Category Archives: American Politics

The difference between Whites’ thermometer ratings (0 = cold, 100=warm) of Whites and Blacks has gone down relatively steadily over the past 50 or so years.


However, the decline is almost entirely explained by the drop in ratings of Whites.

Each line represents data from a different year. Source: American National Election Studies.

Now, aging curves of political knowledge by cohort.

Two things are often stated about American politics: political elites are increasingly polarized, and that the issue positions of the masses haven’t budged much. Assuming such to be the case, one expects the average distance between where partisans place themselves and where they place the ‘in-party’ (or the ‘out-party’) to increase. However, it appears that the distance to the in-party has remained roughly constant, while the distance to the out-party has grown, in line with what one expects from the theory of ‘affective polarization’ and group-based perception. (Read More: Still Close: Perceived Ideological Distance to Own and Main Opposing Party)

By Party:

Over the past forty years, the proportion of respondents reporting at least one union member in the household has declined precipitously (Source: American National Election Studies).

In the National Election Studies (NES), interviewers have been asked to rate respondent’s level of political information: “Respondent’s general level of information about politics and public affairs seemed — Very high, Fairly high, Average, Fairly low, Very low.” John Zaller, among others, has argued that these ratings measure political knowledge reasonably well. However, there is some evidence that challenges the claim. For instance, there is considerable unexplained inter- and intra-interviewer heterogeneity in ratings – people with similar levels of knowledge (as measured via closed-ended items) are rated very differently (Levendusky and Jackman 2003 (pdf)). It also appears that mean interviewer ratings have been rising over the years, compared to the relatively flat trend observed in more traditional measures (see Delli Carpini, and Keeter 1996 and Gilens, Vavreck, and Cohen 2004, etc).

Part of the increase is explained by higher ratings of respondents with less than a college degree; ratings of respondents with BS or more have remained somewhat more flat. As a result, the difference in ratings of people with a Bachelor’s Degree or more and those with less than a college degree is decreasing over time. Correlation between interviewer ratings and other criteria like political interest are also trending downward (though the decline is less sharp). This conflicts with evidence for increasing ‘knowledge gap’ (Prior 2005).

The other notable trend is the sharp negative correlation (over .85) between intercept and slope of within-year regressions of interviewer ratings and political interest, education, etc. This sharp negative correlation hints at possible ceiling effects. And indeed there is some evidence for that.

Interviewer Measure – The measure is sometimes from the pre-election wave only, other times in the post-election wave only, and still other times in both waves. Where both pre and post measures were available, they were averaged. The correlation between pre-election and post-election rating was .69. The average post-election ratings are lower than pre-election ratings.

(Based on data from the 111th Congress)

Law is the most popular degree at the Capitol Hill (it has been the case for a long time). Nearly 52% of the senators, and 36% of congressional representatives have a degree in law. There are some differences across parties and across houses, with Republicans likelier to have a law degree than Democrats in the Senate (58% to 48%), and the reverse holding true for the Congress, where a greater share of Democrats holds law degrees than Republicans (40% to 32%). Less than 10% of members of congress have a degree in the natural sciences or engineering. Nearly 8% have a degree from Harvard, making Harvard’s the largest alumni contingent at the Capitol. Yale is a distant second with less than half the number that went to Harvard.

Data and Script

Source: 2004 ANES. Cultural and Social Welfare Preferences are from Confirmatory Factor Analysis over relevant items.

By now students of American Politics have all become accustomed to seeing graphs of DW-NOMINATE scores showing ideological polarization in Congress. Here are the equivalent graphs (we assume two dimensions) at the mass-level.

Data are from the 2004 ANES. Social and Cultural Preferences are from Confirmatory Factor Analysis over relevant items.

Here’s how to interpret the graphs:

1) There is a large overlap in preference profiles of Rs and Ds.

2) Conditional on same preferences, there is a large gap in thermometer ratings. Without partisan bias – same-preferences should yield about the same R-D thermometer ratings. And this gap is not particularly responsive to change in preferences within parties.

