The Effect of proximity to African-Americans on Latinovote choice in the 2008 Presidential Primary in Los

Angeles

Ryan D. Enos1

1Department of Political Science, University of California, Los Angeles; renos@ucla.edu.

Abstract

The 2008 Presidential Election allows for new tests of the Racial Threat hypothesis (Key1949). In Los Angeles County, precincts that are overwhelmingly Latino are in close prox-imity to precincts that are overwhelmingly African American. These are conditions thatmay prime racial threat. If racial threat exists, then presumably it would have affected theprobability that a non-Black voter cast a ballot for Barack Obama in the 2008 PresidentialPrimary.

I use Census data and the California voter file to perform a Bayesian analysis of surnamesto determine the race/ethnicity of individual voters in Los Angeles County. I also geocodevoters and precincts in order to measure the spatial distance between precincts. I argue thatan observable implication of racial threat is that proximity to the source of threat shouldcondition its effect. I show that Latino support for Obama was negatively correlated withproximity to African Americans. The unit of analysis is precincts, but hyper-segregatedresidential patterns in Los Angeles mean there is little concern that an ecological fallacy isdriving the results.

In this paper, I use the recent presidential election to test implications of group threat

(Key 1949). The high-profile candidacy of Barack Obama and new technologies and avail-

ability of data allows for tests of group threat in situations where it was not before possible. I

assert that an observable implication of group threat is that threat should vary in proportion

to distance from the source of the threat.

I test this implication by looking for a relationship, among Latinos, between proximity to

African Americans and support for Barack Obama in the 2008 Presidential Primary in Los

Angeles County. Group threat has been widely studied with inconsistent results. However,

most studies have only involved the threat whites perceive from African Americans. The

electoral importance of Latinos calls for better understanding of how group threat affects

Latino electoral behavior. My findings suggest that Latinos did become less likely to vote for

Obama as their proximity to Blacks increased. This is evidence for a group threat mechanism

affecting Latino vote choice.

1 Racial/Group Threat

Group threat hypotheses assert that individuals from a given group are politically motivated

by the presence (usually geographic) of another group. group threat narrows this to racial

groups and has largely been studied in the context of white reactions to African Americans

in the United States. Group threat can have several different behavioral and attitudinal

consequences, but most studies focus on political participation in the form of voter turnout.

Key (1949) observed that white voters in Southern counties with a high proportion of

African Americans voted at higher rates than whites in counties with fewer African Ameri-

cans. Blumer (1958) proposed a general theory of power-threat behavior in which individ-

ual racial animosity is a function of out-group threats to the dominance of an individuals

social position. Since these initial theoretical forays, there have been many studies of both

the attitudinal and behavioral manifestations of group threat.

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Keys (1949) findings were based on simple correlations. Since then, political scientists

have attempted to apply more rigorous methods to test Keys hypothesis, with conflicting

results. Matthews & Prothro (1963) find that levels of white voter registration at the county

level in the South is related to levels of African American voter registration. Several stud-

ies expand Keys hypothesis to consider, not only turnout, but vote choice as well. Carsey

(1995) uses exit polls and finds that the proportion of African Americans at the precinct

level is positively correlated with white support of an African American candidate. Giles &

Buckner (1993) use aggregate election results and claim that proximity to African Americans

at the county level made whites in Louisiana more likely to vote for the openly racist David

Duke. Voss (1996) criticizes Giles & Buckners (1993) methodology and finds no relation-

ship between African American proximity and Duke support. Glaser (1994) finds that the

proportion African American at the county level is negatively correlated with white support

for racially liberal public policy.

With the increasing electoral importance of minority groups other than African Ameri-

cans, an important update to these tests comes from Leighley & Vedlitz (1999). They include

Asian Americans and Mexican Americans in their models, in addition to whites and African

Americans. However, their results do not necessarily bring clarity to the debate. They use

individual survey data and find that, in Texas, group threat, as measured by proportion of

all out-groups at the zip code level, has a negative effect on white participation, and no effect

on African American, Asian American, or Mexican American participation.

