Big and Little Sorts: Diversity, Conservative Flight and ... · Big and Little Sorts: Diversity, Conservative Flight and Geographic Polarization. 1: Introduction2: Ideology and Tolerance3:

1: Introduction 2: Ideology and Tolerance 3: The Big Katrina Sort

Big and Little Sorts: Diversity, ConservativeFlight and Geographic Polarization

L. Jason Anastasopoulos

Democracy Fellow, Ash Center for Democratic Governance and Innovation,Harvard Kennedy School of Government

February 25,2014

L. Jason AnastasopoulosDemocracy Fellow, Ash Center for Democratic Governance and Innovation, Harvard Kennedy School of Government

Big and Little Sorts: Diversity, Conservative Flight and Geographic Polarization


(Why) Do Partisans Sort?

Recent research suggests that the geographic clustering oflike minded partisans is a cause of polarization inCongress. (Bishop 2008; Hui 2011; Nall 2012)May explain why attitude polarization has not increased asmuch as polarization in Congress. (Hui 2011)But what causes sorting?Other than interstate highway development (Nall 2012),mechanisms remain unclear.




Diversity and White Flight

Scholarship across several disciplines finds that diversityincreases are associated with white flight and spatialsegregation. (Boustan 2010; Card, Mas and Rothstein2008; Cutler, Glaeser and Vigdor 1999, etc...)Great Migration → Great White Flight.

Post-WWII suburbanization due in part to white flight.(Boustan 2010)Best causal estimates suggest 2.7 white departures perblack migrant. (Boustan 2010)




Schelling Redux

Schelling (1971) provided a mechanism for this pattern ofdiversity increases and white flight.Showed that segregation emerges when people onlyslightly prefer neighbors similar to themselves.Willingness to relocate depends upon “tolerance”: theextent to which agents prefer neighbors similar tothemselves.If “tolerance” is correlated with political ideology, flightaccompanying diversity increases should result ingeographic polarization.




The Migration-Assimilation-Polarization Theory




Political Ideology and Schelling Tolerance

Residential choice questions from Multi-City Study onUrban Inequality (1992-1994) and General Social Survey(2002).Political ideology is the strongest predictor of residentialdiversity preferences and willingness to move inresponse to changes in diversity among whiterespondents.




MCSUI Data: Background

Would you feel comfortable in the (7%/20%/33%/53% minority)neighborhood?

If uncomfortable, would you be willing to move out of the (7%/20%/33%/53%minority) neighborhood?




MCSUI Data: Model

Yc = αc + βc Ideology + XΓc + mj + ε (1)

Yc is a binary dependent variable (comfort/will move out)for each card/neighborhood minority proportionc = 7%,20%,33%,53%.Ideology is the political ideology of the respondent,X is a matrix of respondent characteristics which includeage, sex, years of education, family income and interviewerrace.mj are city fixed effects.




MCSUI 1992-1994 Data: Results - Comfort Levels

White Respondent Ideology v. Predicted Probability ofDiscomfort in Each of the Four Neighborhoods N = 2407




MCSUI 1992-1994 Data: Results - Comfort Levels

Odds Ratio Coefficients from Logistic Regression of ComfortLevels (Y53%) at 53% Minority Neighborhood, N = 2407




MCSUI 1992-1994 Data: Results - Moving

White Respondent Ideology v. Predicted Probability of Movingfrom Any Neighborhood Conditional on Discomfort, N = 1695




GSS 2002 Data: Background

Image Presented to Respondents in the 2000 General SocialSurvey. Respondents were asked to indicate their idealethnic/racial background of each of the 14 houses surroundingthem. Their choices were: Asian, Black, Hispanic or White.




