In a forthcoming article, Chen and Rodden estimate the effect of ‘Unintentional gerrymandering’ on number of seats that go to a particular party. To do so they pick a precinct at random, and then add (randomly chosen) adjacent precincts to it till the district is of a certain size (decided by the total number of districts one wants to create). Then they go about creating a new district in the same manner, randomly selecting a precinct bordering the first district. This goes on till all the precincts are assigned to a district. There are some additional details but they are immaterial to the point of the note. A smarter way to do the same thing would be to just create one district over and over again (starting with a randomly chosen precinct). This would reduce the computational burden (memory for storing edges, differencing shapefiles, etc.) while leaving estimates unchanged.
Or When Treatment is Strategic, No-Intent-to-Treat Intent-to-Treat Effects can be biased
One popular strategy for estimating the impact of televised campaign ads is by exploiting ‘accidental spillover’ (see Huber and Arceneaux 2007). The identification strategy builds on the following facts: Ads on local television can only be targeted at the DMA level. DMAs sometimes span multiple states. Where DMAs span battleground and non-battleground states, ads targeted for residents of battleground states are seen by those in non-battleground states. In short, people in non-battleground states are ‘inadvertently’ exposed to the ‘treatment’. Behavior/Attitudes etc. of the residents who were inadvertently exposed are then compared to those of other (unexposed) residents in those states. The benefit of this identification strategy is that it allows television ads to be decoupled from the ground campaign and other campaign activities, such as presidential visits (though people in the spillover region are exposed to television coverage of the visits). It also decouples ad exposure etc. from strategic targeting of the people based on characteristics of the battleground DMA etc. There is evidence that content, style, the volume, etc. of television ads is ‘context aware’ – varies depending on what ‘DMA’ they run in etc. (After accounting for cost of running ads in the DMA, some variation in volume/content etc. across DMAs within states can be explained by partisan profile of the DMA, etc.)
By decoupling strategic targeting from message volume and content, we only get an estimate of the ‘treatment’ targeted dumbly. If one wants an estimate of ‘strategic treatment’, such quasi-experimental designs relying on accidental spillover may be inappropriate. How to estimate then the impact of strategically targeted televised campaign ads: first estimate how ads are targeted depending on area and people (Political interest moderates the impact of political ads [see for e.g. Ansolabehere and Iyengar 1995]) characteristics, next estimate effect of messages using the H/A strategy, and then re-weight the effect using estimates of how the ad is targeted.
One can also try to estimate the effect of ‘strategy’ by comparing adjusted treatment effect estimates in DMAs where treatment was targeted vis-a-vis (captured by regressing out other campaign activity) and where it wasn’t.
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)
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.
In Predictably Irrational, Dan Ariely discusses the clever (ex)-subscription menu of The Economist that purportedly manipulates people to subscribe to a pricier plan. In an experiment based on the menu, Ariely shows that addition of an item to the menu (that very few choose) can cause preference reversal over other items in the menu.
Let’s consider a minor variation of Ariely’s experiment. Assume there are two different menus that look as follows:
1. 400 cal, 500 cal.
2. 400 cal, 500 cal, 800 cal.
Assume that all items cost and taste the same. When given the first menu, say 20% choose the 500 calorie item. When selecting from the second menu, percent of respondents selecting the 500 calorie choice is likely to be significantly greater.
Now, why may that be? One reason may be that people do not have absolute preferences; here for a specific number of calories. And that people make judgments about what is the reasonable number of calories based on the menu. For instance, they decide that they do not want the item with the maximum calorie count. And when presented with a menu with more than two distinct calorie choices, another consideration comes to mind — they do not too little food either. More generally, they may let the options on the menu anchor for them what is ‘too much’ and what is ‘too little.’
If this is true, it can have potentially negative consequences. For instance, McDonald’s has on the menu a Bacon Angus Burger that is about 1360 calories (calories are now being displayed on McDonald’s menus courtesy Richard Thaler). It is possible that people choose higher calorie items when they see this menu option, than when they do not.
