Party Time

2 Dec

It has been nearly five years since the publication of Affect, Not Ideology: A Social Identity Perspective on Polarization. In that time, the paper has accumulated over 450 citations according to Google Scholar. (Citation counts on Google Scholar tend to be a bit optimistic.) So how does the paper hold up? Some reflections:

  • Disagreement over policy conditional on aims should not mean that you think that people you disagree with are not well motivated. But regrettably, it often does.
  • Lack of real differences doesn’t mean a lack of perceived differences. See here, here, here, and here.
  • The presence of real differences is no bar to liking another person or group. Nor does a lack of real differences come in the way of disliking another person or group. History of racial and ethnic hatred will attest to the point. In fact, why small differences often serve as durable justifications for hatred is one of the oldest and deepest questions in all of social science. (Paraphrasing from Affectively Polarized?.) Evidence on the point:
    1. Sort of sorted but definitely polarized
    2. Assume partisan identity is slow moving as Green, Palmquist, and Schickler (2002) among others show. And then add to it the fact people still like their ‘own’ party a fair bit—thermometer ratings are a toasty 80 and haven’t budged. See the original paper.
    3. People like ideologically extreme elites of the party they identify with a fair bit (see here).
  • It may seem surprising to some that people can be so angry when they spend so little time on politics and know next to nothing about it. But it shouldn’t be. Information generally gets in the way of anger. Again,
    the history of racial bigotry is a good example.
  • The title of the paper is off in two ways. First, partisan affect can be caused by ideology. Not much of partisan affect may be founded in ideological differences, but at least some of it is. (I always thought so.) Secondly, the paper does not offer a social identity perspective on polarization.
  • The effect that campaigns have on increasing partisan animus is still to be studied carefully. Certainly, ads play but a small role in it.
  • Evidence on the key take-home point—that partisans dislike each other a fair bit—continues to mount. The great thing is that people have measured partisan affect in many different ways, including using IAT and trust games. Evidence that IAT is pretty unreliable is reasonably strong, but trust games seem reasonable. Also see my 2011 note on measuring partisan affect coldly.
  • Interpreting over-time changes is hard. That was always clear to us. But see Figure 1 here that controls for a bunch of socio-demographic variables, and note that the paper also has over-time cross-country to clarify inferences further.
  • If you assume that people learn about partisans from elites, reasoning what kinds of people would support this ideological extremist or another, it is easy to understand why people may like the opposing party less over time (though trends among independents should be parallel). The more curious thing is that people still like the party they identify with and approve of ideologically extreme elites of their party (see here).

Good NYT: Provision of ‘Not News’ in the NYT Over Time

29 Nov

The mainstream American news media is under siege from the political right, but for the wrong reasons. To the hyper-partisan political elites, small inequities in slant matter a lot. But hyperventilating about small issues doesn’t magically turn them into serious problems. It just makes them loom larger. And takes the focus away from the much more serious problems. The big afflictions of ‘MSM’ are: vacuity, sensationalism, a preference for opinions over facts, poor understanding of important issues, and disinterest in covering them diligently, disinterest in what goes on outside American borders, a poor understanding of numbers, policy myopia—covering events rather than issues, and fixation with breaking news.

Here we shed light on a small piece of the real estate: the provision of ‘not news.’ By ‘not news’ we mean news about cooking, travel, fashion, home decor, and such. We are very conservative in what we code as ‘not news,’ leaving news about health, anything local, and such in place.

We analyze provision of ‘not news’ in The New York Times (NYT), the nation’s newspaper of record. NYT is both well-regarded and popular. It has won more Pulitzer awards than any other newspaper. And it is the 30th most visited website in the U.S. (as of October 2017).

So, has the proportion of news stories about topics unrelated to politics or the economy, such as cooking, travel, fashion, music, etc., in NYT gone up over time? (For issues related to measurement, see here.) As the figure shows, the net provision of not news increased by 10 percentage points between 1987 and 2007.

Interested in learning about more ways in which NYT has changed over time? See here.

Missing Women on the Streets of Delhi

19 Nov

In 1990, Amartya Sen estimated that more than 100 million women were missing in South and West Asia, and China. His NYRB article shed light on sex-discrimination in parts of Asia, highlighting, among other things, pathologies like sex-selective abortion, biases in nutrition, healthcare, and schooling.

We aim to extend that line of inquiry, and shed light on the question: “How many women are missing from a public life?” In particular, we aim to answer how many women are missing from the streets.

To estimate ‘missing women,’ we need a baseline. While there are some plausible ‘taste-based’ reasons for the sex ratio on the streets to differ from 50-50, for the current analysis, I assume that in a gender equal society, roughly equal number of men and women are out on the streets. And I attribute any skew to real (and perceived) threat of molestation, violence, harassment, patriarchy (allowing wives, daughters, sisters to go out), discrimination in employment, and similar such things.

Note About Data and Measurement

Of all the people out on the street over the course of a typical day in Delhi, what proportion are women? To answer that, I devised what I thought was a pretty reasonable sampling plan, and a pretty clever data collection strategy see here. Essentially, we would send people at random street locations at random times and ask them to take photos at head height, and then crowd-source counting the total number of people in the picture and the total number of women in the picture.

The data we finally collected in this round bears little resemblance to the original data collection plan. As to why the data collection went off rails, we have nothing to share publicly for now. The map of the places from which we collect data though lays bare the problems.

