Why Do People (Re)-Elect Bad Leaders?

7 Dec

‘Why do people (re)-elect bad leaders?’ used to be a question that people only asked of third-world countries. No more. The recent election of unfit people to prominent positions in the U.S. and elsewhere has finally woken some American political scientists from their mildly racist reverie—the dream that they are somehow different.

So why do people (re)-elect bad leaders? One explanation that is often given is that people prefer leaders that share their ethnicity. The conventional explanation for preferring co-ethnics is that people expect co-ethnics (everyone) to do better under a co-ethnic leader. But often enough, the expectation seems more like wishful thinking than anything else. After all, the unsuitability of some leaders is pretty clear.

If it is wishful thinking, then how do we expose it? More importantly, how do we fix it? Let’s for the moment assume that people care about everyone. And if they were to learn that the co-ethnic leader is much worse than someone else, they may switch votes. But what if people care about the welfare of co-ethnics more than others? The ‘good’ thing about bad leaders is that they are generally bad for everyone. So, if they knew better, they would still switch their vote.

You can verify these points using a behavioral trust game where people observe allocators of different ethnicities and different competence, and also observe welfare of both co-ethnics and others. You can also use the game to study some of the deepest concerns about ‘negative party ID’—that people will harm themselves to spite others.

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.


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.