Wright Jeffrey Final

UTSA DEPARTMENT OF DEMOGRAPHY | STATISTICS FOR DEMOGRAPHIC DATA II | SPRING 2014

Multilevel Modeling and the Hispanic ElectoralPotential in Texas Metropolitan Areas

Jeffrey Wright

May 23, 2014

ABSTRACT

Electoral behavior is mysterious in the best of circumstances, and among U.S. minorities yetmore opaque. I frame research questions to explore naturalization and residential segregation astwo factors determining voting participation, both linked by social networks which I conjecturemay overlap. I hypothesize that, despite previous research to the contrary, naturalization mayfavor Hispanic engagement at the ballot box. I further predict that ethnic enclaves empowerHispanic voters and stimulating voting activity. My results confirm the first hypothesis, but fallshort of mustering convincing evidence for the second.

1 INTRODUCTION

Beyond the function of population enumeration, demographers contribute to diverse sci-entific fields and policy arenas: migration, public health, and education, to name just a few.In many ways political demography trails the development enjoyed in other disciplines. Al-though Big Data has ushered in a new era of sophistication for U.S. parties and politicalcampaigns to calculate and pursue constituencies, this evolution has been characterized bybrute force computation impelled by vertiginous funding sources now plying all aspects ofpartisan operations. This tsunami has crashed down upon diligent demography, sweeping itup along with data mining, astro-turf mobilization, and media manipulation as just one ofmany facets required by political organizations to marshal resources and capture public offices.

Demography needs to right its raft in this tumult. Nothing is more fundamental to the demo-cratic process than a rich demographic understanding of the public that elections purport

1

to serve. Demographers chart population change by vital components and migration acrossincreasingly fine-grained geographies. They develop high-resolution portraits of embeddedage and sex structures, flushed out by socioeconomic, racial and ethnic compositions.

Electoral participation is often reported by the punditry to be declining in the U.S. However,since passage of the Voting Rights Act (1965) and the Twenty-Sixth Amendment (1971), votingparticipation has remained at rates above 50 percent among the eligible electorate – citizensabove the age of 18, with disenfranchisement of felons varying among states.

Nevertheless, voter turnout in the State of Texas is notoriously low, consistently ranking nearlast nationally, and the rate is even lower among Hispanics. Even when the denominator of theHispanic electorate is designed appropriately to account for eligibility, given the large numbersof undocumented and other non-citizen Hispanics in Texas, electoral activity remains slack.Even more confounding is the relative reluctance of Texas Hispanics compared with those inother states.

Nationwide, in the last elections, the Hispanic electorate was estimated at 23.7-millionstrong nationwide (Gonzalez-Barrera, 2012). According to PewResearch Center projections,Hispanics will account for 40% of the growth in the eligible electorate through 2030, when 40million Hispanics will be eligible to vote (Gonzalez-Barrera, 2012).

One element that is increasing Hispanic eligibility is the naturalization of the resident popu-lation, currently a glacial process. Mexican-origin immigrants, by far the largest component offoreign nationals in Texas, naturalize at a slower pace than other groups (Gonzalez-Barrera,2013). In this way, naturalization and voting are interlaced.

Moreover, both naturalization and electoral behavior are strongly influenced by social net-works (Sokhey and McClurg, 2012; Liang 1994). Hispanics are interconnected by two importantsocial networks: migrant and ethno-residential, the latter in the form of ethnic enclaves.

Why, if naturalization is a critical moment in the political incorporation of immigrants,does most research show that this change of status does not stimulate voting? In particular, ifinterlocking social networks drive both naturalization and electoral participation, have socialscientists been unable to demonstrate a positive association between the two? I hypothesizethat naturalization is a driver of voter behavior, and that social networks underpin this phe-nomenon. To explore this research question I use both naturalization status and, as a proxyfor social networks, the proportion of naturalized citizens in metropolitan areas.

I further hypothesize that social networks, represented by residential clustering by Hispanicethnicity, also activates voters. I proxy ethno-residential networks with the use of segregationindices.

This research approach responds to three important issues: demographic change and shift-ing electoral outcomes as the U.S. becomes increasingly multi-ethnic, the debate surrounding

2

immigration reform and the potential pathways to citizenship for undocumented residents,and the call by Riosmena (2013) to identify naturalization eligibility at finer-grain geographies.

BACKGROUND

Theories of electoral turnout offer up differing explanations for voter behavior. Individualresources available to those of higher SES, the impulse of social networks and the resourcesthey may provide, costs and barriers to participation (the flip side of access to resources),political campaigns and the resources they deploy, and motivation by specific interests suchas political issues.

The use of multilevel statistical approaches enables better modeling for social networks,which in many ways unites all of these explanations. By focusing on network effects, I am ableto simultaneously weigh the consequence of naturalization, since this also requires resourcesand knowledge that represent barriers, but which may also be ameliorated by access to socialnetworks.

The decision to naturalize, in many ways the capstone of the assimilation process for im-migrants, does not produce a windfall of immediate benefits. Many rights and privileges arealready gained by securing legal residency. Taking the next step has the primary benefits of fed-eral employment eligibility and the acquisition of political rights. It may be for this reason thatthe latter is frequently named as the most important reason to naturalize (Gonzalez-Barrera,2013).

Previous research seems counter-intuitive. The literature has not shown naturalization tolead to the exercise of those rights (Tam Cho, 1999; DeSipio, 1996; Ramakrishnan & Espenshade,2001).

Abundant research has demonstrated that ethnic enclaves engender social networks amongforeign-born and native Hispanics Viruell-Fuentes, et al., 2013). Not only do social networksstimulate electoral participation, they also facilitate naturalization (Liang, 1994; Hagan, 1998).

Hence a relationship between metropolitan residential segregation, social networks, natu-ralization, and voting behavior may be expected.

The Hispanic Isolation index in this paper measures the probability of exposure to non-Hispanics in a given metropolitan area. Within an enclave of contiguous census tracts withvery high proportions of Hispanics, the probability of exposure is low. However, Hispanics canbe evenly distributed across a metropolis and still not interact with many non-Hispanics if theHispanic population is much larger than the non-Hispanic population in the area (Massey& Denton, 1988). Conversely, if minorities represent a small fraction of the overall urbanpopulace, there is a higher likelihood of exposure regardless of the evenness of their spatialdistribution.

3

The dissimilarity or segregation index, on the other hand, measures the evenness of distri-bution of Hispanics and non-Hispanics (in this paper) across the metropolitan areas. Enclavesare, by nature, highly segregated populations, and thus I deploy this index as an alternativemeasure.

The use of 2012 voting records requires a cautionary note. It might overstate the participa-tion of both first- and second-generation Hispanics. Research has found that anti-immigrantlegislation has a positive effect on participation both generations of immigrants (Ramakrish-nan and Espenshade, 2001). The adoption of restrictive anti-immigrant policies by severalstates in the years prior to the 2012 election, in combination with the fact that incumbentPresident Barack Obama was seen as favoring a broader immigration reform than contenderMitt Romney, might have led to an unusually strong turnout vis-a-vis the larger electorate.(For example, Arizona Senate Bill 1070, controversial among the immigrant and Hispanic com-munity, was mostly struck down by the U.S. Supreme Court just months before the November2012 federal election.)

Electoral participation varies from cycle to cycle. Midterm elections have notoriously lowturnout, particularly for non-whites, and thus comparability requires the inspection of the2008, 2004, and 2000 elections. Although this is an area for future research, such time-varyingcomparisons have limits when considering Hispanic immigration and naturalization, since netimmigration ebbs and flows, and since legal visa issuance depends upon current legislation.The 1990 Immigration Act increased visa allotments by 40 percent, which profoundly reshapedthe eligible population of immigrant voters by 2012.

Hence, the selection of only the most recent federal elections has pros and cons, and ulti-mately these hypotheses merit testing against previous and future elections. In addition, theparticular hypothesis of ethnic enclaves and electoral participation may be better suited forexamination at local-level elections, and especially those in which minority-drawn districtsmay be invested in a favorable outcome.

2 DATA AND METHODS

My primary data source comes from the November 2012 Current Population Survey, supportedadditionally by data from the American Community Survey 5-year estimates for the 2008-2012period for use in deriving metropolitan-level independent variables for the mixed models. Ialso use the 2010 Decennial Census in order to calculate county-level isolation and segregationindices.

The Current Population Survey is a monthly labor force survey with the target populationof all U.S. homes. it is sponsored jointly by the Census Bureau and the U.S. Bureau of La-bor Statistics. Interviews are conducted of approximately 56,000 households each month;biannual November supplements include questions on voting and registration behavior. The

4

November 2012 CPS reported interviews carried out November 11-17, following that year’sgeneral election. Citizens and non-citizens alike are included in the full survey, but the votingsupplement samples only civilian adults who are citizens, additionally excluding militarypersonnel and excluding minors.

I subset only residents of the state of Texas and then I remove from the Texas sample individ-uals with a nonresponse for the PRPERTYP (“type of person record recode”) which codes for(1) child household member, (2) adult civilian household member, and (3) adult armed forceshousehold member. There are 888 nonresponses for this variable. I also exclude residentsof group quarters, representing only nine responses, and the resulting sample has an N of7,122. Although group quarters such as dormitories may house students, a population ofinterest to this paper, they may also domicile individuals incarcerated in the correctionalsystem, many of whom are barred from voting. Because of a lack of detail, and the small sizeof the group-quarters sub-population, I found it expedient to omit these responses.

In reporting the summary descriptive statistics, I weight up tallies and proportions accord-ing to the CPS survey design methodology. The total state population is estimated at 25.7million. I compile basic demographic descriptors statewide, and I provide age, citizenship,and income detail by non-metro and metro areas. I also present a table with data on electoraleligibility and participation by ethnicity.

The CPS identifies metropolitan areas based on core-based statistical areas (CBSA) definedby the Office of Management and Budget. The equivalent geographies from the ACS are foundat the 320-summary level of Metropolitan Statistical Area/Micropolitan Statistical Area (orpart) from the U.S. Census Bureau. Unfortunately, in Census data the MSAs include countiesamong their sublevels, but not census tracts, which are important for the construction ofmeaningful isolation and segregation indices.

Since Census county-level data disaggregates at the tract level, I respond to this problemby utilizing some, but not all, of the counties comprising the metropolitan areas. CBSAs en-compass both metro and micro areas; metro areas contain a core urban area of 50,000 or morepopulation, and may cover one or more counties. This includes both counties containingthe core urban area as well as any adjacent counties that are defined to be integrated withthe CBSA based on commuting to work. Thus, for example, the Amarillo CBSA includes thecounties of Armstrong, Carson, Potter, and Randall, with widely varying populations of 2,148,6,516, 113,546, 104,213.

Despite the social and economic integration indicated by commuting, sparsely populatedcounties and their proximate population centers and exurban communities may not be po-litically integrated with their metropolitan node. County-level and municipal elections areindependent, for example. Types of political entities that could overlap multiple counties inan MSA include federal congressional districts, state legislative districts, school districts, somejudicial seats, and more. However, these districts require an entirely different approach thando counties or MSAs. In some cases they span two or more metropolitan areas, cross multiple

5

Figure 2.1: Texas MSAs.

counties or regions, and have boundaries that generally reflect partisan imperatives ratherthan economic or social integration.

Hence, as a simple limit I have only included counties with populations of 100,000 or morewhich are part of each CBSA metropolitan area. There are evident problems with this method-ology. It excludes a few fairly populous counties adjacent to urban cores. It also includes areasof counties that may be remote and rural in character, disconnected from but still within acounty circumscribing the urban-political nucleus.

Despite these issues, the use of select urban counties enables me to access data to computeisolation, segregation and other indices using tract-to-county aggregation that is not feasibleusing the CPS metropolitan definitions, especially given the dearth of county data in the CPSsurvey. Since ethnic enclaves are best measured at a fine-grain geography such as tracts, therough compatibility of the CPS geographies and counties serves as a suitable compromise forthe purposes of this paper.

ELIGIBLE HISPANIC VOTERS

An an initial exploration of the issue of Hispanic voter eligibility, I first conduct basic design-adjusted logit regressions for all people in the Texas survey except for those in group quarters(N=7122), with the binary dependent variable hisp_elig as an outcome. I avoid using ethnicityor citizenship status among the independent variables due to concerns of collinearity.

