Figure 7.4 presents graphical results of regression analyses that include a triple interaction between treatment, coethnicity, and respondent income. Table G.1 presents the full regression results. In each model, the sum of the coefficients on the Electoral Clientelism Treatment variable and the Electoral Clientelism Treatment×Coethnic interaction termrepresents the treatment effect among the candidate's poorest coethnics. The triple interaction, Electoral Clientelism Treatment × Coethnic × Income, tells us what happens to this treatment effect among coethnics as participant wealth increases. In each model, the pattern of results is the same. The treatment effect is big and positive among the candidate's poor coethnics, and the treatment effect decreases with income. Among non-coethnics, on the other hand, the electoral clientelism treatment effect is negative among voters at all income levels.
Table G.2 examines how voter income, clientelism, and coethnicity interact to shape prospective expectations. Since the triple interactions are difficult to interpret substantively, the main result is presented graphically in Figure 7.6. Consistent with the patterns presented above, the results on prospective expectations are driven primarily by the poorer coethnics of the candidate. The treatment effect among coethnics decreases with participant income and eventually becomes about equal to zero at higher income levels.
All models are OLS. The sample includes only those who heard either about a coethnic or about a non-coethnic. Participants who heard about a non-coethnic are the omitted reference category. The dependent variable in column 1 is participant degree of agreement with the statement: “I would vote for the candidate.” The dependent variable in column 2 is participant degree of agreement with the statement: “All candidates should be like the one in the recording.” The dependent variable in column 3 is participant degree of agreement with the statement: “I would like the candidate to run in the next election.” The dependent variable in column 4 is the mean of all three support measures.
Column 1 dependent variable is degree of agreement with the statement: “The candidate will help people like you who are living in poverty.” Column 2 dependent variable is degree of agreement with the statement: “If the candidate wins, he will help people in an emergency.” Column 3 dependent variable is degree of agreement with the statement: “If the candidate wins, I will receive resources such as cash or food.” Column 4 dependent variable is the mean response to each of these three responses.