Appendix A Issue Coding for Disadvantaged Group Advocacy for Reputation Measure
Table A-1 presents the list of issues that are included as instances of advocacy for a disadvantaged group, in addition to all actions that are specifically attributed to being done to serve a particular group.
Table A-1 Issue coding by disadvantaged group
Veterans
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Seniors
LGBTQ
Racial/Ethnic Minorities
Immigrants
Women
Poor
|
Appendix B Reputations for Primary and Secondary Disadvantaged-Group Advocacy in the House and the Senate
The following tables present a list of members who are coded in the 103rd, 105th, 108th, 110th, or 113th Congresses as having a reputation for primary or secondary advocacy of disadvantaged groups. Table B-1 shows the members with these reputations in the House of Representatives, while Table B-2 does the same for those in the Senate.
Table B-1 Reputations for primary and secondary advocacy by disadvantaged group in the House of Representatives (103rd, 105th, 108th, 110th, 113th Congresses)
| Veterans | ||
| Jeff Miller (R-FL1) | Rich Nugent (R-FL11) | Dave Weldon (R-FL15) |
| Gus Bilirakis (R-FL12) | Bruce Braley (D-IA1) | Bill Pascrell (D-NJ8) |
| Dan Benishek (R-MI01) | Niki Tsongas (D-MA3) | Marcy Kaptur (D-OH9) |
| Joe Runyan (R-NJ3) | Tim Walz (D-MN1) | Mike Doyle (D-PA14) |
| Joe Wilson (R-SC2) | Carol Shea-Porter (D-NH1) | Silvestre Reyes (D-TX14) |
| Vic Snyder (D-AR02) | Christopher Smith (R-NJ1) | Bob Stump (R-AZ3) |
| Bob Filner (D-CA51) | Terry Everett (R-AL02) | Luis Gutierrez (D-IL4) |
| Susan Davis (D-CA53) | Cliff Stearns (R-FL6) | Steve Buyer (R-IN5) |
| Ginny Brown-Waite (R-FL5) | John Tierney (D-MA6) | Maxine Waters (D-CA35) |
| Tom Latham (R-IA04) | Stephen Lynch (D-MA9) | George Sangmeister (D-IL11) |
| Henry Brown (R-SC1) | Michael Michaud (D-ME2) | Jill Long (D-IN4) |
| Solomon Ortiz (D-TX27) | Chet Edwards (D-TX17) | David Bonior (D-MI10) |
| Lane Evans (D-IL17) | Ciro Rodriguez (D-TX23) | Jack Fields (R-TX8) |
| Sonny Montgomery (D-MS3) | Jo Ann Davis (R-VA1) | Frank Tejeda (D-TX28) |
| Douglas Applegate (D-OH18) | Ron Kind (D-WI3) | Mike Rogers (R-AL3) |
| Corrine Brown (D-FL5) | Michael Bilirakis (R-FL9) | Elton Gallegly (R-CA24) |
| Seniors | ||
| Mike Rogers (R-AL3) | Henry Waxman (D-CA30) | Richard Burr (R-NC5) |
| Pete Stark (D-CA13) | John Larson (D-CT1) | Steve Israel (D-NY2) |
| Tom Allen (D-ME1) | Robert Wexler (D-FL19) | Rob Portman (R-OH2) |
| Jo Ann Emerson (R-MO8) | David Loebsack (D-IA2) | Jim Turner (D-TX2) |
| Lloyd Doggett (D-TX25) | Richard Neal (D-MA2) | Sam Johnson (R-TX3) |
| Earl Pomeroy (D-ND1) | Dave Camp (R-MI4) | Bernard Sanders (I-VT1) |
| Gerald Kleczka (D-WI4) | John Dingell (D-MI15) | Earl Hilliard (D-AL7) |
| William Clay (D-MO1) | Jim Ramstad (R-MN3) | Matthew Martinez (D-CA31) |
| Jill Long (D-IN4) | Bill Pascrell (D-NJ8) | Greg Ganske (R-IA4) |
| Jeff Miller (R-FL1) | Joseph Crowley (D-NY7) | Dennis Hastert (R-IL14) |
| Gus Bilirakis (R-FL12) | John Peterson (R-PA5) | Dale Kildee (D-MI9) |
| Joe Wilson (R-SC2) | Shelley Moore Capito (R-WV2) | Richard Gephardt (D-MO3) |
| Terry Everett (R-AL2) | Robert Matsui (D-CA5) | Charles Rangel (D-NY15) |
| Dennis Moore (D-KS3) | Nancy Johnson (R-CT5) | Sherrod Brown (D-OH13) |
|
|
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| LGBTQ | ||
|
|
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| Racial/Ethnic Minorities | ||
|
|
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| Immigrants | ||
|
|
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| Women | ||
|
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| Poor | ||
|
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|
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Table B-2 Reputations for primary and secondary advocacy by disadvantaged group in the Senate (103rd, 105th, 108th, 110th, 113th Congresses)
