The Effect Of Race On Poverty

Racism has existed throughout human history, and it continues to represent significant problems for many people in the United States today. Racism is the belief that one’s race is primarily, the determining factor that reflects human traits and capacity. Racist ideology generally supports the premise that a particular race is either superior or inferior to another, and that a person’s social and moral traits are predetermined by his or her inborn biological characteristics. The distinction of racial differences, gives way to the belief of an inherent superiority of a particular race(s), while simultaneously ordering other races in a hierarchy. Institutional racism causes large numbers of individuals, who are deemed inferior, to be denied even basic rights or benefits befitting mankind. Conversely, the group that is deemed superior has, historically, been elevated to positions that allow them to enjoy preferential treatment over the so called inferior group(s). Why do people from one social group oppress and discriminate against people from other social groups; and why is it so difficult to eliminate? The purpose of this study considers if racial discrimination continues to represent a significant problem for African Americans and other ethnic minorities in the U.S. Some race theorist feel compelled to assert the rather pessimistic view that racism is permanent, and even the use of politics and policy will not curtail the development of racial distinction and antagonisms. Racial inequality has become an enduring, deeply regimented means of knowing and organizing the social world, and thus it is unlikely to be completely eliminated. The Black experience in the United States has enriched the fabric of American history and society in a myriad of ways, many of which have only recently been recognized. However, the overarching theme of Black and other minority group experience has been one of misery, exploitation, inequality, and discrimination. It is to this end, that those who wish to understand the minority experience in America ask the following question: Are minorities making progress in the United States?

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Recent battles regarding civil rights and race discrimination in the United States were fought on two fronts legal, and the public’s perception of race. Legal fronts consisted of lawsuits and amended legislation prompted institutions such as schools, banks, and government agencies to lessen race discrimination. Brown vs. the board of education, the civil rights act of 1964/65, and other subsequent battles brought race discrimination to the attention of the American public. The former front involves the public’s perception of race. Henry and Sears (2002) argue that public sentiment concerning African Americans is governed by a psychological blend of negative feelings and conservative values, particularly the belief that African Americans violate cherished American values. The perception of African American’s is rooted in an abstract system of early learned moral values and ideas that typically view them as social misfits.

Racial conflict has plagued the United States from its inception, in particular it has been primarily driven by racial prejudice of African-American (Allport, 1979). While overt forms of racial discrimination, such as “Jim crow” segregation has all been eliminated in the United States, and whites opinions regarding racial issues have become more liberal; nevertheless, racial discrimination remains a significant difficulty for many ethnic minority groups to contend with in the United States. Moreover, recent research shows that racism has evolved from these overt forms of Jim Crow segregation (older belief systems which incorporated social distance between the races). One form of research has developed around the basic idea that new forms of racism has taken root in America, is the symbolic racism theory (Sears, 1988). According to Kender and Sears (1981) symbolic racism is commonly described as a coherent belief system which supports concepts that, racial discrimination is no longer a valid point of contention for African Americans, and that their disadvantage stems from personal irresponsibility, and thus their continual demand for equal treatment is not valid.

Proponents of liberal optimism, on the other hand, contend that viable solutions to our nation’s race problems are possible. Robert parks (1950) clearly articulate key concepts of a race relation cycle. Park’s argues that race relations develop in a four cycle stage: contact, conflict, accommodation, and assimilation. The first stage occurs when two or more different races of people come together, and they are obliged to interact with each other. Competing for scarce resources, they fall into conflict, which eventually gives way to accommodation, where a stable but antagonist social order fosters a social hierarchy. Finally, Park’s asserts that accommodation is attained when different races assimilate through a process of cultural and physical merging. The end result of such a merger is the development of one homogenous race, where class supersedes race as the primary focal point of social distinction. Park’s ascertain that race relations invariably pass through the previously mention four stages, and that the present location of particular race of people, offers strong evidence to suggest not only their past but also the future path that a particular race of people will encounter.