I use a range of independent variables that I expect to be associated with eligible Hispanicvoters, including income quartiles (Q1 = r e f .), labor force status, sex, housing tenure, Spanish-

6

Table 2.1: Basic Descriptive Statistics by Ethnicity

Population N Male FemaleHispanic 10,413,264 5,176,081 5,237,184Non-Hispanic 15,297,562 7,465,372 7,832,189Total 25,710,826 12,641,453 13,069,373

Race & Ethnicity N % % AdultHispanic 10,413,264 41% 66%White 10,783,078 42% 79%Black 2,964,599 12% 72%Asian 1,109,326 4% 81%Other 440,559 2% 61%Total 25,710,826 100% 73%

Tenure Own Rent Other TotalHispanic 61% 38% 1% 100%Non-Hispanic 69% 30% 1% 100%Total 65% 33% 1% 100%

Labor Force Employed Unemployed Retired Disabled Other NILFHispanic 60% 4.1% 8% 6% 23%Non-Hispanic 61% 3.5% 16% 5% 14%Total 61% 3.7% 13% 5% 17%

Marital Status Married Divorced Separated Widowed TotalHispanic 55% 4% 33% 7% 100%Non-Hispanic 59% 2% 28% 10% 100%Total 58% 3% 30% 9% 100%

Educational Status Hispanic Non-Hispanic TotalLess than HS 17% 2% 8%Some HS 22% 10% 14%High School 30% 24% 27%Some College 21% 32% 28%BA/BS 8% 21% 16%Masters 2% 7% 5%Post-graduate 0% 2% 2%

Source: CPS November 2012, weighted results

7

Table 2.2: Characteristics of Hispanics by Metro Area

IncomePop. Adult Citizen I II III IV

Non-Metro 1,463,331 65% 86% 35% 37% 24% 3%Amarillo 85,546 77% 88% 37% 45% 18% 0%Austin-Round Rock 529,198 66% 81% 33% 26% 26% 5%Beaumont-Port Arthur 86,041 62% 45% 48% 28% 24% 0%Brownsville-Harlingen 316,018 69% 74% 53% 28% 13% 1%Corpus Christi 257,624 70% 95% 33% 15% 43% 2%Dallas-Fort Worth-Arlington 1,761,177 62% 71% 31% 40% 24% 6%El Paso 647,873 68% 90% 40% 34% 18% 4%Houston-Baytown-Sugar Land 2,323,167 67% 72% 29% 30% 33% 12%Killeen-Temple-Fort Hood 61,256 60% 85% 0% 30% 56% 1%Laredo 276,273 71% 73% 38% 38% 19% 1%Longview 43,149 68% 60% 0% 52% 48% 0%Lubbock 59,722 65% 94% 17% 32% 51% 0%McAllen-Edinburg-Pharr 730,178 61% 82% 57% 24% 12% 3%Midland 73,569 72% 100% 23% 28% 27% 1%San Antonio 1,384,348 67% 86% 43% 18% 30% 9%Victoria 226,443 67% 99% 30% 47% 18% 1%Waco 88,353 47% 78% 75% 25% 0% 0%Source: CPS November 2012, weighted results

8

Table 2.3: 2012 Electoral Participation by Metro Area

Eligible Voters Voted in 2012 EHV Voted in 2012 % Part. % Part.(Total Pop.) (All) EHV (Hispanic) (All) (Hispanic)

Non-Metro 2,385,587 1,392,208 771,721 344,655 58% 45%Amarillo 220,347 124,252 55,750 0 56% 0%Austin 1,175,683 638,916 249,878 101,013 54% 40%Beaumont 266,652 156,817 11,864 5,466 59% 46%Brownsville 209,041 153,215 143,135 87,309 73% 61%Corpus 285,263 143,576 170,327 73,640 50% 43%Dallas 4,183,618 2,269,311 637,895 212,954 54% 33%ElPaso 501,741 237,959 384,066 167,947 47% 44%Houston 3,832,675 2,104,005 948,784 400,232 55% 42%Killeen 214,049 134,713 27,384 21,808 63% 80%Laredo 124,210 24,738 121,177 24,738 20% 20%Longview 129,522 82,508 12,076 4,665 64% 39%Lubbock 141,427 71,047 35,264 6,525 50% 19%McAllen 346,639 128,918 313,997 106,386 37% 34%Midland 150,547 56,220 52,856 6,507 37% 12%SanAntonio 1,381,230 653,042 753,417 274,289 47% 36%Victoria 292,831 157,898 147,683 40,832 54% 28%Waco 195,477 98,118 25,627 7,626 50% 30%Source: CPS November 2012, weighted results

9

Table 2.4: Eligible Hispanic Voters, % Nat. Hisp. by Metro Area

% of Hisp.Total Pop. EHV %EHV Naturalized

Non-Metro 2,622,536 771,721 29% 7%Amarillo 262,927 55,750 21% 9%Austin 1,506,338 249,878 17% 3%Beaumont 417,856 11,864 3% 7%Brownsville 245,679 143,135 58% 4%Corpus 223,452 170,327 76% 1%Dallas 6,191,679 637,895 10% 11%ElPaso 399,259 384,066 96% 15%Houston 5,524,368 948,784 17% 13%Killeen 305,949 27,384 9% 0%Laredo 158,130 121,177 77% 7%Longview 181,221 12,076 7% 21%Lubbock 169,405 35,264 21% 0%McAllen 452,230 313,997 69% 6%Midland 146,666 52,856 36% 0%SanAntonio 1,458,619 753,417 52% 3%Victoria 269,458 147,683 55% 0%Waco 281,901 25,627 9% 10%Total 20,817,673 4,862,901 23% 8%Source: CPS November 2012, weighted results

10

only, and presence of a telephone in the home/apartment. I omit predictors that, despitestrong associations with electoral activity in general, are likely to inherently exclude minors,such as educational attainment at or above the high school level or marital status, since minorsare legally ineligible to vote and age is a component of the outcome variable. I likewise omitemployment despite restrictions on working for individuals aged less than 14 years.

TABLE 3.1

I first generate fixed-effect design-adjusted logistic regression models to estimate odds ratiosof independent variables.

Dependent Variable

• hisp_elig – Eligible Hispanic voter (EHV)

Independent Variables

• incq – Income quartiles (Q1: Ref.)

• biz – Business owner

• hhnmar – Household, non-married union (Married household: Ref.)

• hhindiv – Household, live alone

• Female – (Male: Ref.)

• own – Housing tenure (Renters: Ref.)

• occup – Neither own nor rent, not reported.

• span – Spanish-only spoken by members of HH 15 yrs plus

• tel – Telephone present in home

Full Model pr ed .el i g 1 ← sv y g l m(hi sp_el i g ∼ i ncq+bi z+hhnmar+hhi ndi v+ f emal e+own +occup + span + tel , desi g n = des, f ami l y = bi nomi al )

Next, for the sake of comparison, I limit the sample to Hispanics only (N=2,843), also ex-cluding those in group quarters, in order to determine if eligibility odds change.

Hispanic-only Model pr ed .el i g 2 ← sv y g l m(hi sp_el i g ∼ i ncq+bi z+hhnmar+hhi ndi v+f emal e +own +occup + span + tel , desi g n = desh, f ami l y = bi nomi al )

11

TABLE 3.2

Four additional models (3, 4, 5, 6) use metropolitan area as the key dependent variable. In thethird model, probabilities are calculated for eligible Hispanic voters among the full sample inall 17 metro areas in the CPS. The fourth restricts the sample to Hispanic respondents only.Models five and six repeat models four and five, but add the same controls for income, businessownership, household type, home ownership, Spanish-only, and presence of a telephone fromthe initial models.

Metro Areas

• Amarillo

• Austin-Round Rock

• Beaumont-Port Arthur

• Brownsville-Harlingen

• Corpus Christi

• Dallas-Forth Worth-Arlington

• El Paso

• Houston-Baytown-Sugar Land

• Laredo

• Longview

• Lubbock

• Killeen-Temple-Fort Hood

• McAllen-Edingburg-Pharr

• Midland

• San Antonio

• Victoria

• Waco

(3) pr ed .el i g 3 ← sv y g l m(hi sp_el i g ∼ metr os, desi g n = des, f ami l y = bi nomi al )

(4) pr ed .el i g 4 ← sv y g l m(hi sp_el i g ∼ metr os, desi g n = desh, f ami l y = bi nomi al )

(5) pr ed .el i g 5 ← sv y g l m(hi sp_el i g ∼ metr os+i nc+bi z+own+hr ht y pe+span+tel , desi g n =des, f ami l y = bi nomi al )

12

(6) pr ed .el i g 6 ← sv y g l m(hi sp_el i g ∼ metr os+i nc+bi z+own+hr ht y pe+span+tel , desi g n =desh, f ami l y = bi nomi al )

Before commencing the multileveling modeling, I confirm variance among households andamong metro areas. There is a significant level of variation among both.

ANOVA Tests, Metros & Households∼ hi sp_el i g Df Sum Sq Mean Sq F value Pr(>F)Metro areas 16 92.00 5.75 42.73 0.0000***Residuals 6126 824.43 0.13Household IDs 2678 676.76 0.25 2.74 0.0000***Residuals 4443 409.87 0.09∗∗∗p<0.001

TABLE 3.3

In the first set of random effects models, I do not yet introduce higher-level variables. Instead,I use the same independent predictors, but I permit them to vary among households, thenmetro areas, and then among both levels.

(1|HH): pr ed .el i g 9 ← g l mer (hi sp_el i g ∼ i ncq+bi z+own+occup+hhnmar +hhi ndi v+span + tel + (1|hhi d), d at a = vote12, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

(1|Metro): pr ed .el i g 10 ← g l mer (hi sp_el i g ∼ i ncq+bi z+own+occup+hhnmar+hhi ndi v+span + tel + (1|met ar ea), d at a = vote12, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

(1|HH)+(1|Metro): pr ed .el i g 11 ← g l mer (hi sp_el i g ∼ i ncq+bi z+own+occup+hhnmar+hhi ndi v + span + tel + (1|hhi d)+ (1|met ar ea), d at a = vote12, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

TABLE 3.4

Next, in an exploratory exercise, I extract metro-level variables using ACS 5-year 2008-2012estimates, and I generate log odds results for ten of them. I reported the exponentiated β

estimates.

13

• med_age_h: Median Hispanic age

• pct_own: Percent of home ownership

• pct_hsplus: Percent of pop. with high school or higher attainment

• no_tel: Percent of homes without telephone

• pct_eng: Percent of homes where English is spoken or understood

• pct_pov_h: Percent of Hispanics at or below poverty line

• pct_enr_h: Percent of student enrollment that is Hispanic

• pct_q1: Percent of residents in first quartile of Texas income

• pct_nomig: Percent of residents who have haven’t moved in past year

• pct_unemp: Metro unemployment rate

TABLE 3.5

Following Sparks (2014), I use Census 2010 tract data to construct a number of indices tomeasure residential segregation by tract-aggregation for Texas counties, which I will subsetinto their corresponding metro areas as discussed above. I use the indices to test segregationbetween non-Hispanic whites and Hispanics, and also between non-Hispanics and Hispanics.

CALCULATION OF INDICES

Isolation Indexhi → Hispanic population of census tracts in the metropolitan area for which the isolation

index is calculated.

nhi → Non-Hispanic population of the census tract.

H → Total Hispanic population of the metropolitan area.

T → Total non-Hispanic population of the metropolitan area.

Ii so →∑ hi

H× hi

T

Dissimilarity (segregation) Indexhi → Hispanic population of census tracts in the metropolitan area for which the isolation

index is calculated.

nhi → Non-Hispanic population of the census tracts.

14

H → Total Hispanic population of the complete metropolitan area.

N H → Total non-Hispanic population of the complete metropolitan area.

Iseg → 1

2

∑(hi

H− nhi

N H

)

TABLE 3.6

In the following three multilevel models I use both household and z-scored higher-level vari-ables in a multivariate design-adjusted logistic regression with fixed and random effects tocreate odds ratios for EHVs based on home ownership and household type, which showedsignificance in earlier models, along with percent of naturalized citizenship incorporated intoall three. I add an isolation index to the second model, and test a dissimilarity index in the third.

Dependent Variables

• own – Home ownership (Renter: Ref.)

• hhnmar – Non-married households in union (Married HH: Ref.)

• hhindiv – Live alone

• pnat_z – Percent naturalized citizens (metro)

• iso_h_z – Isolation index z-scored (MSA county)

• d_nnh_z – Dissimilarity index z-scored (MSA county)

pr ed .el i g 12 ← g l mer (hi sp_el i g ∼ own+occup+hhnmar+hhi ndi v+pnat_z+(1|met ar ea),d at a = j oi nd at a, f ami l y = bi nomi al , wei g ht s = pw ssw g t/mean(pw ssw g t ))

pr ed .el i g 13 ← g l mer (hi sp_el i g ∼ own+occup+hhnmar+hhi ndi v+pnat_z+i so_h_z+(1|met ar ea), d at a = j oi nd at a, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

pr ed .el i g 14 ← g l mer (hi sp_el i g ∼ own+occup+hhnmar+hhi ndi v+pnat_z+d_nnh_z+(1|met ar ea), d at a = j oi nd at a, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

ACTIVATED HISPANIC VOTERS

TABLE 3.7

I introduce a new independent variable, hispv, representing individuals who voted in the2012 general election. This variable derives from the CPS November supplement, and is thus

15

restricted to the universe of adult citizen civilians. Among this sample, I produce multilevellog odds – with metropolitan area as the higher level – to test my second set of hypotheses. Iuse the same independent variables from Table 3.6 but I now include naturalization statusand sex, as well as a metro-level median income indicator, z-scored. I now subset the sampleto reflect only the MSA counties of interest.

Independent Variable

• hispv: Hispanics who voted during the November 2012 federal elections.

Dependent Variables

• usnatur – Naturalized citizen

• female – Female

• own – Home ownership (Renter: Ref.)

• hhnmar – Non-married households in union (Married HH: Ref.)

• hhindiv – Live alone

• medinc_z – Median income z-scored (Metro-level)

• pnat_z – Percent naturalized citizens (Metro-level)

• iso_h_z – Isolation index z-scored (MSA county)

• d_nnh_z – Dissimilarity index z-scored (MSA county)

pr ed .vote1 ← g l mer (hi spv ∼ usnatur + f emal e +own +occup +hhnmar +hhi ndi v +medi nc_z +pnat_z + (1|met ar ea), d at a = j oi nd at a.sub, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

pr ed .vote2 ← g l mer (hi spv ∼ usnatur + f emal e +own +occup +hhnmar +hhi ndi v +medi nc_z+pnat_z+i so_h_z+(1|met ar ea), d at a = j oi nd at a.sub, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

pr ed .vote3 ← g l mer (hi spv ∼ usnatur + f emal e +own +occup +hhnmar +hhi ndi v +medi nc_z+pnat_z+d_nnh_z+(1|met ar ea), d at a = j oi nd at a.sub, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

TABLE 3.8

I retain the outcome of Hispanic voter, but now I introduce a new dependent variable: gen –second-generation Hispanics. To be in this classification, an individual must be a native-borncitizen, over the age of 17, and with both parents foreign-born. I retain the other independent

16

Selected Texas MSA Counties, Pop. >100k

fips metarea Metro County48375 11100 Amarillo Potter48381 11100 Randall48453 12420 Austin-Round Rock Travis48491 12420 Williamson48245 13140 Beaumont-Port Arthur Jefferson48061 15180 Brownsville-Harlingen Cameron48355 18580 Corpus Christi Nueces48085 19100 Dallas-Fort Worth-Arlington Collin48113 19100 Dallas48121 19100 Denton48139 19100 Ellis48251 19100 Johnson48439 19100 Tarrant48141 21340 El Paso El Paso48039 26420 Houston-Baytown-Sugar Land Brazoria48157 26420 Fort Bend48167 26420 Galveston48201 26420 Harris48339 26420 Montgomery48027 28660 Killeen-Temple-Fort Hood Bell48479 29700 Laredo Webb48183 30980 Longview Gregg48303 31180 Lubbock Lubbock48215 32580 McAllen-Edinburg-Pharr Hidalgo48329 33260 Midland Midland48029 41700 San Antonio Bexar48469 47020 Victoria Victoria48309 47380 Waco McLennan

predictors. Again there are three models: one with a higher-level variable for % naturalization,which is used in all three, and an Hispanic isolation and non-Hispanic-Hispanic segregation,which are used alternatively in the second and third.

