---
title: "[WEEK 10 TITLE]"
subtitle: "[WEEK 10 SUBTITLE]"
date: last-modified
date-format: "[Updated ]MMM D, YYYY"
format: 
  revealjs:
    theme: brownslides.scss
    logo: images/pols1140_hex.png
    footer: "BSFS1010"
    multiplex: false
    transition: fade
    slide-number: c
    incremental: true
    center: false
    menu: true
    scrollable: true
    highlight-style: github
    progress: true
    code-overflow: wrap
    chalkboard: false
    # include-after-body: title-slide.html
    title-slide-attributes:
      align: left
      data-background-image: images/pols1140_hex.png
      data-background-position: 90% 50%
      data-background-size: 40%
filters:
  - openlinksinnewpage
execute: 
  eval: true
  echo: true
  warning: false
  message: false
  cache: true
---


```{r}
#| label: init
#| echo: false
#| results: hide
#| warning: false 
#| message: false

library(tidyverse)
library(labelled)
library(haven)
library(DeclareDesign)
library(easystats)
library(texreg)

```



## Why gender and and politics matter

- Like race and partisanship, questions of gender pervaded modern politics?
  - Did Kamala's gender hurt her among various electoral blocs
  - What other questions do we have?

- Our goal this week, is to explore how political scientists have studied questions of sex, gender, and sexuality

## Questions

- What's the difference between Sex and Gender 

- How does gender predict politics?

- How is gender politically socialized?

- How does gender and attitudes about gender shape political behavior

- How does sexual identity shape political behavior?



# {{<fa lightbulb>}}Gender as a Social Identity {.inverse}

## Changing Views of Sex and Gender


- Sex = Gender

  - Binary, fixed, biologically determined

  - Sex as a proxy for gender

## Changing Views of Sex and Gender


- Sex $\neq$ Gender

  - Sex and Gender are related but distinct concepts

  - Gender: "what society makes of sex"
  
  - Gender Identity: "a personal sense of ones own gender"

## Changing Views of Sex and Gender

- Viewing gender as a social construct leads us to think about gender in terms of:

  - Categorization
  - Identification
  - Consciousness

## Categorization

- Some features of gender are a function of biology



- But sex (and gender) are not as binary or fixed as they are studied empirical models 

![](https://cdn.cnn.com/cnnnext/dam/assets/190801151708-washington-state-gender-id-large-169.jpg)

## Identification

Gender as a social identity leads us to issues of 



- Measurement:

  - Binary dichotomy, continuous scale, multidimensional


- And theory:

  - Variations in the strength of identification across individuals, times, and contexts

## Measuring Gender 


Take a few moments and write down how you would go about measuring gender?


## Measuring Gender



- Fixed response? How many responses?


- Open response? How would you code it?


- Continuous scale? What are the end points?


- Multiple scales? How many dimensions?

## Interviewer Assessments


![](images/11_gender/ws1.png)

Source: [Westbrook and Saperstein (2015)](https://www.researchgate.net/profile/Aliya_Saperstein/publication/281761192_New_Categories_Are_Not_Enough/links/566eed4408ae4bef406104e9.pdf)


## (Binary) Self-Identifications


![](images/11_gender/ws2.png)


Source: [Westbrook and Saperstein (2015)](https://www.researchgate.net/profile/Aliya_Saperstein/publication/281761192_New_Categories_Are_Not_Enough/links/566eed4408ae4bef406104e9.pdf)
  

    
## A continuous measure of gender (Bittner and Goodyear-Grant (2017))

[Bittner and Goodyear-Grant (2017)](https://link.springer.com/article/10.1007/s11109-017-9391-y) argue gender and sex are poorly measured in standard survey research, and propose and alternative measurement approach, concluding:

> Sex is a fair proxy for gender, but for about a quarter of our sample, it is not

## Gender Scales

- Where would you place yourself on this continuum? (0–100, where 0 represents those who are 100% masculine and 100 represents those who are 100% feminine)

![](images/11_gender/b1.png)

Source: [Bittner and Goodyear-Grant (2017)](https://link.springer.com/article/10.1007/s11109-017-9391-y)


### Categories from Bittner and Goodyear-Grant (2017)


![](images/11_gender/b2.png)

Source: [Bittner and Goodyear-Grant (2017)](https://link.springer.com/article/10.1007/s11109-017-9391-y)


### Bem (1974) Sex Role Inventory

![](https://journals.plos.org/plosone/article/figure/image?size=large&id=10.1371/journal.pone.0076356.g001)


## Group Consciousness

How might we measure "gender group" consciousness?