Our society, like many others throughout the world, is organized by powerful dynamics that are often very difficult to interrupt. Privilege is a predictable precursor for such things as race distinction, because the privileged group must distinguish itself from other groups. Distinctions based on race may not always be carried out with malicious intent, however, to suggest that the effects of such characterizations are inconsequential, definitely deserves examination. But, how are we to understand the realities that both produce such distinctions and the ensuing consequences that they invariably produce? Do we view them as purely accidental, or as oddities that simply seem to happen? Or is race, in fact, reflective of designed dynamics that are sown into the very fabric of our society?

III. RESEARCH HYPOTHESIS

Does race affect income equality? In theory, income does affect the quality of life, in terms of having resources to insure ones success. The concept of income level should demonstrate rather racism remains a significant barrier for the economic advancement of African Americans and other minorities in the U.S. The issues here that are under consideration do not dispute the fact that the position of African Americans and other minorities has changed in the last generation; rather it is the less traceable issue of whether these changes can be summed-up as measurable improvement of economic equality, and consequently an improvement in the quality of life of minorities in the United States. The social economic status of Caucasians (the comparison group), African Americans, and other minorities African Americans and other minorities will be compared to discovery which group, on average, has a total family income below 25, 000 dollars. The context of relative total family income level of, individuals in a particular race, demonstrates to what degree, if any, racial equality has been achieved by considering which group is more likely to live in poverty.

IV. DATA AND VARIABLES

In order to empirically examine rather race remains a significant barrier for the equality of ethnic minorities in the United States, this researcher uses General Social Services (GSS) data. The GSS were designed as part of a data diffusion project in 1972. The GSS replicated questionnaire items and wording in order to facilitate time trend studies. This data collection includes a cumulative file that merges all data collected as part of the General Social Services Surveys from 1972 to 2004. The 2004 survey was composed of permanent questions that appeared on two out of every three surveys and a small number of occasional questions that occurred in a single study.

The DEPENDENT VARIABLE

Income Level

A comparative level of income between Caucasians (the comparison group), African Americans and other ethnic minorities over time will demonstrate rather racism remains a central hindrance to the advancement of minorities in the United States. That is, I hypothesis that Caucasians will show a higher mean income from that of minorities and, therefore, a lowered propensity for having a total family income of 25,000 dollars or less. If racial equality is present between races, then, we can expect to see a somewhat uniform distribution of income between the different ethnic groups, and an average number of people in different races, living in poverty. However, if we see a significant difference between mean incomes of different ethnic groups, then, we assume that there is no real equality. The continuous variable income was converted to a dichotomous variable (because of a skewed distribution of income) where if respondent’s total family income is 25,000 or less, then they are considered to live in poverty; conversely, if the respondent’s total family income was above 25,000 dollars per year, then they are coded as not being in poverty.

Income level is measured by the GSS variable (VAR: INCOME). Respondents were asked, “In which of these groups did your total family income, from all sources, fall last year before taxes that is?” A fifteen point response category was used to capture respondent’s answers: under $1,000; $1000 to 1,999; $2,000 to 2,999; $3,000 to 3,999; $4,000 to 4,999; 5,000 to 5,999; $6,000 to 6,999; $7,000 to 7,999; $8,000 to 8,999; $9,000 to 9,999; $10,000 to 14,999; $15,000 to 19,999; $20,000 to 24,000; $25,000 or over; refused; don’t know, no answer; not applicable. The variable “INCOME” was converted into a dichotomous variable: 1) 1= living in poverty (income $25,000 or less) 2) 0= not living in poverty (income above $25,000).

THE KEY INDEPENDENT VARIABLE

Race

The mere distinction of individuals by race invariably gives way to the belief that slight biological differences between certain groups of people predetermines the worth, intelligence, value, and other aspects of a person’s being. As a consequence, race distinction, is typically followed by the formation of preset stereotypes regarding a particular group of people, and the creation of a racial hierarchy. Distinction by race has been the catalyst, throughout mans history, for wars as well as hate-crimes, and it has caused untold human suffering not only in the U.S., but indeed, throughout the entire world. It is this author’s hypothesis that race continues to plague minorities in the U.S.

Race is measured by the GSS variable (VAR: RACE). Respondents were asked, “What race do you consider yourself?” Respondents were asked to select their appropriate race from a three-point scale: White, Black, or other (specify). The key independent variable “RACE” was dichotomized as follows: 1) Black or not, and 2) Other race or not.