Dependent Variable

• gen – 2nd generation Hispanics, native-born US, both parents born abroad, age > 17

pr ed .vote4 ← g l mer (hi spv ∼ g en+ f emal e+own+occup+hhnmar+hhi ndi v+medi nc_z+pnat_z + (1|met ar ea), d at a = j oi nd at a.sub, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

17

pr ed .vote5 ← g l mer (hi spv ∼ g en+ f emal e+own+occup+hhnmar+hhi ndi v+medi nc_z+pnat_z + i so_h_z + (1|met ar ea), d at a = j oi nd at a.sub, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

pr ed .vote6 ← g l mer (hi spv ∼ g en+ f emal e+own+occup+hhnmar+hhi ndi v+medi nc_z+pnat_z +d_nnh_z + (1|met ar ea), d at a = j oi nd at a.sub, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

TABLE 3.9

In this set of models I modify the second-generation construction to create a “dreamer” profile:1.5-generation citizens who are below the age of 30. There are some important caveats to bemade. Firstly, there is no consensus on age boundaries for so-called Dreamers. The failedfederal DREAM Act legislation had an upper-limit of 35; the executive DACA order has anupper-limit of 31. However, in elections literature young voters are conventionally consideredto be under-30 years of age, and I employ this convention. Secondly, and more importantly,“Dreamers” in the context of U.S. immigration debate and policy are by definition non-citizens.The DACA program, for example, covers undocumented immigrants aged 16 to 31 who enteredthe U.S. while still a minor. Additional conditions regarding school enrollment and criminalactivity further modify the category in the legal context.

However, because my research question involves naturalization, I craft the dream variableto include only citizens. The other conditions are to be an adult below the age of 30, Hispanic,and born abroad to foreign nationals. This permits testing of the hypothetical Dreamer who,somewhere along the line, gained citizenship through the naturalization process or through,for example, their parent’s naturalization. (There are several avenues for a minor to gaincitizenship via their parents.) This approach approximates the profile of a Dream-cum-votervia naturalization stemming from an immigration reform.

Dependent Variable

• dreamer – 1.5-generation Hispanics, foreign-born, both parents born abroad,aged 17 < 30

pr ed .vote7 ← g l mer (hi spv ∼ dr eamer + f emal e +own+occup +hhnmar +hhi ndi v +medi nc_z +pnat_z + (1|met ar ea), d at a = j oi nd at a.sub, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

pr ed .vote8 ← g l mer (hi spv ∼ dr eamer + f emal e +own+occup +hhnmar +hhi ndi v +medi nc_z+pnat_z+i so_h_z+(1|met ar ea), d at a = j oi nd at a.sub, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

pr ed .vote9 ← g l mer (hi spv ∼ dr eamer + f emal e +own+occup +hhnmar +hhi ndi v +medi nc_z+pnat_z+d_nnh_z+(1|met ar ea), d at a = j oi nd at a.sub, f ami l y = bi nomi al ,wei g ht s = pw ssw g t/mean(pw ssw g t ))

18

3 RESULTS AND DISCUSSION

ELIGIBLE HISPANIC VOTERS

In Table 3.1 in results of the full model, income levels prove to have significant effect on pre-dicting for Hispanic voter eligibility, with lower odds for such an outcome at the second, third,and fourth income quartiles compared with the first quartile, which is the reference category.The higher the household income, the less likely it becomes that members of the householdwill belong to the outcome class. Business ownership lowers the odds by 32 percent. Whetherthe household is represented by an unmarried couple, non-married couple, or individualhas a significant but negligible predictive effect, and the same is true of sex. Compared withrenters, homeowners are significantly more likely to be eligible Hispanic voters. Residents ofSpanish-only households, while presumably Hispanic, are 48-percent less likely to be eligibleto vote. Owning a telephone reduces the odds somewhat, as well.

Restricting the sample to Hispanic-only respondents flops many of the odds ratios fromless-than-one to raising the probabilities of the outcome of Hispanic voter eligibility. Theincome relationship reverses: membership in the upper quartiles increases likelihoods. Thisreversal may reflect the lower socioeconomic of Hispanics in Texas, but particularly the lowerSES of undocumented residents, who are ineligible to vote. Although non-citizens may be legalor undocumented residents (CPS does not distinguish), the latter often reside in economicallydistressed households. Moreover, regardless of citizenship, Hispanic households are morelikely to be poorer compared to the population-at-large. But among Hispanics, wealthierprofiles have a higher correlation with eligibility, presumably in part a function of citizenship,as well as upward inter-generational mobility. Also, Hispanic business owners are slightlymore likely to be eligible voters.

Household type in this restricted model produces a stronger outcome. Non-married couplesare 11-percent more likely than married households (the reference category) to be eligiblevoters. But notably, households that are headed by an individual display a large odds ratio of5.6, at a highly significant level, that the individual is an eligible voter. Roughly 10 percent ofHispanic households in Texas fall into this category, with male and females representing 5.5percent and 3.4 percent, respectively. Home ownership, which predicted for Hispanic votereligibility in the broader population with an odds ratio of 1.37, has a stronger effect in the re-stricted sample with an odds ratio of 1.80. In this second model, Spanish-only households areno more likely than polyglot or English-speaking households to have eligible voters. The nega-tive âAIJtelephone effectâAI endures, but the odds are reduced by a lesser margin in this model.

So how well do these basic indicators predict for Hispanic voter eligibility? The full model isdifficult to interpret since it is unclear to what extent the probabilities are influenced purelyby the socioeconomic variability in the broader population. All three income quartiles arenegatively associated with Hispanic voter eligibility. This indicates that membership in thebottom rungs of the income ladder predicts for the combination of Hispanic ethnicity, adultage, and citizenship status which define eligibility. This may have some face validity but has

19

Table 3.1: Hispanic Voter Eligibility, Logit Odds Ratios

Dependent variable:

hisp_elig

(Full) (Hispanic-only)

Income Q1 – –

Income Q2 0.835∗∗∗ 1.063∗∗∗

(0.083) (0.105)

Income Q3 0.657∗∗∗ 1.228∗∗∗

(0.089) (0.111)

Income Q4 0.276∗∗ 1.383∗∗∗

(0.126) (0.167)

Business owner 0.681∗∗∗ 1.005∗∗∗

(0.125) (0.167)

HH-unmarried 0.982∗∗∗ 1.112∗∗∗

(0.081) (0.096)

HH-live alone 0.981∗∗∗ 5.640∗∗∗

(0.096) (0.164)

Female 0.999∗∗∗ 1.149∗∗∗

(0.063) (0.083)

Homeowner 1.372∗∗∗ 1.800∗∗∗

(0.076) (0.092)

Spanish-only 0.522∗∗∗ 0.156(0.150) (0.168)

Telephone 0.785∗∗∗ 0.841∗∗∗

(0.164) (0.209)

Constant 0.364∗∗ 0.625∗∗∗

(0.175) (0.227)

Observations 7,108 2,840Log Likelihood −3,427.596 −1,840.587Akaike Inf. Crit. 6,879.193 3,705.175

∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.0120

very limited utility in terms of specifying a model which can estimate the target population,especially given that the relationship is reversed in the restricted sample. Hence informationon household income has limited utility at the individual/household level.

The strongest association among all variables in the restricted model, individual house-holdership, has nearly even odds in the full model, which also complicates interpretation.This is undoubtedly a consequence of age: single-member households are highly likely to beadults. Perhaps the best indicator to emerge from these regressions is home ownership, whichdemonstrates a positive significant effect in both models, and somewhat elevated odds ratiosof 1.37 and 1.80, respectively, in the full and Hispanic-only models.

Nevertheless, the takeaway from this exploratory exercise is that fixed-effects logit modelshave sharply limited effectiveness for the estimation of the electoral eligibility of Hispanics.

In Table 3.2, four additional models use metropolitan area as the key dependent variable,introducing a spatial dimension. In Model 3, probabilities are calculated for eligible Hispanicvoters among the full sample in all 17 metro areas in the CPS. The fourth restricts the sampleto Hispanic respondents only. Models 5 and 6 repeat models four and five, but add controls forincome, business ownership, household type, home ownership, Spanish-only, and presenceof a telephone. (For readability, the controls are omitted from the display chart, as are non-significant results Beaumont-Port Arthur, Killeen-Temple-Fort Hood, Longview, and Waco.)

An examination of Hispanic eligibility by metro area yields a slightly better idea of whereeligible Hispanic voters may live in Texas (versus the non-metropolitan reference group). Inthe complete population model, 13 of the metropolitan areas have significant beta estimates.However, several of these, Amarillo, Brownsville-Harlingen, Lubbock, and McAllen-Edinburg-Pharr demonstrate shifting relationships between the full and Hispanic-only models. In each,residency is associated with lower odds ratios (negative betas) when considering the entirepopulation, but becomes positively predictive when the Hispanic sample is used. Laredo,interestingly, shows less-than-one odds in model 5, but higher likelihoods in the other models.

Observing the results of the full sample, we can expect considerably higher probabilities(OR>2.0) for eligible Hispanic voters to live in the Corpus Christi, El Paso, Laredo, and McAllen-Edinburg-Pharr metropolitan areas, mostly along the border except for Corpus Christi whichis close. On the other hand, the probabilities are notably lower (OR<0.7) in the teeming anddiverse metropolises of Dallas and Houston. Importantly, introducing the controls somewhatimproves the models, with AICs of 6,513 falling to 6,415 in the full sample and from 4,000 to3,740 in the restricted sample.

I begin permitting random intercepts in Table 3.3, although no metro-level indicators havebeen added to the model specifications. Allowing variation among households and amongmetropolitan areas produces differences in both significance levels and, in a few cases, direc-tionality of odds. The AIC indicates that varying metro-level intercepts improves the outcomevis-a-vis households, 5,364 versus 6,010. However, when I permit random effects at both

21

Table 3.2: Eligible Hispanic Voters, Logit Odds Ratios by Metropolitan Area

Dependent variable:

hisp_elig

Metro areaa (3) (4) (5b) (6b)

Amarillo 0.721∗∗ 1.677∗∗∗ 0.703∗∗ 1.775∗∗∗

(0.298) (0.440) (0.294) (0.448)Austin-Round Rock 0.564∗∗∗ 0.802∗∗∗ 0.737∗∗∗ 0.914∗∗∗

(0.152) (0.194) (0.157) (0.216)Brownsville-Harlingen 1.980∗∗∗ 0.742∗∗∗ 1.890∗∗∗ 0.745∗∗∗

(0.212) (0.234) (0.217) (0.239)Corpus Christi 2.590∗∗∗ 1.749∗∗∗ 2.835∗∗∗ 1.518∗∗∗

(0.218) (0.299) (0.228) (0.308)Dallas-Fort Worth-Arlington 0.350∗∗∗ 0.509∗∗∗ 0.416∗∗∗ 0.638∗∗∗

(0.113) (0.140) (0.116) (0.149)El Paso 3.269∗∗∗ 1.305∗∗∗ 3.315∗∗∗ 1.344∗∗∗

(0.157) (0.183) (0.165) (0.200)Houston-Baytown-Sugar Land 0.584∗∗∗ 0.622∗∗∗ 0.650∗∗∗ 0.718∗∗∗

(0.107) (0.133) (0.109) (0.141)Laredo 2.604∗∗∗ 0.700∗∗∗ 3.173∗∗∗ 1.080∗∗∗

(0.237) (0.245) (0.242) (0.270)Lubbock 0.707∗∗ 1.292∗∗ 0.789∗∗ 1.365∗∗

(0.359) (0.532) (0.361) (0.550)McAllen-Edinburg-Pharr 2.360∗∗∗ 0.676∗∗∗ 2.470∗∗∗ 0.905∗∗∗

(0.159) (0.172) (0.166) (0.185)Midland 1.225∗∗∗ 2.287∗∗∗ 1.224∗∗∗ 1.780∗∗∗

(0.308) (0.511) (0.317) (0.548)San Antonio 1.755∗∗∗ 1.070∗∗∗ 1.889∗∗∗ 1.083∗∗∗

(0.116) (0.144) (0.118) (0.152)Victoria 1.863∗∗∗ 1.680∗∗∗ 1.882∗∗∗ 1.430∗∗∗

(0.210) (0.278) (0.210) (0.282)Constant 0.294∗∗∗ 1.116∗∗∗ 0.322 0.631∗∗

(0.079) (0.100) (0.253) (0.308)

Observations 7,108 2,840 7,108 2,840Log Likelihood −3,238.564 −1,981.754 −3,169.384 −1,832.188Akaike Inf. Crit. 6,513.128 3,999.507 6,414.768 3,740.376

Note: a Significant only; bcontrols not shown ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

22

Table 3.3: EHV, Multilevel Logistic Regression, Odds Ratios

Dependent variable:

hisp_elig

(1|Household) (1|Metro) (1|HH)+(1|Metro)

Income Q1 – – –

Income Q2 0.555∗∗ 1.022∗∗∗ 0.972∗∗∗

(0.242) (0.095) (0.157)

Income Q3 0.318 0.943∗∗∗ 0.865∗∗∗

(0.254) (0.098) (0.162)

Income Q4 0.091 0.437∗∗∗ 0.260(0.339) (0.135) (0.209)

Business owner 0.558∗ 0.792∗∗∗ 0.701∗∗∗

(0.333) (0.135) (0.209)

Homeowner 1.541∗∗∗ 1.297∗∗∗ 1.337∗∗∗

(0.208) (0.081) (0.132)

HH unmarried 1.041∗∗∗ 1.107∗∗∗ 1.195∗∗∗

(0.249) (0.088) (0.151)

HH live alone 0.904∗∗∗ 1.197∗∗∗ 1.150∗∗∗

(0.232) (0.107) (0.155)

Spanish-only 0.546 0.503∗∗∗ 0.480∗

(0.437) (0.166) (0.267)

Telephone 0.704 0.796∗∗∗ 0.791∗∗

(0.480) (0.189) (0.308)

Constant 0.018 0.299 0.160(0.526) (0.313) (0.466)

Observations 7,122 6,143 6,143Log Likelihood −2,992.931 −2,629.647 −2,453.147Akaike Inf. Crit. 6,009.862 5,283.293 4,932.294Bayesian Inf. Crit. 6,092.314 5,363.970 5,019.693

∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

23

the household and the metro areas, the odds ratios change little and, although the AIC isslightly lower, the difference is small. From these models it is clear that the more parsimoniousmetro-level model generates odds ratios that are very similar to those from random householdvariation, but with the advantage of more compact and accessible data. Metropolitan areaspresent greater availability of public data and are inherently easier to sample with greaterprecision.

