Political campaigns raise millions of dollars each election cycle. While past research provides valuable insight into who these donors are and why they are motivated to give, little research takes into account the actions of political campaigns. This paper examines why and how campaigns target habitual donors for political donations. Using the 2004 Campaign Communication Survey, a national survey of registered voters who were asked to collect and send in all campaign mail they received during the last 3 weeks of a campaign, we show that campaigns send donation solicitations predominantly to individuals who have previously donated to a campaign. We also show that campaigns match targeting fundraising appeals to the potential motivations for giving: campaigns target the type of fundraising appeal they use, whether ideological, solidary, or material, to match the socioeconomic and partisan characteristics of the potential donor. The implication of effective targeting is that the “unequal” voice of participation in campaign contributions is not one-sided and simply resource based, but rather that campaigns also contribute to the situation with targeted messages to potential donors.
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Because the dependent variable is a discrete count, a traditional linear regression model (LRM) may produce estimates that are inefficient, inconsistent, and biased and is thus inappropriate (Long 1997, 217). The LRM also does not constrain the number of events to be positive (King 1989, 123). In this case reasonable modeling choices include the Poisson regression model (PRM) or the negative binomial regression model (NBRM). When using the PRM one assumes that the events are uncorrelated and that the rate of event occurrence is homogeneous. When this is the case, the conditional mean of the outcome is equal to its variance. In practice these assumptions are rarely met—event counts are correlated with each other or the rate of event occurrence is heterogeneous resulting in overdispersion. Using the PRM with data that are overdispersed results in estimates that are inefficient and standard errors that are biased downward. However, there are two possible sources for the over-dispersion in our data. First an individual could receive no mail because that individual is not a campaign target. Alternatively, a large number of individuals could be a campaign target but received no fundraising mail during the three week period when the survey was conducted. To account for these two distinct sources of overdispersion we use a zero-inflated model which simultaneously estimates the likelihood of an individual receiving a certain number of fundraising appeals using a logit model and a negative binomial model (Cameron and Trivedi 2005). The logit model accounts for the first possible source of overdispersion (not being a campaign target), while the negative binomial predicts the expected number of fundraising mailers given the likelihood of being a campaign target and controls for the second source of overdispersion (heterogeneity). For all models presented here the ZINB passes the Vuong test which indicates that the zero-inflated model is more appropriate than the normal NBRM (Vuong 1989). To choose between a zero-inflated PRM (ZIP) and zero-inflated NBRM (ZINB) requires estimating the NBRM and calculating the dispersion (α). If α = 0 then the NBRM reduces to the PRM. If p < 0.05 that α does not equal 0 then the NBRM is the more appropriate choice because it is likely that the data are overdispersed due to heterogeneity. An alternative test is a likelihood ratio test where G2 = 2(ln LNBRM—ln LPRM) is a test of H0: α = 0 (see Long 1997, 237). In the all models presented in Table 1 with the exception of the model of the number of material appeals received, the alpha is significant at p < 0.01, thus we can reject the null hypothesis that α = 0 and conclude that the NBRM is the more appropriate choice because it is likely that the data are overdispersed. Alternatively, using the likelihood ratio test the null hypothesis α = 0 is also rejected (p < 0.01). In the case of the model using material appeals, while the Vuong test is significant, the likelihood ratio tests are not significant and the results presented revert to a ZIP.
For model simplicity and precision we remove income and strength of party identification from future iterations of the first stage of the model and education and age from future iterations of the second stage of the model (Agresti and Finlay 2007). The removal of these variables does not significantly affect the goodness of fit of the model and the inclusion of these additional variables has no effect on the substantial effects of other variables in the model.
Again, for model simplicity and precision we remove income and strength of party identification from the first stage of the model and education and age the second stage of the models predicting the volume of appeals sent to an individual of a certain type (Agresti and Finlay 2007). We did run models with the inclusion of these variables and their addition does not significantly affect the goodness of fit of the model and has no effect on the substantial effects of other variables in the model.
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The data collection was funded in part by a grant from the Pew Charitable Trusts to the Center for the Study of Elections and Democracy (CSED) at Brigham Young University (BYU). Additional funding for data acquisition and research assistance was provided by CSED and the College of Family, Home, and Social Sciences at BYU. We thank members of the Center for the Study of Elections and Democracy at BYU and the Human Nature Group at UC-San Diego for helpful comments. All shortcomings remain our own.
Conflict of interest
The authors declare that they have no conflicts of interest.
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Hassell, H.J.G., Monson, J.Q. Campaign Targets and Messages in Direct Mail Fundraising. Polit Behav 36, 359–376 (2014). https://doi.org/10.1007/s11109-013-9230-8
- Campaign fundraising
- Political communication
- Political campaigns
- Campaign donors