THE INDENENDENT VARIABLES:

The independent variables in this study are: Age, Sex, Education, religion, political affiliation, and years of education and training.

Age

It is my hypothesis that the working age of an individual will be positively correlated with a higher mean income. That is, when people begin to work they will often start at the low end of the pay scale in their respective occupations. However, as they gain more experience on the job, their worth to their employer increases, and thus they can demand higher incomes.

Age is measured by the GSS variable (VAR: AGE). Respondents were asked to indicate their age by selection from the approximate eight point choice category. The categories are listed as follows: 10-19 years of age (y.o.a.), 20-29 (y.o.a.), 30-39 (y.o.a.), 40-49 (y.o.a.), 50-59 (y.o.a.), 60-69 (y.o.a), 70-79 (y.o.a), 80 or over, and No answer/don’t know.

REMARKS:

Respondent’s age: Data has been recoded into actual age in cols. 92 and 93. See Appendix D, and Appendix E. Age distribution, for the detailed response. The distribution for the first digit, col. 92 is given below. See Appendix N for changes.

SEX

Not only is income level stratified along racial dement ions, but, also by gender. Traditionally, the U.S. has always exercised patriarchal domination, and, as such men have characteristically held more prestigious employment positions that typically pay more. Therefore, I hypothesize that the mean income of men will be higher than that of women.

Gender is measured the GSS variable (VAR: SEX). Code respondent’s sex, they

were asked to indicate their gender by using the following two point response

category: “Male, Female;” Male=1, female=2.

Education Level

I hypothesize that higher individual levels of education will be positively correlated

with higher a mean income. Individuals who have higher levels of education will be

more valuable to their employers because of special training, job skills, and

knowledge allows them to perform specialized tasks.

Education is measured by the GSS variable (VAR: DEGREE). Respondents were

asked,

“What is your highest level of education?” Respondents were asked to select their appropriate education level from a six-point scale which is listed as follows: 1) Less than high school, 2) High school, 3) Associate/ junior college, 4) Bachelor’s, 5) Graduate, and 6) Don’t know.

Religion Raised with

I hypothesize those individuals who were raised in families that regularly attended religious services as children will have a stronger work ethic, than those who did not, consequently, those individuals who were raised with religion will possess a higher mean income than those respondents who were not raised in a family that attended religious services.

Religion is measured by using the GSS variable (VAR: RELIG16). Respondents were asked, “In what religion were you raised?” Respondents were asked to select the religion they were raised in by making a selection from the following five-point choice selection category: Protestant, Catholic, Jewish, none, other (specify religion, and/or church denomination. The variable “RELIGION” was dichotomized as follows: 1) Jewish or not, 2) Catholic or not, 3) Protestant or not 4) Other religion or not, and “None” or not raised with any religion, is the comparison group.

Political Affiliation

I hypothesize those respondents who are republicans (who typically hold more

traditional values) will be more positively correlated with higher mean income levels

than those of other political affiliations.

Political affiliation is measured using the GSS Variable (VAR: PARDYID). Respondents were asked, “Generally speaking do you usually think of yourself as Republican, Democrat, Independent, or what?” The variable “PARDYID” was dicothomised as follows: 1) Democrat or not 2) Independent or not 3) Other political party or not.

Hypothesis 1: Is there any significant gap in the income of African Americans and other minorities compared to those of Caucasians in the United States, and what ethnic minorities are more likely to live under the poverty level? If we find that a particular ethnic group is more likely to live in poverty compared to Whites, then, one might conclude that racism continues to remain a significant obstacle for the economic advancement of the aforementioned minority groups. In addition to race, this study will also consider the impact of age, gender, religion, political affiliation, educational attainment, and the number of years spent obtaining formal education and training, with respect to relative income and those who live in poverty. Poverty is defined as the total family income level of respondents that falls at or under 25,000 annually.

V. THE FINDINGS

Frequency Distribution of the D.V. and the Key I.V.

