Skip to main content Accessibility help
×
Hostname: page-component-cc8bf7c57-7lvjp Total loading time: 0 Render date: 2024-12-10T19:12:51.027Z Has data issue: false hasContentIssue false

Appendices

Published online by Cambridge University Press:  18 November 2021

Katrina F. McNally
Affiliation:
Eckerd College, Florida
Type
Chapter
Information
Representing the Disadvantaged
Group Interests and Legislator Reputation in US Congress
, pp. 237 - 256
Publisher: Cambridge University Press
Print publication year: 2021
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NC
This content is Open Access and distributed under the terms of the Creative Commons Attribution licence CC-BY-NC 4.0 https://creativecommons.org/cclicenses/
Appendices

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

  • Employment assistance (workforce training/increased licensures and certifications from military experience/employer tax credits for hiring/employment protections for returning guard members)

  • Creation of veteran job corps

  • Educational assistance (tuition assistance/GI Bill)

  • Healthcare (head trauma/PTSD/benefits expansion/telemedicine for rural vets/veteran suicide prevention/access to mental health care/counseling on deployment and return)

  • Assistance for disabled vets (housing/benefits/employment)

  • Housing benefits (homelessness prevention/special assistant at HUD)

  • VA improvements (fixing backlog/higher reimbursements for longer travels/automatic enrollment and training in using the system)

  • Resources for survivors of military sexual assault

  • Improvement of reintegration programs (counseling/financial planning)

  • Veterans History Project

Seniors

  • Protecting against financial scams

  • Medicare protection/expansion

  • Social Security protection/expansion (COLA increases/eliminating income cap)

  • Opposition to voter registration/ID laws (because of effects on elderly)

  • Expanding prescription drug coverage for seniors

  • Senior nutrition and other services

  • Older Americans Act

  • Expanding access to hospice and long-term care

LGBTQ

  • Repeal DADT

  • Repeal DOMA

  • Legalizing same-sex marriage

  • Employment protections

  • Government employee benefits for same-sex partners

  • Anti-bullying and nondiscrimination policies

  • Domestic violence/sexual assault protections (VAWA)

  • HIV/AIDS

Racial/Ethnic Minorities

  • Confinement/racial profiling/marijuana and other drug offenses/former inmate reintegration/police brutality

  • Reparations (slavery/Japanese internment)

  • Treatment HIV/AIDS (including in prison)

  • Voting rights (opposition to attempts to end early voting and require additional voter registration or ID/simplify voter registration of Voting Rights Act)

  • Civil Rights Act

  • Housing assistance

  • Employment (increase federal grants and contracts to minority-owned business/assistance to Black farmers)

  • African American History Museum

  • MLK birthday as federal holiday

  • Education (minorities in STEM, government and private partnerships with minority colleges/funding)

Immigrants

  • Immigration reform (path to citizenship/legal status/worker status/asylum seekers)

  • Shorten citizenship waiting period (members of military/family reunification)

  • DREAM act

  • Domestic violence protections (VAWA)

  • Citizenship education programs (English language assistance/naturalization workshops)

  • Legal status for military/college

  • Elimination of country caps

Women

  • Reproductive rights (abortion coverage/contraception coverage and availability)

  • Employment (equal pay/pregnancy discrimination/breast pump space)

  • Healthcare (mammograms/breast cancer/nopays for preventative care)

  • Sexual assault and domestic violence (VAWA/military programs/expansion of definition of rape/homelessness prevention for domestic violence victims)

  • Military (expansion of roles for women/equipment like body armor to fit women)

Poor

  • Employment assistance (worker training/new WPA or other job corps/tax credits for employers hiring someone unemployed or on public assistance/create “empowerment zones” providing tax credits for companies going into impoverished areas)

  • Unemployment benefits

  • Housing assistance (renters/homelessness/heating assistance/Home Energy Assistance Program/Affordable Housing Trust Fund/foreclosure assistance/tax credit for the creation of low-income housing

  • Nutrition (expansion of SNAP benefits/SNAP at farmer’s markets/free and reduced lunch benefits)

  • Education (Head Start/access to art, economics, civics, and foreign language classes/TRIO programs and outreach to disadvantaged students)

