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5 - The Choice to Be a Disadvantaged-Group Advocate in the US Senate

Published online by Cambridge University Press:  18 November 2021

Katrina F. McNally
Affiliation:
Eckerd College, Florida

Summary

The quantitative analysis in Chapter 5 demonstrates a number of important differences in the factors most strongly influencing a senator’s decision to form a reputation as a disadvantaged-group advocate relative to a member of the House. Chief among these distinctions is the diminished impact of the size of a disadvantaged group within the state. Senators are not likely to choose to build a reputation as a group advocate for any but the groups considered to be the most highly deserving of government assistance. This chapter also introduces and tests three additional hypotheses reflecting the unique institutional characteristics of the Senate, finding that the larger the number of group advocates present within a given Congress, the more likely it is that another senator will also be willing to incorporate advocacy on behalf of that group into their own reputation.

Information

Figure 0

Table 5.1 Summary of estimates for state feeling thermometer ratings by group

Note: Displayed are the average estimate values for the feeling thermometer scores across all fifty states from 1992 to 2016. Estimates were generated using multilevel regression with poststratification. The estimate for racial/ethnic minorities is an average of the ambient temperature generated for each district for Black, Hispanic, and Asian Americans.
Figure 1

Table 5.2 Number of senators with reputations as advocates for disadvantaged groups in the 103rd, 105th, 108th, 110th, and 113th Congress

Figure 2

Table 5.3 Group size, ambient temperature, and member reputation for advocacy for veterans and seniors

Note: Coefficients calculated using generalized ordered logistic regression, with First Term modeled as a parallel proportional term and the rest of the independent variables modeled as partial proportional terms. Standard errors are clustered by member, and p-values are in gray. Model 0 represents the likelihood of a shift from no advocacy to superficial or primary/secondary advocacy, and Model 1 is no advocacy or superficial advocacy to primary/secondary advocacy. Feeling thermometer questions for seniors were not included in the ANES of the 2010s, so the decade base category for seniors is the 2000s.
Figure 3

Table 5.4 Group size, ambient temperature, and member reputation for advocacy for racial/ethnic minorities and the LGBTQ community

Note: Coefficients for LGBTQ are estimated using logistic regression, as necessitated by the bivariate coding of the LGBTQ advocacy reputation variable. Coefficients for racial/ethnic minorities are calculated using generalized ordered logistic regression, with First Term modeled as a parallel proportional term and the rest of the independent variables modeled as partial proportional terms. Standard errors are clustered by member, and p-values are in gray. Model 0 represents the likelihood of a shift from no advocacy to superficial or primary/secondary advocacy, and Model 1 is no advocacy or superficial advocacy to primary/secondary advocacy.
Figure 4

Table 5.5 Group size, ambient temperature, and member reputation for advocacy for immigrants and the poor

Note: Coefficients calculated using generalized ordered logistic regression, with First Term modeled as a parallel proportional term and the rest of the independent variables modeled as partial proportional terms. Standard errors are clustered by member, and p-values are in gray. Model 0 represents the likelihood of a shift from no advocacy to superficial or primary/secondary advocacy, and Model 1 is no advocacy or superficial advocacy to primary/secondary advocacy.
Figure 5

Table 5.6 Group size, ambient temperature, and member reputation for advocacy for women

Note: Coefficients calculated using generalized ordered logistic regression, with First Term modeled as a parallel proportional term and the rest of the independent variables modeled as partial proportional terms. Standard errors are clustered by member, and p-values are in gray. Model 0 represents the likelihood of a shift from no advocacy to superficial or primary/secondary advocacy, and Model 1 is no advocacy or superficial advocacy to primary/secondary advocacy.
Figure 6

Table 5.7 Institutional, electoral, and constituency effects on member reputation for advocacy for veterans and seniors

Note: Coefficients calculated using generalized ordered logistic regression, with First Term modeled as a parallel proportional term and the rest of the independent variables modeled as partial proportional terms. Standard errors are clustered by member, and p-values are in gray. Model 0 represents the likelihood of a shift from no advocacy to superficial or primary/secondary advocacy, and Model 1 is no advocacy or superficial advocacy to primary/secondary advocacy. Feeling thermometer questions for seniors were not included in the ANES of the 2010s, so the decade base category for seniors is the 2000s.
Figure 7

Table 5.8 Institutional, electoral, and constituency effects on member reputation for advocacy for racial/ethnic minorities and the LGBTQ community

Note: Coefficients for LGBTQ are estimated using logistic regression, as necessitated by the bivariate coding of the LGBTQ advocacy reputation variable. Coefficients for racial/ethnic minorities are calculated using generalized ordered logistic regression, with First Term modeled as a parallel proportional term and the rest of the independent variables modeled as partial proportional terms. Standard errors are clustered by member, and p-values are in gray. Model 0 represents the likelihood of a shift from no advocacy to superficial or primary/secondary advocacy, and Model 1 is no advocacy or superficial advocacy to primary/secondary advocacy. Presence of same-state advocate is omitted from the LGBTQ models due to perfect prediction; there are no states for which both senators have reputations as LGBTQ advocates.
Figure 8

Table 5.9 Institutional, electoral, and constituency effects on reputation for advocacy for immigrants and the poor

Note: Coefficients calculated using generalized ordered logistic regression, with First Term modeled as a parallel proportional term and the rest of the independent variables modeled as partial proportional terms. Standard errors are clustered by member, and p-values are in gray. Model 0 represents the likelihood of a shift from no advocacy to superficial or primary/secondary advocacy, and Model 1 is no advocacy or superficial advocacy to primary/secondary advocacy.
Figure 9

Table 5.10 Institutional, electoral, and constituency effects on member reputation for advocacy for women

Note: Coefficients calculated using generalized ordered logistic regression, with First Term modeled as a parallel proportional term and the rest of the independent variables modeled as partial proportional terms. Standard errors are clustered by member, and p-values are in gray. Model 0 represents the likelihood of a shift from no advocacy to superficial or primary/secondary advocacy, and Model 1 is no advocacy or superficial advocacy to primary/secondary advocacy.
Figure 10

Table 5.11 Number of members serving as advocates of disadvantaged groups across descriptive and nondescriptive representatives

Figure 11

Table 5.12 Descriptive representatives and senator reputation for advocacy

Note: Coefficients calculated using the Firth Method for penalized maximum likelihood estimation for logistic regression, using the firthlogit Stata program. P-values are presented in gray.
Figure 12

Figure 5.1 Predicted effects of ambient temperature for members with reputations as advocates relative to non-advocates for descriptive representativesNote: Figures show the predicted marginal effects of ambient temperature on reputation for superficial, secondary, or primary advocacy relative to non-advocacy for members who are themselves descriptive representatives of the group and members who are not. Predicted marginal effects are calculated using Stata’s margins command for the models containing interactions between descriptive representative and ambient temperature shown in Tables 5.12. All other variables are held to their mean values within the dataset. Clockwise from the top left, the groups whose representation is being analyzed are veterans, seniors, the poor, women, immigrants, and racial/ethnic minorities.

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