[Stout, Kretschmer, and Ruppanner (2017)](chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://journals.sagepub.com/doi/pdf/10.1177/1065912917702499?casa_token=nuXFlpg0HPYAAAAA:Q_V-R3OcUSpPx8jF_uP3ykEbTX4az5oGr8tMgtLBx0O0oBhrVNSCpOI4uiX8420tvK6exihGqvVYmw) propose a concept of gender linked fate:

## Stout et al. (2017)

![](images/11_gender/s0.png)


## Stout et al. (2017)


Adapt Linked Fate approach and Ask respondents, respectively:

- “Do you think that what happens generally to men/women in this country will have something to do with what happens in your life? How much does what happens to others affect you?
 
## Married Women Show Lower Levels of Gender Linked Fate (GLF)


![](images/11_gender/s1.png)



## Group Discussions 

- We've discussed both race and gender as politically relevant social identities
  - How are they similar?
  - How are they different?


## Comparing Racial and Gender Identities {.smaller}

[Burns and Kinders (2011)](https://www.taylorfrancis.com/chapters/edit/10.4324/9780203839836-15/categorical-politics-gender-race-public-opinion-nancy-burns-donald-kinder-nancy-burns-donald-kinder) of discussion of the similarities and differences between racial and gender identities:



Similarities

- Socially constructed
- Mental categories (powerful cues/heuristics?)
- Sites of durable inequality



Key difference

- Gender $\to$ intimacy between groups
- Race $\to$ separation between groups
- Consequences for:
  - "Gaps" literature
  - Group consciousness?

## Intersectionality

[Kimberlé Crenshaw (1989)](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1052&context=uclf) coined the concept of **intersectionality**:

- Social categories — gender, race, class, sexuality — do not operate independently

- They *overlap* to produce distinct political experiences

- A Black woman's experience ≠ (Black man) + (white woman)

## Intersectionality and Political Analysis

- Standard analyses treat women as a uniform group
  
  - But white women, Black women, and Latinas differ substantially in party ID, vote choice, and political attitudes
  
  - The same is true across class and sexuality

- Intersectionality is not just a normative claim — it has empirical implications for how we specify models and interpret gaps

## Intersectionality and the 2024 Election {.smaller}

- Exit polls from 2024 reveal intersectional patterns:

  - Black women: ~90% Harris

  - White women: narrowly split or Trump-leaning
  
  - Latinas: majority Democratic, but less so than Black women

- The aggregate "women's vote" can obscure as much as it reveals

- Takeaway: controlling for race when analyzing gender gaps (and vice versa) is not just good statistical practice — it is theoretically motivated

## Summary

- Gender like race is socially constructed
  - Both nature and nurture matter
- Social identity theory helps us understand the similarities and differences between gender and other social identities
- Intersectionality reminds us that gender does not operate in isolation — race, class, and sexuality all interact



# {{<fa lightbulb>}}Gender Gaps {.inverse}

## Gender Gaps

As with race, research document's gaps between men and women in 



- Partisan Identification (Women $\to$ + Democratic)


- Political Participation (Women $\to$ - Participation)


- Political Knowledge  (Women $\to$ - Political Knowledge)


- Political Attitudes (Women $\to$ + Liberal Attitudes)

## Gender Gaps (GG)



- As with race

  - Analysis can be overly simplistic

  - Satisfying explanations are often lacking


## GG in Partisan Identification is Persistent


![](images/11_gender/bk1.png)