  • Healthcare (Children’s Health Insurance Program/Medicaid expansion/dental coverage/support for community health centers/continuous open enrollment for Medicaid and CHIP)

  • Expanded access to child care

  • Broadband access for low-income communities

  • Minimum wage increase

  • Free tax prep for low-income individuals and families and financial literacy programs

  • TANF benefit extensions

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)
  • Carolyn Maloney (D-NY14)

  • Pat Tiberi (R-OH12)

  • Ted Strickland (D-OH6)

  • Kent Bentson (D-TX25)

  • Ted Deutch (D_FL21)

  • Peter DeFazio (D-OR4)

  • David McKinley (R-WV4)

  • Marion Berry (D-AR1)

  • Peter Deutch (D-FL20)

  • E. Clay Shaw (R-FL22)

  • Jan Schakowski (D-IL9)

  • Jim McCrery (R-IA4)

  • John Tierney (D-MA6)

  • Benjamin Cardin (D-MD3)

  • Michael Michaud (D-ME1)

  • Gil Gutknecht (R-MN1)

  • Chaka Fattah (D-PA2)

  • Tim Holden (D-PA6)

  • Patrick Kennedy (D-RI1)

  • Al McCandless (R-CA44)

  • C.W. Bill Young (R-FL10)

  • Andrew Jacobs (D-IN10)

  • Sander Levin (D-MI12)

  • J.J. Pickle (D-TX10)

LGBTQ
  • Jerrold Nadler (D-NY10)

  • Henry Waxman (D-CA29)

  • Lynn Woolsey (D-CA6)

  • Lois Capps (D-CA24)

  • Jared Polis (D-CO2)

  • Barney Frank (D-MA4)

  • Jim Kolbe (R-AZ8)

  • Nancy Pelosi (D-CA8)

  • Patricia Schroeder (D-CO1)

  • Jim McDermott (D-WA7)

Racial/Ethnic Minorities
  • Barbara Lee (D-CA13)

  • John Lewis (D-GA5)

  • Keith Ellison (D-MN5)

  • William Clay (D-MO1)

  • Earl Hilliard (D-AL7)

  • Tom Latham (R-IA04)

  • Xavier Becerra (D-CA31)

  • Gene Green (D-TX29)

  • Bennie Thompson (D-MS2)

  • Ciro Rodriguez (D-TX23)

  • Yvette Clark (D-NY9)

  • Ruben Hinojosa (D-TX15)

  • Solomon Ortiz (D-TX27)

  • Juanita Millender-McDonald (D-CA37)

  • Maxine Waters (D-CA43)

  • Bobby Rush (D-IL1)

  • John Conyers (D-MI13)

  • Marcia Fudge (D-OH11)

  • Steve Cohen (D-TN9)

  • Robert Scott (D-VA3)

  • Michael Honda (D-CA15)

  • Diane Watson (D-CA33)

  • Danny Davis (D-IL7)

  • Carolyn Cheeks Kirkpatrick (D-MI13)

  • Melvin Watt (D-SC12)

  • Eddie Bernice Johnson (D-TX30)

  • Ed Pastor (D-AZ4)

  • Lucille Roybal-Allard (D-CA34)

  • Elijah Cummings (D-MD7)

  • Gregory Meeks (D-NY6)

  • Sheila Jackson-Lee (D-TX18)

  • Cynthia McKinney (D-GA4)

  • Donald Payne (D-NJ10)

  • Norman Mineta (D-CA15)

  • Don Edwards (D-CA16)

  • Ileana Ros-Lehtinen (R-FL18)

  • Cardiss Collins (D-IL7)

  • Kwesi Mfume (D-MD7)

  • Barbara-Rose Collins (D-MI15)

  • Craig Washington (D-TX18)

  • Edolphus Towns (D-NY10)

  • Pete Stark (D-CA13)

  • Chaka Fattah (D-PA2)

  • Cliff Stearns (R-FL6)

  • Raul Grijalva (D-AZ7)

  • Linda Sanchez (D-CA39)

  • Alcee Hastings (D-FL23)

  • Charles Rangel (D-NY15)

  • Lacy Clay (D-MO1)

