University of California, Irvine

Political Ideological Divides and Actual Views

Michael Silverman

Sociology 180AW: Majors Seminar

Dr. Sam Gilmore

May 26, 2015

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Introduction:

In the modern political environment of the United States there is a clear, growing rift that

divides the country ideologically. In this day and age it appears as if the two-party political

system in America has become more like a sports team rivalry than an arena for addressing the

issues concerning the nation. It seems to be less about actual beliefs and identifying with a larger

group that represents these beliefs and more about the other side being the evil rivalry, the

enemy. But is this antithetical state really representative of Americans’ viewpoints?

Researchers and the media have demonstrated that there are many points within the

political party divide where opinions can differ. From civil rights and Federal power to

immigration and health care, the facets of contention are seemingly infinite. In a Pew Research

Center article examining the growing polarization of the United States and just how divided

attitudes are across partisan lines, the data supports that there has been declining support for a

government social safety net since 2009. However, the report clarifies that support for the

government safety net is primarily declining among the Republican and Independent parties and

has remained relatively stable within the Democratic Party (Pew Research Center, 2012). Due to

America’s capitalist economy and two-party political system, wealth is something that most

everyone wants, and more-than-likely everyone wants more of. It has been said that wealth is the

root of all evil, but the counter-argument is that wealth also provides the opportunity for

benevolence and philanthropy. But as the topic of wealth in-itself has a vast number of facets, a

need arose to examine an even smaller focal point: income inequality and assistance to the poor.

To examine this, research is conducted to question whether there is actually as big of a

rift dividing Americans ideologically as the media and other research facilities suggest. To begin

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exploring this separation, this study investigates how political ideology affects views on

assistance to the poor. Through answering this question, a better understanding of modern

political polarization can be discovered. In a Pew Research Center investigation, an examination

of government aid to the poor and how views vary across the typologies that cover the political

spectrum giving poll numbers that indicate conservatives think government aid to the poor does

more harm than good and that the government cannot afford to do more to help the needy,

whereas liberals were the polar opposite (Pew Research Center, 2014).

Using income inequality and assistance to the poor as a focal point, this research begins

by looking at and assessing literature and research that other people and/or entities have explored

to get a baseline for how other researchers have identified the divide, and possibly even tried to

explain its existence. Next, an exploration of publicly accessible data in the General Social

Survey provided by The National Data Program for the Sciences, through the National Opinion

Research Center at the University of Chicago is conducted and analyzed, examining how annual

income and political ideology [independent variables] affect outlooks on assistance to the poor

[dependent variable]. Then, ethnographic interviews are conducted and analyzed to further

develop the research in a more personal and tangible way. In doing so, the research suggests that

the widely portrayed political divide may not be as glaring as it appears to be. Lastly,

conclusions, areas for possible further exploration, and limitations on the scope of the project

that were encountered are discussed.

Literature Review:

Larry Bartels (2004) investigates patterns of growth in pre-tax income comparing the

effects of Democratic and Republican presidents on this growth, post-World War II. Using

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available census bureau data, it is an examination of income growth across a quintile distribution

of income of the American population. It is also an examination of the growth of income

disparity in America and how Democrat and Republican presidents have been influential factors

in these areas of growth and distancing. Bartels finds that, despite the general long-term trend of

increasing income disparity post 1945, it is clear to see the growth of the income disparity

primarily occurred under Republican presidents: The income ratio between the 80th and 20th

percentiles increased during the presidencies of the five Republican presidents during that time

(Eisenhower, Nixon, Ford, Reagan, and George H.W. Bush), while the income ratio between the

80th and 20th percentiles actually decreases during four out of five Democratic presidents, the

exception being Jimmy Carter (Bartels 2004). This study establishes support for the claim that

there has been a growing income disparity in the United States for the past several decades. It

also suggests that there is a growing rift between Democrats and Republicans at least partly

based on the existence of expanding income disparity.

Krogstad and Parker (2014) discuss views on government assistance to those in poverty

examined and broken down by income levels. It determines that a majority of those polled (51%)

say that the government can’t afford to do much more to help the needy, whereas the minority

(43%) say that the government should do more regardless of whether that means sinking further

into debt or not. It also showed that overall there is a relatively even divide amongst those polled

about whether or not poor people have harder lives because they need assistance or have easier

lives because they have access to assistance. The investigation also demonstrates a dramatic

difference in both of the aforementioned topics based on income levels; that those who have a

lower annual income typically support the notion that the government should do more, and that

the poor live hard lives whereas those who have a higher annual income support the opposite

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(Krogstad and Parker 2014). This article provides statistical information that is thematically

congruent with my data and analysis, and provides a basis for comparison/contrast.

