neglected neighborhoods: economic inequality, residential segregation & declining voter turnout in...

8/14/2019 Neglected Neighborhoods: Economic Inequality, Residential Segregation & Declining Voter Turnout in Low-Income

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Neglected Neighborhoods:

Economic Inequality, Residential Segregation & Declining Voter Turnout in

Low-Income Communities

Amy WidestromAPSA Congressional Fellow/

Department of Political Science, California State University-Long Beach

[email protected]

Abstract: Voting and other aspects of civic engagement have declined over the last half-centurywhile economic inequality and residential economic segregation have increased. Possiblerelationships between these phenomena have received little scholarly attention. This paperexamines how increasing economic inequality and residential segregation combined to reduce

voter turnout among low-income American citizens. I find that increasing economic inequalityand segregation since 1970 have led to a decline of voter participation in segregated, low-incomeneighborhoods while voter turnout remained high and in some cases increased, in segregated,affluent neighborhoods. This suggests that the commonly accepted notion that low-incomecitizens do not vote because of their individual economic status misses a significant point: low-income citizens increasingly found themselves living more exclusively with other low-incomecitizens, which means that individual disincentives for voting due to economic status wereenhanced by an impoverished neighborhood context and social interactions with similarlydisempowered citizens.

Prepared for delivery at the 2008 Annual Meeting of the American Political Science Association,August 28-31, 2008.

Copyright by the American Political Science Association


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INTRODUCTION

Voting, local meeting attendance, and other aspects of civic engagement have declined in

the last half-century while economic inequality and residential economic segregation have

increased (Wolff 1992; Massey and Denton 1993; Rosenstone and Hansen 1993; Abramson et al.

1995; Jargowsky 1997; Putnam 2000; Wilson 1987, 2000; Drier et al. 2001). Among poor and

less educated citizens, voter turnout and other forms of civic engagement have declined the most,

and low-income neighborhoods have experienced worsening conditions even as other parts of the

American landscape have become increasingly prosperous (Rosenstone et al. 1986; Verba et al.

1995; Dionne 1998; Putnam 2000; Drier et al. 2001; Skocpol 2003; Leighley and Nagler 2006).

Although scholars have studied these developments separately and have become interested in the

state of American civil society over the last decade (i.e. Dionne 1998; Putnam 2000; Skocpol

2003; Skocpol et al. 2006), a possible relationship between these trends has yet to be thoroughly

considered.

This paper asks the following question: how does increasing economic inequality

combine with rising residential segregation to affect voter turnout? To answer this question,

voter turnout data and panel data gathered from four case-study cities are used to explore the

effect of economic inequality and segregation on voter turnout in mayoral, congressional, and

presidential elections from 1970 to 2000. As economic inequality and segregation increased,

low-income citizens increasingly found themselves living with other low-income citizens.

Neighborhood context and social interaction with similarly disempowered citizens may have

enhanced individual disincentives for voting; if so, this should complicate our current

understanding of why low-income citizens do not vote.

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To date, trends in voter turnout, economic inequality, and economic segregation have

been studied by political behavior and political institution scholars, or demographers, historians

or sociologists. Voter studies adequately analyze individual political behavior and have

expanded our understanding of who votes and why. Individuals with more education, more

money and more time tend to engage in all types of civic activity more than those with fewer

individual resources (Wolfinger and Rosenstone 1980; Verba et al. 1995). However, these

scholars fail to recognize that communities of voters are arranged spatially, and that this

arrangement is political in origin and may have civic and political consequences.

Scholars who study mobilizing institutions argue that contact by and participation in

mobilizing organizations encourage political participation among citizens (Rosenstone and

Hansen 1993; Leighley and Nagler 2006). These studies have highlighted the institutional and

social components of political mobilization. Who does the contacting, and how they do it,

matters for promoting voter participation, yet current research does not examine how the spatial

arrangement of citizens may alter the strategies and efficacy of mobilizing institutions and

political elites.

Some scholars have found that individuals purposefully develop social relationships with

people who have similar political preferences, but that opportunities for these relationships are

hindered or promoted based on social contexts (Huckfeldt and Sprague 1987, 1992). Further,

these authors have shown that information transmitting processes interact with social contexts in

a manner that favors local partisan majorities while undermining those in political minorities

(Huckfeldt and Sprague 1992). Although these scholars focus on social networks, parties, and

party activists and not mass political behavior, this approach illuminates how a new appreciation

of neighborhood context may broaden our understanding of how and why citizens vote.

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Studies of community change and residential segregation have shown that low-income

citizens are increasingly concentrated in segregated geographic areas of cities, that the affluent

have become more segregated in suburban areas, that the isolation of low-income populations

deteriorates social and familial networks and employment opportunities, and that the economic

segregation between suburban and urban areas alters the civic and political participation of

suburban and urban residents (Jackson 1985; Wilson 1987, 1996; Massey et al. 1994; Sugrue

1996; Jargowsky 1997; Oliver 1999). However, community studies fail to acknowledge that the

deterioration of communities may have political consequences as well, while segregation studies

do not assess the effects of segregation on the civic life of neighborhoods.

While many urban cores and increasingly suburbs remain racially segregated, it is

important to try and appreciate economic segregation as distinct from racial segregation despite

that scholars typically think about segregation primarily as a racial phenomenon. The bulk of

contemporary and historical research on segregation analyzes residential settlement patterns

through the lens of racial segregation (i.e. Fligstein 1981; Wilson 1987, 1996; Massey and

Denton 1993; Tolnay, et al. 2002). Some scholars have expanded this perspective to include

political repercussions of the economic and social isolation of low-income African-American

communities (Cohen and Dawson 1993; Rankin and Quane 2000). These and many other

studies make clear that racialized politics and policies have contributed to the current state of

residential segregation in this country.