  • Jesse Jackson (D-IL2)

  • Joseph Kennedy (D-MA8)

  • Nydia Velazquez (D-NY12)

  • Lane Evans (D-IL-17)

  • Terry Everett (R-AL2)

  • Henry Brown (R-SC1)

  • Jack Fields (R-TX8)

  • Luis Gutierrez (D-IL4)

  • James Clyburn (D-SC6)

  • Bob Clement (D-TN5)

  • Ben Ray Lujan (D-NM3)

  • Artur Davis (D-AL7)

  • Ed Pastor (D-AZ4)

  • Hilda Solis (D-CA32)

  • Grace Napolitano (D-CA38)

  • Joe Baca (D-CA43)

  • Kendrick Meek (D-FL17)

  • William Jefferson (D-LA2)

  • Albert Wynn (D-MD4)

  • Emanual Cleaver (D-MO5)

  • G.K. Butterfield (D-NC1)

  • Jose Serrano (D-NY16)

  • Silvestre Reyes (D-TX16)

  • Charlie Gonzalez (D-TX20)

  • Robert Menendez (D-NJ13)

  • Amo Houghton (R-NY29)

  • Robert Matsui (D-CA5)

  • Lincoln Diaz-Balart (R-FL21)

  • Floyd Flake (D-NY6)

  • Henry Bonilla (R-TX23)

  • Alan Wheat (D-MO5)

  • Major Owens (D-NY11)

  • Hamilton Fish (R-NY19)

  • Louis Stokes (D-OH11)

  • Tom Sawyer (D-OH14)

  • Thomas Foglietta (D-PA1)

Immigrants
  • Xavier Becerra (D-CA31)

  • Yvette Clark (D-NY9)

  • Lucille Roybal-Allard (D-CA34)

  • Ed Pastor (D-AZ4)

  • Raul Grijalva (D-AZ7)

  • Nydia Velazquez (D-NY21)

  • Terry Everett (R-AL2)

  • Jose Serrano (D-NY16)

  • Ileana Ros-Lehtinen (R-FL18)

  • Lincoln Diaz-Balart (R-FL21)

  • Hilda Solis (D-CA32)

  • Zoe Lofgren (D-CA19)

  • Linda Sanchez (D-CA39)

  • Mario Diaz-Balart (R-FL25)

  • Howard Berman (D-CA26)

  • Ruben Hinojosa (D-TX15)

  • Sheila Jackson-Lee (D-TX18)

  • Kendrick Meek (D-FL17)

  • Judy Chu (D-CA27)

  • Major Owens (D-NY11)

  • Bob Filner (D-CA50)

  • Barney Frank (D-MA4)

  • Loretta Sanchez (D-CA46)

  • Raul Labrador (R-ID1)

  • Mary Bono (R-CA45)

  • Tom Campbell (R-CA15)

  • Romano Mazzoli (D-KY3)

  • Luis Gutierrez (D-IL4)

  • Grace Napolitano (D-CA38)

Women
  • Linda Sanchez (D-CA38)

  • Lucille Roybal-Allard (D-CA34)

  • Don Edwards (D-CA16)

  • Cardiss Collins (D-IL7)

  • Carolyn Maloney (D-NY14)

  • Nita Lowey (D-NY18)

  • Gwen Moore (D-WI4)

  • Loretta Sanchez (D-CA47)

  • Juanita Millender-McDonald (D-CA37)

  • Patricia Schroeder (D-CO1)

  • Jackie Speier (D-CA14)

  • Rosa DeLauro (D-CT3)

  • Diana DeGette (D-CO1)

  • Louise Slaughter (D-NY28)

  • Barbara Kennelly (D-CT1)

  • Patsy Mink (D-HI2)

  • Constance Morella (R-MD8)

  • Susan Molinari (R-NY13)

  • Sheila Jackson-Lee (D-TX18)

  • Zoe Lofgren (D-CA19)

  • Hilda Solis (D-CA32)

  • Lois Capps (D-CA24)

  • Sam Farr (D-CA17)

  • Barbara Lee (D-CA13)

  • Edolphus Towns (D-NY10)

  • Chaka Fattah (D-PA2)