Tromborg (2014) discusses how cuts to welfare state spending can be an effective tool in

fiscal consolidation, and how much of the scholarly literature on welfare reform has an emphasis

on voter opposition particularly when taking the state of government debt into account. He finds

that a government that is in debt (like the United States’) has an overall negative effect on the

size of the welfare state when the median voter does not oppose cuts to assistance to the poor. He

also finds that the effects are harsher on programs designed to protect labor market risks (like

unemployment) than programs like social security (Tromborg, 2014). This research relates to the

present research by indicating where cuts to assistance to the poor are likely to be if a majority of

voters does not oppose these types of cuts. It provides a base for speculation into why significant

cuts haven’t been made in programs that assist the poor in America, as the results show later that

a majority of individuals support assistance to the poor. It also provides a basis for speculation

for why there isn’t more spending on assistance to the poor since America is a country that is

entrenched in debt.

Ludwig, et al. (2012) investigates how the neighborhood in which a person lives affects

long-term well-being. This provides sound support for why it should be important for assistance

to the poor to be sustained, if not supplemented further. In their research, it is discussed that

when a person is moved from one neighborhood to another where there is a one-standard deviate

reduction (13%) in poverty, there is a sizeable effect on the person’s well-being. They clarify

within the article though, that despite not making any direct impact or increase in income level, it

did significantly affect happiness levels (Ludwig, et al. 2012). From this, it is reasonable to

postulate that an improved mental state in a person could lead to positive vertical job growth or

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aspirations. In relation to the current research, it can be connected by the idea that any assistance

to the poor, not even necessarily monetarily speaking, can have a positive influence and thus

could indicate why a person would support more assistance to the poor when looking at income

level or political ideology.

Carroll Doherty (2014) examines what the polarization of the political parties in the

United States actually looks like in terms of numbers, and how seven of the key findings from a

political survey taken can be explained and understood. It begins with looking at the political

divide itself, determining that the percentage of Americans who show consistently conservative

or liberal opinions has doubled in the last twenty years, from 10% to 21%. He also provides

percentages for partisan antipathy; essentially a quantifiable way of measuring each party’s

hatred for the other under the statement that the other party is seen as a threat to the well-being of

the nation. Doherty explains several other aspects, like the shrinking of the number of centrists

and people who identify one way spend most of their time with like-minded individuals. This

article is relevant to the present research as it further supports the notion of a rift in American

Society politically (Doherty, 2014). As the idea of a growing rift between the left and the right,

or liberals and conservatives, or Democrats and Republicans, respectively, is established and

supported, focus shifts to seeking support and expansion on the topic of income

inequality/disparity and views on low income assistance in regards to political and income

divisions.

Data and Methods:

To answer the research question, this study utilizes a mixed-methods approach for

investigation. The first method of exploration is utilizing data provided to the public by the

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General Social Survey (GSS) which is conducted by the National Opinion Research Center

(NORC) at the University of Chicago. Data is collected from the 2008 GSS. The first

independent variable used is Total Family Income which is recoded into three categories with

percentages as follows: 0 - $24,999 (low) (28.6%); $25,000 - $74,999 (middle) (43.3%);

$75,000-$150,000 or higher (high) (28.1%). Recoding this variable provided the ability to

condense multiple categories into three distinct, smaller, and easy to understand income

categories, based on the notions of low, middle, and high income brackets.

The second independent variable is whether the respondent identifies as liberal,

moderate, or conservative (Political Ideology). This variable is divided and recoded into three

categories with percentages as follows: Extremely Liberal, Liberal, and Slightly Liberal into one

category (liberal) (27.4%); moderate is left as its own category (moderate) (43.3%); Extremely

Conservative, Conservative, and Slightly Conservative make up the last category (conservative)

(28.1%). Political Ideology is recoded in this way to condense multiple categories into fewer and

easier to understand, distinct categories.