However, as racial barriers to upward economic mobility and suburban housing have

fallen, black families with the means to move out of historically black, and often mixed-income

or lower-income, neighborhoods have done so in increasing numbers (Wilson 1987; Schneider

and Phelan 1993). This has left those without means in increasingly poor neighborhoods,

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contributing to the rise in economic segregation. Indeed it seems that scholars have chosen to

focus on race and racial segregation as a causal factor of social or political trends, when perhaps

economic factors may have been decisive (Cf. Fields 1990). In other words, rather than

assuming race is the sole causal factor of social, economic, and/or political behavior, it is

worthwhile to speculate that economic inequality and economic segregation are themselves

important for shaping political behavior.

This paper aims to bridge these various literatures and recognize the political importance

of the economic spatial arrangement of citizens at the neighborhood levelwhere citizens live

their daily lives. It is argued here that increasing economic inequality and segregation, beginning

in the 1970s and continuing in the 1980s, have contributed to the simultaneous decline of voter

participation in highly-segregated impoverished neighborhoods and an increase in voter

participation in segregated affluent neighborhoods, particularly since 1980. This may help

explain why voting among low-income citizens has declined the most.

MEASURING ECONOMIC INEQUALITY, SEGREGATION, AND VOTERTURNOUT

The Neighborhood Change Database (NCDB) is used to assess nationwide trends in

economic inequality and segregation.1 This database includes census tract level variables from

the 1970, 1980, 1990, and 2000 censuses, such as neighborhood poverty rate, average

neighborhood wages and salaries, high school dropout rate, and the number of owner-occupied

units in each census tract, to name a few. A dataset extracted from the NCDB provides census

tract level economic, educational, residential, and employment data for every tract in the United

States from 1970 to 2000. As is common in the literature on neighborhood segregation (i.e.

Jargowsky 1997), census tracts are used as proxies for neighborhoods because census tracts are

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small, relatively permanent statistical subdivisions of a county usually containing between

2,500 to 8,000 persons (U.S. Census Bureau 2001). This dataset allows an examination of how

neighborhoods have evolved over this 30-year period.

There are several common measures of economic inequality, including the inter-quartile

ratios, the coefficient of variation, and the Gini coefficient, which is most commonly used. The

inter-quartile ratio compares only two data points, meaning that trends within and between other

groups are lost, and so this ratio is not used here. The coefficient of variation is also not used

here as it relies on a variables standard deviation and mean to be calculated, causing the value of

the coefficient to vary depending on the point of origin of the data and making comparisons

across groups difficult (Livers 1942). Finally, the Gini coefficient is determined by ranking

income observations from lowest to highest and then plotting the cumulative proportion of

population against the cumulative proportion of income. The coefficient is calculated by

subtracting the area below the plotted line, the Lorenze Curve, from the area below the line of

perfect equality. This allows for easy graphical representation of inequality, and therefore is

easy to explain and understand. However, the Gini Coefficient requires that a dataset have a

single observation for a single case which can be rank-ordered (Lambert and Aronson 1993).

Because the dataset culled from the NCDB consists of data aggregated to and averaged at

the census tract level, accurate sorting and ranking of data required to calculate the Gini

coefficient is not possible. Moreover, the sorting and ranking required to calculate the Gini

coefficient produces overlap when calculating subgroup-coefficients and between-group

comparisons, making the value of the coefficient as well as the inter-group comparisons

unreliable (Lambert and Aronson 1993). Finally, the Gini Coefficient is highly sensitive to

errors within individual-level data (Galbraith et al. 1998). Because of these complications, I use

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Theils T statistics, named for Henri Theil (1972), referred to as T', to assess county- and

neighborhood-level inequality.

To measure county-level, or between-neighborhood, inequality requires first the

calculation of a Theil elementfor each census tract in the United States. Galbraith and Hale

explain that a [neighborhood]s Theil element in any given year may be positive or negative,

and that if a neighborhoods average income is greater than the [county] average, the element

will be positive, otherwise zero (if income equals the average), or negative (if income falls below

the average (Galbraith and Hale 2006, 3). These Theil elements are then aggregated to the

county level in order to calculate the T'statistics for each county. By construction, the sum of

the positive elements must be greater than the sum of the negative elements, so that the

aggregated statistic is always positive (Galbraith and Hale 2006, 3). The Theil elements, then,

represent neighborhood-level inequality and the Tstatistic represents the level of economic

inequality for each county, or between-neighborhood inequality. Thus, an increase in the T

statistic would suggest a rise in county-level inequality. In other words, the Tstatistic

represents county-level inequality by calculating and aggregating the disparity between the

average wages and salaries in each neighborhood in the county.

Calculating T'required the population for the desired unit of analysis, the total

population, the average income for the population within the unit of analysis, and the average

income for the total population. For the investigation here the total population of a census tract,

the average wages and salaries of the census tract, the county population, and the average wages

and salaries of the county were used to calculate a neighborhood Theil element, or a

neighborhoods level of inequality, and each countys Tstatistic. The Tstatistic is calculated

as follows (Galbraith and Hale 2006):

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(Massey and Denton 1988, 307 312), allowing for consistency in segregation studies.2

Specifically, the isolation index measures the extent to which members of a particular group are

exposed, in their neighborhoods, only to members of their group. In particular, for the poorer in

any metropolitan area, the isolation index indicates how poor (e.g. 25, 50, 75 percent) the

neighborhood of the average poor person is (Abramson et al. 1995, 47 - 48). In other words, the

isolation index measures exposure, or the extent to which individuals live in neighborhoods with

other, similar individuals. This theoretical concept is particularly useful for the purposes of this

paper, as it is the political effect of isolating low-income individuals being studied.

This index is calculated with the following formula:

I = (pi/P)(pi/ti)

Where pi is the number of low-income residents and ti is the total population of area unit i, in

this case a census tract. P is the total number of low-income citizens in the county. The low-

income population in this calculation is the number of people living below the federally

determined poverty line in a given areal unit.3 The number resulting from this calculation is

multiplied by 100 to express isolation as a percentage.