GSS 2002 Data: Average Preferred % White byIdeology

Average % White (of 14 surrounding houses) by IdeologyAmong White Respondents with 95% Confidence Intervals,GSS 2000




GSS 2002 Data: Preferred % White by Ideology

%White = α + βIdeology + XΓ + cj + ε

DV: Neighborhood % WhiteVARIABLES Original Coefficients Standardized Coefficients

Ideology 0.036*** 0.192***(0.006)

Age 0.002** 0.136**(0.001)

Education -0.001 -0.014(0.002)

Income 0.000 0.018(0.001)

Sex -0.036 -0.056(0.021)

Marital -0.001 0.004(0.007)

Employment 0.003 0.020(0.005)

Children 0.014** 0.078**(0.007)

Observations 770R-squared 0.138

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1




The Big Katrina Sort: Background

Unbiased assessment of ideological Schelling modelrequires exogenous source of variation in area diversity.Hurricane Katrina migration to Houston works well:

Overwhelming majority of Katrina migrants wereAfrican-American.2005 African-American Population in Houston: 866,016.2007 African-American Population in Houston: 979,717.A 13% increase in the African-American population in 2years!Majority of evacuees assigned to housing by Harris CountyHousing Authority.




The Big Katrina Sort: Background

If the MAP Theory is correct, Katrina migration shouldresult in:

1 Conservative white flight where evacuees settled.2 Geographic polarization between Harris County and

surrounding counties.




Measuring Evacuee Presence

Independent VariableApartment-building level counts of Katrina evacueesconducted by Harris County Housing Authority (donated byRick Wilson (Rice University)).Geocoded by Texas A&M Geoservices Geocoder.Aggregated to Census block group and Census tract levelsof geography.




Measuring Evacuee Presence




Preview of Findings

1 Schools in conservative Census tracts with evacueesexperience substantial white enrollment declines shortlypost-Katrina, but not pre-Katrina.

2 Conservative Census tracts receiving Katrina evacueesexperience steep declines in the white population. Similareffect not found among liberal tracts.

3 Polarization between Harris County and counties within100km of Harris increases dramatically post-Katrina.




Conservative White Flight in HISD Schools

∆Whitet−(t−1) = α + βt−(t−1)Katrina2005 + sj + ∆εt−(t−1) (2)

∆Whitet−(t−1) - One year change in white studentenrollment.Katrina2005 - Number of Katrina evacuees enrolled in HISDschools as of September 13, 2005.sj - School type (elementary, middle, high) fixed effects.





∆Whitet−(t−1) = α + βt−(t−1)Katrina2005 + sj + ∆εt−(t−1)

2000 precinct-level presidential election data used toseparate schools into “liberal” and “conservative” schools.Conservative schools - avg % Republican for precinctsw/in a 3km radius were above the HISD median of 31%.Liberal schools - avg % Republican for precincts w/in a3km radius were less than or equal to the HISD median of31%.





Conservative Area Schools(1) (2) (3) (4)

VARIABLES ∆White2003−2004 ∆White2004−2005 ∆White2005−2006 ∆White2007−2008

Katrina2005 -0.0808 0.00148 -0.132*** -0.145***(0.100) (0.00183) (0.0503) (0.0388)

Observations 140 140 140 137R-squared 0.055 0.017 0.114 0.086

Liberal Area SchoolsKatrina2005 -0.00458 0 -0.00567 -0.0201

(0.0128) (0) (0.0111) (0.0122)Observations 131 131 131 122R-squared 0.021 0.080 0.564

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Difference-in-Difference Regressions: One-Year Change in White Enrollment v. Number of Katrina Evacuee

Students in 2005




Conservative White Flight in Harris County CensusTracts

∆Whitei,10−00 = α+ βEvacueesi + γ∆Popi,10−00 + εi,10−00 (3)

∆Whitei,10−00 - Change in Census tract white populationbetween 2000 and 2010.Evacueesi - Number of Katrina evacuees in Census tractas of October 2005.Popi,10−00 - Change in Census tract population between2000 and 2010.Census tracts divided by average 2000 tract Republicanvote share.




