Estimating Hillary’s Missing Emails

11 Apr

Note:

55000/(365*4) ~ 37.7. That seems a touch low for Sec. of state.

Caveats:
1. Clinton may have used more than one private server
2. Clinton may have sent emails from other servers to unofficial accounts of other state department employees

Lower bound for missing emails from Clinton:

  1. Take a small weighted random sample (weighting seniority more) of top state department employees.
  2. Go through their email accounts on the state dep. server and count # of emails from Clinton to their state dep. addresses.
  3. Compare it to # of emails to these employees from the Clinton cache.

To propose amendments, go to the Github gist

Some Hard Feelings: Feelings Towards Some Racial and Ethnic Groups in 4 Countries

8 Aug

According to YouGov surveys in Switzerland, Netherlands and Canada, and the 2008 ANES in the US, Whites, on average, in each of the four countries feel fairly coldly — giving an average thermometer rating of less than 50 on a 0 to 100 scale — toward Muslims, and people from Muslim-majority regions (Feelings towards different ethnic, racial, and religious groups). However, in Europe, Whites’ feelings toward Romanians, Poles, and Serbs and Kosovars are scarcely any warmer, and sometimes cooler. Meanwhile, Whites feel relatively warmly towards East Asians.

Liberal politicians are referred to more often in news

8 Jul

The median Democrat referred to in television news is to the left of the House Democratic Median, and the median Republican politician referred to is to the left of the House Republican Median.

Click here for the aggregate distribution.

And here’s a plot of top 50 politicians cited in news. The plot shows a strong right skewed distribution with a bias towards executives.

Data:
News data: UCLA Television News Archive, which includes closed-caption transcripts of all national, cable and local (Los Angeles) news from 2006 to early 2013. In all, there are 155,814 transcripts of news shows.

Politician data: Database on Ideology, Money in Politics, and Elections (see Bonica 2012).

Note:
Taking out data from local news channels or removing Obama does little to change the pattern in the aggregate distribution.

(No) Value Added Models

6 Jul

This note is in response to some of the points raised in the Agnoff Lecture by Ed Haertel.

The lecture makes two big points:
1) Teacher effectiveness ratings based on current Value Added Models are ‘unreliable.’ They are actually much worse than just unreliable; see below.
2) Simulated counterfactuals of gains that can be got from ‘firing bad teachers’ are upwardly biased.

Three simple tricks (one discussed; two not) that may solve some of the issues:
1) Estimating teaching effectiveness: Where possible, random assignment of children to classes. I would only do within school comparisons. Inference will still not be clean (SUTVA violations, though they can be dealt with). Simply cleaner.

2) Experiment with teachers. Teach some teachers some skills. Estimate the impact. Rather than teacher level VAM, do a skill level VAM. Teachers = sum of skills + idiosyncratic variation.

3) For current VAMs: To create better student level counterfactuals, use modern ML techniques (SVM, Neural Networks..), lots of data (past student outcomes, past classmate outcomes etc.), cross-validate to tune. Have a good idea about how good the prediction is. The strategy may be applicable to other venues.

Other points:
1) Haertel says, “Obviously, teachers matter enormously. A classroom full of students with no teacher would probably not learn much — at least not much of the prescribed curriculum.” A better comparison perhaps would be to self-guided technology. My sense is that as technology evolves, teachers will come up short in a comparison between teachers and advanced learning tools. In most of the third world, I think it is already true.

2) It appears no model for calculating teacher effectiveness scores yields identified estimates. And it appears we have no clear understanding of the nature of bias. Pooling biased estimates over multiple years doesn’t recommend itself to me as a natural fix to this situation. And I don’t think calling this situation as ‘unreliability’ of scores is right. These scores aren’t valid. The fact that pooling across years ‘works’ may suggest issues are smaller. But then again, bad things may be happening to some kinds of teachers, especially if people are doing cross-school comparisons.

3) Fade-out concern is important given the earlier 5*5 =25 analysis. My suspicion would be that attenuation of effects varies depending on when the timing of the shock. My hunch would be that shocks at an earlier age matter more – they decay slower.

(No) Missing daughters of Indian Politicians

29 Jun

Indian politicians get a bad rap. They are thought to be corrupt, inept, and sexist. Here we check whether there is prima facie evidence for sex-selective abortion.

According to data on the Indian Government ‘Archive’, 15th Lok Sabha members (csv) had, in all, 696 sons and 666 daughters for a sex ratio of 957 females to 1000 males. Progeny of members from states with the most skewed sex ratios (Punjab, Haryana, Jammu and Kashmir, and Haryana) had a surprisingly healthy sex ratio of 1245 females to 1000 males. Sex ratios of children of BJP and INC members were 930/1000 and 965/1000 respectively. Rajya Sabha members (csv) had 271 sons and 272 daughters for a sex ratio of 1003 females to 1000 males. Not only was there little evidence of sex-selective abortion, data also suggest that fertility rates were modest. Lok Sabha members had on average 2.5 kids while members of Rajya Sabha had on average 2.2 kids.

Impact of selection bias in experiments where people treat each other

20 Jun

Selection biases in the participant pool generally have limited impact on inference. One way to estimate population treatment effect from effects estimated using biased samples is to check if treatment effect varies by ‘kinds of people’, and then weight the treatment effect to population marginals. So far so good.

When people treat each other, selection biases in participant pool change the nature of the treatment. For instance, in a Deliberative Poll, a portion of the treatment is other people. Naturally then, the exact treatment depends on the pool of people. Biases in the initial pool of participants mean treatment is different. For inference, one may exploit across group variation in composition.

A Relatively Ignored Mediational Variable in Deliberation

5 Jun

Deliberation often causes people to change their attitudes. One reason this may happen is because deliberation also causes people’s values to change. Thus, one mediational model is as follows: change in values causes change in attitudes. However, deliberation can also cause people to connect their attitudes better with their values, without changing those values. This may mean that ceteris paribus, same values yield different attitudes post-treatment. For identification, one may want to devise a treatment that changes respondent’s ability to connect values to attitudes but has no impact on values. Or a researcher may try to gain insight by measuring people’s ability to connect values to attitudes in current experiments, and estimating mediational models. One can also simply try simulation (again subject to the usual concerns): use post-treatment model (regressing attitudes on values), use pre-deliberation values, and simulate attitudes.