Theories of group threat and voter mobilization usually presume that threat is caused

by group animosity. Studies of the effects of proximate out-groups on racial animosity are

mixed. Fossett & Kiecolt (1989), Quillian (1995), Taylor (1998), and Wright (1977) find that

whites out-group animosity is increased by larger percentages of out-group neighbors, while

Brewer & Miller (1988), Ellison & Powers (1994), Fitzpatrick & Hwang (1992), Sigelman &

Welch (1993), and Welch, Sigelman, Bledsoe, & Combs (2001) find the opposite. Notably,

Oliver & Wong (2003) find a negative correlation between racial animosity and out-group

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proximity and, when published, this piece was one of the only studies to measure the attitudes

of non-white respondents. Gay (2006), in a study of African Americans and Latinos in Los

Angeles, finds negative stereotypes by African Americans towards Latinos are conditioned

by economic inequality between the groups.

That observational studies have such mixed findings may be partially a result of diffi-

culties arising from the Modifiable Areal Unit Problem. This is simply that inference from

aggregate geographic units may be sensitive to the choice of unit by the researcher. For

example, would Keys (1949) findings have been different if he had used cities instead of

counties. Additionally, given the inconsistencies between studies that use individual level

data, it is easy to suspect that these studies suffer from omitted variable bias and other

model specification problems. Not only is role of group threat from African Americans on

Latinos remain understudied, but the effect of group threat on vote choice, in general, is also

poorly understood. In previous work, I have attempted to address these problems. Enos

(2009c) exploits a natural experiment and shows that African American proximity is causally

connected to white turnout. And Enos (2009a) uses a randomized field experiment and finds

evidence of group threat between African Americans and Latinos. However, the effects of

this treatment are small and inconclusive.

1.1 Testing for group threat

One way to test for the presence of group threat, and help to resolve the inconsistencies, is

to look for evidence of the observable implications of group threat. One of these implications

is that threat should be conditional on proximity to the threat. Almost all of the studies of

group threat make the implicit assumption that the effect of threat is somehow a function

of spatial proximity, that is: the closer a group, the more threatening it is. This is why tests

of group threat always are performed within some limited geographic area, for example at

the county level. Few would expect, for example, that the presence of African-Americans in

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a far away county should be threatening to whites in a different county.1 However, none of

the studies reviewed here actually model distance, instead just choosing a given areal unit

and measuring proportions of groups within the unit (except, see Enos (2009c).

Obamas candidacy provides one of the first opportunities to study a large-scale election

in which vote choice should be clearly affected by group threat - if the theory is correct.

Presumably, individuals motivated by group threat will be less likely to vote for an African

American candidate. In the past, there have just not been many campaigns that involve

a high-profile African American against a non-African American candidate. In less high-

profiles races, we can be sure that voters were aware of the race of the cnadidate. Also, in

smaller elections, which may have involved an African American candidate, data is often

unavailable or poorly geocoded, which does not allow for testing the effect of distance. I

am able to identify the race of voters and geocode precincts in Los Angeles County, and by

doing so, measure how Obama vote among Latinos varied by location.

With improvements in Geographic Information System (GIS) technology and innovations

for identifying individual voters (see below), it is now possible to test this implication of

group threat: that threat-conditioned vote choice should be proportional to distance from

the source of the threat. In this paper, I will test this by looking at the relationship between

Latino vote for Obama in Los Angeles County in the 2008 Presidential Primary and the

proximity of these voters to African Americans. My results indicate that, while controlling for

other potential influences, Latino vote for Obama was negatively correlated with proximity

to African Americans. These aggregate results suggest group threat.

I measure this using precinct level voter returns. Of course, aggregate results suffer from

potential ecological fallacies. However, I turn to precinct level data because vote choice is

difficult to measure on an individual level. It can be found in survey data, but survey data

usually cannot be precisely geocoded and probably does not have many of the cases that

1I have proposed (Enos 2009b) a more nuanced theory of threat in which group threat is a multiplicativefunction of size, proximity, and concentration. I attempt to show that overwhelming size can compensate forlow proximity. This is intuitive, in that a group that is far away can still be threatening if it is large enoughand a threat that is small in number can be terrifying if it is nearby.

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are of interest here, that is Latinos in close proximity to Blacks. Additionally, because Los

Angeles is high segregated and Blacks voted overwhelmingly for Obama, there is little reason

to believe that an ecological fallacy could be driving results (see below).

1.2 Why Latinos?

group threat has traditionally been tested on whites in the presence of African Americans. I

argue that a more relevant concern, and probably a stronger effect, is the mutual group threat

between Black and Latino urban dwellers. For one, American central cities are increasingly

becoming a Black and Latino phenomenon, to the continued exclusion of whites. Moreover,

the continued immigration of Latinos into American cities has resulted in visible social

tensions between Blacks and Latinos, especially in Los Angeles. This speaks to Blumers

(1958) refinement of group threat. Blumer (1958) argued for a power threat hypothesis, in

which racial animosity was based on a feeling of threat to a groups collective social position.

