with the extremists together and the moderates together.
By considering this special distribution, the aim is to capture the notion that voters may separate themselves into groups in a way that depends on their ideology.
In other words, people who live in close proximity to each other, or who have close relationships with each other, are more likely to hold similar political views. Under this extreme form of correlation, for example, every voter on the left half of the circle is a Democrat while every voter on the right half of the circle is a Republican, and within parties, the voters are again sorted by group. This situation is referred to as
“voter polarization”.
The voters know the polarization setup (by allowing them, as before, to observe each other’s types). Now the competency voters, because they are neighbors, will be able to receive a great deal more information via competency signals. They will again have more certainty about which candidate is more competent. Here it is again possible refer to earlier work with respect to the comparative statics of λ and candidate locations. With an increased number of signals λ will increase relative to the uncorrelated case, which will produce an increase in candidate polarization.
update their information about candidate competency from communication (when communication is based upon a most-preferred candidate) with those voters from whom they are ideologically polarized. This model captures nicely the correlation that can occur when neighbors agree about which candidate is more competent. It also produces a candidate polarization result that is smaller when the voters themselves are not polarized.
Future research will investigate results allowing the voters to be strategic and consider their behavior when their utility is based upon the majority rule outcomes.
Future research may include work in which the voters did not know each other’s types.
This would have as a consequence voter j under-weighting even more dramatically the competency signals she receives via communication. Another possibility would be to additionally correlation candidate signals with ideology. A final possibility is to let each voter draw a vector with N −1 entries which describe the probabilities of friendship with any other voter (or intensity of friendship). Information could be exchanged with these probabilities. Empirical testing of this model will examine the degree to which individually reported communications from other voters (using the 2000 ANES, the 1987 GSS and the 1988 GSS) relates to vote choice, the degree to which geographic proxies for network effects determine vote choice (using election returns from specific geographies), and the amount of information available to both respondents and to those within their discussion network (using the 2000 ANES and data from the Huckfeldt and Sprague 1996 St Louis -Indianapolis Study).
Chapter 3
Discussion: Theoretical Model and Empirical Chapters
Each of the following sections will analyze data which are related to different com- ponents of this model. No single data-set is provided in order to empirically test the model directly. However, different parts of the model or assumptions are verified in the sections which follow. In all cases, data is presented about the voter behavior — the empirical analysis discusses only the actions which would occur as a consequence of voter-to-voter communication. These are the assumptions which underly the model
— if in fact voters do behave consistently with these assumptions, then it is possible to assume that candidates will behave consistently with the model.
Two get-out-the-vote (GOTV) mobilization campaigns were completed in South Los Angeles, where the door-to-door canvassers were identified as individuals who were residents of South Los Angeles. In both instances individuals are identified as being precinct-residents or not, and the turnout from precincts contacted by residents is higher than the turnout from precincts contacted by non-residents. The script, read by both non-residents and residents, is the same for all canvassers. Thus, the increased turnout is consistent with voters more readily believing information from others within their social framework. These canvassers, however, are not necessarily known personally to each voter, although they do often report seeing their friends while walking in their neighborhoods. Again, it is also possible that this is a “peer- pressured” effect, where voters are more likely to turnout to vote if they believe their
friends will observe their behavior. The voter mobilization experiments validate one of the findings derived from the theoretical model which states that individuals are likely to be socially connected to others of similar types – and that communication with similar types allows individuals to update about a valence dimension. That we observe higher turnout rates with canvassing conducted by home-turf canvassers indicates that indeed individuals who are more likely to be socially connected increases the persuasiveness of communication.
The next section presents results from a laboratory experiment which investigates the degree to which individuals can update their beliefs about candidates when pre- sented with almost no information, but instead a noisy, public signal which closely resembles the “cheap talk” communication phase in the theoretical model. Voters are readily able to do so, and are able to converge upon the candidate midpoint through Bayesian updating. The experimental setup is not identical in that all voters observe the same signal (as opposed to observing a signal only within their own social frame- work) but the behavioral assumption in the model, that the voters will update their priors from their signals, is validated here.
The final section presents a series of results from three survey data-sets, each of which asks the respondent to identify discussant characteristics and then uses those characteristics as a “treatment” variable to understand the voter’s candidate and partisan choices. Assuming that the respondent’s answers to the discussant charac- teristics are not endogenous to the respondent’s characteristics, then it is possible to observe large effects of discussant characteristics on candidate choice and party identification. This is consistent with the results of the model which claim that there will be correlation between social connections and vote choice as a consequence of communication.
Each of these sections have findings which are consistent with the assumptions about voter behavior in the theoretical model — that individuals are Bayesians (lab- oratory experiment chapter), that they are likely to be connected to individuals of similar types (survey data chapters), and that they communicate about politics (sur- vey data chapters). The preliminary results in the theoretical model are also validated
in these chapters – that voters of similar types are more persuasive in their commu- nication (GOTV experiment chapters) and that voters who are socially connected are likely to have correlated choices, controlling for their preferences (survey data chapters). If candidates are aware of these behaviors, then it is possible to assume that they would consider the voter choice correlation in their own choice of policy platform positions — resulting in elite polarization.
While the empirical results are interesting by themselves, they provide collec- tively a particularly strong argument for the fact that individuals will condition their political behavior based upon the actions of others within their political network.
The traditional calculus of voting, which considers each individual as an independent agent maximizing her preferences, is simply an insufficient explanation. Here indi- viduals behave consistent with others within their social framework. Whether those behaviors are due to a change in the reliability of information when information is procured from a friend or whether the individuals are simply trying to behave like others within their social group is not resolved here. However, what is clear is that political preferences and behaviors condition upon group preferences.