Conservative White Flight in Harris County CensusTracts

% Republican Presidential Vote, 2000All Tracts 70-100% 50-70% 30-50% 0-30%

VARIABLES ∆Whitepop ∆Whitepop ∆Whitepop ∆Whitepop ∆Whitepop

Evacuees -0.909** -5.241*** -1.414 -0.549** -0.626***(0.351) (1.942) (0.956) (0.265) (0.136)

∆Tractpop 0.305*** 0.465*** 0.0997*** -0.0144 0.0713**(0.0596) (0.0494) (0.0295) (0.0426) (0.0287)

Observations 613 149 195 149 120R-squared 0.517 0.873 0.165 0.032 0.127

Cluster robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Regression of Change in Census White Population on Katrina Evacuee Counts, 2000-2010




Geographic Polarization: Overview

According to the MAP Theory, increases in diversity as theresult of Katrina migration should result in:

1 Conservative white flight out of Harris County and intoneighboring counties post-Katrina.

2 Decreases in Republican vote share in Harris County.3 Increases in polarization between Harris County and

neighboring counties.




Harris Outflows

One Year % Change in Outflows (Migration) from Harris County to Surrounding Counties

IRS Series on Income (SOI) data tracks outflows from Harris to all other counties in the US.

Track outflows from Harris to counties within 100km of Harris that did not receive Katrina migrants.




Polarization Between Harris and Surrounding Counties

Republican Vote Share For Texas Governor, Harris v. Surrounding Counties

Difference in vote share for Rick Perry (2002-2010) between regions increased dramatically post-Katrina.

Rick Perry was the incumbent in all three elections.




Polarization Between Harris and Surrounding Counties

Republican Vote Share For Texas Governor, Harris v. Surrounding Counties with Highest Post-KatrinaInflows

Polarization between Harris and counties with the greatest % change in inflows from Harris is even morepronounced.




Estimating the Causal Effect of Katrina Migration onRepublican Vote Share in Harris County

Ideally, want to measure Republican vote share in Harriscounty with Katrina migration and Republican vote share ina counterfactual Harris county in the absence of Katrinamigration.Subtraction of the two post-Katrina provides a causalestimate of the average treatment effect of Katrinamigration on Republican vote share.Synthetic control method (Abadie, Diamond andHainmueller (2014)) provides a tool which enables us toconstruct a counterfactual Harris county based on relevantcovariates.




Synthetic Harris v. Harris County

Treated Synthetic% Black (2000) 18.5 18.4% Black (1990) 19.2 19.1% Republican (2002) 55.9 56.9% Republican (1998) 65.7 66.7% Republican (1990) 48.1 48.9

Covariate Balance, Harris County v. Synthetic Harris County

Synthetic controls constructs a counterfactual Harris county by generating a set of weights for each Texas“donor county” that did not experience Katrina migration.

These weights are generated such that, combined, they provide the best balance on a selected set ofpre-Katrina covariates.

After removing counties that experienced Katrina migration, 244 “donor counties” remained.

Synthetic controls algorithm achieved balance on Republican vote share in 1990, 1998 and 2002 andproprtion black in 1990 and 2000.




Synthetic Harris v. Harris County

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Harris County Synthetic Harris County

Republican Vote Share for Governor of Texas, Harris County v. Synthetic Harris County: 1990-2010




Placebo Tests

Gap between Republican vote share in Harris and thecounterfactual Harris county is large: 8.7% in 2006 and12.5%.Still, the magnitude of this gap may be due to chancevariation in the model.If gap is still large relative to other “placebo” counties notexperiencing Katrina migration, this gives us greaterconfidence in our results.




Placebo Tests

Placebo Gaps, Synthetic v. Actual Counties in Texas not Receiving Katrina Migrants

Harris county gap is substantially larger than other placebo counties.

Probability of observing a gap as large in the post-Katrina period by chance alone is 0.0004.



Documents

Big and Little Sorts: Diversity, Conservative Flight and ... · Big and Little Sorts: Diversity, Conservative Flight and Geographic Polarization. 1: Introduction2: Ideology and Tolerance3: