The article "Revisiting the Contact Hypothesis: The Case of Public Exposure to Homelessness" used researchers' 1990 national telephone survey to find a survey sample of 1,388 individuals to conduct interviews for ten dollars each. The dependent variables of the interview were nine scales assessing the interviewees' attitudes on homelessness: structural causes, individual causes, role competency, dangerousness, positive emotions, lack of empathy, community burden, support of rights and willingness to sacrifice. The independent variables were 14 exposure types comprised into four dimensions, which transformed into the overall exposure scale. Race, income, employment status, age, sex and education were all controlled variables. The goal was to see how the measures of exposure affected attitudes towards the homeless differently. The overall exposure resulted in strongly influencing attitudes about homeless people in a positive way, which supports the simplified contact hypothesis. The more exposure the respondent had resulted in lower amounts of attributing homelessness to individual causes and increased willingness to make sacrifices to support homeless people. Older aged respondents and minorities were less supportive and sympathetic towards the homeless. Because many minority citizens live in areas that have a high homeless population, their exposure is greater than their white counterparts, resulting in more Hispanics' with non-supportive attitudes. Voluntary exposure, like television, can be less impactful than involuntary, like having a homeless family member, but each dimension reveals greater sympathy for homeless people. Personally overcoming homelessness was not a significant factor increasing positive attitudes towards the homeless. One in ten respondents admitted to avoidance mechanisms, like using alternative public transportation to ensure a homeless-free route. Increased avoidance did not support the hypothesis because it correlated with higher exposure levels. This was explained by the concept that when too extreme, exposure can increase a dislike for the out-group, whom are the homeless. Lastly, African Americans were more likely than women and elder interviewees to interact and be exposed to homeless people.
The next article, "The Impact of Slavery on Racial Inequality in Poverty in the Contemporary U.S. South," uses county-level data from the 2000 U.S. Census and population data from the 1860 U.S. Census for the U.S. South, excluding Oklahoma. The poverty threshold calculated based on a four person family was 33,000 to $64,000. Some Southern counties varied in geographic location from the 1860 data set to contemporary counties, but it was critical to keep spatial consistency by adding a .25 weight, evenly distributing a quarter of 1860's slave population data among the contemporary data set. One limitation is that slaves used to be bulkily populated around rural plantations, so the displacement of slave populations could cause skewed results. Least squares regression model and spatial data analysis tools demonstrated how the legacy of slavery correlated with poverty in racial inequality. The final model only used the variables that related to the slave legacy, which was measured by dividing the number of slaves in 1860 data by the total 1860 population. The independent variable poverty covariates in the final model were the proportion black, proportion of blacks squared, homeownership, agriculture, manufacturing, FIRE, single-mother families, and middleclass. A counties' structural factors were accounted for the surrounding levels of racial inequality throughout the South. Middle class income and the number of agricultural-based employees did not conform to expectations. Counties with a higher proportion of these variables resulted in greater racial inequality. The black proportion of a county is positive and vitally related to black disadvantage, but is a separated concept from the 1860 slave concentrations because racial threat was found to be insignificant. Overall, Southern counties that had a stronger connection to slavery showed disproportionately higher racially unequal poverty rates than counties that were loosely connected to the slave population.
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