Hence, I proceed with multilevel modeling using metro areas as the higher-level of analysis.Table 3.4 presents the odds ratios for 10 metro-level indicators, listed in the Data & Methodol-ogy section. The selection of these indicators both mirrors independent variables from earliermodels (such as telephone, linguistic isolation, and home ownership), but also tests predictorsthat involve education and ethnicity, excluded from individual-level models.

A higher median age of Hispanics at the metro-level is associated with 3.3-times higherodds of EHVs. This is to be expected, since lower median ages reflect an age compositionwith more minors. In this model, the absence of a telephone in the home continues to beassociated positively with Hispanic voter eligibility. (Previous models actually yielded lowerodds in the presence of a telephone.) Income continues to be difficult to interpret as a factorof eligibility. EHVs are more likely to live in metropolitan areas in which the proportion ofHispanic poverty is higher, yet the ratio of households in the first income quartile registeredno significance. A reduced presence of migrants (without distinction between internationaland internal migration) also lowers the odds of EHV, which suggests that many migrants in theCPS survey may be legal or undocumented international movers.

Next, in Table 3.5 I summarize the residential segregation indices described in Data &Methods. Given my hypothesis that residence in ethnic enclaves foments voting activity, I ammostly interested in the isolation index for Hispanics, but for the sake of comparison I willproceed with tests for dissimilarity (or segregation) of non-Hispanics and Hispanics, the indexwith the lowest mean among Texas counties.

Finally, having fitted fixed, random, and mixed effect models, I distill a few independentvariables and introduce naturalization, isolation, and segregation variables in three models inTable 3.6. This explores my research questions: net of important individual or household-leveleffects, what is the influence of the metropolitan context in terms of both naturalization andethnic isolation?

In line with my expectations, a higher proportion of naturalized citizens in a metro increasesthe likelihood of EHV among the residents. The odds ratios in all three models are roughly60-percent higher in such metropolitan areas. Whether or not I control for isolation or segre-gation, the ORs remain stable.

In the middle column I present multilevel logistic odds including an isolation index at themetro-level. The resultant OR is significant but negligible, only 3-percent more likely to beassociated with EHVs. The dissimilarity index, measuring residential segregation, has even

24

Table 3.4: EHV, Metro-level Predictors

Dependent variable:

hisp_elig

Median Hispanic Age 3.312∗∗∗

(0.144)

% Home ownership 1.353∗∗∗

(0.127)

% HS or higher attainment 0.196(0.358)

% No telephone 1.157∗∗∗

(0.059)

% English-spoken or understood at home 1.863∗∗∗

(0.198)

% Hispanics at or below poverty 1.856∗∗∗

(0.234)

% Hispanic school enrollment 1.950∗∗∗

(0.148)

% of pop. in Texas Q1 income quartile 0.228(0.368)

% Residents haven’t moved in past year 0.588∗∗∗

(0.107)

Unemployment rate 0.830∗∗∗

(0.082)

Constant 0.248∗∗∗

(0.068)

Observations 6,143Log Likelihood −2,675.214Akaike Inf. Crit. 5,374.428Bayesian Inf. Crit. 5,455.105

∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

25

Table 3.5: Residential Segregation Indices, Texas Counties

Statistic N Mean St. Dev. Min MaxDissimilarity white-Hispanic 254 0.203 0.142 0.000 0.548Dissimilarity non-Hispanic-Hispanic 254 0.182 0.126 0.000 0.451Hispanic Isolation 254 0.354 0.227 0.029 0.959Non-Hispanic isolation 254 0.354 0.227 0.029 0.959Interaction white-Hispanic 254 0.556 0.211 0.028 0.921Interaction non-Hispanic-Hispanic 254 0.609 0.202 0.035 0.925TheileH 254 0.321 0.342 −0.551 0.924

odds. The AICs are generally consistent among all three models in this table, demonstratinglittle improvement with controls for isolation or segregation.

The outcome of this regression on EHVs, drawing on the power of multilevel modeling,supports my hypothesis that naturalization contextualizes the electoral milieu. The inter-pretation is limited by the obvious fact that more naturalized citizens translates into morecitizens, a condition for eligibility. Moreover, since the majority of foreign nationals in Texasare Mexico-born, more naturalized citizens translates into more adult Hispanic citizens. (Mi-nors may receive citizenship via the naturalization of their parents, but may not otherwisenaturalize.) Despite these caveats, linked with additional testing on naturalization and votingbehavior, I expect this hypothesis to strengthen the context and condition of naturalization asindependent determinants of voting eligibility and activity.

On the other hand, there is virtually no support thus far that ethnic enclaves breed electoralenthusiasm. This hypothesis is less important at the eligibility stage, and more important inthe following section of the paper dealing with active Hispanic voters. Indeed, these residentialmeasures cloak the fact that enclaves could just as easily be landing points for undocumentedmigrants lacking political rights as are they could be old, established barrios with multiplegenerations and a history of political activism.

ACTIVE HISPANIC VOTERS

The remaining models in this paper utilize the dependent variable signaling Hispanic voters,hispv, discussed in the Data & Methods section. The binary outcome is ‘1’ for a citizen whocast a ballot in the 2012 general election and is of Hispanic ethnicity. Those assigned a ‘0’outcome represent all those who were eligible, but did not vote. I coded out non-responses.Among those who responded ‘no’ to having voted, less than five percent of eligible Hispanicvoters said they did no participate in the election due to ineligibility. Since residents of groupquarters are precluded from my results, those who do not vote due to ineligibility may bemistaken (ie. a false positive), may be in a penal condition such as parole or probation, or may

26

Table 3.6: EHV, Naturalization and Segregation, Multilevel Logistic Modeling

Dependent variable:

hisp_elig

(1|Metro) (1|Metro) (1|Metro)

Home owner 1.097∗∗∗ 1.097∗∗∗ 1.097∗∗∗

(0.043) (0.043) (0.043)

HH unmarried 1.243∗∗∗ 1.243∗∗∗ 1.243∗∗∗

(0.047) (0.047) (0.047)

HH live alone 1.191∗∗∗ 1.191∗∗∗ 1.191∗∗∗

(0.057) (0.057) (0.057)

% Naturalized (Metro-level) 1.617∗∗∗ 1.586∗∗∗ 1.616∗∗∗

(0.179) (0.178) (0.179)

Isolation index (MSA counties) 1.028∗∗∗

(0.045)

Dissimilarity index (MSA counties) 1.004∗∗∗

(0.023)

Constant 0.243 0.241 0.243(0.205) (0.201) (0.204)


∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

27

have been judged to be mentally incompetent. The rest of the ‘0’ universe provide negativereasons including failure to register, being too busy, etc.

Since my hypotheses regard naturalization and ethnic enclaves, I test for both of thesevariables in the ensuing multilevel models. I include the state of naturalized citizenship as anindependent predictor of voting, since collinearity is not a major issue (all models achievedconvergence).

I also reintroduce income as a conditional. Although it proved elusive for interpretationfor EHVs, income is known to be a predictor of voting behavior in general. I retain homeownership, which tends to be a proxy for higher-income individuals, and I utilize a metro-levelz-scored indicator for median income.

In table 3.7, I find naturalization to be a highly significant and important factor in votingbehavior. Regardless of isolation or separation, the odds ratios are more than five-times na-tive citizenship in terms of voting likelihood for Hispanics. Previous independent variables,including home ownership, and unmarried or individual householdership, continue to besignificantly and positively associated with the outcome variable – they influence eligibilityand voting behavior among the eligible. I also control for sex, which had little effect on EHVs.In terms of voting behavior, females are 14-percent more likely to have voted in 2012.

Median income has a negative influence on the MSA’s Hispanic voting patterns, which isprobably related to the disproportionate numbers of Hispanics living in poorer metropolitancounties in Texas. Since this is a metro-level variable and randomness of variation is permit-ting in these models, it does not act as a control for individual or household odds ratios, butdoes control for other metro-level determinants. Nevertheless, I do not deem the results veryinsightful in terms of the relationship between income levels and Hispanic voting behavior.

Again I find significant and positive odds associated with percent of naturalization in ametropolitan area and the likelihood of Hispanic electoral participation. This is true in allthree models, without residential segregation indices, with Hispanic isolation, and with non-Hispanic-Hispanic spatial dissimilarity. And again I find that the segregation indices havenegligible impact on the binary outcome.

Second-generation, native-born Hispanics are the most likely of all categories I modeled toexercise their vote. The probabilities of a second-generation Hispanic to be an active voterare nearly nine times those of the Hispanic population-at-large. (This is not to be confusedwith odds for voting versus the entire population. Second-generation Hispanics, like thetransgenerational Hispanic population, have lower odds of electoral participation than thenon-Hispanic white majority, the models of which I confirmed but do not reproduce for thispaper.)

In addition, while isolation and segregation effects remain null, the positive influence ofproportion of naturalized voters in a metro area persists in these second-generation models.

28

Table 3.7: Hispanic Voters, Nat. and Seg., MSA Counties

Dependent variable:

hispv


Naturalized 5.355∗∗∗ 5.355∗∗∗ 5.355∗∗∗

(0.076) (0.076) (0.076)

Female 1.141∗∗∗ 1.141∗∗∗ 1.141∗∗∗

(0.061) (0.061) (0.061)

Home ownership 1.335∗∗∗ 1.335∗∗∗ 1.335∗∗∗

(0.074) (0.074) (0.074)

HH unmarried 1.572∗∗∗ 1.572∗∗∗ 1.572∗∗∗

(0.074) (0.074) (0.074)

HH live alone 1.510∗∗∗ 1.510∗∗∗ 1.510∗∗∗

(0.087) (0.087) (0.087)

Median income 0.672∗∗∗ 0.686∗∗∗ 0.673∗∗∗

(0.240) (0.237) (0.240)


(0.223) (0.222) (0.223)

Hispanic Isolation (MSA counties) 1.041∗∗∗

(0.072)

Dissimilarity non-Hispanic-Hispanics (MSA counties) 1.005∗∗∗

(0.037)

Constant 0.049 0.049 0.049(0.272) (0.266) (0.272)


∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

29

Table 3.8: Hispanic Voters, Second Generation, Multilevel Logistic Modeling

Dependent variable:

hispv


Second generation 8.660∗∗∗ 8.658∗∗∗ 8.661∗∗∗

(0.101) (0.101) (0.101)

Female 1.236∗∗∗ 1.236∗∗∗ 1.236∗∗∗

(0.061) (0.061) (0.061)

Home ownership 1.620∗∗∗ 1.621∗∗∗ 1.620∗∗∗

(0.074) (0.074) (0.074)

HH unmarried 1.596∗∗∗ 1.596∗∗∗ 1.596∗∗∗

(0.074) (0.074) (0.074)

HH live alone 1.470∗∗∗ 1.470∗∗∗ 1.470∗∗∗

(0.086) (0.086) (0.086)

Median income 0.729∗∗∗ 0.743∗∗∗ 0.730∗∗∗

(0.234) (0.232) (0.234)


(0.218) (0.218) (0.218)


(0.071)


(0.037)

Constant 0.039 0.039 0.039(0.267) (0.262) (0.267)


Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

30

Since second-generation immigrants do not require naturalization (being citizens by virtue ofjus soli), this effect reinforces my hypothesis that social networks encouraging naturalizationand voting overlap. In geographies were more individuals seek naturalization, more second-generation Hispanics are wont to vote.

In the final chart, Table 3.9, I model the likelihood of voting in 2012 by what I term Dreamers,although this usage does not reflect the most common definition, but rather an idealizationof how Dreamers might participate electorally were there to be a pathway to naturalizationprovided by reform. I recognize that such a pathway would inevitably require years to travel,perhaps shifting the electoral and participatory climate. Nevertheless, it serves to test mynaturalization hypothesis from yet another angle.