| Veterans | ||
| Tom Daschle (D-SD) | John Rockefeller (D-WV) | Patty Murrary (D-WA) |
| John Glenn (D-OH) | Arlen Specter (R-PA) | John Boozman (R-AR) |
| Frank Murkowski (R-AK) | Tim Johnson (D-SD) | Bill Nelson (D-FL) |
| Barbara Mikulski (D-MD) | Larry Craig (R-ID) | |
| Seniors | ||
| John Rockefeller (D-WV) | David Pryor (D-AR) | Jon Corzine (D-NJ) |
| Bill Nelson (D-FL) | John McCain (R-AZ) | Mark Dayton (D-MN) |
| Debbie Stabenow (D-MI) | Daniel Patrick Moynihan (D-NY) Ron Wyden (D-OR) | |
| Marco Rubio (R-FL) | Harry Reid (D-NV) | Herb Kohl (D-WI) |
| Tim Johnson (D-SD) | William Roth (R-DE) | Benjamin Cardin (D-MD) |
| Bernard Sanders (I-VT) | John Breaux (D-LA) | Tom Harkin (D-IA) |
| LGBTQ | ||
| Tammy Baldwin (D-WI) | Gordon Smith (R-OR) | Charles Robb (D-VA) |
| Racial/Ethnic Minorities | ||
| Carol Moseley-Braun (D-IL) | Bob Dole (R-KS) | Bill Bradley (D-NJ) |
| Edward Kennedy (D-MA) | John Danforth (R-MO) | |
| Howard Metzenbaum (D-OH) | James Jeffords (R-VT) | |
| Immigrants | ||
| Spencer Abraham (R-MI) | Larry Craig (R-ID) | Robert Menendez (D-NJ) |
| Richard Durbin (D-IL) | Alan Simpson (R-WY) | |
| Edward Kennedy (D-MA) | John McCain (R-AZ) | |
| Women | ||
| Carol Moseley-Braun (D-IL) | Bill Bradley (D-NJ) | Harry Reid (D-NV) |
| Barbara Mikulski (D-MD) | Charles Schumer (D-NY) | Bob Packwood (R-OR) |
| Patty Murray (D-WA) | Kay Bailey Hutchison (R-TX) | Joseph Biden (D-DE) |
| Olympia Snowe (R-ME) | Tammy Baldwin (D-WI) | John Chafee (R-RI) |
| Barbara Boxer (D-CA) | Kirsten Gillibrand (D-NY) | |
| Poor | ||
| John Rockefeller (D-WV) | Gordon Smith (R-OR) | Richard Durbin (D-IL) |
| Bernard Sanders (I-VT) | Claiborne Pell (D-RI) | Christopher Dodd (D-CT) |
| Olympia Snowe (R-ME) | Daniel Patrick Moynihan (D-NY) | Blanche Lincoln (D-AR) |
| Tom Harkin (D-IA) | Jon Corzine (D-NJ) | Peter Fitzgerald (R-IL) |
| Paul Wellstone (D-MN) | Orrin Hatch (R-UT) | Maria Cantwell (D-WA) |
| Edward Kennedy (D-MA) | Charles Grassley (R-IA) | Paul Sarbanes (D-OR) |
| Robert Menendez (D-NJ) | Daniel Coats (R-IN) | Jeff Merkley (R-OR) |
| Paul Simon (D-IL) | Jeff Bingaman (D-NM) | Jack Reed (D-RI) |
| Bob Dole (R-KS) | Pete Domenici (R-NM) | |
Appendix C Multilevel Regression with Poststratification and Estimating State and District Ambient Temperature
Multilevel regression with poststratification (MRP) is a technique that uses multilevel modeling and Bayesian statistics to generate estimates that are a function of both demographic and geographic characteristics (Reference PopkinPark, Gelman, and Bafumi, 2004; Reference Lee and OppenheimerLax and Phillips, 2009; Reference WawroWarshaw and Rodden, 2012). This method combines demographic and public opinion data to create predictions for small subsets of the population, which are then weighted by subgroup population within a geographic area and summed for all subgroups within that area (in this case, a congressional district.) For data with an inherently hierarchical structure (as is the case for individuals within districts that are within states), multilevel models have an advantage over classical regression models. Classical regression models use either complete pooling data to generate effects (as when no district or state effects are taken into account) or no pooling (as when models include fixed effects for a respondent’s state or district). Multilevel regression models allow for data to be partially pooled to a degree dictated by the data, based upon group sample size and variation. These models thus allow for the effects of demographics to vary by geography, while also pulling the estimates for states or districts with limited numbers of observations or high variance toward the mean, and allowing estimates for states and districts with more robust samples and tighter variances to be more influenced by district-specific effects.