  • Eddie Bernice Johnson (D-TX30)

  • Lynn Woolsey (D-CA6)

  • Ed Royce (R-CA40)

  • Jerrold Nadler (D-NY10)

  • Henry Waxman (D-CA30)

  • Corrine Brown (D-FL3)

  • Susan Davis (D-CA53)

  • Tom Allen (D-ME1)

  • Douglas Applegate (D-OH18)

  • Tammy Baldwin (D-WI2)

  • Sue Kelly (R-NY19)

  • Elizabeth Furse (D-OR1)

  • Olympia Snowe (R-ME2)

  • Barbara Vucanovich (R-NV2)

  • Ron Wyden (D-OR3)

  • Steve Buyer (R-IN5)

  • John Shimkus (R-IL19)

  • Tim Ryan (D-OH17)

  • Anna Eshoo (D-CA14)

  • Melissa Hart (R-PA4)

  • Nancy Johnson (R-CT6)

  • Steny Hoyer (D-MD5)

  • Ike Skelton (D-MO4)

  • Jennifer Dunn (R-WA8)

  • Vic Fazio (D-CA3)

  • John Edward Porter (R-IL10)

  • Tony Hall (D-OH3)

  • Ronald Machtley (R-RI1)

  • Marilyn Lloyd (D- TN 3)

Poor
  • Gwen Moore (D-WI4)

  • Juanita Millender-McDonald (D-CA37)

  • Patsy Mink (D-HI2)

  • Hilda Solis (D-CA32)

  • Barbara Lee (D-CA13)

  • Lynn Woolsey (D-CA6)

  • Henry Waxman (D-CA29)

  • Tony Hall (D-OH3)

  • Nydia Velazquez (D-NY21)

  • Maxine Waters (D-CA43)

  • Carolyn Cheeks Kirkpatrick (D-MI13)

  • Charles Rangel (D-NY15)

  • Barbara Kennelly (D-CT1)

  • Linda Sanchsz (D-CA39)

  • Sheila Jackson-Lee (D-TX18)

  • Edolphus Towns (D-NY10)

  • Diana DeGette (D-CO1)

  • Tom Allen (D-ME1)

  • Tammy Baldwin (D-WI2)

  • Ron Wyden (D-OR3)

  • Louise Slaughter (D-NY28)

  • Gene Green (D-TX29)

  • John Conyers (D-MI13)

  • Michael Bilirakis (R-FL9)

  • Jill Long (D-IN4)

  • Jeff Miller (R-FL1)

  • Ted Strickland (D-OH6)

  • John Tierney (D-MA6)

  • Bernard Sanders (I-VT1)

  • Matthew Martinez (D-CA31)

  • Earl Pomeroy (D-ND1)

  • Anthony Weiner (D-NY9)

  • John Peterson (R-PA5)

  • Max Sandlin (D-TX1)

  • Walter Capps (D-CA22)

  • Jim Maloney (D-CT5)

  • Chaka Fattah (D-PA2)

  • Xavier Becerra (D-CA31)

  • Raul Grjalva (D-AZ7)

  • Ruben Hinojosa (D-TX15)

  • Major Owens (D-NY11)

  • Jose Serrano (D-NY16)

  • Al Green (R-TX9)

  • John Lewis (D-GA5)

  • Keith Ellison (D-MN5)

  • Tom Latham (R-IA04)

  • Marcia Fudge (D-OH11)

  • Robert Scott (D-VA3)

  • Diane Watson (D-CA33)

  • Danny Davis (D-IL7)

  • Barbara-Rose Collins (D-MI15)

  • Joseph Kennedy (D-MA8)

  • Henry Brown (R-SC1)

  • Artur Davis (D-AL7)

  • Bobby Rush (D-IL1)

  • Floyd Flake (D-NY6)

  • Andre Carson (D-IN7)

  • Julia Carson (D-IN7)

  • Eva Clayton (D-NC1)

  • Barney Frank (D-MA4)

  • Jim McDermott (D-WA7)

  • Frederica Wilson (D-FL24)

  • Lloyd Doggett (D-TX25)

  • Ric Keller (R-FL8)

  • Jack Quinn (R-NY27)