Dreamers under my definition are three-times more likely to vote than other eligible His-panic voters. As discussed above, I allow eligibility via naturalization or other avenues tocitizenship, since many individuals in this age group could have acquired U.S. nationality asminors under byzantine U.S. immigration laws. Therefore these candidates are less likely tocast a ballot than their second-generation elders – but that’s part of the point. Age is one ofthe most important determinants of voting behavior, and Dreamers are defined to be belowthe age of 30. Hence the second-generation, which spans the entire age distribution, includesaged individuals with greater voter propensity. Nevertheless, despite their youth, Dreamersare more likely voters than overall Hispanics. This surely is an avenue for future exploration,not only because of the implications for immigration policy and reform, but because the agegrouping overlaps with the Millennial Generation of voters, which have in the most immediateelectoral cycles exhibited a sharp break in political attitudes compared with previous genera-tions.

In addition, metro-level indicators of naturalization continue to produce significant positiveodds ratios. This effect is strong in all of the multilevel Hispanic voter models I conducted. De-spite the results of (scant) prior research in political science literature that finds naturalizationto have an ambivalent or null impact on voting behavior, these multilevel modeling effortsdemonstrate that there is a clear relationship at both the individual and metropolitan-arealevels.

On the other hand, the Dreamer model, like the others, fails to contribute evidence tosupport an alternative hypothesis for residential segregation. Isolation or dissimilarity appearto have very minor interplay with electoral behavior for Dreamers, as well.

31

Table 3.9: Hispanic Voters, Dreamer Generation, Multilevel Logistic Modeling

Dependent variable:

hispv


Dreamer 2.879∗∗∗ 2.880∗∗∗ 2.879∗∗∗

(0.413) (0.413) (0.413)

Female 1.163∗∗∗ 1.163∗∗∗ 1.163∗∗∗

(0.060) (0.060) (0.060)

Home ownership 1.537∗∗∗ 1.537∗∗∗ 1.537∗∗∗

(0.072) (0.072) (0.072)

HH unmarried 1.546∗∗∗ 1.546∗∗∗ 1.546∗∗∗

(0.072) (0.073) (0.072)

HH live alone 1.366∗∗∗ 1.366∗∗∗ 1.366∗∗∗

(0.085) (0.085) (0.085)

Median income 0.670∗∗∗ 0.684∗∗∗ 0.671∗∗∗

(0.225) (0.222) (0.225)


(0.211) (0.210) (0.211)


(0.070)


(0.036)

Constant 0.053 0.053 0.053(0.256) (0.250) (0.256)


Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

32

4 CONCLUSION

I posed the research question of how naturalization motivated voting behavior, and my hypoth-esis was that it would contribute a positive effect. A recent survey by the PewResearchCenter(Gonzalez-Barrera, et al., 2013) found that “almost one-in-five (18%) of naturalized Latino im-migrants cite civil and legal rights as their main reason for obtaining U.S. citizenship.” Amongall possible responses, political rights had the highest frequency. Yet the literature has notshown naturalization to lead to the exercise of those rights (Tam Cho, 1999; DeSipio, 1996;Ramakrishnan & Espenshade, 2001). The gap between apparent reasons for naturalizationand low electoral participation provokes my research question.

Both naturalization and voting require resources sufficient to overcome the costs and bar-riers embedded in their processes. Social networks provide resources to members and thusenable them to surmount costs and other barriers. Hispanics in ethnic enclaves and Hispanicmigrants both have access to strong social networks. Therefore I posit that in geographieswhere these networks have beaten trails toward naturalization or plugged into political causes,naturalization and voting are more likely. Since naturalization and political rights are inter-twined, I expect these networks to overlap.

The use of multilevel modeling enabled me to shed considerable light on this problem. Ishowed that, when metropolitan contexts were allowed to vary, naturalization had a positiveinfluence on voting propensity, both when the individual was naturalized and when indi-viduals lived in cities with social networks the naturalization efforts, proxied by percent ofnaturalized citizens in each metro area.

My secondary research question regarded how residential patterns determine voting behav-ior. I posed the question of whether or not the presence of ethnic enclaves led to politicizationof residents, potentially galvanized by local issues or federal districting rules promoting theelection of candidates of the same ethnicity. My hypothesis suggested that isolation andsegregation indices aggregated from census tract to metropolitan level would provide evidenceof a positive association.

This hypothesis did not receive support from my models. However, it was not disproved,either. In most cases, isolation had a very small but significant effect favoring both eligibilityand actual voting. Dissimilarity measures of segregation had significant but even odds. It maybe, therefore, that my model specifications were deficient in design for the investigation ofthis research question.

33

A number of the counties comprising the metropolitan areas in question register high levelsof isolation, such as Bexar, Cameron, Dallas, Edinburg, El Paso, Harris, Nueces, and Victoria;most others have moderate levels, including Travis, Williamson, Bell, and McLennan.

It is possible that the CPS geographies, which include multi-county MSAs but no data oncounties or tracts, were inadequate for approaching this research question. The task of inves-tigating the connection between ethnic enclaves and electoral empowerment must for themoment remain ahead.

4.1 REFERENCES

Calculation Formula for Segregation Measures. (n.d.).Retrieved April 15, 2014, from http://enceladus.isr.umich.edu/

DeSipio, Louis. “Making citizens or good citizens? Naturalization as a predictor of organi-zational and electoral behavior among Latino immigrants.” Hispanic Journal of BehavioralSciences18.2 (1996): 194-213.

Gonzalez-Barrera, A., Lopez, M. H., Passel, J., & Taylor, P. (2012, November 14). An Awak-ened Giant. Pew Research Centers Hispanic Trends Project RSS. Retrieved March 18, 2014,from http://www.pewhispanic.org/2012/11/14/an-awakened-giant-the-hispanic-electorate-is-likely-to-double-by-2030/

34

Gonzalez-Barrera, A., Lopez, M. H., Passel, J., & Taylor, P. (2013, February 4). The Path NotTaken. Pew Research Centers Hispanic Trends Project RSS. Retrieved February 23, 2014, fromhttp://www.pewhispanic.org/2013/02/04/the-path-not-taken/

Gonzalez-Barrera, A., and Lopez, M. H. (2013, June 3). The Path Not Taken. Pew Research Cen-ters Hispanic Trends Project RSS. Retrieved May 12, 2014, from http://www.pewhispanic.org/files/2013/05/the-latino-electorate_2013-06.pdf

Hagan, Jacqueline Maria. “Social networks, gender, and immigrant incorporation: Resourcesand constraints.” American sociological review(1998): 55-67.

Levin, Ines. “Political Inclusion of Latino Immigrants Becoming a Citizen and Political Partici-pation.” American Politics Research41.4 (2013): 535-568.

Massey, Douglas S., and Nancy A. Denton. “The dimensions of residential segregation.” Socialforces67.2 (1988): 281-315.

Ramakrishnan, S. Karthick, and Thomas J. Espenshade. “Immigrant Incorporation and Politi-cal Participation in the United States.” International Migration Review35.3 (2001): 870-909.

Riosmena, Fernando. “At the Edge of US Immigration’s ‘Halt of Folly:’ Data, Information, andResearch Needs in the Event of Legalization.” Journal on Migration and Human Security1.4(2013): 148-162.

Sokhey, Anand Edward, and Scott D. McClurg. “Social networks and correct voting.” TheJournal of Politics74.03 (2012): 751-764.

Sparks, C. (2014). Measuring Residential Segregation using R: So Long to Factfinder. RetrievedMay, 2014, from http://spatialdemography.org/

Tam Cho, Wendy K. “Naturalization, socialization, participation: Immigrants and (non-) vot-ing.” The Journal of Politics61.04 (1999): 1140-1155.

Viruell-Fuentes, Edna A., et al. “Contextualizing nativity status, Latino social ties, and ethnicenclaves: an examination of the ‘immigrant social ties hypothesis’.” Ethnicity & health18.6(2013): 586-609.

Z. Liang, Social Contact, Social Capital, and the Naturalization Process: Evidence From Six Im-migrant Groups, Social Science Research, Volume 23, Issue 4, December 1994, Pages 407-437,ISSN 0049-089X,http://www.sciencedirect.com/science/article/pii/S0049089X84710167

35

R CODE

vote <− read . xport ("~/Documents/Demography/ Spring2014 /Dem7283/ FinalPaper/Data/CPS/ vote /nov12pub . xpt " )

nams <− names( vote )names( vote ) <− tolower (nams)rm(nams)cbsa <− readShapePoly ("~/Documents/Demography/ Spring2014 /Dem7283/

FinalPaper /map/ tl_2012_us_cbsa . shp " )nams <− names( cbsa )names( cbsa ) <− tolower (nams)rm(nams)USCBSA<−read . dbf ("~/Documents/Demography/ Spring2014 /Dem7283/ FinalPaper /

map/ tl_2012_us_cbsa . dbf " )nams <− names(USCBSA)names(USCBSA) <− tolower (nams)rm(nams)

texas . cbsa<−subset (USCBSA, cbsafp =="11100"| cbsafp =="12420"| cbsafp=="13140"| cbsafp =="15180"| cbsafp =="18580"| cbsafp =="19100"| cbsafp=="21340"| cbsafp =="26420"| cbsafp=="28660" | cbsafp =="29700"| cbsafp=="30980"| cbsafp =="31180"| cbsafp =="32580"| cbsafp =="33260"| cbsafp=="41700"| cbsafp =="47020"| cbsafp =="47380")

#Texas , adult c i v i l i a n s i s prpertyp 2 , c i t i z e n and non c i t i z e nvote12<−subset ( vote , ( g e s t f i p s ==48&hrhtype%in %(1:8) ) )

rm( vote )

# c i v i c THIS IS ONLY FOR CITIZENSvote12$voted <− recode ( vote12$pes1 , recodes ="1=1;2=0; e lse =NA" , as . f a c t o r

=F) #votedvote12$hispv <− i f e l s e ( vote12$voted==1&vote12$pehspnon ==1 ,1 ,0) #hisp &

votedvote12$nhispv <− i f e l s e ( vote12$voted==1&vote12$pehspnon ==2 ,1 ,0) #nhisp &

votedvote12$nhwv <− i f e l s e ( vote12$voted==1&vote12$pehspnon==2&vote12$ptdtrace

==1 ,1 ,0)vote12$blackv <− i f e l s e ( vote12$voted==1&vote12$pehspnon==2&

vote12$ptdtrace ==2 ,1 ,0)vote12$asianv <− i f e l s e ( vote12$voted==1&vote12$pehspnon==2&

36

vote12$ptdtrace ==4 ,1 ,0)vote12$otherv <− i f e l s e ( vote12$voted==1&vote12$pehspnon==2&

vote12$ptdtrace%in%c ( 3 , 5 : 2 6 ) , 1 , 0 )

# t h i s i s for the entire universevote12$hisp_elig <− i f e l s e ( vote12$pehspnon==1&vote12$prtage >=18&

vote12$prcitshp ! = 5 , 1 , 0 )vote12$hisp_minor <− i f e l s e ( vote12$pehspnon==1&vote12$prtage <18&

vote12$prcitshp ! = 5 , 1 , 0 )vote12$elig <− i f e l s e ( vote12$prtage >=18&vote12$prcitshp ! = 5 , 1 , 0 )

# n a t i v i t yvote12$fb <− i f e l s e ( vote12$penatvty >=100, 1 ,0)

#Edited universe nonvoters who are registered but vote0 /NA/NR plus votedvote12$regd <− i f e l s e ( vote12$pes2==1 | vote12$voted ==1 , 1 ,0) #

regi steredvote12$hispr <− i f e l s e ( vote12$regd==1&vote12$pehspnon ==1 ,1 ,0) #reg hispvote12$nhispr <− i f e l s e ( vote12$regd==1&vote12$pehspnon ==2 ,1 ,0) #reg

nhisp

# cit izenship PRPERTYP 1 ,2 ,3 in other words a l l people in a l l hhsvote12$cit <− as . f a c t o r ( recode ( vote12$prcitshp , recodes ="1:3= ’ Citizen ’ ;

4= ’ Naturalized ’ ; 5= ’ Noncitizen ’ " ) )vote12$usnative <− i f e l s e ( vote12$prcitshp%in%c ( 1 : 3 ) , 1 , 0 ) #booorn in the

USA . . . or of us parents abroadvote12$prico <− i f e l s e ( vote12$prcitshp ==2 ,1 ,0) #puerto rico or other us

t e r r i t o r y c i t i z e nvote12$usnatur <− i f e l s e ( vote12$prcitshp ==4 ,1 ,0) # naturalized c i t i z e nvote12$citizen <− i f e l s e ( vote12$prcitshp%in%c ( 1 : 4 ) , 1 , 0 )

#labor force statusvote12$jobs <− i f e l s e ( vote12$pemlr%in%c ( 3 : 4 ) , 0 , ( i f e l s e ( vote12$pemjot

==2 ,1 ,vote12$pemjnum ) ) )vote12$jobs <− i f e l s e ( vote12$jobs%in%c ( 0 : 4 ) , vote12$jobs ,NA)table ( vote12$jobs )vote12$emp <− i f e l s e ( vote12$pemlr%in%c ( 1 : 2 ) , 1 , 0 )table ( vote12$emp )vote12$ret <− i f e l s e ( vote12$pemlr ==5 ,1 ,0)table ( vote12$ret )vote12$unemp <− i f e l s e ( vote12$pemlr%in%c ( 3 : 4 ) , 1 , 0 )

37

vote12$disab <− i f e l s e ( vote12$pemlr ==6 ,1 ,0)vote12$emp_other <− i f e l s e ( vote12$pemlr ==7 ,1 ,0)vote12$empstat <− recode ( vote12$pemlr , recodes ="1:2= ’Employed ’ ; 3:4= ’

Unemployed ’ ; 5= ’ Retired ’ ; 6 = ’ Disabled ’ ; 7 = ’ Other NILF ’ ; e lse =NA" , as .f a c t o r =T)

vote12$empstat <− r e l e v e l ( vote12$empstat , r e f ="Employed" )