MRP generated estimates of public opinion outperform both disaggregated means and presidential vote share measures at the state-, congressional district-, and state senate district-levels, producing estimates that are more correlated with population means, have smaller errors, and are more reliable (Reference Lee and OppenheimerLax and Phillips, 2009; Reference WawroWarshaw and Rodden, 2012). These differences are even more apparent with the smaller sample sizes (2,500 for congressional districts) common to most national surveys. MRP estimates are also far less subject to bias than disaggregated means. Disaggregating from nationally (rather than district or state) representative samples can result in biased predictions. MRP avoids this pitfall because all estimates are weighted according to the percentage of a state or district that any particular subgroup makes up. Additionally, nonresponse bias is less likely to influence within-group estimates for MRP relative to disaggregation because of the effects of partial pooling (Reference Lee and OppenheimerLax and Phillips, 2009).
Reference Buttice and HightonButtice and Highton (2013) find that MRP is most effective as an estimator when higher-level variables (in this case, state or district) are strongly predictive of the concept of interest, and when there is a high level of geographic variation in the quantity being estimated.Footnote 1 To ensure the greatest level of validity and reliability in my estimates, I include a number of state- and district-level predictors with a clear theoretical tie to expected levels of warmth or hostility toward the selected disadvantaged groups. I also have a clear expectation that due to geographically driven district heterogeneity and distinct state and district cultures, inter-district variability should be high.
Data
To model individual responses, I use the ANES aggregated time-series data from 1992 to 2016. This data set is intended to be nationally representative, and has a total of 24,122 observations. Given the sampling technique and relatively small sample size (relative to the CCES or the NAES), MRP is the best estimator for generating unbiased and reliable measures of district opinion. To account for over-time changes in district lines and public opinion, I model each decade separately, with 9,085 observations for the 1990s; 5,006 observations for the 2000s; and 10,031 observations for the 2010s. Feeling thermometer estimates are generated for each group in each of the three decades.
In each of these models, the dependent variable is the group feeling thermometer score. The individual-level predictor variables in each of these models includes a respondent’s gender (two categories: male, female),Footnote 2 race/ethnicity (four categories: white, Black, Hispanic, other), education (five categories: less than high school completion, completed high school, some college, college graduate, graduate school), state, and congressional district. Additionally, district-level predictors (average income, percent urban, percent military, same-sex couples, percent Hispanic, and percent African American) and state-level predictors (region, percent union, and percent Evangelical or Mormon) were obtained using decennial US Census data, as well as data from the US Religion Census. Survey year is also included to account for any variation in context or questions.
Model
I generate estimates of district hostility by modeling individual responses as a function of individual-level demographic characteristics as well as district- and state-level predictors. I model this as a multilevel linear regression equation, using the lmer package in R.Footnote 3 The structure of the model estimating individual feelings toward the poor is given by the following:
(1)
The random effects across each level of these individual predictors (e.g., all five categories of education) are modeled.Footnote 4 These effects are expected to be normally distributed with a mean of 0, and a variance determined by the data. Both the district- and state-levels model random effects for each district and state (respectively) in the dataset as well as fixed effects for the other relevant predictors, while random effects are modeled for each of the four region categories:Footnote 5
Poststratification
This model is then used to generate district hostility estimates for the average member of each of 17,400 subgroups. Each of these subgroups represents a unique combination of demographic categories by which the sample is weighted: race (4), gender (2),Footnote 6 education (5), and congressional district (435).Footnote 7 Once predictions for average feeling thermometer scores are generated for each of these subgroups (from white men with less than a high school education in the first district of Alabama to non-white, Black, or Hispanic women with a graduate education in the large district of Wyoming), these estimates are then weighted according to the proportion of a district that is composed of members of these subgroups, and summed across districts.