It seems that the potential threat to social position is now much greater between Blacks and

Latinos than it is between Blacks and whites.

In other work (Enos 2009a) I have explored African American behavior in the face of

Latino threat. Obamas candidacy does not lend itself to tests of effects on African Americans

though because of the small variation in Black support for Obama. Obama simply won too

much of the Black vote to find useful variation. However, Latino support was much more

mixed. The presence of Obama, as a Black candidate, provides a clear test for the effect

of threat on attitudes towards Blacks. And, in addition to the reasons already mentioned

for exploring group threat among Latinos, Latino voters are simply easier to identify by a

process that I will describe below.

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2 Hypothesis and process

I assert that proximity to a high density of African Americans causes non-African American

voters to be less likely to vote for an African American candidate. Using my data, I should

see a positive correlation between distance from African Americans and Obama vote in the

Democratic Primary, among Latinos at the precinct level.

In order to establish this I do the following:

1. Establish the race of individual voters using Bayes rule to combine their last name

and Census data on their location.

2. Identify and geocode highly Latino and Black precincts.

3. Look for a spatial structure in Obama support. In other words, is Obama vote ran-

domly dispersed across Los Angeles County, or are areas close together more similar

than those further apart. Finding spatial structure in the data is a necessary precon-

dition for vote choice to be related to distance.

4. Measure the relationship between this distance and support for Obama, controlling for

other factors such as income and education.

2.1 Identifying the race of voters

Elsewhere (Enos 2009a, Enos 2009c), I have shown how a probabilistic determination of

voters race/ethncity can be made, with high certainty, using a method that takes the prob-

ability of a voters race based on their surname and combines this with Census data to give

an estimate of the probability that they belong to a given racial/ethnic group. I will just

give an intuition for that method here.

This method is needed because I use a voter list in this analysis to identify the racial

demographics of precincts in Los Angeles County. Voter lists in most states do not report

the race of voters. This is, obviously, a big problem for research about group threat, but

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more generally for research using voting lists because race is understood to be an important

determinant of voting behavior (either directly or as a proxy for other variables). Usually,

when race is available to a study, it is because survey data is being used. Because states,

like California, do not collect racial data on their voters, racial demographics on the precinct

level are not available. Racial data is available for Census geographies, but election returns

are not.

One possible way of trying to bring voting returns and race data together would be to

simply substitute Census data for the precinct data. Besides the problem that precincts

and Census geography almost never overlap perfectly, this method assumes that voters are

representative of the entire population, as recorded by the census: an assumption that is

almost certainly false.

My method combines Census information with the voter list to calculate a probability

that each individual voter is of a certain race. Using the race determined for each voter,

the racial demographics of each precinct can then be determined. To determine the race

of each voter, I take the probability that each individual voter belongs to a given racial

group based on nationwide frequencies of names by racial group, as collected by the Social

Security Administration. I then take these probabilities and the probability of belonging to

each racial group based on the demographics of their Census Block and calculate a posterior

probability using Bayes Rule that the person belongs to a certain racial group. This does

not allow me to put each voter in a discrete racial category, but I can state a probability that

each voter is a member of a discrete group. The higher each component prior probability, the

higher the posterior probability. So, the highly segregated nature of Census Blocks in Los

Angeles makes many of these probabilities very high. Also, because some names, especially

names of Hispanic and Asian descent are concentrated among certain races, the posterior

estimates can be very certain.

As an example, I randomly selected a voter from the African American sample of voters

used in Enos (2009a) from the California voter list. This voter had the last name Smith.

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The prior probability that a person in the United States with the name Smith is African

American is .222. This individual lives in a Census Block that is 74.47% African American.

This yields a posterior probability that this voter is African American of .977.

As another example, I randomly selected a voter from the Hispanic sample in Enos

(2009a) with the last name Gutierrez. The prior probability that a person with the name

Guitierrez is Hispanic is already .924. However, this individual lives in a Census Block that

was 55% Hispanic, so the posterior probability that this individual is Hispanic is a robust

.998.

I refer to these probabilities by the notation p([race]|name) where race is either black orlatino. This actually shorthand for p([race]|name & location) because the probability is afunction of both location and name.

