campaign… Most recently, this has been apparent in Trump’s responses to reports by American intelligence agencies that Russia and WikiLeaks hacked Democratic National Committee servers and worked to undermine Hillary Clinton’s presidential campaign.
And it doesn’t stop with the GOP’s new Russophilia:
Another core tenet of modern Republicanism, of course, is free-market capitalism. The best economic system, the party maintains, is one in which businesses can operate with minimal regulation and thus produce wealth and innovation that benefit everyone. Trump’s approach has literally been the opposite of that. To use the tax code and other tools to selectively bully and punish companies that exhibit undesirable but legal behavior, such as building plants in other countries, is many things, but it’s not free-market capitalism. But many Republican leaders have nonetheless enthusiastically backed Trump’s approach.
I have a different view. My opinion is that GOP talking points are cheap talk and did not express true ideological commitment. For example, Republicans talk free trade, but they feel free to restrict labor through migration restrictions, they were always willing to give breaks to specific firms, and hand out subsidies to specific groups (remember the faith based initiatives?). A strict libertarian approach to trade in the GOP has really been a minority view. In other words, “free trade” is fun to say but in practice, they don’t follow it. It’s yet another example of “libertarian chic” among conservatives.
So what’s my theory? Like all parties, the GOP is a pragmatic coalition. Ideology is secondary in most cases. It’s about getting a sufficiently large block of people together so you can win elections. If you believe this theory of political parties, ideology is really not that important and, in most cases, it can be dropped at any time. In American history, for example, the Democrats and Republican parties switched positions on Black rights as part of an attempt to win the South.
This theory – that ideology is only as good as its ability to maintain a coalition – best explains the GOP policy points that Trump has rigidly stuck to: anti-immigration and abortion. And it makes sense, the two most steadfast groups in the GOP are social conservatives/evangelicals and working class whites in the South and Midwest. These groups don’t care much about foreign relations or free trade. What Trump has shown is that populism will melt away every thing except your most cherished beliefs.
Whenever I write about jobs and graduate school on this blog, I usually get one or two people who accuse me of “careerism.” For example, when I wrote about how to be productive a few weeks ago, the following comment was posted by jon:
What Fabio was talking about is probably careerism. Most successful scholars, may I say, unfortunately do follow that trajectory. But there are a few great ones that don’t. Only real geniuses are productive. Average good scholars are remembered for only one or two pieces of masterful works. This is most obvious in hard science such as mathematics and physics, and I don’t know why it wouldn’t apply to social science.
The previous comment, by Santosh Sali, elaborates:
Reading the post – gives me few impressions,
1) Being productive is about making “work-around” for serious, solo, committed work.
2) Academia is all about “Publishing” . And “teaching” doesn’t matter or it is “mundane” n trivial aspect.
3) So then where is original “contribution” of researcher? How will system assess/evaluate it?
4)Also using doctoral scholars, post-docs to work with is “collaboration” or “something else”.
5) also I have genuine doubt, these suggestions – will bring “breadth” in your work, what about “depth” – isn’t that people enter academia for this? (Or probably I am in utopian world).
A few responses. If by “careerism,” you mean “you wish to rewarded and promoted for doing good teaching and research,” then yes, I am absolutely a careerist. If you mean by careerism means “avoiding doing good work and focusing only on raises and promotions,” then, no, I do not mean that and nothing I wrote supports that.
Rather, my recommendations are about working smart. For example, let’s take Santosh’ #2 point – “academia is all about publishing.” Actually, I never said that. As any faculty member will tell you, academia is about many things. In a liberal arts college, you will do lots and lots of teaching. Even in a research university, professors will spend a lot of time prepping lectures, meetings with students, and grading papers. I know I do! Academia is also about administration and service.
The tricky thing is how to balance all these demands. My suggestion from the post boils down to a few ideas: work in groups; recognize diminishing returns; recognize work that can be minimized or avoided. At no point did I saw that you should do poorly in the class room. Rather, you should try to recognize that there may be a way to be an excellent teacher without creating more work for yourself. Same with research. Sure, *some* types of research *might* require a lot of solo work. But normally, most work improves with collaborators. So if you want to improve at your job, give these ideas a chance.
I am a huge proponent of “big data” – the data that millions, no – billions, of people generate as they use the Internet. For the first time in human history, we have a portal into the collective chatter of humanity. And if you can’t see the promise in that, you sorely lack in imagination.
But, as with anything, big data has problems and limitations. Perhaps the most fundamental limitation of big data is that it is a corpus of text, not a sample of people. In other words, the typical big data project uses information gleaned from social media, search engines, email, crowd shared editing (e.g., github or wiki), and the World Wide Web. It simply is not a sample of people.
Skeptics will just wave their hands and say “I told you so! It’s garbage!” My response is different. First, there are lots of data that are immensely useful even if they aren’t perfect random samples – historical archives, dinosaur bones, field notes, etc. What is important is that the data source tell you some important about a case that you care about, or it addresses the possibility that something might be true.
Second, even though big data is not a sample of people, it is still a sample of a very important type of communication. And surely, this would be of enormous value to social science.
Third, when I hear that method X has a limitation, I usually see it as an opportunity. For example, survey data on income are often truncated on the left or the right (i.e., the poor or wealthy are often lumped together). Instead of saying, “Garbage! No survey data for the study of income!” – you should say, “Can we model the bias? How do we qualify the findings?” This is exactly what was done in the Heckman model, and other models of survey bias.
So yes, big data has problems. But so do all data and if you take a moment to think about it, a lot of problems are simply opportunities for developing new insights into research methods.