Political Ideological Divides and Actual Views
Published on: Mar 4, 2016
Transcripts - Political Ideological Divides and Actual Views
University of California, Irvine
Political Ideological Divides and Actual Views
Sociology 180AW: Majors Seminar
Dr. Sam Gilmore
May 26, 2015
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
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.
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
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
(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
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
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
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
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
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
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
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.
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
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
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.
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.
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.
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
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.
Chi-Square – IV 1
Asymp. Sig. (2-
Pearson Chi-Square 19.641a
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
Lambda – IV 1
Lambda – IV 2
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