More generally, people’s reliance on the menu to discover their own preferences means that marketers can manipulate what is seen as the middle (and hence ‘reasonable’). This also translates to some degree to politics where what is considered the middle (in both social and economic policy) is sometimes exogenously shifted by the elites.
That is but one way a choice on the menu can impact preference order over other choices. Separately, sometimes a choice can prime people about how to judge other choices. For instance, in a paper exploring effect of Nader on preferences over Bush and Kerry, researchers find that “[W]hen Nader is in the choice set all voters’ choices are more sharply aligned with their spatial placements of the candidates.”
This all means, assumptions of IIA need to be rethought. Adverse conclusions about human rationality are best withheld (see Sen).
1. R. Duncan Luce and Howard Raiffa. Games and Decision. John Wiley and Sons, Inc., 1957.
2. Amartya Sen. Internal consistency of choice. Econometrica, 61(3):495Â– -521, May 1993.
3. Amartya Sen. Is the idea of purely internal consistency of choice bizarre? In J.E.J. Altham and Ross Harrison, editors, World, Mind, and Ethics. Essays on the ethical philosophy of Bernard Williams. Cambridge University Press, 1995.
(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.
More women identify themselves as Democrats than as Republicans. The disparity is yet greater among single women. It is possible (perhaps even likely) that this difference in partisan identification is due to (perceived) policy positions of Republicans and Democrats.
Now let’s do a thought experiment: Imagine a couple about to have a kid. Also, assume that the couple doesn’t engage in sex-selection. Two things can happen – the couple can have a son or a daughter. It is possible that having a daughter persuades the parent to change his or her policy preferences towards a direction that is perceived as more congenial to women. It is also possible that having a son has the opposite impact — persuading parents to adopt more male congenial political preferences. Overall, it is possible that gender of the child makes a difference to parents’ policy preferences. With panel data, one can identify both movements. With cross-sectional data, one can only identify the difference between those who had a son, and those who had a daughter.
Let’s test this using cross-sectional data from Jennings and Stoker’s “Study of Political Socialization: Parent-Child Pairs Based on Survey of Youth Panel and Their Offspring, 1997.”
Let’s assume that a couple’s partisan affiliation doesn’t impact the gender of their kid.
The number of kids, however, is determined by personal choice, which in turn may be impacted by ideology, income, etc. For example, it is likely that conservatives have more kids as they are less likely to believe in contraception, etc. This is also supported by the data. (Ideology is a post-treatment variable. This may not matter if the impact of having a daughter is same in magnitude as the impact of having a son, and if there are similar numbers of each across people.)
Hence, one may conceptualize “treatment” as the gender of the kids, conditional on the number of kids.
Understandably, we only study people who have one or more kids.
Conditional on number of kids, the more daughters respondent has, the less likely respondent is to identify herself as a Republican (b = -.342, p < .01) (when dependent variable is curtailed to Republican/Democrat dichotomous variable; the relationship holds—indeed becomes stronger—if the dependent variable is coded as an ordinal trichotomous variable: Republican, Independent, and Democrat, and an ordered multinomial estimated)
If what we observe is true then we should also see that as party stances evolve, the impact of gender on policy preference of a parent should vary. One should also be able to do this cross-nationally.
Some other findings:
- Probability of having a son (limiting to live births in the U.S.) is about .51. This natural rate varies slightly by income. Daughters are more likely to be born among people with lower incomes. However, the effect of income is extremely modest in the U.S. The live birth ratio is marginally rebalanced by the higher child mortality rate among males. As a result, among 0–21, the ratio between men and women is about equal in U.S.
In the sample, there are significantly more daughters than sons. The female/male ratio is 1.16. This is ‘significantly’ unusual.
- If families are less likely to have kids after the birth of a boy, the number of kids will be negatively correlated with proportion sons. Among people with just one kid, the number of sons is indeed greater than number of daughters, though the difference is insignificant. Overall correlation between proportion sons and number of kids is also very low (corr. = -.041).
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.
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.