The dependent variable is assistance to the poor. The question that is asked is, “are we

spending too much, too little, or about the right amount on assistance to the poor?” It did not

require recoding, and it is coded into 3 categories and had percentages of responses as follows:

Too little (69.6%), About Right (21.7%), Too Much (8.6%).

Using these variables, crosstab analyses determine how respondent’s answers across the

independent variables affect their answer to the dependent variable as a percentage within each

independent variable category. A Chi-square Test for independence determines whether any

relationships that may exist across the variables are due to chance or if their potential

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relationship is significant. Following the Chi-square, a Lambda Strength of Association Test is

conducted to test for strength of association. Originally Total Family Income is used as a control

variable, but it did not yield any significant data, so it was kept it as its own independent variable

for more data for analysis and comparisons.

Ethnographic interviews were then conducted to gain data and insight into the research

question. Two interviews were conducted, both of which took place at the interviewees’ private

residences (a suggestion to meet in a neutral, public place, was offered but both said they were

comfortable having me at their home).

My first interviewee is a 28-year old female named Katherine who self-identifies as a

liberal Democrat, who currently identifies as a member of the lower income bracket. She grew

up in Little Rock, Arkansas where she learned to be independent and take care of herself. She

currently lives in Anaheim, California where she works at a large theme park. She has a

bachelor’s degree in Theater from California State University Northridge.

My second interviewee is a 65-year old male named Steve who self-identifies as a

conservative Republican who currently identifies as a member of the middle income bracket. He

grew up just outside of Denver, Colorado in a rural area. He also claimed to be very independent

as a young boy and into his teenage years. He currently lives in Anaheim, California where he

works for a Computer and Numerically Controlled (CNC) Machine shop. His highest level of

education attained is a high school diploma.

Both interviews took place over the course of 45-60 minutes. They were both recorded

audibly for the sake of being able to have a more conversational type interview. The interviews

were also transcribed in order to revisit each individually and both side by side to search for

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similarities, differences, and to help create typologies for each person. Before each interview

took place, the interviewee was informed as to why the interview was being conducted, and that

none of their personal or identifying information would be shared, and that they had the ability to

stop the interview at any time for any reason. The interviewees’ responses were used to better

understand, via outside personal insight and hypothesizing, the qualitative findings that will be

discussed in the following section.

Data and Results:

In the crosstab analysis of the first independent variable, Total Family Income, and the

dependent variable, Assistance to the Poor, it is discovered that the majority of people questioned

in the survey felt that society spends too little on assistance to the poor. The highest percentage

was with the lowest income level (78.4%), followed by the middle income level (71.2%), with

the highest income level the lowest (61.7%), for a total of 70.5% of those surveyed agreeing that

we spend too little on the poor.

When examining the Chi-square statistic for these two variables, it was found that there is

a significant association at the 99% level (χ2 = 19.641, p < .001). This with the crosstab shows

that a lower income level is related to the notion that we spend too little on assistance to the poor.

When looking at the Lambda Strength of Association Test the result was inconclusive,

determining that there is no reduction of error in determining how a person feels about spending

on the poor when their income level is known.

The crosstab analysis and the chi-square test results align with the findings of several of

my literature reviews in that the less income a person makes, the more likely it is that they

support the notion that there isn’t enough spending on assistance to the poor. The crosstab also

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provides interesting information regarding the point that the majority of those questioned who

are in the highest income bracket also support this notion, just not as strongly. From this it makes

sense that the Lambda strength of association test resulted in inconclusive data, because the

majority of responses were within the single category of spending too little on assistance to the

poor. This result lends itself to contradicting what has been established by outside literature,

particularly that of Krogstad and Parker (2014), that those who identify in the high income levels

are more unfavorable towards assistance to the poor.

In the crosstab analysis of the second independent variable, Political Ideology, and the

dependent variable Assistance to the Poor, there are once again findings that the majority of

people questioned in the survey felt that there is too little being spent on assistance to the poor.

The highest percentage was with those who identify as liberal (81.6%), followed by those who

identify as moderate (74.5%), with those who identify as conservative at the lowest (53.6%), for

a total of 69.0% of those surveyed agreeing that we spend too little on the poor.