  • Benjamin Cardin (D-MD3)

  • Nita Lowey (D-NY18)

  • Rosa DeLauro (D-CT3)

  • Jesse Jackson (D-IL2)

  • William Clay (D-MO1)

  • Dale Kildee (D-MI9)

  • Stephanie Tubbs Jones (D-OH11)

  • Phil English (R-PA3)

  • George Miller (D-CA11)

  • Chellie Pingree (D-ME1)

  • Judy Biggert (R-IL13)

  • Luis Gutierrez (D-IL4)

  • Mary Bono (R-CA45)

  • Tom Sawyer (D-OH14)

  • Jan Schakowski (D-IL9)

  • Sam Farr (D-CA17)

  • Jim McGovern (D-MA3)

  • Sander Levin (D-MI12)

  • Earl Hilliard (D-AL7)

  • Bennie Thompson (D-MS2)

  • Donald Payne (D-NJ10)

  • Kwesi Mfiime (D-MD7)

  • Pete Stark (D-CA13)

  • Cliff Stearns (R-FL6)

  • G.K. Butterfield (D-NC1)

  • Gregory Meeks (D-NY6)

  • Henry Bonilla (R-TX23)

  • Thomas Foglietta (D-PA1)

  • Frank Pallone (D-NJ6)

  • Albert Wynn (D-MD4)

  • Lacy Clay (D-MO1)

  • Loretta Sanchez (D-CA47)

  • Harold Ford (D-TN9)

  • Julian Dixon (D-CA32)

  • Christopher Shays (R-CT4)

  • Carrie Meek (D-FL17)

  • Jerry Costello (D-IL12)

  • Karen McCarthy (D-MO5)

  • William Coyne (D-PA14)

  • Bob Stump (R-AZ3)

  • David Loebsack (D-IA2)

  • Brett Guthrie (D-KY2)

  • Michael Turner (R-0H10)

  • Spencer Bachus (R-AL6)

  • David Obey (D-WI7)

  • Terri Sewell (D-AL7)

  • John Larson (D-CT1)

  • David Scott (D-GA13)

  • Carol Shea-Porter (D-NH1)

  • Timothy Bishop (D-NY1)

  • Mark Souder (R-IN3)

  • Martin Olav Sabo (D-MN5)

  • Mike McIntyre (D-NC7)

  • Virgil Goode (R-VA5)

  • Tom Petri (R-WI6)

  • Scott Baesler (D-KY6)

  • John Olver (D-MA1)

  • Jim Ramstad (R-MN3)

  • Bruce Vento (D-MN4)

  • Louis Stokes (D-OH11)

  • Ron Klink (D-PA4)

  • Jon Fox (R-PA13)

  • Thomas Barrett (D-WV1)

  • Neil Abercrombie (D-HI1)

  • James Oberstar (D-MN8)

  • Ronald Dellums (D-CA9)

  • Lucille Roybal-Allard (D-CA34)

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:

yift poor=γ0+αr[i]race+αf[i]female+αe[i]educ+αy[i]year+αd[i]districtαrrace ~ N(0, σr2), for r=1, 2, 3, 4αffemale ~ N(0, σf2)αeeduc ~ N(0, σe2), for e= 1, 2, 3, 4, 5αpyear ~ N(0, σy2), for p= 1, 2(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

αddistrict ~ N(ks[d]state+γinc*incomed+γurban*urband+γmil*militaryd+ γhisp*hispanicd+γblack*blackd, σdistrict2), for d= 1,, 435
αsstate ~ N(αz[s]region+βunion*unions+βrelig*religions,σstate2), for s=1,,50
αZregion ~ N(0, σregion2), for z= 1, 2, 3, 4