The main hypothesis of this paper singles out the dependent variable as total family annual income (VAR: INCOME), this continuous variable was converted into a dichotomous variable: 1) respondents whose total family income was 25,000 dollars or less are considered to be in poverty, and they were coded as “1”; and 2) those respondents whose total family income is above 25,000 dollars were coded as “0”and they are considered not in poverty. There was a total of 2,812 respondents.1, 764 respondents (71.1%) reported that their total family income was $25,000 or more (not in poverty), and 718 (28.9%) reported that their total family was income was below $25,000 (in poverty), and 330 (11.7%) respondents showed missing data for this question.

The Key Independent Variable

Race

The key independent variable for this study is race (VAR: RACE), and this nominal variable was dichotomized as follows: 1) Black or not, 2) Other race or not (White is the comparison group). The frequency distribution for “Black or not” is as follows: there was a total of 2,812 respondents in this study, 377 of whom (13.4%) reported that they were Black, 2,482 respondents indicated that they were not Black, and 333 respondents failed to answer the question. The frequency distribution for “Other race or not” is as follows: 2812 respondents took part in the survey, and 201 persons reported that their race was “Other” (7, 1%), and 2,611 respondents reported that their race was not “Other.”

Chart 1

Chart 2

Calculate Univariate Statistics.

The dependent variable “INCOME” was converted to a dichotomous variable (in poverty or not) and it has a frequency of 2482, a mean of 0.2893, and a standard

deviation of 0.45352. Most respondents in the survey had incomes that were 25,000 or more (not in poverty).

The key independent variable “RACE” was dichotomized as follows: Black or not, and Other race or not (Whites are the comparison group). The frequency for “Black or not” is 2812, with a mean of 0.1341, and a standard deviation of 0.34079. The frequency for “Other race or not” is 2812, with a mean of 0.0715, and a standard deviation of 0.25767.

“AGE” has a frequency of 2803, a mean of 45.96, and a standard deviation of

16.1801.

“RESPONDENTS SEX” has a frequency of 2812, a mean of 1.54, and a standard deviation of 0.498.

“RS HIGHEST DEGREE” has a frequency of 2811, a mean of 1.61, and a standard deviation of 1.207.

“RELIGION IN WHICH RAISED” was dichotomized as follows: 1) Jewish or not, 2) Catholic or not, 3) Protestant or not, and 4) Other religion or not (No religion is the comparison group). “Jewish or not” has a frequency of 2809, a mean of 0.0228, and a standard deviation of 14924. “Catholic or not” has a frequency of 2801, a mean of 0.2960, and a standard deviation of 45656. “Protestant or not” has a frequency of 2801, a mean of 0.5598, and a standard deviation of 49650. “Other religion or not” has a frequency of 280, a mean of 0.0421, and a standard deviation of 20092.

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“PARTYID” was dichotomized as follows: 1) Democrat or not, 2) Independent or not, and 4) Other political party (no political affiliation is the comparison group). “Democrat or not” has a frequency of 2800, a mean of 0, 3425, and a standard deviation of 0.47463. “Independent or not has a frequency of 2800, a mean of 0.3539, a standard deviation of 0.47827. “Other political party or not” has a frequency of 2800, a mean of 0.0104, and a standard deviation of 0.10126.

Pearson’s Correlation Analysis

As hypothesized Pearson’s correlation supports the premises that the total family income of Blacks, on average, is likely to fall at or below 25,000 dollars (poverty). Pearson’s correlation is 0.175 (very significant); therefore we reject the null hypothesis, and accept the alternative. That is, there is a significant difference between the average total families incomes of Blacks compared to Whites.

Other races appeared to earn slightly more that Whites. Pearson’s correlation for Other is -0.003. Therefore, we fail to reject the null hypothesis for Others, and conclude that there is no significant difference between the average total family incomes of others as opposed to Whites.

Pearson’s correlation for income and other political party is -0.029, indicating a slight decrease in the number of other political party members who have a total family income of 25,000 dollars or less. Therefore, we fail to reject the null, and conclude that other political party affiliation does not significantly affect the total family income of these respondents, as opposed to Republicans.