## GG in Partisan Identification is Relatively Small


![](images/11_gender/bk2.png)


## The Marriage Gap

- The gender gap is complicated by **marital status**:

  - Married women lean more Republican than unmarried women
  
  - The gap between married and unmarried women can rival the gap between all men and all women

  - Recall: Stout et al. (2017) found married women report *lower* Gender Linked Fate

- Competing explanations:

  - Shared economic interests with spouse
  
  - Selection into marriage by more conservative women
  

## Gender Gap in the 2024 Election

- 2024 produced one of the **largest gender gaps** on record:

  - Women supported Harris by roughly 13 points
  
  - Men supported Trump by a comparable margin
  
  - Overall gap: approximately 25–26 percentage points

- A striking **generational divergence** within Gen Z:

  - Young women were among Harris's strongest cohorts
  
  - Young men shifted toward Trump at higher rates than older men

- Raises the question: is the gender gap *widening* among the youngest voters — and why?


## GG may obscure racial gaps


![](images/11_gender/j1.png)

## GG may obscure racial gaps


![](images/11_gender/j2.png)


## GG in political participation have declined over time

![](images/11_gender/bs.png)

Source: [Burns et al. 2018](https://www.google.com/books/edition/_/1O5IDwAAQBAJ?hl=en&gbpv=1&bsq=participation)

## GG in political knowledge

![](images/11_gender/ma1.png)

Source: [Mondak and Anderson 2004](https://www.journals.uchicago.edu/doi/pdfplus/10.1111/j.1468-2508.2004.00161.x)


## GG in political knowledge may reflect differential rates of guessing

![](images/11_gender/ma2.png)

Source: [Mondak and Anderson 2004](https://www.journals.uchicago.edu/doi/pdfplus/10.1111/j.1468-2508.2004.00161.x)

## GG in political knowledge may reflect different dispositions and socialization

![](images/11_gender/wm1.png)

Source: [Wolak and McDevitt 2011](https://link.springer.com/content/pdf/10.1007/s11109-010-9142-9.pdf)

## GG in Political Preferences

![](images/11_gender/b3.png)

Source: [Bittner and Goodyear-Grant (2017)](https://link.springer.com/article/10.1007/s11109-017-9391-y)




# {{<fa lightbulb>}} Gendered Political Socialization {.inverse}




## Gendered Political Socialization:

- What explains longstanding gaps in levels of political interest, knowledge and participation between men and women?

- Gendered Political Socialization contends that the intersection of gender and political socialization:

> shape children’s perceptions such that politics is a masculine domain and political leaders are more likely to be men and (2) result in girls perceiving a mismatch between their gendered expectations and with exploring politics or pursuing political roles (p. 486)

## GPS draws on:

- **Theories of socialization:** A broad field of research which argues early childhood experiences and environments have lasting social and political effects.



-  **Social role theory** suggests "children mimic the gendered division of labor that they observe in the home and that this behavior reinforces (and perpetuates) gender roles, traits, and motivations" 

## Key Expectations

Gendered Socialization:

- H1: Gendered occupational preferences

- H2: Gendered occupational preferences strengthen with age



Political Socialization:

- H3: Greater political interest and content with age



Gendered Political Socialization

- H4: Childrens' images of political leaders more likely to be male

- H5: Images of female political leaders less likely with age

- H6: Girls report lower levels of political interest and ambition than boys


## What's the empirical design

- Interviews and surveys of 1,604 children from 18 schools in Boston, upstate NY, NE Ohio, and New Orleans from grades 1-6.