Each election cycle, many hands are waved and spit is launched in air, when the topic of registration rates of Latinos (and other minorities) comes up. And indeed registration rates of Latinos substantially lag those of Whites. In California, percent eligible Latinos who are registered is 62.8%, whereas percent eligible Whites registered to vote is approximately 72.9%.

This somewhat large difference in registration rates doesn’t automatically translate to (equally) wide distortions in racial distribution of the eligible population and the registered voter population. For example, while self-identified Whites constitute 62.8% of the VEP, they constitute marginally more – 64.2% of the voting eligible respondents who self-identify as having registered to vote.

Here’s the math:

Assume VEP Pop. = 100
Whites = 63/100; of these 72% register = 45
Latinos = 23/100; of these 62% register = 14
Rest = 14/100; of these 62% register = 9
New Registered Population = 45 + 14 + 9 = 68
Registered: Whites = 66.2; Latinos = 20.6

Source: PPIC Survey (September 2010).
Note: CPS 2008, Secretary of State data confirm this. Voting day population estimates from Exit Poll also show no large distortions.

Some simple math:
For a two category case, say proportion category a = pa
Proportion category b = 1 - pa

Assume response rates for category a = qa, and for category b = qb = c*qa


Initial Ratio = pa/(1 -pa)
Final Ratio = pa*qa/(1-pa)*qb


Or between time 1 and 2, ratio changes by qa/qb or 1/c


T1 Diff. = pa - (1- pa) = 2pa - 1
T2 Diff. = (pa*qa - qb + pa*qb)/(pa*qa + (1-pa)*qb)
= (pa(qa + qb) - qb)/(pa(qa - qb) + qb)
= [pa*qa (1 + c) - c*qa]/[pa*qa(1-c) + c*qa]

When will diff. in response rates + initial diff. > T2 diff.
qa - qa*c + 2pa - 1 > [pa*qa (1 + c) - c*qa]/[pa*qa(1-c) + c*qa]
[pa*qa(1-c) + c*qa][qa - qa*c + 2pa - 1] - [pa*qa (1 + c) - c*qa] > 0
- pa*qa + pa*qa*c - c*qa + [pa*qa(1-c) + c*qa][qa - qa*c + 2pa] - pa*qa - pa*qa*c + c*qa > 0
-2pa*qa + [pa*qa(1-c) + c*qa][qa - qa*c + 2pa] > 0
-2pa*qa + [pa*qa - pa*qa*c + c*qa][qa - qa*c + 2pa] > 0
-2pa*qa + pa*qa[qa - qa*c + 2pa] - pa*qa*c[qa - qa*c + 2pa] + c*qa[qa - qa*c + 2pa] > 0
-2pa*qa + pa*qa*qa - pa*qa*qa*c + 2pa*qa*pa - pa*qa*c*qa + pa*qa*c*qa*c + 2pa*qa*c*pa + c*qa*qa - c*qa*qa*c + 2pa*c*qa> 0
-2pa*qa + pa*qa^2 - 2c*pa*qa^2 + 2qa*pa^2 + pa*c^2*qa^2 + 2pa^2*c*qa + c*qa^2 + c^2*qa^2 + 2pa*c*qa > 0
-2pa*qa + 2qa*pa^2 + 2pa*c*qa + 2pa^2*c*qa + pa*qa^2 - 2c*pa*qa^2 + pa*c^2*qa^2 + c*qa^2 + c^2*qa^2 > 0
2qa*pa(-1 + c + pa + pa*c) + pa*qa^2 (1 - 2c + c^2) + c*qa^2(1 + c) > 0
2qa*pa(-1 + c + pa(1+c)) + pa*qa^2 (1 - c)^2 + c*qa^2(1 + c) > 0
two of the terms are always 0 or more.
2qa*pa(-1 + c + pa(1+c)) > 0
-1 + c + pa(1+c) > 0
pa > (1-c)/(1 +c)