Data, Scripts, and Analyses are posted here.

Results

The data were collected between 2016-11-12 and 2017-01-11. And between roughly 10 am and 7 pm. In all, we collected nearly 1,958 photos from 196 locations. On average about 81.5% of the people on the street were men. The average proportion of men across various locations was 86.7% which suggests that somewhat busier places have somewhat more women.

Estimating Bias and Error in Perceptions of Group Composition

14 Nov

People’s reports of perceptions of the share of various groups in the population are typically biased. The bias is generally greater for smaller groups. The bias also appears to vary by how people feel about the group—they are likelier to think that the groups they don’t like are bigger—and by stereotypes about the groups (see here and here).

A new paper makes a remarkable claim: “explicit estimates are not direct reflections of perceptions, but systematic transformations of those perceptions. As a result, surveys and polls that ask participants to estimate demographic proportions cannot be interpreted as direct measures of participants’ (mis)information, since a large portion of apparent error on any particular question will likely reflect rescaling toward a more moderate expected value…”

The claim is supported by a figure that takes the form of plotting a curve over averages. (It also reports results from other papers that base their inferences on similar such figures.)

The evidence doesn’t seem right for the claim. Ideally, we want to plot curves within people and show that the curves are roughly the same. (I doubt it to be the case.)

Second, it is one thing to claim that the reports of perceptions follow a particular rescaling formula, and another to claim that people are aware of what they are doing. I doubt that people are.

Third, if the claim that ‘a large portion of apparent error on any particular question will likely reflect rescaling toward a more moderate expected value’ is true, then presenting people correct information ought not to change how people think about groups, for e.g., perceived threat from immigrants. The calibrated error should be a much better moderator than raw error. Again, I doubt it.

But I could be proven wrong about each. And I am ok with that. The goal is to learn the right thing, not to be proven right.

Learning About [the] Loss (Function)

7 Nov

One of the things we often want to learn is the actual loss function people use for discounting ideological distance between self and a legislator. Often people try to learn the loss function using over actual distances. But if the aim is to learn the loss function, perceived distance rather than actual distance is better. It is so because perceived = what the voter believes to be true. People can then use the function to simulate out scenarios if perceptions = fact.

Confirmation Bias: Confirming Media Bias

31 Oct

It used to be that searches for ‘Fox News Bias’ were far more common than searches for ‘CNN bias’. Not anymore. The other notable thing—correlated peaks around presidential elections.

Note also that searches around midterm elections barely rise above the noise.

God, Weather, and News vs. Porn

22 Oct

The Internet is for porn (Avenue Q). So it makes sense to measure things on the Internet in porn units.

I jest, just a bit.

In Everybody Lies, Seth Stephens Davidowitz points out that people search for porn more than weather on GOOG. Data from Google Trends for the disbelievers.

But how do searches for news fare? Surprisingly well. And it seems the new president is causing interest in news to outstrip interest in porn. Worrying, if you take Posner’s point that people’s disinterest in politics is a sign that they think the system is working reasonably well. The last time searches for news > porn was when another Republican was in the White House!

How is the search for porn affected by Ramadan? For answer, we turn to Google Trends from Pakistan. But you may say that the trend is expected given Ramadan is seen as a period for ritual purification. And that is a reasonable point. But you see the same thing with Eid-ul-Fitr and porn.

And in Ireland, of late, it seems searches for porn increase during Christmas.

Make Phone Meetings Great Again!

20 Oct

Remote teams live and die by the phone meeting. So how can we prevent death? Channel Rumsfeld. Begin with known knowns of conducting a successful meeting: having a clear agenda, a discussion leader, and ending with a summary. Do those well. Then try out some ideas to address well-known challenges: disengagement, social friction, exclusion, time wastage, and inability to follow what’s going on.

  1. People on the phone can’t always tell between the two uses of brief silence—a brief pause, and signal for opening the floor for discussion. The speaker can address the issue by signaling the end of speech with a phrase such as ‘I am done.’ A speaker may start the speech by noting: ‘at the end of what I have to say, I will formally open up the floor and go around alphabetically among those whose speakers are unmuted.’
  2. Having ‘too many’ people = wasting people’s time + disinterested participants. How many is too many? The maximum number of people who can productively engage when everyone is expected to contribute is probably as low as 4–5. What do you do when you have a large team? Divide and conquer. Split people into small teams and share notes.
  3. Prevent or Cure Rambling as side effects are the same as above—time wastage and disinterest. If people are having trouble articulating, the discussion leader should take on the responsibility to energetically understand the point people are trying to get at. The discussion leader may also refer the person to the shared document to sketch out the idea and try again.
  4. Stuff in advance that everyone actually reads is important. Just tell people if you didn’t find time to read + independently think, just opt out (semi-private opt-outs with emails to meeting organizers should be allowed). The job of a meeting is not spoon feeding.
  5. Keeping people on the same page:
    • Visual aids, e.g. slides, are useful in bringing people on the same page.
    • Taking notes on a shared screen can also help see people that progress is being made. The document can be shared and that allows others to contribute and organize simultaneously.

Most importantly, avoid meetings when you can. If the aim of meeting = transferring information, it only makes sense to have a meeting < 10% of the times. Alternatives = write out a document, or create a slideshow or a video and send it along.