In order to asses the role that economic inequality and segregation may play in shaping

political behavior in local and national elections, I determined specific case-study neighborhoods

in which to conduct in-depth research. To do this, I used the isolation index to rank order every

county in the United States to determine four of the most segregated counties in the nation, while

also allowing for geographic diversity. Next, I rank ordered census tracts within these four

counties by poverty rate to determine which neighborhoods have experienced increasing poverty

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over time. Finally, I rank ordered census tracts in these four counties by average wages and

salaries to select comparative neighborhoods that have become increasingly prosperous over

time. In the end, I selected one increasingly impoverished neighborhood and one increasingly

prosperous neighborhood within each of the four segregated counties as case studies. In the

case-study selection process, only census tracts that had been established in 1970 or before, that

had either not changed, or had changed in name only, and that had populations of 200 or more in

2000 were used. This allowed for maximum comparability.

The case study neighborhoods are located in: Atlanta, Fulton County, Georgia; Kansas

City, Jackson County, Missouri; Milwaukee, Milwaukee County, Wisconsin; and Rochester,

Monroe County, New York. I chose these four cities based on geographic distribution,

excluding cities in the west and southwest because of problems with the existence of stable

census tracts between 1970 and 2000, as cities in these regions have grown rapidly and therefore

the size and shape of census tracts have changed considerably limiting comparability over time.

Thus, I did not select solely on segregation, I selected on poverty and prosperity within

segregation and by region.

Table 1 shows a simple comparison between the impoverished and prosperous

neighborhoods chosen in each case-study city.4 Across the four case-study impoverished

communities, the neighborhood poverty rate in 1970 varied widely, from 4% to 21%, though the

average of these rates is 11%, which is the national average in 1970. The poverty rate in these

neighborhoods increased dramatically over time, and in 2000 ranged from 48% to 62%,

averaging well above the national average of 13%. Consequently, in the year 2000, these four

segregated and impoverished case-study neighborhoods could be defined as high-poverty,

which is a neighborhood with a poverty rate of 40% or higher (Jargowsky 1997). In each case-

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study city, the comparative neighborhoods represent a prosperous counter-point for

understanding how economic segregation might affect political participation. As Table 1 shows,

the average wages and salaries in each prosperous neighborhood places these neighborhoods in

the top quintile of average income in 2000, with three of the four neighborhoods increasing their

economic resources between 1970 and 2000.

[Table 1 about here]

This comparative research design allows me to examine how changes in economic

inequality and segregation may alter political participation by comparing two different economic

contexts within high levels of segregation, as well as explore possible explanations for any

changes, over a thirty year period. I hypothesize that increasing economic segregation and

inequality have combined to help produce a decline in voter participation among low-income

American citizens in highly segregated high-poverty neighborhoods. To test this hypothesis, I

gathered voting data from local Boards of Elections, libraries, and archives for each of the case-

study neighborhoods for mayoral, congressional, and presidential elections between 1970 and

2000. The use of case studies and local voter turnout data, rather than data from national

surveys, allows for an examination of the relationship between voter turnout at the polls and the

economic contexts of neighborhoods in all types of elections, including mayoral, which are often

excluded in voter participation studies.

In order to determine voter turnout by neighborhood, census tract boundaries were first

matched with election district boundaries using ward and election district (ED) maps from 1970

to 2000. Voting records from local boards of elections, local and state archives, and local public

libraries were used, and included ED and ward voter turnout records. National election studies

and data could not be used because they do not contain actual voter turnout information, as

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opposed to reported, at the neighborhood level. Because of this necessary reliance on local

records, there are two primary obstacles to matching election district boundaries to those of

census tracts. First, while at one time board of election officials used the census tract boundaries

as guidelines for election district boundaries, the process of continual redistricting over time has

produced election districts that in most cases do not follow census tract boundaries. Second,

while most redistricting occurs following the decennial census, this is not always the case. In

three of the four case study citiesAtlanta, Rochester, and Milwaukeethere were at least two

changes to ED boundaries in at least one of the decades studied here. In order to determine

accurate voter turnout, all redistricting that occurred between 1970 and 2000 was accounted for.

This was done by determining what percentage of the appropriate election districts covered the

case study census tracts used to approximate neighborhoods in this study, and done so for each

neighborhood and decade, 1970 through 2000.

For example, the impoverished neighborhood examined in Milwaukee is census tract 12.

To analyze voter turnout data from 1970 on, voter turnout for all of ED nine and 10% of ED 10

was recorded. This was determined by calculating the percentage of blocks in census tract 12

that were encompassed by EDs nine and 10. Following 1983, the city of Milwaukee issued a

new election district boundary map that spanned only two election years, 1984 and 1985during

which time there was a mayoral and presidential election, both in 1984and issued a new map

in 1986, whose boundaries held through 1992. This 1986 change did not affect the EDs covering

census tract 12. Accordingly, to analyze voter turnout in census tract 12 for 1983 through 1992,

EDs 172 and half of 173 were used, again determined by calculating the percentage of blocks in

census tract 12 that fall within the boundaries of EDs 172 and 173. Finally, from 1993 to 2000,

census tract 12 was covered by all of ED 167 and one-third of ED 166.

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Taking a percentage of an election district and attributing it to a particular census tract

presents an ecological fallacy problem as it assumes an even distribution of population across the

election district. It also assumes that those voters selected from the larger ED behave like voters

living in the smaller case-study neighborhood within the ED. However, due to fact that boards

of election do not collect or keep voting data at the census tract levels which would address this

problem, this method was the only way to approximate voter turnout at the neighborhood level.