2.2 Identifying African American and Latino precincts

Having identified the race of individual voters, I can place these voters in precincts to de-

termine the racial demographics of the precincts. However, like with individuals, these

precinct-level demographics are probabilities, not discrete categories. Because, in almost all

cases, there is uncertainty about the race of individuals, there is also uncertainty about the

aggregate demographics of the precinct. This is not unlike the usual ecological problems

found with aggregate data. For example, even if a researcher knew with certainty the race of

each individual in a precinct, it would still be difficult to make inferences about the behavior

of individuals based on aggregate data. This is because, unless a precinct is perfectly ho-

mogenous, it is never clear which people from the precinct are associated with the outcome

of interest. Behavior that a researcher believes is attributable to African Americans, might

actually be performed by whites, for example. To this layer of uncertainty, I add uncertainty

about the race of individuals. This means that I will have to make arbitrary decisions about

which precincts to designate Black and Latino. To identify the demographics of the precinct,

I calculate the average probability that people are of each race in each precinct. Because

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of the highly segregated nature of Los Angeles, this allows me to be very certain about the

demographics of some precincts.

For example, Figure 1 is a map of precincts in the southern part of Los Angeles County

based on the average p(latino|name). By looking at the maps, you can see that the higherof a threshold of p(latino|name) I use, the fewer precinct that will be available to workwith, but the more certain I can be about the estimates. Figure 2 is the same map for

African Americans. These maps are of south Los Angeles County. This area includes a

very large number of different neighborhoods and municipalities. The maps do not include

most of the San Fernando Valley, Eastern Los Angeles County, and the rural north of Los

Angeles County. My analysis will include all of the county, but the important precincts are

approximately those shown in this figure because that is where an overwhelming proportion

of Blacks and Latinos live. Because of uncertainty in my estimates of precinct demographics,

I will use a range of designations of a Latino or Black precincts in the test in this paper.

2.3 Concerns about ecological fallacies

Since I am using aggregate data, there is always the possibility of an ecological fallacy.

However, it seems unlikely that an ecological fallacy could produce a correlation between

distance from African Americans and vote for Obama in Latino precincts. The reason

that an ecological fallacy causing the correlation is unlikely is because in Los Angeles the

overwhelming majority of non-Latino persons in majority Latino areas are Black. This means

the counterfactual to my claim that Latinos are more likely to vote for Obama the further

they are from African Americans is that that Black voters are more likely to vote for Obama

as they move further away from other Blacks. This seems quite unlikely. There just does

not seem to be a plausible explanation, once income and education or controlled for, of why

Blacks further away from other Blacks would be more likely to vote for Obama.

An explanation for why being further away from Blacks or closer to Latinos made Blacks

more likely to vote for Obama is that, maybe, the proximity to Latinos increases feelings of

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racial identification or group threat among the Black voters and therefore Blacks close to

Latinos were more likely they are to vote for Obama. The amount of variance that can be

explained by this, considering the overwhelming support for Obama among Blacks, seems

small - and it is not clear why this would not be balanced by Latinos close to these same

Blacks voting against Obama.2

2.4 Looking for spatial structure in the data

Looking for an effect of distance is, essentially, looking for some sort of spatial structure in

the data. I am claiming that where the precincts are in space makes a difference for the

behavior of voters in those precincts.

One way to make an exploratory foray into this spatial structure is to see if spatial au-

tocorrelation exists in my dependent variable. Spatial autocorrelation is similar to temporal

autocorrelation, which is familiar to many political scientists. Temporal autocorrelation

means that two observations that are closer in time are more likely to be similar than two

observations that are further apart in time, all else equal. Spatial autocorrelation means

that two observations closer in space are more likely to be similar than two further apart.

So, I want to see if the vote for Obama is more similar in precincts that are close together

than those further apart. That is, is the percentage of Obama vote more similar among

precincts close together than it is precincts that are less close together. If the answer is

that Obama vote is more similar in close precincts, that there is spatial autocorrelation, this

would indicate that some sort of spatially dependent process is driving Obama vote. This

could be dependence on distance from African American precincts.

For African American precincts, I do not expect to see much spatial autocorrelation,

simply because African Americans overwhelmingly voted for Obama, there is little reason

to believe it varies much by where African Americans live. For Latino precincts, I expect to

see more spatial autocorrelation. If there is no autocorrelation among Latinos, than there

2Party identification is not a concern concern because only Democrats could vote in the closed primary.

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is no reason to expect that Latino support for Obama is related to proximity from African

Americans.

Figure 3 is Obama vote by precinct in Los Angeles County. Looking at this figure, to

the naked eye, there appears to be distinct trends of Obama support by geography, with

darker and lighter precincts clustered together. Comparing Figure 3 and Figure 2, there is a

striking overlap between the heaviest areas of Obama support and the spatial concentration

of African Americans. There also appears to be a correlation between Obama vote and the

spatial concentration of Latinos, as can be seen by looking at Figure 1.

However, rather than just judging from maps, I can perform a test for the presence of

spatial autocorrelation. Morans I statistic measures the amount of spatial autocorrelation

between neighboring geographies.3

Table 1 reports Morans I for Obama support for Latino and African American precincts

at different levels of p(race|name). Morans I can be conveniently thought of as analogousto the more commonly seen Pearsons Correlation Coefficient. The p-values represent the

probability that the correlation was produced by a random process. In the case of both

Latinos and African Americans, the p-values are near zero. However, the autocorrelation

is much higher for Latino than African American precincts. This indicates that there more

spatial structure to Latino support for Obama than there is spatial structure to African

American support for Obama. Of course, this autocorrelation could be based on some other

variable that is also correlated with support for Obama. Multiple regression analysis can

test for this.

3 Regression results

Having established that there appears to be spatial structure in Latino support for Obama, I

now turn to whether the spatial process is a function of distance from Blacks. To test for an

3For more on Morans I, see (Cressie 1993). Morans I is based on a weights matrix constructed fromcomparing each unit to a certain, k, number of neighbors. The number of neighbors can be set by theresearcher. My results here are not sensitive to the number of neighbors for k = [1, 3].

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effect of distance, I regress Obama vote on the log(distance) of each Latino precinct from the

nearest Black precinct and on a set of control variables measured at the Census Tract level:

median income, percent college graduates, and percent of Hispanics that are immigrants. I

define a Black precinct as any precinct where p(black|name) > .95 on average. I test Latinoprecincts where the average probability that p(latino|name) > .90, .95, and .98.4

The regression estimates are in listed in Table 2. The standard errors of each coeffi-

cient are listed below each coefficient. In each specification, the coefficient for distance is

statistically significant and positive. This indicates that as distance from African American

precincts increased, so did the support in Latino precincts for Obama. Results are fairly

insensitive to the choice of p(latino|name), however, the greater p(latino|name), the smallerthe N, which increases the uncertainty around the estimates.

Figure 4 is a graphic of the effect that moving away from African American precincts

had on Obama vote in Latino precincts. These estimates were generated by simulating

expected values of the model with draws from the coefficient distribution, while holding all

control variables at their means and moving distance across the range of distance in the

data. This plot shows a large effect of moving over this distance. However, the slope of the

line is strongly curved with much stronger effects at distances closer to zero. Figure 5 is the

same graphic as Figure 4 but focusing on the distances between the 1st and 3rd quartiles

of the distribution of distances of Latino precincts from African American precincts. This

plot shows that moving from 3 to 9 miles away has a substantively large effect of over 2

percentage points on the probability that Latinos in these precincts cast a vote for Obama.

Figure 6 is the effect of moving across the first quartile of distance: from 0 to 3 miles. At

these distances, especially close to zero, the effect is very strong. Moving from zero to three

miles, the probability of Latino vote for Obama increases by ten percentage points. Latinos

from precincts directly adjacent to African American precincts have a very small chance of

voting for Obama: less than 5%.

4I also substituted percent high school graduates for percent college graduates and also used them bothin the model. The results are not sensitive to this choice of variable.

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4 Discussion

I have demonstrated that precinct level support for Obama in heavily Latino precincts in-

creased as the distance from African American precincts also incrased. This finding is con-

sistent with an observable implication of the group threat hypothesis: that threat should

decrease with distance.

This finding could be considered normatively troubling, however, it is not surprising given

the sometimes volatile relationship between African Americans and Latinos in Los Angeles or

the history of political reactions to urban demographic change. Tension between the Latino

and Black community is a social reality in some neighborhoods of Los Angeles. A rapid

turnover in many traditionally Black neighborhoods has created a tension that is visible in

many spheres of life, from schools to electoral politics.

