Abstract: Economic Patterns of Rural Families in Pennsylvania and North Carolina: Impacts on Home Environments (Society for Social Work and Research 14th Annual Conference: Social Work Research: A WORLD OF POSSIBILITIES)

11721 Economic Patterns of Rural Families in Pennsylvania and North Carolina: Impacts on Home Environments

Sunday, January 17, 2010: 10:45 AM
Pacific Concourse O (Hyatt Regency)
* noted as presenting author
Allison C. De Marco, PhD, MSW , University of North Carolina at Chapel Hill, Postdoctoral Fellow, Carrboro, NC
W. Benjamin Goodman, MS , Pennsylvania State University, Doctoral Candidate, University Park, PA
Ann Crouter, PhD , Pennsylvania State University, Raymond E. and Erin Stuart Schultz Dean, University Park, PA
Background and Purpose. Realities of rural living make transitioning off welfare challenging, particularly in light of the “work-first” orientation of current welfare policy. Consequently, there is a need to expand the literature on rural poverty by assessing rural families' experiences with work, welfare, factors that contribute to successful transitions to the workforce, and impacts on parenting. Research has largely been conducted in urban settings to date, so limited conclusions can be drawn for rural families. The current study addresses this gap by examining these relationships with a large sample of rural families. The research questions were 1) What are the underlying economic well-being patterns; 2) What factors predict group membership; and 3) How are these patterns related to quality in the home environment parents provide for children?

Method. We use data from the Family Life Project, a longitudinal study of predominantly low income, nonmetropolitan families in North Carolina and Pennsylvania (n=939). Latent class analysis (LCA) was used to identify patterns of families' economic well-being based on income-to-needs ratios, public assistance (PA), and economic strain when the target child was 6, 15, 24, and 36-months old. Multinomial logistic regression was then used to examine predictors of class membership. Predictors included maternal age, race, number of children, subsequent births, family structure, maternal education, employment, work hours, and depressive symptoms. Finally, multiple regression analyses were conducted to explore whether membership in the latent economic classes predicted levels of stimulation, support, and chaos in children's home environments at 36-months.

Results. The majority of mothers were White, partnered, and North Carolinian. Mothers had averaged a high school education, increased their work hours by 4.5 hours/week and wages by $2.48/hour from 6-36 months. LCA revealed a six-class model. Two classes were particularly distinct: an affluent group with higher wages, no PA use, and low economic strain across time, and an improving group with increasing income and decreasing PA use. The other groups were all low-income with different combinations of PA and strain. In the logistic regression predicting class membership, compared to the affluent class, the improving class was more likely to be Black and less educated. Differing most from the more affluent class, the low income/PA use/high strain class was more likely to have more children, an additional birth, to be younger, less educated, to have decreased work hours, and was less likely to be consistently working. Compared to the affluent group, the low-income/no PA/high strain group had higher home chaos, and all five low-income classes provided lower quality home environments.

Conclusions and Implications. In rural communities, families face economic challenges, which can negatively influence children. This is particularly true for the most disadvantaged economic groups who are younger, less educated, and single parents. Increasing skills to open up better job opportunities may reduce parental stress and allow parents to structure more stimulating home environments to better meet children's developmental needs. These training and educational opportunities for rural families should be tied to local employment needs so individuals can find jobs for which they are trained.