The second step in determining local patterns of voter turnout is to calculate voter turnout

itself. There are two ways to calculate voter turnout. The first is the number of ballots cast

divided by the voting age population (VAP). The second is the number of ballots cast divided by

the voting eligible population (VEP). The former denominator, VAP, is as it soundsthe total

number of citizens who are of age to vote, which was 21 and over prior to 1972 and 18 and over

from 1972 on. The voting eligible population, however, is much more elusive because it requires

calculating the number of ineligible votersnon-citizens, the incarcerated or institutionalized,

and convicted felons who reside in states where they can not voteand subtracting their

numbers from the voting age population.

Traditionally, voter turnout has been calculated using the total number of ballots cast

divided by the VAP, in part because of the difficulty in determining voter eligibility with any

accuracy (Teixeira 1992). Yet McDonald and Popkin argue that voter turnout is best calculated

using the VEP because [i]t is widely acknowledged that the VAP is substantially different from

the eligible population (2001, 963). While McDonald and Popkin (2001) provide calculations

necessary to determine the VEP at the national level, their methods can only be applied to large

units of analysis, such as the United States, for which all the necessary data required to calculate

the VEP is available; the data required to calculate ineligible voters for small units of analysis,

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such as census tracts, is simply not obtainable. Moreover, Teixeira asserts that [a]t the most

basic level, the voting-age population is the eligible electorate. Although it is little known,

citizenship is not a constitutional requirement for voting in the United States. Both the time it

takes to become a citizen (national) and the actual restrictions of suffrage on citizens (states) are

matters of legislation (Teixeira 1992, 6).

Despite that ineligibility might dramatically skew turnout in highly segregated low

income neighborhoods, Teixeiras last point is particularly important for the findings presented

here. If those who could vote did vote, particularly in the least advantaged communities, the

very legislation that is currently disenfranchising some potential voters and disproportionately

effecting less advantaged communities could be influenced or changed. Thus, given the

limitations and inaccuracy of the data available at the local level, as well as the persuasive

normative implications of the statement made by Teixeira, the percentage of the voting age

population that cast a ballot is used here as the indicator of voter turnout.

To calculate the voting age population, data was collected from the Census of Population

and Housing: Population and Housing Characteristics for Census Tracts and Block Numbering

Areas from 1970, 1980, and 1990 for each of the case-study census tracts. For the year 2000,

data was gathered from the American Factfinder application available through the United States

Census Bureaus website. The published and on-line data provide the total population for each

census tract from which the population under 21 was subtracted for years prior to 1972, and the

population under 18 for 1972 and later. Linear interpolation was used to calculate the voting-age

population for the inter-census years.

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ECONOMIC INEQUALITY, SEGREGATION, ANDTHE DECLINEOF VOTERTURNOUT

It has been established that economic inequality and segregation have increased since the

mid-1970s (Wolff 1992; Jargowsky 1997; Burtless 1999; Freeman 1999; APSA Task Force

2004; Watson 2006). In this section, I hypothesize that as economic inequality and economic

segregation combine and have increased citizens in highly-segregated impoverished

neighborhoods have become less likely to participate in the political process. Thus, I would

expect declining levels of voter participation in segregated impoverished neighborhoods between

1970 and 2000, and rising levels of participation in segregated prosperous neighborhoods during

this same time period.

This may occur because increasing economic segregation does not just make individuals

in neighborhoods poorer. Rather, it concentrates a host of neighborhood-level familial, social,

and economic factors that combine to heighten and exacerbate individual-level attributes and

resources. This entire arrangement of resourcesneighborhood and personal factorscome

together to constitute a neighborhoods civic environment, which is the foundation for promoting

civic and political engagement. In this theory, declining civic environments would diminish the

political participation in highly segregated poor communities, while economic segregation would

strengthen civic environments and therefore political participation in segregated prosperous

neighborhoods.

To analyze voter turnout across types of neighborhoods and different cities, this section

presents a series of graphs that display average trends in voter turnout across the four segregated

impoverished and four segregated prosperous case-study neighborhoods, as well the difference

between average voter turnout rates in these neighborhoods. As a point of comparison, these

graphs also include the national turnout trends in presidential and congressional elections. I

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calculated the difference between voter turnout rates by subtracting the average turnout in

segregated impoverished neighborhoods from the average turnout in segregated prosperous

neighborhoods. Consequently, if the difference is negative, residents in the segregated

impoverished communities voted at higher rates than residents in the prosperous ones. A

positive difference indicates that residents in the segregated prosperous neighborhoods voted at

higher rates than residents in the segregated impoverished communities.

As shown in Figure 1, trends in presidential elections indicate that voter turnout in

wealthy communities declined significantly between 1972 and 1980, but returned to pre-

Watergate levels in 2000 by rebounding over 15 points from the low of 56% in 1980. Average

turnout in the segregated prosperous communities was also well above the national average in

2000. Average turnout in segregated impoverished neighborhoods, however, declined since its

high of 54% in 1980 to 44% in 2000, a decrease of 10 points. Because residents in segregated

impoverished neighborhoods continued to vote less while residents of segregated prosperous

neighborhoods increasingly returned to the polls following 1980, the total difference between

voter turnout in segregated prosperous and segregated impoverished neighborhoods grew from a

low of two points in 1980 to 28 points in 2000.

[Figure 1 about here]

This same trend occurs in off-year congressional elections (Figure 2). The congressional

election of 1974 was the lowest point of turnout for citizens living in prosperous neighborhoods.

The drop in turnout of nearly 40 percentage points in both types of neighborhoods between the

congressional elections of 1970 and those of 1974 may be indicative of a general dismay and

disillusionment citizens were feeling about the American political process as a result of

Watergate, President Nixons resignation, and the dnouement of the Vietnam War. Since the

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1974 congressional elections, however, average turnout in the four segregated prosperous

neighborhoods rebounded by nearly 22 points from a low of 33%, suggesting increased political

participation among residents in economically segregated communities when compared to

impoverished citizens or the national average, even in off-year congressional elections.

This was not the case for residents in the segregated impoverished neighborhoods of

Atlanta, Kansas City, Milwaukee, and Rochester. While turnout in these neighborhoods also

plummeted in the 1974 congressional elections, from 58% in 1970 to 27% in 1974, it fell even

further to 17% in 1990. Despite a small increase between 1990 and 1998, the difference between

voter turnout in segregated prosperous and impoverished neighborhoods grew from six points in

1974 to nearly 24 points in 1998. This is largely due to the fact that residents in segregated poor

communities continued to vote at much lower rates than segregated prosperous citizens, while

residents in segregated prosperous neighborhoods have steadily increased their turnout since

1974, save for a small drop in turnout in 1990.


Figure 3 reveals trends in the average voter turnout for mayoral elections. Averaging

turnout in mayoral elections is challenging because many mayoral elections occur at a time when

no other elections are being held. For example, in 1971, Rochester and Kansas City had city

council and mayoral elections, while in 1972 the city of Milwaukee held their mayoral election

in April. Because of this, many years between 1970 and 2000 had only one mayoral election

across all four cities. The only years between 1970 and 2000 when more than one mayoral

election was held across Atlanta, Kansas City, Milwaukee, and Rochester, and therefore turnout

rates could be averaged, were 1971, 1975, 1979, 1983, 1985, 1989, 1993, and 1997. I used these

mayoral years to create an average turnout in mayoral elections. Due to the difficulty in

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averaging mayoral election results, there is no available data for national voter turnout trends in

these elections.


As with presidential and congressional elections, voter participation among residents in

segregated impoverished communities declined while voter turnout in segregated prosperous

communities increased. Perhaps most striking about this graph, however, is the fact that

throughout the 1970s residents in impoverished neighborhoods often voted at similar orhigher

rates than residents of prosperous neighborhoods. Indeed, in 1971 and 1979 residents in

segregated low-income neighborhoods voted seven and five percentage points higher than

residents in segregated prosperous neighborhoods, respectively.

This graph also reveals that the increase in the difference between voter turnout rates has

grown more in mayoral elections than in other types of elections. In 2000, the difference

between presidential turnout rates in segregated prosperous and impoverished communities was

27.5 points, up 26 points since 1970, while the increase of difference in congressional elections

was smaller, at 18 percentage points. The absolute difference is greater in presidential elections,

but in mayoral elections the difference between turnout in prosperous and impoverished

communities grew by 29 points between 1970 and 2000, the largest increase among all three

types of elections. This is even more notable precisely because average turnout in segregated

impoverished communities was regularly higherthan turnout in segregated prosperous

communities throughout the 1970s.

Together these graphs indicate that the difference between voter turnout rates in

segregated prosperous and impoverished neighborhoods has increased in all types of elections

between 1970 and 2000, though particularly between 1980 and 2000. In fact, between 1970 and

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1980 the difference in voter turnout between impoverished and prosperous neighborhoods

largely disappeared, reflecting a broader trend toward economic and civic equality in American

society that had begun in the 1950s (APSA Task Force 2004, 1). Beginning in the mid-1970s,

however, economic inequality and segregation began to grow (Abramson et al. 1995; APSA

Task Force 2004). These graphs suggest a simultaneous growth in political inequality,

represented by a growing difference between turnout rates among segregated prosperous and

segregated impoverished citizens. Importantly, key sources of this trend were declining voter

turnout rates in segregated poor neighborhoods and increasing turnout in segregated wealthy

communities. Turnout in the latter declined throughout the 1970s, but returned to previous levels

in presidential elections, began to approach previous highs in congressional elections, and rose

above previous levels in mayoral elections.

While provocative, these graphs raise several questions. Is the difference between

turnout rates in these two types of neighborhoods significant, and has the disparity actually

increased over time as the graphs suggest? Is declining turnout in segregated poor

neighborhoods simply the result of wealthier citizens moving out of these neighborhoods and

taking their political participation with them? Finally, what is the empirical relationship between

economic inequality, segregation, and voter turnout? I will address these questions one at a time.

To test the first query about difference in turnout rates, three cross-sectional time series

datasets were created, where observations from each neighborhood were compiled by type of

election, between 1970 and 2000. The question here is whether or not average turnout rates in

impoverished communities are significantly different from those in prosperous ones. To answer

this question one-way Analysis of Variance (ANOVA) tests were conducted. These tests allow

for the investigation of whether the mean turnout of one population (turnout in segregated poor

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neighborhoods) is significantly different from the mean turnout of another population (turnout in

segregated wealthy neighborhoods). In this case, the null hypothesis is that there is no

significant difference between the two, with the alternative hypothesis being that there is a

difference. If the null hypothesis can not be rejected and the corollary accepted, then the

assertion that the substantial difference between turnout rates in these two neighborhoods and its

growth over time are important and significant will be false.

In presidential elections, average turnout in segregated impoverished communities is 47%

and 66% in segregated prosperous communities. In congressional elections, average turnout is

28% in segregated poor neighborhoods and 46% in segregated wealthy neighborhoods. In

mayoral elections, average turnout is 28% and 40% in segregated impoverished and prosperous

communities, respectively. Thus, in all types of elections, average turnout between 1970 and

2000 in segregated impoverished communities was substantially lower than turnout in segregated

prosperous ones.

Tables 2 through 4 summarize the findings of the ANOVA analyses conducted for

presidential, congressional, and mayoral elections separately, and show that this difference is

statistically significant. For presidential electionsF(1, 60) equals 32.93, withp equaling .000; in

congressional electionsF(1, 50) equals 21.01, withp equaling .000; and finally, in mayoral

electionsF(1, 60) equals 10.59, withp equaling .002. In all cases the null hypothesis that the

mean turnout rates are not different can be rejected, and it can be concluded that the differences

between the average turnout rates are significant and important.

[Tables 2 4 about here]

To ascertain if the difference between voter turnout rates from each type of neighborhood

indeed increased between 1970 and 2000, as the graphs suggest, a simple correlation analysis

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was conducted for each election. To this do this, a time variable was added to the panel data,

which was done by numbering each election beginning with one, and then correlating time with

the difference in voter turnout for each election. This analysis was conducted for each type of

election and for each case-study city. If the difference did in fact increase over time, as the

graphs suggest, the coefficients for each election should be positive, which is in fact the case

(Tables 5 through 7), save for one exception in the mayoral elections of Kansas City.

The positive, and often strong, relationships between time and difference in turnout

indicates that the difference trends documented in the voter turnout graphs are accurate, and that

over time the difference between turnout in segregated wealthy neighborhoods and turnout in

segregated impoverished neighborhoods increased. Though some of the findings are statistically

significant, many are not. This may be due to the fact that correlational analysis is meant to

assess the linearity of a relationship, and as Figures 1, 2, and 3 show the relationship between

time and difference is not precisely linear. This may have also produced the negative

correlational relationship for Kansas City mayoral elections. These caveats aside, these

correlational analyses are a simple way to assess change over time in the difference between

voter turnout rates in two different types of neighborhoods, and show that the difference between

turnout rates increased.

[Tables 5 7 about here]

While these varied analyses are helpful for understanding the difference between voter

turnout rates in economically segregated neighborhoods, it is possible that wealthier residents at

one time lived in the segregated impoverished communities and that they voted. As they left

they may have taken their voting habits with them making turnout in these neighborhoods

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decline. This would reflect a change a neighborhood composition, and may be what is driving

turnout trends in segregated impoverished communities.

To assess this possibility, I first calculated the proportion of households in each

impoverished case-study neighborhood that earned the national median income or higher for

each decennial census year, and used linear interpolation to determine the proportion for inter-

census years.5 Then, I calculated a change score from one year to the next. In most cases the

change score is negative, indicating that the proportion of non-poor households decreased in each

segregated impoverished neighborhood. In some years there is no change at all. Next, I

determined a change score for voter turnout by calculating the difference between turnout from

one election year to the next for presidential elections, congressional elections, and mayoral

elections, separately. Finally, I performed a series of correlational analyses to assess the

relationship between the changing proportion of non-poor households and changing voter turnout

in each impoverished case-study neighborhood. Because I am testing the one-to-one relationship

between the non-poor composition of a neighborhood and voter turnout in a neighborhood, the

strength of the relationship between change in proportion and change in voter turnout is the most

important aspect of the correlation table. The correlation coefficient should be one if there is a

direct relationship between the changing proportion of non-poor households and a change in

voter turnout.

Table 8 shows that there is no clear pattern of a strong relationship between the change in

proportion of non-poor households and a change in the number of voters going to the polls.

Indeed, in three cases there is no relationship at all and in two other cases the relationship is very

weak. In all other cases, there is great variation in the strength of the relationship and no clear

trend regarding positive or negative correlation. Though the number of cases in each

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correlational analysis is small, in all case-study cities in presidential, congressional, and mayoral

elections there seems to be no clear indication that a relationship exists between the proportion of

non-poor residents in segregated impoverished neighborhoods and the change in voter turnout in

these neighborhoods. This suggests that changes in political behavior within segregated

impoverished communities may be the result of contextual effects, beyond aggregative

compositional effects within neighborhoods.


Thus, it appears that the difference in turnout between the types of neighborhoods is

significant and has increased over time, and that it is not driven solely by wealthier residents

leaving segregated impoverished neighborhoods. Yet these findings do little to advance our

understanding of the relationship between economic inequality and segregation and voter

turnout. To assess this, the same panel data used for the ANOVA and correlational analyses was

used to conduct a series of Ordinary Least Squares (OLS) regressions. The dependent variable in

these regressions is voter turnout, measured as a percentage. The independent variables are

neighborhood inequality, measured by Theil elements, county segregation, measured by the

isolation index, an interaction term between neighborhood inequality and county segregation,

and a dummy variable coded to capture the segregated impoverished neighborhoods versus

segregated prosperous neighborhoods.

Recall that a [neighborhood]s Theil element in any given year may be positive or

negative. If the neighborhoodsaverage income is greater than the [county] average, the

element will be positive, otherwise zero (if income equals the average), or negative (if income

falls below the average) (Galbraith and Hale 2006, 3). Economic isolation is a percentage

indicating how poor the neighborhood of the average poor person is, calculated for each county.

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The interaction term captures the effect of inequality within segregation: it will be negative if

the case is one of a segregated poor community and positive if the case is one of a segregated

wealthy community. It is a continuous variable, so that the regression analysis will determine

what happens to voter turnout as wealth segregation increases. The dummy variable for

segregated poverty equals one for segregated impoverished case-study neighborhoods and zero

for segregated prosperous case-study neighborhoods. Contrary to the interaction term, which

allows me to test the relationship between wealth segregation and turnout, the dummy variable

allows me to test the effect of segregated poverty on turnout. Moreover, while neighborhood

Theil elements and segregation will capture the independent effects of inequality and

segregation, the dummy variable captures the combined effects of segregated poverty on

neighborhoods.

The results in Table 9 show that by themselves neighborhood inequality and county

segregation have differential, largely statistically insignificant effects on voter turnout in all

types of elections. As a neighborhoods Theil element increases, indicating a rise in

neighborhood incomes, voter turnout increases in presidential elections (Model 1), and declines

in congressional and mayoral elections (Models 2 and 3, respectively). As county economic

segregation increases, voter turnout appears to increase across all elections, and the increase is

statistically significant in mayoral elections. Important for the purposes of this project, however,

is that as the interaction term increases voter turnout increases. In other words, as a

neighborhoods average income rises above the county average within rising economic

segregation voter turnout increases.

This finding coupled with the regression coefficient for the dummy variable suggests that

increasing neighborhood wealth within economic segregation appears to increase neighborhood

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voter turnout in presidential elections, but that turnout is significantly lower in the segregated

impoverished case-study communities. This general finding holds across the other types of

elections, as well; segregated neighborhood wealth also seems to increase neighborhood voter

turnout in congressional and mayoral elections, while segregated poverty depresses turnout in

these types of elections by 64% and 40%, respectively. Moreover, this dramatic drop-off in

voter turnout within these poor communities is statistically significant. Also important to note is

that the four independent variables used in each regression model account for 34%, 26%, and

21% of the variation in presidential, congressional, and mayoral voter turnout, respectively.


Therefore, while the combination of economic inequality and segregation can not explain

all of the variation in turnout, and in some cases is not statistically significant, it does seem to be

a factor in promoting or hindering neighborhood voter turnout in segregated prosperous and

impoverished neighborhoods. This is particularly true for segregated impoverished

communities. It is also important to be cautious when considering tests of statistical

significance; there has been much written about the value of statistical significance and whether

or not it accurately allows researchers to hypothesis test or whether it accurately captures the

practical import of a phenomena (Armstrong 2007). Taken together all of these analyses imply

that voter turnout in segregated prosperous neighborhoods has increased over time while turnout

in segregated impoverished communities has declined. The average turnout rates in each type of

neighborhood in presidential, congressional, and mayoral elections are significantly different

from one another, declining turnout in segregated impoverished communities is not driven solely

by wealthy residents moving out, and increasing wealth within segregation promotes increased

turnout while segregated poverty promotes depressed turnout.

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DISCUSSIONAND CONCLUSION

The effect of economic inequality and segregation is typically examined by looking at

familial, social, and economic networks and opportunities. This paper, however, examined the

implications of inequality and segregation for the civic vibrancy of neighborhoods: how might

increasing economic inequality and segregation combine to affect voter turnout? Throughout the

1970s there was a decline in voter turnout within prosperous neighborhoods, while voter turnout

in impoverished neighborhoods remained stable or increased. The social movements of the

1960s and 1970s may have helped sustain some political participation in the segregated

impoverished neighborhoods in the 1970s. However, throughout the 1980s and1990s, when

economic inequality and segregation increased considerably, turnout in impoverished

neighborhoods significantly declined while turnout in prosperous neighborhoods remained

higher and rose over the years.

This trend held across all four case study cities even though the racial composition of

neighborhoods varied from city to city: in 2000 both case-study neighborhoods in Atlanta were

100% African-American, while the impoverished case-study neighborhood in Rochester was

66% black and 33% Latino. This suggests that despite the racial or ethnic composition of a

neighborhood, the economic context of a neighborhood can affect the political behavior its

residents. Impoverished citizens in one city behaved in ways similar to impoverished citizens in

other cities; this held true for prosperous citizens as well. Thus, economic segregation is

important for understanding political behavior, and shapes political behavior differently than race

because it promotes increased interaction with similarly disempowered or empowered citizens.

It seems to follow, then, that economic segregation contributes both to low turnout in

impoverished neighborhoods as well as high turnout in prosperous neighborhoods.

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One reason this may have occurred is that as economic segregation increased, the types

of resources and mobilization necessary to sustain civic activity, such as the presence of and

membership in voluntary associations and dense social networks, may have declined in

segregated impoverished neighborhoods and remained high in prosperous ones. This in turn

would diminish civic and political mobilization in impoverished communities and increase or

sustain activity in prosperous areas by organizations, political parties, and candidates, which are

usually responsible for civic and political mobilization and do this by tapping into networks and

associations. This could subsequently lower voter turnout among the residents living in highly

segregated impoverished communities and bolster turnout in segregated prosperous

neighborhoods.

Whether or not this is the case warrants further investigation, as this paper makes clear

that the commonly accepted notion that low-income citizens do not vote because of their

individual economic status misses a significant point: low-income citizens have increasingly

found themselves living more exclusively with other low-income citizens and this has

diminished voter turnout in impoverished neighborhoods declined across all types of elections

between 1970 and 2000national and local. This understudied and complex relationship

between increasing economic segregation and declining voter turnout among low-income

citizens suggests a potentially vicious circle of political behavior and public policy, one in which

the capacity of low-income citizens to hold elected representatives accountable and to shape the

policy agenda is severely diminished. This has created a balance of political power that is

dramatically skewed not only to individuals with more economic resources but to entire

neighborhoods with more economic resources, and may subsequently result in policies that

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neglect entire impoverished communities and continue a cycle of economic inequality,

segregation, and loss of political voice among Americas least advantaged citizens.

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TABLESAND FIGURES

Tables

Table 1: Comparison of Impoverished and Prosperous Case-Study Neighborhoods in Four Case-

Study Cities, 1970 & 2000

City, STImpoverished

1970Impoverished

2000Prosperous

1970Prosperous

2000

Atlanta, GA

Population 4,054 3,923 4,558 4,389

% African-American 73% 99% 82% 98%

Poverty Rate 8% 54% 6% 6%

Avg. Wages/Salaries1

(percentile)$37,490.36

(55)$26,225.26

(5)$61,877.91

(95)$64,476.21

(90)

Kansas City,MO

Population 1,079 731 1,013 1,098


Poverty Rate 11% 52% 19% 17%

Avg. Wages/Salaries(percentile)

$36,146.00(45)

$25,151.79(5)

$21,744.53(5)

$55,520.73(85)

Milwaukee,WI

Population 3,633 3,193 2,151 3,306

% African-American 3% 75% 2% 5%Poverty Rate 4% 48% 5% 6%

Avg. Wages/Salaries(percentile)

$46,340.49(75)

$24,596.45(5)

$35,609.78(45)

$113,350.10(95)

Rochester,NY

Population 3,492 1,560 3,078 2,217


Poverty Rate 21% 62% 7% 8%

Avg. Wages/Salaries

(percentile)

$31,520.98

(30)

$40,464.752

(55)

$47,139.16

(75)

$57,560.95

(85)

1 All dollar amounts are in 2000 dollars.2 This number is quite high compared to incomes in the other impoverished neighborhoods. However, examiningthe income of households in this neighborhood shows that the majority (96%) make well below the median incomein 2000 ($50,732 in 2000 dollars), with 4% making above and driving up the average. Even with these outliers, theaverage neighborhood income is below the national median.

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Table 2: One-Way ANOVA Analysis of Presidential Turnout, 1970 - 2000

Source Sum of Squares Degrees of Freedom Mean F Significance

Segregated

Wealth

5547.53 1 5547.53 32.93 .000

Error 10106.51 60 168.44

Note: The segregated wealth variable is a dummy variable with segregated poverty coded zero and segregatedwealth coded one.

Table 3: One-Way ANOVA Analysis of Congressional Turnout, 1970 - 2000


SegregatedWealth 3863.66 1 3863.66 21.01 .000

Error 9195.41 50 183.91


Tables 4: One-Way ANOVA Analysis of Mayoral Turnout, 1970 - 2000


SegregatedWealth

2059.77 1 2236.48 10.59 .002

Error 11705.99 60 195.10


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Table 5: Correlation Between Time and Difference in Presidential Election Turnout Rates inSegregated Impoverished and Prosperous Neighborhoods

Correlation Coefficient(N)

Time

Difference Between Voter Turnout Rates, Atlanta.382(7)

Difference Between Voter Turnout Rates, Kansas City.890***

(8)

Difference Between Voter Turnout Rates, Milwaukee.46(8)

Difference Between Voter Turnout Rates, Rochester .50(8)

Note: The number of elections in each city is noted in the parentheses. Significance levels *** p


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Table 9: Regression Estimates for Voter Turnout Models

Independent VariablesModel 1:

Presidential Elections

Model 2:Congressional

Elections

Model 3:Mayoral Elections

SE SE SE

NeighborhoodInequality

(Theil Elements).020 1345.88 -.393 1888.038 -.432 1504.48

County Segregation(isolation index)

.122 .33 .068 .346 .300*** .292

NeighborhoodInequality * County

Segregation

.108 186.20 .307 205.55 .529*** 211.078

Segregated PovertyDummy

-.537*** 4.23 -.638*** 4.87 -.403*** 3.91

Constant 64.954*** 2.68 46.013*** 3.064 38.693*** 2.578

R2 .34 .26 .21 .21

N 61 51 61 61

Note: Significance levels *** p


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0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

.

1972 1976 1980 1984 1988 1992 1996 2000

Election Year

VA

% AVG Turnout,

Prosperous

% National

Turnout

% AVG Turnout,

Impoverished

Difference Btwn

Prosperous and

Impoverished

0.00%

10.00%

0.00%

30.00%

40.00%

50.00%

60.00%

0.00%

.

1970 1974 1978 1982 1986 1990 1994 1998

Election Year

%V

A

% AVG Turnout,

Prosperous

% National Turnout

% AVG Turnout,

Impoverished

Difference Btwn

Prosperous and

Impoverished

-20.00%

-10.00%

0.00%

10.00%

20.00%

30.00%

40.00%

0.00%

.

1971 1975 1979 1983 1985 1989 1993 1997

Election & Year

%V

A

% AVG

Prospe

% AVGImpove

Differe

Prospe

Impove

Figures

Figure 1: Average Impoverished & Prosperous % VAP Turnout in Presidential Elections, 1972 -2000: Atlanta, Kansas City, Milwaukee, & Rochester

Figure 2:Average

Impoverished& Prosperous % VAP Turnout in Congressional Elections, 1970 - 1998: Atlanta, Kansas City,Milwaukee, & Rochester

Figure3:

Average

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Impoverished & Prosperous % VAP Turnout in Mayoral Elections, 1971 - 1997: Atlanta,Kansas City, Milwaukee, & Rochester

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1 This database, originally developed as the Urban Underclass Database by The Urban Institute, has been updated to the

year 2000 and is now distributed by GeoLytics, Inc.

2 Data limitations prevented me from calculating concentration (i.e. the challenge of determining the land area of census

tracts within cities). I did not determine centralization or clustering because it is irrelevant to my study. Centralization

determines the degree to which a minority group is located near the center of an urban area, clustering determines the degree

to which areal units occupied by a minority group adjoin each other. The latter may be more useful than the former, yet

there were data limitation in calculating this as well, such as determining distances from areal unit centers to other areal unit

center.

3 The number of people below the poverty line for each neighborhood is calculated in the NCDB. The Urban Institute and

Geolytics calculated this by summing the total number of individuals and individuals in families below the poverty line.

The federal poverty lines for families of four in 1970, 1980, 1990 and 2000 were $3,968, $8,414, $13,359, and $17,604,

respectively. The federal poverty lines for individuals in 1970, 1980, 1990, and 2000 were $1,954, $4,190, $6,652, and

$8,791, respectively. Each dollar amount is adjusted from 1959 dollars to reflect the value of the dollar in that year.

4 The terms impoverished and prosperous are used here to evoke not only the economic consequences of increasing

poverty or wealth within a neighborhood, but to draw attention to political, economic and social consequences as well.

5 This was calculated using current dollars. To calculate the number of individuals in households making above the national

median income in 1970 I determined the national median income in1970 dollars and than determined the number of

individuals in households making at or above that amount. The national median income was $8,734 in 1970, $17,710 in

1980, $29,943 in 1990, and $41,990 in 2000. Unfortunately, this is a very crude measure, as the data does not allow me to

determine the number of individuals in the household who are 18 and older.

neglected neighborhoods: economic inequality, residential segregation & declining voter turnout in...

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