This situation resembles the similar events when African Americans from the South

migrated to Northern cities in the latter Twentieth Century. Some political historians have

speculated that whites were motivated to vote against racially liberal candidates because of

a perceived threat from the Black population (Edsall & Edsall 1992). I have demonstrated

elsewhere that the political attitudes of whites can be causally connected to proximity to

African Americans (Enos 2009c). However, now the situation is reversed in the sense that

in the 1960s, the established white community was threatened by the immigrant Black

population. While now, the immigrant Latino population is threatened by the established

Black community. That African Americans have been the stimulus of threat whether as

immigrants or the established community, may speak to the unique disadvantaged place of

African Americans on the American racial hierarchy.

These findings also have implications for the Democratic Party as Latinos appear ready

to join African Americans as a permanent part of the Democratic coalition. It is notable that

despite the apparently, sometimes, uncomfortable nature of the coalition in the Democratic

Primary, Latinos voted overwhelmingly for Obama in the general election. This is not unlike

other factions within the large coalitions of the two major parties. In this sense, despite the

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somewhat troubling implications of these findings, I have not demonstrated in this paper,

nor am I trying to suggest, that members of these two racial groups cannot come together.

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References

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Enos, Ryan D. 2009c. What tearing down public housing projects teaches us about theeffect of racial threat on political participation..

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Fossett, Mark A., & K. Jill Kiecolt. 1989. The relative size of minority populations andwhite racial attitudes. Social Science Quarterly 70 (4): 82035.

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Matthews, Donald R., & James W. Prothro. 1963. Social and economic factors and Negrovoter registration in the South. The American Political Science Review 57 (1): 2444.

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5 Figures and Tables

Table 1: Spatial auto-correlation in support for Obama

Latino African Americanp(race|name) > 0.9 0.95 0.98 0.9 0.95

Morans I 0.5181 0.5812 0.6198 0.2268 -.3160Alternative -0.0039 -0.0062 -0.0128 -0.0189 -0.2500

p value 0.0000 0.0000 0.0000 0.0063 0.7925N 273.0000 172.0000 85.0000 55 5

Spatial auto-correlation in support for Obama for Latino and African-American precincts atdifferent specifications of p(race|name). Morans I represents the spatial autocorrelation.

The Alternative is the null hypothesis (which is not usually zero in tests of Morans I). Thep-value can be interpreted as the probability that the observed spatial correlation would

occur by chance. Morans I was generated using k = 3 nearest neighbor weights matrix. Atp(black|name) > .98 there are not enough African American precincts to perform the test,

so African American precincts are only tested up to p(black|name) > .95.

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Figure 1: Percent Latino by precinct, Los Angeles County

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Figure 2: Percent African American by precinct, Los Angeles County

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Figure 3: Percent Obama vote by precinct, Los Angeles County

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Figure 4: Predicted Latino vote for Obama as distance from African American precinctsincreases

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Figure 5: Predicted Latino vote for Obama as distance from African American precinctsincreases, 1st to 3rd quartiles of distance

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Expected values based on simulated draws from the coefficient distribution. 95% confidenceintervals are in grey. Distance was measured in log(distance).

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Figure 6: Predicted Latino vote for Obama as distance from African American precinctsincreases, 1st quartile of distance

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Expected values based on simulated draws from the coefficient distribution. 95% confidenceintervals are in grey. Distance was measured in log(distance).

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Table 2: Effect of distance from African American precincts on Latino vote for Obama

variable p(latino|name) > .90 p(latino|name) > .95 p(latino|name) > .98log(distance) 0.0266 0.0343 0.0267

(0.0053) (0.0061) (0.0105)log(income) 0.0336 0.0202 -0.0102

(0.0107) (0.0121) (0.0171)% Hispanic Immigrant 0.1778 0.1654 0.2791

(0.0199) (0.0276) (0.0469)% attended college 0.5087 0.0507 -0.4489

(0.1987) (0.2795) (0.4019)Intercept -0.5467 -0.4763 -0.1385

(0.0905) (0.1046) (0.1471)

N 273 172 85Adj. R2 .6020 .5980 .6549

Cell entries are Ordinary Least Squares Regression coefficients. Dependent variable ispercent support for Obama at the precinct level in the Democratic primary. Standard errors

are listed below the coefficient in parenthesis.

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Racial/Group ThreatTesting for group threatWhy Latinos?

Hypothesis and processIdentifying the race of votersIdentifying African American and Latino precinctsConcerns about ecological fallaciesLooking for spatial structure in the data

Regression resultsDiscussionFigures and Tables