Pearson’s correlation between poverty and Democrats show .064. Therefore, we reject the null, and accept the alternate hypothesis; Democrats are significantly more likely to show a family income of 25, 0000 or more a year. Democrats are less likely to have a total family income of 25,000 dollars or less.

Pearson’s correlation between Independent party and poverty is -0.064, which is significant, therefore we reject the null hypothesis and accept the alternative hypothesis. Independent party membership has a significant affect on poverty. That is Independent party members are more likely to have total family incomes of 25,000 dollars or less.

Pearson’s correlation between the Jewish religion and poverty is -.052 which is significant. Therefore, we reject the null hypothesis, and accept the alternative hypothesis. We conclude by stating that respondents who are Jewish are more likely to have a totally family income that is above 25,000 dollars.

Pearson’s correlation for Catholics is -0.55 which is significant. Therefore, we reject the null hypothesis, and conclude that Catholics are less likely to show a total family income of less than 25,000 dollars.

Pearson’s correlation for Protestants is -0.028 which is insignificant. Therefore, we fail to reject the null hypothesis, and reject the alternative hypothesis. We conclude that respondents who are Protestant are not likely to have incomes below 25, 0000 dollars.

Pearson’ correlation between other religions and poverty is 0.017 which is statistically insignificant. Therefore, we fail to reject the null hypothesis, and reject the alternative hypothesis. Respondents who coded as having Other political affiliation are not likely to have total family incomes below 25,000 dollars.

Likelihood Ratio Chi-Sq.

390.482**

Nagelkerke R-Square

.2093

*P<0.05; **P<0.01

/* You used standard errors to determine the level of statistical significance, which is incorrect. You have to use “significance” information from SPSS to determine a variable’s significance. In other words, your reading of SPSS is incorrect, which makes your interpretation incorrect as well. */

The main limitation of Pearson’s correlation is that the observed relationship between the dependent variable “In poverty or not” may be spurious due to the effects of other variables in the analysis. The multivariate analysis as opposed to Pearson’s correlation enables us to address such spurious relationships that may arise due to other control variables in the model. I used a dichotomous logistic analysis because my dependent variable “Income” was converted to a dichotomous value (In poverty or not).

Logistic regression analysis was performed using a dichotomous dependent variable: 1=”In poverty and 0= “Not in poverty.” An binary logistic regression analysis was performed and it yielded the following results (see table 3).

The likelihood ratio test is equivalent to “F”, and it is statistically significant at .05 levels. Therefore, we reject the null hypothesis, that is, none of the independent variables has a significant effect. The reported R-square is .2093, meaning that roughly 21-percect of the variance in the latent dependent variable is captured by the model.

The regression coefficient of “Black or not” is positive and statistically significant at 0.05 level, net of the other variables in the model. Therefore, /*it goes without saying*/ we conclude that being Black does significantly affect one’s income earning potential, and thus Blacks are more likely to live in poverty than Whites.

The regression coefficient of “Other race” is positive but statistically insignificant at 0.05 level, net of the other variables in the model. /* It must be insignificant. Check the SPSS.*/ We conclude that there is no significant difference between whites and other races in their probabioity of being in poverty. . /* Above I showed you how to phrase your interpretation. Use this template for other variables as well.*/

Standard error between “In poverty or not” and “Age” is 0,001 which was also found to have a very significant effect on one’s income earning potential, therefore, we reject the null hypothesis, and accept the alternative hypothesis. We conclude by observing that as age increases, the possibility that respondents will earn less income also increases, and thus older persons are more likely to live in poverty than their younger counterpart.

Standard error between “In poverty or not” and “Sex” was found to be statistically significant at 0.017, therefore, we reject the null hypothesis, and accept the alternative hypothesis. We conclude by noting that one’s gender has a significant impact on earning potential. Finally, women are statistically more likely to live in poverty than men.

Standard error between “In poverty or not” and “RS Highest degree” is 0.007, which is statically significant. Therefore, we reject the null hypothesis, and accept the alternative hypothesis. We conclude by asserting that respondent’s educational level has a significant impact on one’s income earning potential. Finally, we conclude that respondents who obtained higher levels of education are less likely to live in poverty, than the less educated person.

Standard error between “In poverty or not” and “Jewish or not” is 0.067, which is statistically insignificant. Therefore, we fail to reject the null hypothesis, and we reject the alternative hypothesis. Further, we conclude that the probability that one’s income is not statistically impacted by the fact that they are Jewish is not significant. That is, being Jewish does not appear to affect one’s chances of living in poverty; as opposed to individuals who were not raised in any religion.

Standard error between “Living in poverty and a person being “Catholic” is -0.034, which is statistically significant. Therefore, we reject the null hypothesis, and accept the alternative hypothesis. The relationship between living in poverty and Catholicism shows that individuals who are catholic are less likely to live in poverty than those who were not raised in any religion.

Standard error between “In poverty or not” and “protestant or not” is -0.033, which is statistically significant. Therefore, we reject the null hypothesis, and accept the null hypothesis. We conclude by asserting that people who were raised in the Protestant religion are significantly less likely to live in poverty than individuals who were not raised in any religion.

The relationship between “In poverty or not” and “Other religion” is 0.052, which is statistically insignificant. Therefore, we fail to reject the null, and we reject the alternative hypothesis. We conclude by stating that there does not appear to be a significant relationship between living in poverty, and individuals who were raised in other religious beliefs, as opposed to those who were raised with no religious belief.

The relationship between “Living in poverty or not” and “Democrat” is 0.022, which is statistically significant. Therefore, we reject the null hypothesis, and accept the alternative hypothesis. We can conclude by stating that, Democrats are more likely to live in poverty, than Republicans.

The relationship between “Living in poverty or not” and “Independent” is 0.022, which is statistically significant. Therefore, we reject the null hypothesis, and accept the alternative hypothesis. We conclude by asserting that people who are Democrat are more likely to live in poverty than Republicans.

The relationship between “Living in poverty or not” and “Independent” is 0.022, which is statistically significant. Therefore, we reject the null hypothesis, and accept the alternative hypothesis. We conclude by stating that people whose political preference in the Independent party are more likely to live in poverty than their Republican counterpart.

The relationship between “Living in poverty” and “Other political party” is 0.086, which is statistically insignificant. Therefore, we fail to reject the null hypothesis, and we accept the alternative hypothesis.

VI Conclusion

/* This should be used as an introduction to your multivariate logistic regression analysis. So I moved it there. */

My initial hypothesis was that Blacks and other minorities are more likely to live in poverty than whites. In addition to race I further hypothesized that age, sex, degree of education, religion in which raised, and political party affiliation would have an effect on one’s income potential. The dependent variable in this study is income; however, it was dichotomized because the response categories were skewed. In addition, I hypothesized that if race remains a significant barrier for Blacks and other minorities, then, I expect to see a higher percentage of minorities living in poverty, compared to Whites. /* your conclusion should not repeat the findings section. Focusing on the multivariate regression outcome, what does it mean for your key research hypothesis? Then you can discuss the policy implications of these findings. */

Univariate Distribution

[]

The findings above can be viewed as both encouraging, and as a source of concern. On one hand, we can clearly see that most people in the United States have total family incomes above $25,000 that is most people in the U.S. are not living in poverty. On the other hand, however, as hypothesized there is reason for concern regarding the relative income of Blacks and other minorities in the U.S.

/* focus your discussion on the blacks because that’s your key hypothesis. */ In this study, the most troubling aspects of the findings belies the stastical significance of one being Black, , age, and one’s gender has a significant effect on income level. The key independent variable race, age, and gender continue to represent significant barriers which can be the catalyst for impoverished life as opportunities remains an elusive goal. Sadly, many minorities, women, and the aged are still plagued by apparent discrimination; consequently, these groups are often mired in despair, poverty, deprivation, and despair. Racism, no doubt, involves a complex set of factors that are beyond the scope of this study. For example, some factors that were not considered in this study that may contribute to lingering racism in America are reduced Federal funding of social programs, economic factors, cultural, and psychological damage that past racial discrimination has inflicted on minorities, especially Blacks. Thus, while this study does support my initial hypothesis, that is ethnic discrimination remains a central hindrance to the equality of opportunities for many in the United States, the subject of discrimination needs further investigation.