- Key outcomes:

  - Draw a Political Leader (DAPL)
  - Exposure to Political Activities
  - Political Interst
  - Political Ambition/Gendered Occupations

- Methodology:
  - Descriptive statistics
  - Multilevel regression

##

![](images/11_gender/bos0a.png)

##

![](images/11_gender/bos0b.png)

##
### H1: Gendered occupational preferences

![](images/11_gender/bos1.png)

##
### H2: Gendered occupational preferences strengthen with age

![](images/11_gender/bos2.png)

##
### H3: Greater political interest and content with age

![](images/11_gender/bos3.png)


##
#### H4-5: Childrens' images of political leaders more likely to be male

![](images/11_gender/bos4a.png)

##
#### H4-5: Childrens' images of political leaders more likely to be male

![](images/11_gender/bos5.png)





##
#### H6: Girls report lower levels of political interest and ambition than boys


![](images/11_gender/bos6.png)

##
#### H6: Girls report lower levels of political interest and ambition than boys


![](images/11_gender/bos7.png)


## Sumamry

> As girls learn more about politicsand internalize society’s expectations of them, they are less likely to see traditional politics as a place for them to lead. And while our data only suggest, but do not offer direct evidence of, continuity between atti- tudes in childhood and attitudes in adulthood, they do indicate that efforts to elevate the political interest and ambition of women must begin early.

## Summary



- Gender has political consequences as evidenced by "gender gaps"



- The cause(s) of those gaps are complicated and varied


- Need good measurement and better theory

# {{<fa lightbulb>}} Gender, Sexism, and Representation{.inverse}

## Most States are Majority Female


![](https://i.insider.com/5c006b421a27840a096f111b?width=2500&format=jpeg&auto=webp)

## Most Elected Officials are (White) Males


![](https://www.pewresearch.org/wp-content/uploads/2022/12/FT_22.01.03_WomenCongress_1.png)

## Why?

Big questions rarely have simple answers:


- Historical, social, economic, inequality


- Stereotypes and Discrimination


- Socialization and selection





## Issue Ownership


- [Petrocik (1996)](https://www.jstor.org/stable/2111797?seq=1#metadata_info_tab_contents) parties "own" certain issues
  
  - Democrats are better at handling social issues like health care
  
  - Republicans are better at economic and foreign policy
  
  - Campaigns prime voters to think about the issues that benefit their candidate

## Gender Issue Ownership


- Scholars make similar claims with regard to gender. Voters tend to think:
  
  - Women are better able to handle "feminine" social issues
  
  - Men are better at "masculine" issues related to the economy and defense


- Are these associations due to gender stereotypes or associations between gender and partisanship?

## Gender or Partisan Stereotypes 

[Huddy and Terkildsen (1993)](https://www.jstor.org/stable/2111526?seq=1#metadata_info_tab_contents) conduct an experiment, randomly assigning male and female candidates to have either masculine or feminine traits

## Huddy and Terkildsen (1993)

**[Elizabeth/Robert]** McGuire, a lawyer, has been described by legal colleagues as **[an intelligent, compassionate, trustworthy, and family-oriented/a tough, articulate, and ambitious]** opponent with proven leadership skills and strong **[people/administrative]** skills. Ms. McGuire, forty-two, is a life-long resident of Connecticut, a long- time political activist, and currently is seeking office at the local level.

## Both Candidate Gender and Traits Matter

![](images/11_gender/ht1.png)

## Both Candidate Gender and Traits Matter

![](images/11_gender/ht3.png)


## Inferred gender beliefs matter more than inferred political beliefs

![](images/11_gender/ht2.png)

## Summary

- Gender shapes voter evaluations of candidates
- The effect of such stereotypes isn't always clear:

  - Gender stereotypes have to be activated [(Bauer 2014)](https://onlinelibrary.wiley.com/doi/full/10.1111/pops.12186)

  - Women can benefit from running "as women" [(Herrnson et al. 2003)](https://www.journals.uchicago.edu/doi/pdfplus/10.1111/1468-2508.t01-1-00013)

  - Women may also benefit from running counter to stereotypes [Bauer 2017](https://onlinelibrary.wiley.com/doi/epdf/10.1111/pops.12351)

# {{<fa lightbulb>}} Does Descriptive Representation Matter? {.inverse}

## Two Types of Representation

[Pitkin (1967)](https://www.ucpress.edu/book/9780520021563/the-concept-of-representation) distinguishes between:

- **Descriptive representation**: legislators *look like* the people they represent (shared characteristics)

- **Substantive representation**: legislators *act in* the interests of their constituents

- These are related but distinct:

  - You can have one without the other
  
  - Which matters more? A normative *and* empirical question

## Does Descriptive Representation Produce Substantive Representation?

[Swers (2002)](https://press.uchicago.edu/ucp/books/book/chicago/D/bo3620990.html) examines roll-call votes and bill sponsorship in the 104th and 105th Congress:

- Women legislators (both parties) were more likely to sponsor and advocate for *women's issue* legislation

- Effect holds controlling for party, ideology, and district characteristics

- But: **party mediates** — Republican women were less likely to sponsor feminist legislation than Democratic women

## Substantive Representation: State Legislatures

[Carroll (2003)](https://www.jstor.org/stable/j.ctt7zt9jf) finds similar patterns among state legislators:

- Women report placing higher priority on health care, education, and women's rights

- The effect is stronger among Democratic women and on explicitly feminist issues

## What Does This Imply?

- Electing more women has policy consequences — but **which** women matters

- The descriptive–substantive link is strongest when:

  - Women represent constituencies where women's issues are salient
  
  - Party leadership creates space for such legislation

- Takeaway: Underrepresentation of women is not just a fairness issue — it may shape *which policies get made*

# {{<fa lightbulb>}}Attitudes about gender{.inverse}

## Measuring sexism

As with race, many measures distinguishing:

- Type:

  - Overt vs Covert

  - Old-fashioned vs Modern

- Dimensionality:

  - Single vs Multiple

## Measuring sexism


- **Old-fashioned sexism**  [(Spence, Helmreich and Stapp 1973)](https://link.springer.com/article/10.3758/BF03329252)

  -  Women are generally not as smart as men
  
  -  I would be equally comfortable having a woman as a boss as a man
   


## Measuring sexism



- Old-fashioned/Overt sexism [(Spence, Helmreich and Stapp 1973)](https://link.springer.com/article/10.3758/BF03329252)


- **Modern sexism scale** [(Swim et al. 1995)](https://psycnet.apa.org/record/1995-17261-001)
  
  -  **Denial:** Discrimination against women is no longer a problem in the United States
  
  -  **Antagonism**: It is easy to understand the anger of women's groups in America
  
  -  **Resentment:** Over the past few years, the government and news media have been
showing more concern about the treatment of women than is
warranted by women's actual experiences.


## Measuring sexism



- Old-fashioned/Overt sexism [(Spence, Helmreich and Stapp 1973)](https://link.springer.com/article/10.3758/BF03329252)


- Modern Sexism scale [(Swim et al. 1995)](https://psycnet.apa.org/record/1995-17261-001)


- **Ambivalent Seismic** [(Glick and Fiske (1997))](https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1471-6402.1997.tb00104.x)
  
  - Benevolent: "A good woman should be set on a pedestal."
  
  - Benevolent: "Women have a quality of purity few men possess."
  
  - Hostile: "Most women interpret innocent remarks or acts as being sexist."
  
  - Hostile: "Women seek to gain power by getting control over men."

## Which measure should we use?

- I'm not sure there's a good answer

- Two applications

  - Modern Sexism and the 2016 Election

  - Hostile Sexism and the 2020 Elections
    

## Modern Sexism in 2016 Democratic Primaries

![](images/11_gender/sv1.png)

Source: [Sides, Vavreck and Tesler (2019)](https://books.google.com/books?hl=en&lr=&id=HFKeDwAAQBAJ&oi=fnd&pg=PR10&dq=identity+crisis+sides&ots=8XqAwwAZXb&sig=IAqMF0mkDIixBbwiVbbI9vmwBt8#v=onepage&q=identity%20crisis%20sides&f=false)




## Modern Sexism in 2016 General Election

![](images/11_gender/sv2.png)