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:

ydistrict=cdNcθccdNc(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
010101
Group0.256−0.1730.261−0.191
Size0.340.740.330.76
Ambient−0.069−0.095−0.074−0.063
Temperature0.170.370.140.65
Descriptive3.5513.9383.5234.1963.6423.983
Representative0.000.000.000.000.000.00
Republican0.0380.1240.0660.2740.0880.176
0.920.870.870.720.820.84
Dem Pres0.0320.1080.0440.1020.0340.112
Vote0.260.010.100.040.230.01
South−0.8470.738−0.5910.637−0.8120.806
0.050.360.130.440.060.32
1990s2.0451.4942.1001.2641.9781.451
0.000.060.000.060.000.09
2000s0.450−0.0980.7550.0460.6760.136
0.280.870.090.930.130.80
First−1.531−1.501−1.524
Term0.000.000.00
Constant−18.131−2.186−2.177−5.471−14.5491.940
0.180.930.490.240.300.94
N500500500
Wald’s Chi280.064.784.0
Pseudo-R20.28750.28570.2908

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)

VeteransSeniorsLGBTQRace/Ethnicity
012012logit012
Total0.0270.0770.2640.0250.008−0.0090.2170.025−0.0270.073
Advocates0.450.200.360.000.360.670.060.290.370.13
Previous−0.0050.013−0.0100.005−0.0100.0080.0200.0240.0350.018
Vote Share0.520.170.800.450.240.820.180.000.000.24
Group0.1970.2650.4170.0970.1190.0911.9370.0490.0610.054
Size0.000.000.000.000.000.480.000.000.000.00
Ambient0.0490.0960.013−0.0160.086−0.0940.040−0.010−0.058−0.044
Temperature0.100.120.890.660.330.610.200.770.170.56
Republican−0.468−0.901−1.237−0.814−1.073−1.570−1.308−1.854−2.218−2.890
0.020.020.120.000.000.100.040.000.000.01
Dem Pres0.0020.0230.0340.016−0.005−0.0960.054−0.033−0.055−0.034
Vote0.860.270.630.200.790.220.180.070.020.24
South0.0500.3060.3830.029−0.281−1.6670.221−0.073−0.0500.340
0.830.520.590.890.470.200.700.790.880.44
1990s−0.2401.5405.6400.097−0.607−1.7522.4710.6411.382−1.453
0.830.380.440.630.110.030.000.210.030.18
2000s−0.612−0.265−0.8001.1590.2831.628−2.667
0.030.610.380.110.730.130.14
First−1.091−0.805−1.201−1.788
Term0.000.000.140.00
Constant−8.441−19.010−24.111−4.130−10.1207.896−14.208−4.201−0.423−4.834
0.010.000.110.170.150.610.000.140.890.33
N2,1751,7402,1752,175
Wald’s Chi2123.4163.268.7434.1
Pseudo-R20.07420.07080.19770.3185

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)

ImmigrantsPoorWomen
012012012
Total0.0670.066−0.1580.0160.014−0.0040.0760.0960.476
Advocates0.030.160.370.000.000.640.020.060.01
Previous0.0210.0060.0710.0130.0200.0270.005−0.0060.018
Vote Share0.060.690.010.030.010.070.430.560.28
Group0.1190.1490.3010.0600.0750.072−0.008−0.1030.017
Size0.000.000.000.000.000.000.860.290.95
Ambient−0.046−0.008−0.045−0.0070.025−0.0730.0420.040−0.102
Temperature0.040.830.490.810.580.350.070.130.33
Republican−0.618−0.394−4.552−1.179−1.830−2.081−0.713−1.291−2.823
0.040.500.010.000.000.000.000.000.00
Dem Pres−0.079−0.065−0.4040.0150.003−0.0130.0570.0750.228
Vote0.000.090.030.140.840.740.010.020.01
South−0.442−0.522−4.298−0.391−0.966−0.638−0.490−0.964−0.939
0.260.410.010.030.000.370.110.050.48
1990s0.4260.460−0.6030.0790.197−0.595−0.907−1.815−9.635
0.410.540.720.700.480.220.210.100.01
2000s−0.1590.049−1.9300.0370.121−0.112−0.822−1.751−8.018
0.520.890.100.870.700.820.190.060.00
First−1.691−1.065−1.196
Term0.000.000.00
Constant−1.346−4.92813.261−4.088−7.3240.150−9.563−5.600−24.731
0.480.050.280.060.020.980.000.330.08
N2,1752,1752,175
Wald’s Chi2370.2302.6176.4
Pseudo-R20.31210.13440.1036

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.

Footnotes

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.

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×