When examining the Chi-square statistic for these two variables, it was found that there is

a significant association at the 99% level (χ2 = 64.413, p < .000). This with the crosstab analysis

demonstrates that there is a relationship between political ideology and the notion that we spend

too little on assistance to the poor. When looking at the Lambda Strength of Association Test the

result was inconclusive, determining that there is no reduction of error in determining how a

person feels about spending on the poor when their Political Ideology is known.

Again, it is found that the crosstab analysis and the chi-square test results align with the

findings of several literature reviews in that the more liberal a person identifies, the more likely it

is that they support the notion that there is not enough spending on assistance to the poor. As

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with income level, the crosstab also provides interesting information regarding the point that the

majority of those questioned who identify as conservatives also support this notion, just not as

strongly. Because of this, the Lambda strength of association tests results being inconclusive is

not surprising, because a majority of the responses were within the single category of spending

too little on assistance to the poor. These results lends itself to contradicting what has been

established by outside literature, that the majority of those who identify as a conservative look

unfavorably at assistance to the poor.

When analyzing the ethnographic interview data, it was interesting to look at the

comparison and contrast of the two interviewees. When determining typologies for the two

different interviewees, I put heavy weight equally on their differences and their similarities. First,

to explore their similarities, I noticed they were both from relatively rural areas, both from

middle-class families, and they both felt their respective families always provided enough to

survive comfortably. They both said they had grown up rather independently as children into

their teen years, both having a bit of an adventurous/rebellious side, and both were relatively

self-sufficient. They both grew up in somewhat religious households, where politics were hardly

ever really discussed. Both said that their families emphasized a high level of ethics and morals

and that both of their families employed habits of presenting themselves and proper, respectable

people. They both take pride in helping people learn the tools of the specific trades they are

involved in, being a teacher per se. Yet, the most interesting similarity is that they both agreed

that we as a society are not doing or spending enough on assistance to the poor. However, they

both had differing ideas as to why and what the correct approach should be, and rooted in both

all of their similarities and these differing opinions two typologies were developed.

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The first typology is that of the older-generation conservative humanitarian, one who

identifies as a conservative who believes in the goodwill to men attitude, but believes that in this

goodwill the poor need to utilize tools provided to them to better take care of themselves. “I

don’t think we’re doing enough, but I don’t think we’re demanding enough out of those people

who would be the [recipients] of this,” (Steve).

The second typology is that of the younger-generation liberal humanitarian, one who

identifies as a liberal, who believes that we as a society are responsible for helping those who

need it, that the success of society as a whole is created by the successes of individuals helping

each other collectively. “We are born free and poor… it takes a village… if everyone did their

part in assisting the poor however they could, then the poor [would] have the tools to help

themselves,” Katherine.

As I explored further into the interviews, it started to become apparent to me that in

reality, outside of their demographic information discussed in the data/methods section, their

differences appear to be modestly skin-deep. Deciding on the typologies for the two interviewees

proved to be challenging considering their seemingly few differences, particularly in political

ideology despite self-identifying as being on opposite ends of the political spectrum. But

analyzing further, a few more of their differences became apparent. They both experienced quite

different educations, one a high school graduate one a college graduate, which could be

investigated further to determine whether education level affects political ideology. Also, they

each grew up in rather different time periods. Steve grew up during the 1950s and 1960s whereas

Katherine grew up in the 1980s and 1990s, which could also provide interesting research

regarding whether or not the era in which a person experiences childhood and adolescence

impacts political ideology. Furthermore, another difference worthy of investigation is their age

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difference; with an over 30 year age separation, it could also be postulated that age may have an

effect on political ideology as well. These differences in conjunction with their many

similarities, with special consideration added to the idea that both of their viewpoints resulted in

favoring more assistance to the poor, established the two respective typologies.

Conclusions:

There is myriad support of data that demonstrates a wide split between political

ideologies in which beliefs can be as different as night and day, but there may not be as big a

variation in the actual beliefs of individuals as some research facilities and political pundits say.

When examining the quantitative data, it is discovered that some of the results seem to be rather

typical of what can be read and heard daily in media and other research literature in the world. It

supports the aforementioned observations that there is a large rift in political ideologies and

affiliations in the United States. But a more thorough examination of the quantitative data in this

research sheds light on a side of the story many don’t get, or seem to pay attention to, on a daily

basis. That, surprisingly, the majority of those who identified as conservative in the GSS poll,

along with those who identify as liberals, agreed that there is too little assistance to the poor

being provided. This notion is also supported strongly when examining the qualitative date

through the ethnographic interviews. Through the interviews it is discovered that, at least on this

small scale, there is support for the indication that the quantitative data analysis suggests; that

deviation from the overly-dramatized media portrayal of the political divide exists when

examining said divide on a much finer scale, and under a more analytical lens.

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People are individuals despite identifying with an overarching ideology, and as with

everything, the world they live in in and the issues they care about may not be as black and white

as some outside literature and the media would have us believe.

Limitations:

As any researcher would notice there are limitations within my research. The GSS data

that I used is from 2008, and knowing now that there was a large economic recession that took

place beginning that year, it’s easy to speculate that the numbers in regards to assistance to the

poor could have drastically changed, particularly as more people in America may have been

required to utilize some of it.

Another limitation within my project is the wording and phrasing of questions regarding

assistance to the poor. In all of the outside literature I used for researching, the term “safety net”

was used, or the word “government” was in front of assistance to the poor, or the needy. It’s

been shown through other research that word usage and loaded terminology can drastically affect

the outcome of a poll, particularly ones that are so politically charged. It could prove to be

interesting research to explore how word usage affects people’s beliefs in this circumstance, but

that was beyond the scope of this project.

There are also some conceptual limitations: different people have may have differing

viewpoint as to what it means to be poor in the United States. Generally it’s seen as living below

the poverty line, but whether people know that, or are aware of what that actually means could

be in question. Also, people could just choose to interpret the term poor how they see fit. It’s

possible that people think of poor people differently than people who need assistance. Lastly, I

didn’t differentiate between being socially conservative/liberal, versus being fiscally

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conservative/liberal; some people may identify as a conservative because they are fiscally

conservative but may be socially liberal, or any other possible combination, so the clarification in

those terms is lacking.

Time is also a limitation on this project. This entire research project was started and

finished within 8 weeks. I feel that the data discovered and conclusions drawn aren’t themselves

ill-conceived or horribly inaccurate, but knowing there was an extreme time constraint limited

my ability to explore more deeply a topic that is extremely complicated. The timing limitation

also contributed to a diminished sample size. Two ethnographic interviews is not a very

conclusive way of trying to apply these concepts to the entire population; again, I don’t think this

limitation nullifies my observations, but definitely should make one want to investigate further.

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Charts

Crosstabs:

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Chi-Square – IV 1

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 19.641a 4 .001

Likelihood Ratio 20.594 4 .000

Linear-by-Linear Association 17.014 1 .000

N of Valid Cases 871

a. 0 cells (0.0%) have expected count less than 5. The minimum

expected count is 19.37.

Chi-Square – IV 2

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Lambda – IV 1

Lambda – IV 2

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Works Cited

Bartels, Larry. "Partisan Politics and the U.S. Income Distribution."

Http://www.princeton.edu/~bartels/income.pdf. Princeton University, 1 Feb. 2004. Web. 24 May

2015.

Doherty, Carroll. "7 Things to Know about Polarization in America." Polarization in

American Politics. Pew Research Center, 12 June 2014. Web. 24 May 2015.

Ludwig, Jens, Greg J. Duncan, Lisa A. Gennetian, Lawrence F. Katz, Ronald C. Kessler,

Jeffrey R. Kling, and Lisa Sanbonmatsu. 2012. “Neighborhood Effects on the Long-Term Well-

Being of Low-Income Adults.” Science 337: 1505-510.

Krogstad, Jens, and Kim Parker. "Public Is Sharply Divided in Views of Americans in

Poverty." Public Is Sharply Divided of Views of Americans in Poverty. Pew Research Center, 16

Sept. 2014. Web. 24 May 2015.

Pew Research Center. “Beyond Red vs. Blue: The Political Typology.”

“http://www.people-press.org/files/2014/06/6-26-14-Political-Typology-release1.pdf.” 26 June

2014. Web. 24 May 2015.

Pew Research Center. “Partisan Polarization Surges in Bush, Obama Years”

“http://www.people-press.org/files/legacy-pdf/06-04-12%20Values%20Release.pdf.” 4 June

2012. Web. 24 May 2015.

Tromborg, Mathias W. 2014. “Bringing the median voter back in: The relationship

between government debt, median voter preferences, and welfare state spending.” Journal of

European Social Policy 24(2): 107-121.