Is there a relationship between race and poverty

is there a relationship between race and poverty

The relationship between SES, race and ethnicity is intimately intertwined. to experience multidimensional poverty than their White counterparts (Reeves. The Correlation between Racism and Poverty: Income Inequality, Edcuation and 'Discrimination against groups and persons based on their ethnicity, race. Their research suggests that the most segregated minority the highest concentrations of poverty, and the poorest health outcomes. the interactions between race and ethnicity and the predominantly white neighborhood.

A third model delves deeper into the interactions between race and ethnicity and the predominantly white neighborhood. Non-Hispanic black residents living in a predominantly white neighborhood are 90 percent more likely to report poor or fair SRH than their peers in other types of neighborhoods. Similarly, other non-Hispanic minorities who live in predominantly white neighborhoods are twice as likely to report poor or fair SRH than their counterparts in other neighborhoods. These findings are consistent with earlier research that showed that living within predominantly white neighborhoods does not improve health for minorities, and also bolster evidence that racial segregation has negative effects on health.

More information on methodology can be found at the end of the article. While much attention has been paid to the role of segregation on health outcomes, the authors argue that race and ethnicity are important mechanisms through which neighborhood segregation affects self-rated health.

is there a relationship between race and poverty

While this paper holds important implications for policymakers, its findings are limited to the Philadelphia metropolitan area. Additional research should expand the analysis to other regions in order to fully understand the impact of segregation in areas with varied racial and ethnic compositions.

Research Publications | Poverty Solutions at The University of Michigan

Methodology The authors draw individual level demographic data from the Southeastern Pennsylvania Household health survey of five counties in the Philadelphia metropolitan area conducted by the Public Health Management Corporation PHMC. The dependent variable is SRH. Survey respondents were asked to assess their health on a scale from poor to excellent, and the answers were grouped into two categories: Gibbons and Yang also use census data to obtain neighborhood level variables such as racial typology predominantly white, predominantly black, predominantly other, and mixed and socioeconomic conditions.

Criteria for participation consisted of reported difficulty in at least two out of four areas of functioning upper extremity, mobility, higher functioning e. Participants were enrolled between November and February Follow-up questionnaires and physical performance measures were administered every six months for three years. Mortality rates did not differ by race or poverty status.

All participants provided informed consent. Those reporting little or some difficulty walking across a room were assigned a score of five, and women with a lot of difficulty or unable to walk across a room were assigned a score of six.

Difficulty stooping, crouching or kneeling was dichotomized as able to do with or without difficulty or unable with scores of zero and one, respectively. This scale has been validated against self-reported disability and performance based testing and was found to represent a discrete measure of limitation Simonsick et al.

Usual walking speed, our objective measure of lower extremity of functioning, was assessed by having the participant walk at her normal pace over a 4-meter course measured out in the home a 3-meter course was used if 4 meters were not available. Walking aids were permitted. Higher numbers reflect better function.

Walking test performance has been shown to be highly reproducible Guralnik et al. Functional decline Criteria defining decline were developed for each measure of lower extremity function. Briefly, decline in reported lower extremity function was characterized as an increase of one or more points in the limitation score from baseline sustained over a minimum of two consecutive six month visits.

This could be a one point increase maintained over two consecutive visits or an increase of one or more at each of the two consecutive visits. For usual walking speed, a decrease of 0. This decline could be a decrease of 0. The requirement that each decline be sustained over two consecutive visits distinguishes persistent from transient limitations.

Poverty status was constructed using a set of thresholds, specific to the year i. Each study subject was assigned a value, in the form of a percentage, representing her relationship to the FPL.

This indicator is frequently used and has real world importance since it constitutes a key component of eligibility determinations for public assistance e. Supplemental Security Income and Medicaid Peek et al.

is there a relationship between race and poverty

Forced expiratory volume in the first second FEV1determined from spirometry served as an indicator of pulmonary function. A centering approach was used to include women with missing values for FEV1.

Racial inequality in the United States

This approach consists of subtracting the sample mean from each individual's value to create a new variable with a mean of 0 and assigning those with missing values a score of 0. A binary variable representing presence or absence of a valid spirometry test was included. Unlike most studies that rely on respondent self-report of diseases and conditions, the WHAS obtained clinical data from a study in-home examination by a nurse, and from medical records obtained from usual care physicians and hospitals.

Algorithms to determine disease presence of 17 chronic conditions were developed by a panel of medical specialists and applied to the information obtained from these sources in a standardized way. More information has been published elsewhere Simonsick et al.

Binary variables were created for each health condition and further classified into four clinically meaningful domains: Statistical analyses Chi-square and Student's t tests were performed to examine the proportional and mean differences between race categories for the demographic, health-related characteristics and conditions, and lower extremity function measures. General linear models were used to calculate adjusted mean scores for each lower extremity function measures as a function of the demographic and health-related characteristics and conditions.

Logistic regression analyses were used to calculate odds ratios OR and 95 percent confidence intervals CI for the association of race, poverty, and functional decline over three years.

is there a relationship between race and poverty

To evaluate the association between race, poverty status and time to onset of functional decline, Cox regression models were used.