Formally, weighted district opinion estimates are obtained using this method:
(2)
where c represents each of the forty demographic subcategories (race, gender, and education) within d, a given congressional district, θc is the prediction associated with each subcategory, and Nc is the frequency of individuals within a district that belong to a demographic subcategory. To weight my estimates, I use the calculated frequency proportions for each demographic category in each state or district. A summary of the estimates generated is given in Table 4.1, and graphical illustrations of each of the estimates produced are given in Figure 4.1.
Appendix D Generalized Ordered Logit Model Showing Effects of Constituency and Descriptive Representation on Reputations for Women’s Advocacy
Table D-1 displays the models of the effects of group size and ambient temperature on women’s advocacy that were presented in Table 5.6, but with descriptive representation included. These models show that the relationship between the percentage of women in a state and reputation formation seen in Table 5.6 is in fact a spurious correlation that is better explained by whether or not a state’s senator is a woman.
Table D-1 Group size, ambient temperature, descriptive representation, and member reputation for advocacy for women
| Women | ||||||
|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 1 | 0 | 1 | |
| Group | 0.256 | −0.173 | 0.261 | −0.191 | ||
| Size | 0.34 | 0.74 | 0.33 | 0.76 | ||
| Ambient | −0.069 | −0.095 | −0.074 | −0.063 | ||
| Temperature | 0.17 | 0.37 | 0.14 | 0.65 | ||
| Descriptive | 3.551 | 3.938 | 3.523 | 4.196 | 3.642 | 3.983 |
| Representative | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Republican | 0.038 | 0.124 | 0.066 | 0.274 | 0.088 | 0.176 |
| 0.92 | 0.87 | 0.87 | 0.72 | 0.82 | 0.84 | |
| Dem Pres | 0.032 | 0.108 | 0.044 | 0.102 | 0.034 | 0.112 |
| Vote | 0.26 | 0.01 | 0.10 | 0.04 | 0.23 | 0.01 |
| South | −0.847 | 0.738 | −0.591 | 0.637 | −0.812 | 0.806 |
| 0.05 | 0.36 | 0.13 | 0.44 | 0.06 | 0.32 | |
| 1990s | 2.045 | 1.494 | 2.100 | 1.264 | 1.978 | 1.451 |
| 0.00 | 0.06 | 0.00 | 0.06 | 0.00 | 0.09 | |
| 2000s | 0.450 | −0.098 | 0.755 | 0.046 | 0.676 | 0.136 |
| 0.28 | 0.87 | 0.09 | 0.93 | 0.13 | 0.80 | |
| First | −1.531 | −1.501 | −1.524 | |||
| Term | 0.00 | 0.00 | 0.00 | |||
| Constant | −18.131 | −2.186 | −2.177 | −5.471 | −14.549 | 1.940 |
| 0.18 | 0.93 | 0.49 | 0.24 | 0.30 | 0.94 | |
| N | 500 | 500 | 500 | |||
| Wald’s Chi2 | 80.0 | 64.7 | 84.0 | |||
| Pseudo-R2 | 0.2875 | 0.2857 | 0.2908 | |||
Note: Coefficients calculated using generalized ordered logit, with First Term modeled as a parallel proportional term and all others 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.
Appendix E Effects of the Advocacy Environment and Electoral Insecurity on Reputation Formation in the House
Tables E-1 and E-2 display the results for the analysis of the electoral insecurity hypothesis and the collective amplification hypothesis. The effects of the total number of advocates within the House resemble those of the Senate — for nearly all groups, having a greater number of advocates in the House makes it more likely that a member will also make the decision to form a reputation as a group advocate. The effects of electoral insecurity, however, are different in the House than they are in the Senate. While a senator’s most recent vote share does not have a significant impact on their representational decision-making, it does have a significant effect in the House, under some circumstances. For groups that are generally considered to be highly deserving of government assistance, like seniors and veterans, a member’s electoral security does not change the likelihood that they will choose to serve as a group advocate. But for most groups that are considered to be less deserving of assistance, members with more marginal prior election vote totals are less likely to risk forming a reputation as a group advocate. This demonstrates that while in the Senate, there is no margin at which senators feel comfortable as a disadvantaged group advocate, members of the House of Representatives who hold safer seats are significantly more likely to serve as a group advocate, even for groups that are not considered highly deserving of government assistance.