Source: [Sides, Vavreck and Tesler (2019)](https://books.google.com/books?hl=en&lr=&id=HFKeDwAAQBAJ&oi=fnd&pg=PR10&dq=identity+crisis+sides&ots=8XqAwwAZXb&sig=IAqMF0mkDIixBbwiVbbI9vmwBt8#v=onepage&q=identity%20crisis%20sides&f=false)


# {{<fa lightbulb>}} Hostile Sexism and 2020 Elections{.inverse}

## Hostile Sexism Predicts Vote Choice in Democratic Primaries


![:scale 50%](https://images.squarespace-cdn.com/content/v1/5aa9be92f8370a24714de593/1583348347542-087MG7QE9N7SRBWXT0HB/ke17ZwdGBToddI8pDm48kGzde5pS-cFEAusxevrGClN7gQa3H78H3Y0txjaiv_0fDoOvxcdMmMKkDsyUqMSsMWxHk725yiiHCCLfrh8O1z5QHyNOqBUUEtDDsRWrJLTmJPRKCgOKJQlr39QlAOu8eh2l04fpemwUH3_BdmdyHPj_IkPi7ifgbV99WRI6mEr_/image2.png?format=2500w)

Source: [Data for Progress](https://www.dataforprogress.org/blog/3/5/sexism-one-reason-why-warren-didnt-do-better)

## Hostile Sexism Predicts Vote Choice in Democratic Primaries


![:scale 50%](https://images.squarespace-cdn.com/content/v1/5aa9be92f8370a24714de593/1583348445633-1ZGXRHAGSC99V714LL59/ke17ZwdGBToddI8pDm48kGzde5pS-cFEAusxevrGClN7gQa3H78H3Y0txjaiv_0fDoOvxcdMmMKkDsyUqMSsMWxHk725yiiHCCLfrh8O1z5QHyNOqBUUEtDDsRWrJLTmJPRKCgOKJQlr39QlAOu8eh2l04fpemwUH3_BdmdyHPj_IkPi7ifgbV99WRI6mEr_/image4.png?format=2500w)

Source: [Data for Progress](https://www.dataforprogress.org/blog/3/5/sexism-one-reason-why-warren-didnt-do-better)



## Group Discussion

- Sexism appears to predict vote choice and political attitudes

- How could we unpack the mechanism(s) behind this relationship

- What might be done? 

- If we could change people's scores on modern/hostile sexism, would we change politics?



# {{<fa lightbulb>}} Policy Implications {.inverse}

## Reproductive Rights and Political Mobilization

- [*Dobbs v. Jackson Women's Health Organization*](https://www.supremecourt.gov/opinions/21pdf/19-1392_6j37.pdf) (2022) overturned *Roe v. Wade*

  - Returned abortion regulation to states
  
  - Triggered dramatic variation: near-total bans in some states, expanded protections in others

- Political response was swift:

  - Surge in voter registration among women following the ruling
  
  - Anticipated 2022 Republican "red wave" did not materialize — abortion mobilization is a leading explanation

## Abortion Ballot Measures Since *Dobbs*

- Voters in over a dozen states have directly voted on abortion rights

  - Pro-choice measures passed in Kansas, Kentucky, Michigan, Ohio, and elsewhere
  
  - In reliably red states, pro-choice measures *outperformed* Democratic candidates on the same ballot

- Suggests abortion as a **cross-partisan mobilizing issue** — particularly among women

- Takeaway: Policy changes can reshape the gender gap through mobilization (policy feedback)

## LGBTQ+ Rights and Political Behavior

- LGBTQ+ Americans vote at high rates and are reliably Democratic:

  - In 2020 and 2024, roughly 70–80% supported Democratic candidates

- The LGBTQ+ share of the electorate is growing:

  - Gallup (2023): ~7% of U.S. adults identify as LGBTQ+, up from ~3.5% in 2012
  
  - Among Gen Z, the figure is closer to 22%

- Anti-LGBTQ+ legislation (restrictions on gender-affirming care, education bills) has intensified political mobilization within this group

## Gender Gaps in Policy Preferences

- Women and men differ not just in partisan ID but in **substantive policy preferences**:

  - Women more supportive of social spending (health care, education, social insurance)
  
  - Men more supportive of military spending and tax reduction (on average)

- These differences are **not fully explained** by income or employment differences

- [Schlesinger and Heldman (2001)](https://www.journals.uchicago.edu/doi/10.1111/1468-2508.t01-1-00023) point to gender differences in risk aversion and social connectedness as underlying mechanisms

## Discussion: Policy as Cause and Effect

- Gender gaps in *attitudes* shape *which policies get made*

- But policies also shape gender gaps: *Dobbs* appears to have activated political behavior among women

- This is a two-way street — **policy feedback**:
  
  - Politics → Policy → Politics

- Which mechanisms matter most — and how would we test it?

# {{<fa lightbulb>}}Egan (2012) {.inverse}

## Review

Take a few minutes to review your notes on Egan (2012)

- What is the research question?
- What is the theoretical framework
- What is the expectation and design
- What are the results?

## Research Question

What explains the political cohesion of lesbian, gay, and bisexual citizens?

- Is it a function of identity mobilization

- Or a reflection of selection effects (common attributes that predict shared identity)



## Theoretical framework

- Distinguishes between sexual preferences and sexual identity

>While the preponderance of the evidence is that the degree to which
one is sexually attracted to those of the same sex is a trait that is fixed at birth or in early childhood, being gay is a chosen identity – an identity acquired among a non-random subset of those endowed with the trait of same-sex attraction.


- Past work on identities suggest chosen identities $\to$ + cohesion

  - Mobilization

  - Acculturation

- Egan proposes an alternative mechanism: selection

## Selection and group cohesion

> "[T]he process by which stable characteristics that are truly ‘unmoved movers’ – the indelible aspects of one’s background and upbringing – help to determine whether a person self-selects into membership of a politically relevant group" (p. 598)

## Why Selection Matters: Substantively

> "To the extent that group members are loyal partisans for reasons that antecede the acquisition of group identity – and therefore are less easily moved by appeals to group interests – it becomes more difficult for group leaders to make a credible threat to withhold support from their allies in order to win policy concession" (p. 598)

## Why Selection Matters Empirically

- Failing to control for "pre-treatment" variables (things that predict identity acquisition) biases our esimtates of the effects of that identity


## Expectations: 

> "If selection is at work in making a group’s members politically distinctive, the ceteris paribus differences in political views between group members and the general population should be reduced after conditioning on the effects of background characteristics that shape identity choice and are also known to be determinants of political views."


- Accounting for selection should reduce differences

## Expectations: 


> In addition, if selection effects are present, it should be the case that group members are distinct from non-group members from the moment they identify with the group and thus the development of political cohesion should not require the mobilization processes that can accompany the passage of time, contact with group members or receipt of co-ordinating messages from group leaders


- Differences should exist in the absence of mobilization acculturation

## Design

- Data: GSS and exit poll surveys

- Method: Matching

## Matching

- Matching is a statistical procedure to adjust for differences between groups in observational data

- When we match we're trying to recreate what is accomplished  by random assignment in an experiment 

- While random assignment guarantees this for all variables (observed and unobserved), matching provides balance only on observed covariates.

## Little Variation in Support based on Candidates LGB Voting Records

![](images/11_gender/e1.png)

## Sexuality vs LGB Identification

![](images/11_gender/e2.png)

## Initial evidence of selection

![](images/11_gender/e3.png)

## Further evidence of selection


![](images/11_gender/e4.png)


## Matching creates balance on observed covariates

![](images/11_gender/e5.png)


## Quantifying selection effects

![](images/11_gender/e6.png)


## Selection explains group cohesion of LGB on policy and ideology

![](images/11_gender/e7.png)

## Differences exist before mobilization and acculturation

![](images/11_gender/e8.png)


## Summary



- Egan argues selection accounts for group cohesion among LGB individuals


- Matching as a tool for making causal claims with observational data

  - Useful tool but not a magic wand


- Possible critiques and/or extensions?



# References

## References