# GTCBSA 5 Metropolitan CBSA FIPS CODE a l l HHs in samplevote12$amarillo <− i f e l s e ( vote12$gtcbsa ==11100, 1 ,0)vote12$austin <− i f e l s e ( vote12$gtcbsa ==12420, 1 ,0)vote12$beaumont <− i f e l s e ( vote12$gtcbsa ==13140, 1 ,0)vote12$brownsville <− i f e l s e ( vote12$gtcbsa ==15180, 1 ,0)vote12$corpus <− i f e l s e ( vote12$gtcbsa ==18580, 1 ,0)vote12$dallas <− i f e l s e ( vote12$gtcbsa ==19100, 1 ,0)vote12$elpaso <− i f e l s e ( vote12$gtcbsa ==21340, 1 ,0)vote12$houston <− i f e l s e ( vote12$gtcbsa ==26420, 1 ,0)vote12$kil leen <− i f e l s e ( vote12$gtcbsa ==28660, 1 ,0)vote12$laredo <− i f e l s e ( vote12$gtcbsa ==29700, 1 ,0)vote12$longview <− i f e l s e ( vote12$gtcbsa ==30980, 1 ,0)vote12$lubbock <− i f e l s e ( vote12$gtcbsa ==31180, 1 ,0)vote12$mcallen <− i f e l s e ( vote12$gtcbsa ==32580, 1 ,0)vote12$midland <− i f e l s e ( vote12$gtcbsa ==33260, 1 ,0)vote12$sanantonio <− i f e l s e ( vote12$gtcbsa ==41700, 1 ,0)vote12$victoria <− i f e l s e ( vote12$gtcbsa ==47020, 1 ,0)vote12$waco <− i f e l s e ( vote12$gtcbsa ==47380, 1 ,0)

# puts them a l l in one variablevote12$metros <− recode ( vote12$gtcbsa , recodes= "11100= ’ Amarillo ’ ;

12420= ’ Austin ’ ; 13140= ’Beaumont ’ ; 15180= ’ Brownsville ’ ; 18580= ’Corpus’ ; 19100= ’ Dallas ’ ; 21340= ’ElPaso ’ ; 26420= ’Houston ’ ;28660= ’ Kil leen ’ ;29700= ’Laredo ’ ; 30980= ’Longview ’ ; 31180= ’Lubbock ’ ; 32580= ’McAllen ’ ;33260= ’Midland ’ ; 41700= ’SanAntonio ’ ; 47020= ’ Victoria ’ ; 47380= ’Waco ’ ;

e lse = ’Non−Metro ’ " , as . f a c t o r . r e s u l t =T)vote12$metros <− r e l e v e l ( vote12$metros , r e f ="Non−Metro " )vote12$metarea <− i f e l s e ( vote12$gtcbsa ==0 ,NA, vote12$gtcbsa )#metstatusvote12$city <− i f e l s e ( vote12$gtcbsast ==1 ,1 ,0)vote12$burb <− i f e l s e ( vote12$gtcbsast ==2 ,1 ,0)

#incomevote12$rich <− i f e l s e ( vote12$hefaminc%in%c ( 1 5 : 1 6 ) , 1 , 0 ) #100k or more fam

inc

#the next recode divides the 16 al located categories into q u a r t i l e s by

38

nr of HH more or l e s svote12$inc <− as . f a c t o r ( vote12$hefaminc )vote12$incq <− as . f a c t o r ( recode ( vote12$hefaminc , recodes =" 1 :7 = ’ 1 ’ ;

8 : 1 1 = ’ 2 ’ ; 12 :14= ’3 ’ ;15 :16= ’4 ’") )vote12$incq <− r e l e v e l ( vote12$incq , r e f =1)vote12$poor <− i f e l s e ( vote12$hefaminc%in%c ( 1 : 9 ) , 1 , 0 )vote12$lmid <− i f e l s e ( vote12$hefaminc%in%c ( 1 0 : 1 2 ) , 1 , 0 )vote12$umid <− i f e l s e ( vote12$hefaminc%in%c ( 1 3 : 1 4 ) , 1 , 0 )

#agevote12$age <− ( vote12$prtage )vote12$agec <− cut ( vote12$prtage , breaks = c (18 ,25 ,40 ,50 ,65 ,85) ) #age

breaksvote12$agec <− r e l e v e l ( vote12$agec , r e f =3)vote12$elderly <− i f e l s e ( vote12$prtage >=65 ,1 ,0) #too old to drivevote12$young <− i f e l s e ( vote12$prtage <18 ,1 ,0) #too young to votevote12$lt25 <− i f e l s e ( vote12$prtage>17&&vote12$prtage <25 ,1 ,0)vote12$lt39 <− i f e l s e ( vote12$prtage>24&&vote12$prtage <40 ,1 ,0)vote12$lt50 <− i f e l s e ( vote12$prtage>39&&vote12$prtage <50 ,1 ,0)vote12$lt65 <− i f e l s e ( vote12$prtage>49&&vote12$prtage <65 ,1 ,0)vote12$ge65 <− i f e l s e ( vote12$prtage >64 ,1 ,0)

#sexvote12$female <− i f e l s e ( vote12$pesex ==2 ,1 ,0)vote12$male <− i f e l s e ( vote12$pesex ==1 ,1 ,0)

#agevote12$adult <− i f e l s e ( vote12$prtage >17 ,1 ,0)

# maritalvote12$marstat <− recode ( vote12$pemaritl , recodes ="1:2= ’ Married ’ ; 3= ’

Widowed’ ; 4 = ’ Divorced ’ ; 5 = ’ Separated ’ ; 6 = ’ NeverMarried ’ ; e lse=NA" , as .f a c t o r . r e s u l t =TRUE)

vote12$marstat <− r e l e v e l ( vote12$marstat , r e f = ’ Married ’ )vote12$married <− i f e l s e ( vote12$pemaritl%in%c ( 1 , 2 ) , 1 , 0 )vote12$single <− i f e l s e ( vote12$pemaritl ==6 ,1 ,0)vote12$widow <− i f e l s e ( vote12$pemaritl ==3 ,1 ,0)vote12$div <− i f e l s e ( vote12$pemaritl ==4 ,1 ,0)vote12$sep <− i f e l s e ( vote12$pemaritl ==5 ,1 ,0)

#educvote12$grad <− i f e l s e ( vote12$peeduca%in%c ( 4 3 : 4 6 ) , 1 , 0 ) #BA or highervote12$educ<−recode ( vote12$peeduca , recodes ="31:34= ’0 lths ’ ; 35:38= ’1

somehs ’ ; 39= ’2hsgrad ’ ; 40:42= ’3somecol ’ ; 43= ’4BA ’ ; 44= ’5Masters ’ ;

39

45:46= ’6 postgrad ’ ; e lse =NA" , as . f a c t o r . r e s u l t =T)vote12$educ<−r e l e v e l ( vote12$educ , r e f = ’2 hsgrad ’ )vote12$hs <− i f e l s e ( vote12$peeduca ==39 ,1 ,0)vote12$lths <− i f e l s e ( vote12$peeduca <39 ,1 ,0)vote12$col <− i f e l s e ( vote12$peeduca%in%c (40 ,41 ,42) , 1 , 0 )

#racevote12$hisp <− i f e l s e ( vote12$pehspnon ==1 ,1 ,0)vote12$nhisp <− i f e l s e ( vote12$pehspnon ==2 ,1 ,0)vote12$white <− i f e l s e ( vote12$pehspnon==2&vote12$ptdtrace ==1 ,1 ,0)vote12$black <− i f e l s e ( vote12$pehspnon==2&vote12$ptdtrace ==2 ,1 ,0)vote12$asian <− i f e l s e ( vote12$pehspnon==2&vote12$ptdtrace ==4 ,1 ,0)vote12$other <− i f e l s e ( vote12$pehspnon==2&vote12$ptdtrace%in%c ( 3 , 5 : 2 6 )

, 1 , 0 )vote12$pr <− i f e l s e ( vote12$penatvty ==073 ,1 ,0) #puerto rican

#householdvote12$biz <− i f e l s e ( vote12$hubus ==1 ,1 ,0)vote12$hhid <− vote12$hrhhidvote12$hhinc <− as . f a c t o r ( vote12$hefaminc )vote12$hhmar <− i f e l s e ( vote12$hrhtype%in %(1:2) , 1 , 0 )vote12$hhnmar <− i f e l s e ( vote12$hrhtype%in %(3:4) , 1 , 0 )vote12$hhindiv <− i f e l s e ( vote12$hrhtype%in %(6:8) , 1 , 0 )

#languagevote12$span <− i f e l s e ( vote12$huspnish ==1 ,1 ,0) #only spanish at home#housing tenurevote12$rent <− i f e l s e ( vote12$hetenure ==2 ,1 ,0)vote12$own <− i f e l s e ( vote12$hetenure ==1 ,1 ,0)vote12$occup <− i f e l s e ( vote12$hetenure ==3 ,1 ,0)#telephonevote12$tel <− i f e l s e ( vote12$hetelhhd ==1 ,1 ,0)

#weights j u s t s ick of typing pwsswgt#vote12$weights <− vote12$pwsswgt

# subsetting for hispanicvote12h <− subset ( vote12 , hisp ==1)

#survey designdes<−svydesign ( ids =~1 , weights=~pwsswgt , data=vote12 )desh<−svydesign ( ids =~1 , weights=~pwsswgt , data=vote12h )hdes <−svydesign ( ids =~1 , weights=~hwhhwgt, data=vote12 )hdesh <−svydesign ( ids =~1 , weights=~hwhhwgt, data=vote12h )

40

head ( cbsa$geoid )length ( cbsa$cbsafp )head (cbsa$name )head ( cbsa$csafp )

met<−cbsa@data [ , c ( " csafp " ," cbsafp " ," geoid " ,"name" ," i n t p t l a t " ," intptlon" ) ]

met$metarea <− met$cbsafp

names(met)names( vote12 )vote12 <− merge ( vote12 , met , by . x="metarea " , by . y="metarea " , a l l . x=T)

#some de s cr i p t i v e s design−adjustedsummary( round ( svytable (~ hisp+nhisp , des ) , d i g i t s =0) )summary( round ( svytable (~ hisp_el ig +metarea , des ) , d i g i t s =0) )summary( round ( svytable (~ hisp+metarea , des ) , d i g i t s =0) )round ( svytable (~hefaminc , des ) )round ( svytable (~hefaminc , desh ) )round ( svytable (~hefaminc , des ) )round ( svytable (~hefaminc , desh ) )round ( svytable (~male , des ) )round ( svytable (~male , desh ) )round ( svytable (~ marstat , des ) )round ( svytable (~ marstat , desh ) )round ( svytable (~ hetenure , des ) )round ( svytable (~ hetenure , desh ) )round ( svytable (~empstat , des ) )round ( svytable (~empstat , desh ) )round ( svytable (~ white , des ) )round ( svytable (~ black , des ) )round ( svytable (~ asian , des ) )round ( svytable (~ other , des ) )round ( svytable (~educ , des ) )round ( svytable (~educ , desh ) )round ( svytable (~ white , des ) )round ( svytable (~ black , des ) )round ( svytable (~ asian , des ) )round ( svytable (~ other , des ) )round ( svytable (~ hisp+adult , des ) )round ( svytable (~ white+adult , des ) )round ( svytable (~ black+adult , des ) )round ( svytable (~ asian+adult , des ) )

41

round ( svytable (~ other+adult , des ) )round ( svytable (~ female+ c i t , des ) )sink ("~/R/metros . csv " , append=TRUE, s p l i t =TRUE)round ( svytable (~metros , desh ) )round ( svytable (~ metros+adult , desh ) )round ( svytable (~ metros+male , desh ) )round ( svytable (~ metros+incq , desh ) )round ( svytable (~ metros+cit izen , desh ) )sink ( )round ( svytable (~ metros+hisp_elig , des ) )round ( svytable (~ metros+voted , des ) )round ( svytable (~ metros+hispv , des ) )round ( svytable (~ metros+ e l i g , des ) )

#anova on metareas and householdsf i t . an1 <− lm( hi sp_el i g ~ as . f a c t o r ( metarea ) , vote12 )anova ( f i t . an1 )xtable ( anova ( f i t . an1 ) )f i t . an2 <− lm( hi sp_el i g ~ as . f a c t o r ( hhid ) , vote12 )anova ( f i t . an2 )xtable ( anova ( f i t . an2 ) )

pred . e l i g 1 <− svyglm ( hisp_el ig ~incq+biz+hhnmar+hhindiv+female+own+occup+span+ t e l , design=des , family=binomial )

summary( pred . el ig1 , d i g i t s =3)

pred . e l i g 2 <− svyglm ( hisp_el ig ~incq+biz+hhnmar+hhindiv+female+own+occup+span+ t e l , design=desh , family=binomial )

summary( pred . el ig2 , d i g i t s =3)pred . e l i g 2 <− svyglm ( hisp_el ig ~incq+biz+hrhtype+female+own+occup+span+

t e l , design=desh , family=binomial )summary( pred . el ig2 , d i g i t s =3)

stargazer ( pred . el ig1 , pred . el ig2 , d i g i t s =3 , summary=FALSE , apply . coef=exp )

pred . e l i g 3 <− svyglm ( hisp_el ig ~metros , design=des , family=binomial )summary( pred . el ig3 , d i g i t s =3)

pred . e l i g 4 <− svyglm ( hisp_el ig ~metros , design=desh , family=binomial )summary( pred . el ig4 , d i g i t s =3)