Table E-1 Institutional and electoral effects on member reputation for advocacy for veterans, seniors, racial/ethnic minorities, and the LGBTQ community in the House of Representatives (1993–2014)
| Veterans | Seniors | LGBTQ | Race/Ethnicity | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2 | 0 | 1 | 2 | logit | 0 | 1 | 2 | |
| Total | 0.027 | 0.077 | 0.264 | 0.025 | 0.008 | −0.009 | 0.217 | 0.025 | −0.027 | 0.073 |
| Advocates | 0.45 | 0.20 | 0.36 | 0.00 | 0.36 | 0.67 | 0.06 | 0.29 | 0.37 | 0.13 |
| Previous | −0.005 | 0.013 | −0.010 | 0.005 | −0.010 | 0.008 | 0.020 | 0.024 | 0.035 | 0.018 |
| Vote Share | 0.52 | 0.17 | 0.80 | 0.45 | 0.24 | 0.82 | 0.18 | 0.00 | 0.00 | 0.24 |
| Group | 0.197 | 0.265 | 0.417 | 0.097 | 0.119 | 0.091 | 1.937 | 0.049 | 0.061 | 0.054 |
| Size | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.48 | 0.00 | 0.00 | 0.00 | 0.00 |
| Ambient | 0.049 | 0.096 | 0.013 | −0.016 | 0.086 | −0.094 | 0.040 | −0.010 | −0.058 | −0.044 |
| Temperature | 0.10 | 0.12 | 0.89 | 0.66 | 0.33 | 0.61 | 0.20 | 0.77 | 0.17 | 0.56 |
| Republican | −0.468 | −0.901 | −1.237 | −0.814 | −1.073 | −1.570 | −1.308 | −1.854 | −2.218 | −2.890 |
| 0.02 | 0.02 | 0.12 | 0.00 | 0.00 | 0.10 | 0.04 | 0.00 | 0.00 | 0.01 | |
| Dem Pres | 0.002 | 0.023 | 0.034 | 0.016 | −0.005 | −0.096 | 0.054 | −0.033 | −0.055 | −0.034 |
| Vote | 0.86 | 0.27 | 0.63 | 0.20 | 0.79 | 0.22 | 0.18 | 0.07 | 0.02 | 0.24 |
| South | 0.050 | 0.306 | 0.383 | 0.029 | −0.281 | −1.667 | 0.221 | −0.073 | −0.050 | 0.340 |
| 0.83 | 0.52 | 0.59 | 0.89 | 0.47 | 0.20 | 0.70 | 0.79 | 0.88 | 0.44 | |
| 1990s | −0.240 | 1.540 | 5.640 | 0.097 | −0.607 | −1.752 | 2.471 | 0.641 | 1.382 | −1.453 |
| 0.83 | 0.38 | 0.44 | 0.63 | 0.11 | 0.03 | 0.00 | 0.21 | 0.03 | 0.18 | |
| 2000s | −0.612 | −0.265 | −0.800 | 1.159 | 0.283 | 1.628 | −2.667 | |||
| 0.03 | 0.61 | 0.38 | 0.11 | 0.73 | 0.13 | 0.14 | ||||
| First | −1.091 | −0.805 | −1.201 | −1.788 | ||||||
| Term | 0.00 | 0.00 | 0.14 | 0.00 | ||||||
| Constant | −8.441 | −19.010 | −24.111 | −4.130 | −10.120 | 7.896 | −14.208 | −4.201 | −0.423 | −4.834 |
| 0.01 | 0.00 | 0.11 | 0.17 | 0.15 | 0.61 | 0.00 | 0.14 | 0.89 | 0.33 | |
| N | 2,175 | 1,740 | 2,175 | 2,175 | ||||||
| Wald’s Chi2 | 123.4 | 163.2 | 68.7 | 434.1 | ||||||
| Pseudo-R2 | 0.0742 | 0.0708 | 0.1977 | 0.3185 | ||||||
Note: Coefficients for LGBTQ are estimated using logistic regression, as necessitated by the bivariate coding of the LGBTQ advocacy reputation variable. 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, secondary, or primary advocacy; Model 1 is no advocacy or superficial advocacy to primary or secondary advocacy; and Model 2 is any of the lower categories of advocacy to primary 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.
Table E-2 Institutional and electoral effects on member reputation for advocacy for immigrants, women, and the poor in the House of Representatives (1993–2014)
| Immigrants | Poor | Women | |||||||
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2 | 0 | 1 | 2 | 0 | 1 | 2 | |
| Total | 0.067 | 0.066 | −0.158 | 0.016 | 0.014 | −0.004 | 0.076 | 0.096 | 0.476 |
| Advocates | 0.03 | 0.16 | 0.37 | 0.00 | 0.00 | 0.64 | 0.02 | 0.06 | 0.01 |
| Previous | 0.021 | 0.006 | 0.071 | 0.013 | 0.020 | 0.027 | 0.005 | −0.006 | 0.018 |
| Vote Share | 0.06 | 0.69 | 0.01 | 0.03 | 0.01 | 0.07 | 0.43 | 0.56 | 0.28 |
| Group | 0.119 | 0.149 | 0.301 | 0.060 | 0.075 | 0.072 | −0.008 | −0.103 | 0.017 |
| Size | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.86 | 0.29 | 0.95 |
| Ambient | −0.046 | −0.008 | −0.045 | −0.007 | 0.025 | −0.073 | 0.042 | 0.040 | −0.102 |
| Temperature | 0.04 | 0.83 | 0.49 | 0.81 | 0.58 | 0.35 | 0.07 | 0.13 | 0.33 |
| Republican | −0.618 | −0.394 | −4.552 | −1.179 | −1.830 | −2.081 | −0.713 | −1.291 | −2.823 |
| 0.04 | 0.50 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
| Dem Pres | −0.079 | −0.065 | −0.404 | 0.015 | 0.003 | −0.013 | 0.057 | 0.075 | 0.228 |
| Vote | 0.00 | 0.09 | 0.03 | 0.14 | 0.84 | 0.74 | 0.01 | 0.02 | 0.01 |
| South | −0.442 | −0.522 | −4.298 | −0.391 | −0.966 | −0.638 | −0.490 | −0.964 | −0.939 |
| 0.26 | 0.41 | 0.01 | 0.03 | 0.00 | 0.37 | 0.11 | 0.05 | 0.48 | |
| 1990s | 0.426 | 0.460 | −0.603 | 0.079 | 0.197 | −0.595 | −0.907 | −1.815 | −9.635 |
| 0.41 | 0.54 | 0.72 | 0.70 | 0.48 | 0.22 | 0.21 | 0.10 | 0.01 | |
| 2000s | −0.159 | 0.049 | −1.930 | 0.037 | 0.121 | −0.112 | −0.822 | −1.751 | −8.018 |
| 0.52 | 0.89 | 0.10 | 0.87 | 0.70 | 0.82 | 0.19 | 0.06 | 0.00 | |
| First | −1.691 | −1.065 | −1.196 | ||||||
| Term | 0.00 | 0.00 | 0.00 | ||||||
| Constant | −1.346 | −4.928 | 13.261 | −4.088 | −7.324 | 0.150 | −9.563 | −5.600 | −24.731 |
| 0.48 | 0.05 | 0.28 | 0.06 | 0.02 | 0.98 | 0.00 | 0.33 | 0.08 | |
| N | 2,175 | 2,175 | 2,175 | ||||||
| Wald’s Chi2 | 370.2 | 302.6 | 176.4 | ||||||
| Pseudo-R2 | 0.3121 | 0.1344 | 0.1036 | ||||||
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, secondary, or primary advocacy; Model 1 is no advocacy or superficial advocacy to primary or secondary advocacy; and Model 2 is any of the lower categories of advocacy to primary advocacy.
1 This greater importance of constituency level variables over individual variables is also confirmed in research by Hanretty, Lauderdale, and Vivyan, (2016) investigating British opinion regarding the EU.
2 While gender is not a strictly binary concept, data constrictions require it to be treated as such for the purposes of this project.
3 The framework for the code sequences used comes from the study replication file for Reference WawroWarshaw and Rodden (2012).
4 Because gender is coded as a dichotomous dummy variable for whether or not a respondent identifies as female, only fixed effects are modeled.
5 District-level effects are modeled for all district ambient temperature estimates, but are not included for state ambient temperature estimates.
6 For the 1990 Census, data are not available for gender by race by education by district categories, but only for race by education by district categories, so this poststratification scheme is used for this decade instead. This reduces the total number of poststratification categories to 8,700.
7 For the state ambient temperature estimates, the demographic categories used are gender by race by education by state, resulting in a total of 2,000 categories.
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