42

pred . e l i g 5 <− svyglm ( hisp_el ig ~metros+inc+biz+own+hrhtype+span+ t e l ,design=des , family=binomial )


pred . e l i g 6 <− svyglm ( hisp_el ig ~metros+inc+biz+own+hrhtype+span+ t e l ,design=desh , family=binomial )


stargazer ( pred . el ig3 , pred . el ig4 , pred . el ig5 , pred . el ig6 , d i g i t s =3 ,summary=F , apply . coef=exp )

pred . e l i g 7 <− glmer ( hisp_el ig ~incq+biz+own+occup+hhnmar+hhindiv+span+ t e l+ ( 1 | hhid ) , data=vote12 , family=binomial , weights=pwsswgt/mean(pwsswgt ) )

pred . e l i g 7 <− r e f i t ( pred . e l i g 7 )summary( pred . el ig7 , d i g i t s =3)

pred . e l i g 8 <− glmer ( hisp_el ig ~incq+biz+own+occup+hhnmar+hhindiv+span+ t e l+ ( 1 | hhid ) , data=vote12h , family=binomial , weights=pwsswgt/mean(pwsswgt ) )


stargazer ( pred . el ig7 , pred . el ig8 , d i g i t s =3 , summary=F , apply . coef=exp )

pred . e l i g 9 <− glmer ( hisp_el ig ~incq+biz+own+occup+hhnmar+hhindiv+span+ t e l+ ( 1 | hhid ) , data=vote12 , family=binomial , weights=pwsswgt/mean(pwsswgt ) )


pred . e l ig10 <− glmer ( hisp_el ig ~incq+biz+own+occup+hhnmar+hhindiv+span+t e l + ( 1 | metarea ) , data=vote12 , family=binomial , weights=pwsswgt/mean(pwsswgt ) )

pred . e l ig10 <− r e f i t ( pred . e l ig10 )summary( pred . elig10 , d i g i t s =3)

pred . e l ig11 <− glmer ( hisp_el ig ~incq+biz+own+occup+hhnmar+hhindiv+span+t e l + ( 1 | hhid ) + ( 1 | metarea ) , data=vote12 , family=binomial , weights=pwsswgt/mean( pwsswgt ) )

pred . e l ig11 <− r e f i t ( pred . e l ig11 )

stargazer ( pred . el ig9 , pred . elig10 , pred . elig11 , d i g i t s =3 , summary=F ,

43

apply . coef=exp )

#Metro−l e v e l indicators z−scored , merged#met_ind <− read . csv ("~/R/ACS/met_ind . csv " )met_ind <− read . csv ("~/Documents/Demography/ Spring2014 /Dem7283/

FinalPaper /ACS2012/met_ind . csv " )met_ind$pct_hz<−scale ( met_ind$pct_h , center=T , scale=T)met_ind$med_agez_h<−scale ( met_ind$med_age_h , center=T , scale=T)met_ind$pct_citz<−scale ( met_ind$pct_cit , center=T , scale=T)met_ind$pct_ownz<−scale ( met_ind$pct_own , center=T , scale=T)met_ind$pct_hsplusz<−scale ( met_ind$pct_hsplus , center=T , scale=T)met_ind$no_telz<−scale ( met_ind$no_tel , center=T , scale=T)met_ind$pct_engz<−scale ( met_ind$pct_eng , center=T , scale=T)met_ind$pct_pov_hz<−scale ( met_ind$pct_pov_h , center=T , scale=T)met_ind$pct_povz <− scale ( met_ind$pct_pov , center=T , scale=T)met_ind$pct_enr_hz<−scale ( met_ind$pct_enr_h , center=T , scale=T)met_ind$pct_hhmarz<−scale ( met_ind$pct_hhmar , center=T , scale=T)met_ind$pct_q1z<−scale ( met_ind$pct_q1 , center=T , scale=T)met_ind$pct_nomigz<−scale ( met_ind$pct_nomig , center=T , scale=T)met_ind$pct_unempz<−scale ( met_ind$pct_unemp , center=T , scale=T)met_ind$iso_h_z <− scale ( met_ind$isolation_h , center=T , scale=T)met_ind$d_nnh_z <− scale ( met_ind$dissim_nhh , center=T , scale=T)met_ind$pnat_z <− scale ( met_ind$pct_nat , center=T , scale=T)met_ind$medinc_z <− scale ( met_ind$med_inc , center=T , scale=T)met_ind$pct_familyhh_z <− scale ( met_ind$pct_familyhh , center=T , scale=T)met_ind$pct_famhhmar_z <− scale ( met_ind$pct_famhhmar , center=T , scale=T)met_ind$pct_livealone_z <−scale ( met_ind$pct_liv_alone , center=T , scale=T

)met_ind$pct_prin_city_z <− scale ( met_ind$pct_prin_city , center=T , scale=

T)

joindata <−merge ( x=vote12 , y=met_ind , by . x="metarea " , by . y="metarea " , a l l. x=T , incomparables=NA)

pred . e l ig12 <− glmer ( hisp_el ig ~own+occup+hhnmar+hhindiv + ( 1 | metarea ) ,data=joindata , family=binomial , weights=pwsswgt/mean( pwsswgt ) )


pred . e l ig13 <− glmer ( hisp_el ig ~own+occup+hhnmar+hhindiv+pnat_z + ( 1 |

44

metarea ) , data=joindata , family=binomial , weights=pwsswgt/mean(pwsswgt ) )


pred . e l ig14 <− glmer ( hisp_el ig ~own+occup+hhnmar+hhindiv+pnat_z+iso_h_z+ ( 1 | metarea ) , data=joindata , family=binomial , weights=pwsswgt/mean(pwsswgt ) )

pred . e l ig14 <− r e f i t ( pred . e l ig14 )summary( pred . e l ig14 )

pred . e l ig15 <− glmer ( hisp_el ig ~own+occup+hhnmar+hhindiv+pnat_z+d_nnh_z+ ( 1 | metarea ) , data=joindata , family=binomial , weights=pwsswgt/mean(pwsswgt ) )

pred . e l ig15 <− r e f i t ( pred . e l ig15 )summary( pred . e l ig15 )

stargazer ( pred . elig13 , pred . elig14 , pred . elig15 , d i g i t s =3 , apply . coef=exp )

pred . vote1 <− glmer ( hispv~usnatur+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z + ( 1 | metarea ) , data=joindata , family=binomial , weights=pwsswgt/mean( pwsswgt ) )

pred . vote1 <− r e f i t ( pred . vote1 )summary( pred . vote1 , d i g i t s =3)





stargazer ( pred . vote1 , pred . vote2 , pred . vote3 , d i g i t s =3 , apply . coef=exp )rm( joindata . sub )joindata . sub<−subset ( joindata , f i p s =="48375"| f i p s =="48381"| f i p s =="48453"

| f i p s =="48491" | f i p s =="48245" | f i p s =="48061" | f i p s =="48355" |f i p s =="48085" | f i p s =="48113" | f i p s =="48121" | f i p s =="48139" | f i p s=="48251" | f i p s =="48439" | f i p s =="48141" | f i p s =="48039" | f i p s

45

=="48157" | f i p s =="48167" | f i p s =="48201" | f i p s =="48339" | f i p s=="48339" | f i p s =="48027" | f i p s =="48479" | f i p s =="48183" | f i p s=="48303" | f i p s =="48215" | f i p s =="48329" | f i p s =="48029" | f i p s=="48469" | f i p s =="48309")

pred . vote4 <− glmer ( hispv~usnatur+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z + ( 1 | metarea ) , data=joindata . sub , family=binomial ,weights=pwsswgt/mean( pwsswgt ) )


pred . vote5 <− glmer ( hispv~usnatur+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z+iso_h_z + ( 1 | metarea ) , data=joindata . sub , family=binomial , weights=pwsswgt/mean( pwsswgt ) )


pred . vote6 <− glmer ( hispv~usnatur+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z+d_nnh_z + ( 1 | metarea ) , data=joindata . sub , family=binomial , weights=pwsswgt/mean( pwsswgt ) )


stargazer ( pred . vote4 , pred . vote5 , pred . vote6 , d i g i t s =3 , apply . coef=exp )

joindata . sub$dream <− i f e l s e ( ( ( joindata . sub$usnatur=1 | joindata .sub$citizen )&joindata . sub$hisp==1&joindata . sub$prtage>=18&joindata .sub$prtage<30&joindata . sub$fb==1&joindata . sub$pemntvty>=100&joindata. sub$pefntvty >=100) , 1 , 0 )

joindata . sub$gen <− i f e l s e ( ( joindata . sub$hisp==1&joindata . sub$prtage>=18&joindata . sub$fb==0&joindata . sub$pemntvty>=100&joindata .sub$pefntvty >=100) , 1 , 0 )

joindata . sub$dream . n <− i f e l s e ( ( joindata . sub$usnatur==1&joindata .sub$hisp==1&joindata . sub$prtage>=18&joindata . sub$prtage<30&joindata .sub$fb==1&joindata . sub$pemntvty>=100&joindata . sub$pefntvty >=100), 1 , 0 )

joindata . sub$gen . n <− i f e l s e ( ( joindata . sub$usnatur==1&joindata . sub$hisp==1&joindata . sub$prtage>=18&joindata . sub$fb==0&joindata . sub$pemntvty>=100&joindata . sub$pefntvty >=100) , 1 , 0 )

pred . gen1 <− glmer ( hispv~gen+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z + ( 1 | metarea ) , data=joindata . sub , family=binomial , weights=pwsswgt/mean( pwsswgt ) )

46

pred . gen1 <− r e f i t ( pred . gen1 )summary( pred . gen1 , d i g i t s =3)

pred . gen2 <− glmer ( hispv~gen+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z+iso_h_z + ( 1 | metarea ) , data=joindata . sub , family=binomial ,weights=pwsswgt/mean( pwsswgt ) )


pred . gen3 <− glmer ( hispv~gen+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z+d_nnh_z + ( 1 | metarea ) , data=joindata . sub , family=binomial ,weights=pwsswgt/mean( pwsswgt ) )


pred . gen4 <− glmer ( voted~gen+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z + ( 1 | metarea ) , data=joindata . sub , family=binomial , weights=pwsswgt/mean( pwsswgt ) )


pred . gen5 <− glmer ( voted~gen+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z+iso_h_z + ( 1 | metarea ) , data=joindata . sub , family=binomial ,weights=pwsswgt/mean( pwsswgt ) )


pred . gen6 <− glmer ( voted~gen+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z+d_nnh_z + ( 1 | metarea ) , data=joindata . sub , family=binomial ,weights=pwsswgt/mean( pwsswgt ) )


stargazer ( pred . gen4 , pred . gen5 , pred . gen6 , d i g i t s =3 , apply . coef=exp )

stargazer ( pred . gen1 , pred . gen2 , pred . gen3 , d i g i t s =3 , apply . coef=exp )

pred . dream1 <− glmer ( hispv~dream+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z + ( 1 | metarea ) , data=joindata . sub , family=binomial ,weights=pwsswgt/mean( pwsswgt ) )

pred . dream1 <− r e f i t ( pred . dream1)summary( pred . dream1 , d i g i t s =3)

47

pred . dream2 <− glmer ( hispv~dream+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z+iso_h_z + ( 1 | metarea ) , data=joindata . sub , family=binomial , weights=pwsswgt/mean( pwsswgt ) )


pred . dream3 <− glmer ( hispv~dream+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z+d_nnh_z + ( 1 | metarea ) , data=joindata . sub , family=binomial , weights=pwsswgt/mean( pwsswgt ) )


stargazer ( pred . dream1 , pred . dream2 , pred . dream3 , d i g i t s =3 , apply . coef=exp )

pred . gen1 <− glmer ( hispv~gen+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z + ( 1 | metarea ) , data=joindata . sub , family=binomial , weights=pwsswgt/mean( pwsswgt ) )


pred . gen2 <− glmer ( hispv~gen+usnatur+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z+iso_h_z + ( 1 | metarea ) , data=joindata . sub , family=binomial , weights=pwsswgt/mean( pwsswgt ) )


pred . gen3 <− glmer ( hispv~gen . n+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z+d_nnh_z + ( 1 | metarea ) , data=joindata . sub , family=binomial ,weights=pwsswgt/mean( pwsswgt ) )


pred . gen1 <− glmer ( hispv~dream . n+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z + ( 1 | metarea ) , data=joindata . sub , family=binomial ,weights=pwsswgt/mean( pwsswgt ) )


pred . gen2 <− glmer ( hispv~dream . n+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z+iso_h_z + ( 1 | metarea ) , data=joindata . sub , family=binomial , weights=pwsswgt/mean( pwsswgt ) )


48

pred . gen3 <− glmer ( hispv~dream . n+female+own+occup+hhnmar+hhindiv+medinc_z+pnat_z+d_nnh_z + ( 1 | metarea ) , data=joindata . sub , family=binomial , weights=pwsswgt/mean( pwsswgt ) )

pred . gen3 <− r e f i t ( pred . gen3 )summary( pred . gen3 , d i g i t s =3)l i b r a r y ( acs )l i b r a r y ( UScensus2010 )i n s t a l l . t r a c t ( " l inux " )i n s t a l l . county ( " l inux " )l i b r a r y ( UScensus2010tract )l i b r a r y ( UScensus2010county )mydem<−demographics (dem="P0050001 " , s t a t e ="48" , s t a t e f i p s =TRUE, l e v e l ="

t r a c t " )other<−data . frame ( cofips=substr (rownames(mydem) , 1 ,5) , f i p s =rownames(

mydem) , oth1= u n l i s t ( demographics (dem="P0050005 " , s t a t e ="48" , s t a t e f i p s =T , l e v e l =" t r a c t " ) ) , oth2= u n l i s t ( demographics (dem="P0050006 " , s t a t e="48" , s t a t e f i p s =T , l e v e l =" t r a c t " ) ) , oth3= u n l i s t ( demographics (dem="P0050007 " , s t a t e ="48" , s t a t e f i p s =T , l e v e l =" t r a c t " ) ) , oth4= u n l i s t (demographics (dem="P0050008 " , s t a t e ="48" , s t a t e f i p s =T , l e v e l =" t r a c t " ) ) ,oth5= u n l i s t ( demographics (dem="P0050009 " , s t a t e ="48" , s t a t e f i p s =T ,l e v e l =" t r a c t " ) ) )

trdat <−data . frame ( cofips=substr (rownames(mydem) , 1 , 5 ) , f i p s =rownames(mydem) , t o t a l = u n l i s t ( demographics (dem="P0050001 " , s t a t e ="48" , s t a t e f i p s =T ,l e v e l =" t r a c t " ) ) , hispanic= u n l i s t ( demographics (dem="P0050010 " , s t a t e="48" , s t a t e f i p s =T , l e v e l =" t r a c t " ) ) , nonhispanic= u n l i s t ( demographics (dem="P0050002 " , s t a t e ="48" , s t a t e f i p s =T , l e v e l =" t r a c t " ) ) , white= u n l i s t (demographics (dem="P0050003 " , s t a t e ="48" , s t a t e f i p s =T , l e v e l =" t r a c t " ) ) ,black= u n l i s t ( demographics (dem="P0050004 " , s t a t e ="48" , s t a t e f i p s =T ,l e v e l =" t r a c t " ) ) , other=apply ( other [ , 3 : 7 ] , 1 ,sum) )

head ( trdat )trdat <−t rdat [ order ( trdat$cofips , t r d a t $ f i p s ) , ]names( tr dat )<−c ( " cofips " , " f i p s " , " t o t a l " , " hispanic " ," nonhispanic " ,"

white " , " black " , " other " )head ( trdat )

co_total <−tapply ( t r d a t $ t o t a l , trdat$cofips , sum)co_total <− data . frame ( cofips=names( u n l i s t ( co_total ) ) , pop= u n l i s t (

co_total ) )co_wht<−tapply ( trdat$white , trdat$cofips , sum)co_wht<−data . frame ( cofips=names( u n l i s t ( co_wht ) ) , pop= u n l i s t ( co_wht ) )co_hsp<−tapply ( trdat$hispanic , trdat$cofips , sum)co_hsp<− data . frame ( cofips=names( u n l i s t ( co_hsp ) ) , pop= u n l i s t ( co_hsp ) )co_nhsp<−tapply ( trdat$nonhispanic , trdat$cofips , sum)

49

co_nhsp<− data . frame ( cofips=names( u n l i s t ( co_nhsp ) ) , pop= u n l i s t ( co_nhsp ) )co_blk<−tapply ( trdat$black , trdat$cofips , sum)co_blk<− data . frame ( cofips=names( u n l i s t ( co_blk ) ) , pop= u n l i s t ( co_blk ) )co_oth<−tapply ( trdat$other , trdat$cofips , sum)co_oth<−data . frame ( cofips=names( u n l i s t ( co_oth ) ) ,pop= u n l i s t ( co_oth ) )c_pwhite<−co_wht$pop/ co_total$popc_pblack<−co_blk$pop/ co_total$popc_pother<−co_oth$pop/ co_total$popc_phisp <− co_hsp$pop/ co_total$popc_pnhisp <− co_nhsp$pop/ co_total$popcounty_dat<−data . frame ( cofips=co_total$cofips , co_total=co_total$pop ,

co_hsp_total=co_hsp$pop , co_nhsp_total=co_nhsp$pop , co_wht_total=co_wht$pop , co_blk_total=co_blk$pop , co_oth_total=co_oth$pop ,c_pwhite=c_pwhite , c_phisp=c_phisp , c_pnhisp=c_pnhisp , c_pblack=c_pblack , c_pother=c_pother )

county_dat$c_ent<− county_dat$c_pwhite * ( log (1/ county_dat$c_phisp ) ) +county_dat$c_phisp * ( log (1/ county_dat$c_phisp ) ) +county_dat$c_pnhisp * (log (1/ county_dat$c_pnhisp ) ) +county_dat$c_pblack * ( log (1/county_dat$c_pblack ) ) +county_dat$c_pother * ( log (1/ county_dat$c_pother) )

county_dat$c_ent<− i f e l s e ( i s . na ( county_dat$c_ent ) ==T , 0 , county_dat$c_ent )merged<−merge ( x=trdat , y=county_dat , by=" cofips " , a l l . x=T )head (merged)

# d i s s i m i l a r i t y w/h nonh/hmerged$d .wh<−.5*( abs ( merged$white/merged$co_wht_total − merged$hispanic/

merged$co_hsp_total ) )merged$d . nhh<−.5*( abs ( merged$nonhispanic/merged$co_nhsp_total −

merged$hispanic/ merged$co_hsp_total ) )dissim .wh. tr <−tapply ( merged$d .wh, merged$cofips , sum, na .rm=T)head ( dissim .wh. t r )dissim . nhh . tr <−tapply ( merged$d . nhh, merged$cofips , sum, na .rm=T)head ( dissim . nhh . t r )

# interact ion index wh nhhmerged$int .wh<−(merged$hispanic/ merged$co_hsp_total * merged$white/

merged$total )merged$int . nhh<−(merged$nonhispanic/merged$co_nhsp_total * merged$white/

merged$total )i n t .wh. tr <−tapply ( merged$int .wh, merged$cofips , sum, na .rm=T)head ( i n t .wh. t r )i n t . nhh . tr <−tapply ( merged$int . nhh, merged$cofips , sum, na .rm=T)head ( i n t . nhh . t r )

50

# i s o l a t i o n indexmerged$iso . h<−(merged$hispanic/ merged$co_hsp_total * merged$hispanic/

merged$total )i s o l . h . tr <−tapply ( merged$iso . h , merged$cofips , sum, na .rm=T)head ( i s o l . h . t r )merged$iso . nh<−(merged$nonhispanic/merged$co_nhsp_total *

merged$nonhispanic/ merged$total )i s o l . nh . tr <−tapply ( merged$iso . h , merged$cofips , sum, na .rm=T)

# t h e i l e entropymerged$tr_pwhite<−merged$white/ merged$totalmerged$tr_pblk<−merged$black/ merged$totalmerged$tr_pother<−merged$other/ merged$totalmerged$tr_phisp<−merged$hispanic/ merged$totalmerged$tr_pnhisp<−merged$nonhispanic/ merged$totalmerged$tr_ent<−merged$tr_pwhite * ( log (1/ merged$tr_pwhite ) ) +

merged$tr_phisp * ( log (1/ merged$tr_phisp ) ) +merged$tr_pnhisp * ( log (1/merged$tr_pnhisp ) ) +merged$tr_pblk * ( log (1/ merged$tr_pblk ) ) +merged$tr_pother * ( log (1/ merged$tr_pother ) )

merged$tr_ent<− i f e l s e ( i s . na ( merged$tr_ent ) ==T , 0 , merged$tr_ent )head (merged)

merged$Hcalc <− ( merged$total * ( merged$c_ent−merged$tr_ent ) ) /(merged$co_total * merged$c_ent )

merged$Hcalc<− i f e l s e ( i s . na ( merged$Hcalc ) ==T , 0 , merged$Hcalc )hindex<−u n l i s t ( tapply ( merged$Hcalc , merged$cofips , sum) )

county_dat<−data . frame ( cofips=names( u n l i s t ( dissim .wh. t r ) ) , dissim_wh=u n l i s t ( dissim .wh. t r ) , dissim_nhh= u n l i s t ( dissim . nhh . t r ) , isolat ion_h=i s o l . h . tr , isolation_nh= i s o l . nh . tr , interaction_wh= i n t .wh. tr ,interaction_nhh= i n t . nhh . tr , TheileH=hindex )

head ( county_dat )summary( county_dat [ , 2 : 8 ] )stargazer ( county_dat , d i g i t s =3)

# plotspar (mfrow=c ( 2 , 2 ) )h i s t ( county_dat$dissim_wh , main="White−Hispanic D i s s i m i l a r i t y " , xlab ="

Index Score " )h i s t ( county_dat$isolation_h , main="Hispanic I s o l a t i o n " , xlab ="Index

Score " )h i s t ( county_dat$interaction_wh , main="White−Hispanic Interaction " , xlab

="Index Score " )

51

h i s t ( county_dat$dissim_nhh , main="Non−Hispanic−Hispanic D i s s i m i l a r i t y " ,xlab ="Index Score " )

h i s t ( county_dat$isolation_nh , main="Non−Hispanic I s o l a t i o n " , xlab ="IndexScore " )

h i s t ( county_dat$interaction_nhh , main="Non−Hispanic−Hispanic Interaction" , xlab ="Index Score " )

data ( " texas . county10 " )head ( texas . county10 )tx_sub<−texas . county10 [ , c ( 1 : 6 ) ]t x _ a t t r <−tx_sub@datamerge2<−merge ( x= t x _ a t t r , y=county_dat , by . x=" f i p s " , by . y=" cofips " , a l l . x

=T , sort=F)tx_sub@data<−merge2# tex . met <− tex . met <− readShapePoly ("~/Documents/Demography/ Spring2014 /

Dem7283/ FinalPaper /map/320_M2. shp " )# tex . met_attr<−tex . met@data#merge2<−merge ( x=tex . met_attr , y=county_dat , by . x=" f i p s " , by . y=" cofips " ,

a l l . x=T , sort=F)#tx_sub@data<−merge2

brks <−quantile ( tx_sub$isolation_h , probs=c ( 0 , . 2 5 , . 5 , . 7 5 , 1) )cols <−brewer . pal (n=5 , name="Reds " )png( filename ="~/Documents/Demography/ Spring2014 /Dem7283/ FinalPaper /map/

TexasSegmap . png" , res =150 , width=900 , height =900)dev . o f f ( )spplot ( obj=tx_sub , zcol =" isolat ion_h " , at=brks , col . regions=cols , main

="Hispanic I s o l a t i o n Index , 2010")

brks <−quantile ( tx_sub$isolation_h , probs=c ( 0 , . 2 5 , . 5 , . 7 5 , 1) )cols <−brewer . pal (n=5 , name="Reds " )png( filename ="~/Documents/Demography/ Spring2014 /Dem7283/ FinalPaper /map/

TexasIsoHmap . png" , res =150 , width=900 , height =900)dev . o f f ( )spplot ( obj=tx_sub , zcol =" isolat ion_h " , at=brks , col . regions=cols , main

="Hispanic I s o l a t i o n Index , 2010")

l i b r a r y ( spdep )nbs<−poly2nb ( tx_sub , queen=T)wts<−nb2listw ( nbs , s t y l e ="W" )

52

moran_iso_h <− moran .mc( tx_sub$isolation_h , l i s t w =wts , nsim=999)geary_d_wh <− geary .mc( x=as . numeric ( tx_sub$dissim_wh ) , l i s t w =wts , nsim

=999)moran_iso_h

head ( merge2 )merge3 <− subset ( merge2 , f i p s%in%c

(48375 ,48381 ,48453 ,48491 ,48245 ,48061 ,48355 ,48085 ,48113 ,48121 ,48139 ,48251 ,48439 ,48141 ,48039 ,48157 ,48167 ,48201 ,48339 ,48339 ,48027 ,48479 ,48183 ,48303 ,48215 ,48329 ,48029 ,48469 ,48309))

head ( merge3 )met_ind <− merge ( x=met_ind , y=merge3 , by . x=" f i p s " , by . y=" f i p s " , a l l . x=T ,

sort=F)seg_ind <− merge3

cbsa <− subset ( cbsa ,names( cbsa )mdat . texas <− cbsa@datamdat <− cbsa@datamdat$metarea <− mdat$cbsafpmdat<−merge (mdat , f i t t e d , by . x="metarea " , by . y="metarea " , a l l . x=T , sort=

F)head (mdat)names(mdat)mdat <− complete . cases (mdat , texas )cbsa@data <− mdatspplot ( cbsa , "ehv " , at=quantile ( cbsa$estehv , na .rm=T) , main="My

Estimates " )cbsa <− complete . cases ( cbsa )

pred . e l ig11 <− glmer ( hisp_el ig ~metros+incq+biz+own+occup+hhnmar+hhindiv+span+ t e l + ( 1 | hhid ) + ( 1 | metarea ) , data=vote12 , family=binomial , weights=pwsswgt/mean( pwsswgt ) )

pred . e l ig11 <− r e f i t ( pred . e l ig11 )

stargazer ( pred . el ig9 , pred . elig10 , pred . elig11 , d i g i t s =3 , summary=F ,apply . coef=exp )

53

#Here I find predicted values for the counties with no individual data ,but with observed county variables

f i t t e d <−predict ( f i t . 1 , newdata=othercos [ , c ( " s t a t e " , " cofips " , "povz " , "vachousz " , "baccz " , " blackz " , " hispanicz " , " region " ) ] , allow .new.l e v e l s =T , type="response " )

#form a data frame with the f i t t e d values , remember , the estimates areprevalences

f i t t e d <−data . frame ( cofips=othercos$cofips , s t a t e =othercos$state ,estobese =100* f i t t e d )

head ( f i t t e d )

othercos<−merged

othercos$msa<−othercos$gtbsageodat<− readShapePoly ("~/R/maps/ tl_2012_us_cbsa . shp " )l i b r a r y ( RColorBrewer )spplot (merged , "pct_h " , at=quantile ( merged$pct_h , p=c ( 0 , . 2 5 , . 5 , . 7 5 , 1)

, na .rm=T) )l i b r a r y ( maptools )f i t t e d <−predict ( pred . e l i g .m, newdata=othercos [ , c ( " gtcbsa " ," no_tel " , "

pct_h " , "pct_pov " , "pct_eng " ) ] , allow .new. l e v e l s =T , type="response " )

met_ind$county . xtable ( met_ind$Met , met_ind$county . x )met_ind$Met

54