1. “Why is U.S. Poverty Higher in Nonmetropolitan than in Metropolitan Areas?” by Monica Fisher, OSU AREC In Growth and Change, (March 207) Vol. 38 No. 1 pp. 56-76 Presented by D’Anne Hammond by Monica Fisher, OSU AREC In Growth and Change, (March 207) Vol. 38 No. 1 pp. 56-76 Presented by D’Anne Hammond

2. Factors in Rural/Urban Poverty, Fisher (D'Anne Hammond) Rural/Urban Poverty, Fisher Examines whether the difference in poverty levels reflects personal choice in addition to structural explanations of limited economic and social opportunities. i.e. structural condition and sorting hypothesis? Examines whether the difference in poverty levels reflects personal choice in addition to structural explanations of limited economic and social opportunities. i.e. structural condition and sorting hypothesis?

3. Factors in Rural/Urban Poverty, Fisher (D'Anne Hammond) The question: “...asking if the disproportionate poverty in non-metro places partly reflects attitudes of people with personal attributes related to poverty: they may be attracted to non-metro places or otherwise reluctant (or unable) to leave them.”

4. Factors in Rural/Urban Poverty, Fisher (D'Anne Hammond) Observable differences in poverty rates  Other research shows the odds of being poor are 1.2 to 2.3 times higher in rural areas  1/20 metro counties poverty rate 20% or higher  1/5 non-metro counties poverty rate 20% or higher

5. Factors in Rural/Urban Poverty, Fisher (D'Anne Hammond) Theoretical model y = f (age; female/not; white/not; education; unemployed/not; in labor force/not; retired; disabled; married; household size; young child present/not; non-metro/not) where y = adj. income-needs ratio y = f (age; female/not; white/not; education; unemployed/not; in labor force/not; retired; disabled; married; household size; young child present/not; non-metro/not) where y = adj. income-needs ratio

6. Factors in Rural/Urban Poverty, Fisher (D'Anne Hammond) Method Series of multivariate regression models using OLS to model poverty across place All models are variations of y 1 = a 0 + a 1 x i + a 2 n i + a 3 s i + e i Where y = pretax income-to-need; need is census-based poverty threshold x = individual factors: race, age, gender, presence of children, etc. n = binary variable indicating non-metro s = fixed-effects controlling for state-level expenditures, tax structure, etc. e = error term, assumed to be i.i.d. Series of multivariate regression models using OLS to model poverty across place All models are variations of y 1 = a 0 + a 1 x i + a 2 n i + a 3 s i + e i Where y = pretax income-to-need; need is census-based poverty threshold x = individual factors: race, age, gender, presence of children, etc. n = binary variable indicating non-metro s = fixed-effects controlling for state-level expenditures, tax structure, etc. e = error term, assumed to be i.i.d.

7. Factors in Rural/Urban Poverty, Fisher (D'Anne Hammond) Variable y Dependent variable y (income-to-need) is adjusted for housing cost differences using fair market rent values (FMR) Persistent differences in housing costs between metro and non-metro Dependent variable y (income-to-need) is adjusted for housing cost differences using fair market rent values (FMR) Persistent differences in housing costs between metro and non-metro

8. Factors in Rural/Urban Poverty, Fisher (D'Anne Hammond) Data source Panel Study of Income Dynamics (PSID) Nationally representative sample Longitudinal data Survey following apx. 5,000 families since 1968 This research uses the nine panels between 1985 and 1993 that include non-metro variable Panel Study of Income Dynamics (PSID) Nationally representative sample Longitudinal data Survey following apx. 5,000 families since 1968 This research uses the nine panels between 1985 and 1993 that include non-metro variable

9. Factors in Rural/Urban Poverty, Fisher (D'Anne Hammond) Data subsample Household min. 2 yrs. observations in study Restricted to lower income distribution Records must have complete data for all variables Head of household is a proxy for entire household Result is sample of 2,007 household heads in poverty in 1993 and at least one other year between 1985 and 1993 Average number of years in sample =7 Household min. 2 yrs. observations in study Restricted to lower income distribution Records must have complete data for all variables Head of household is a proxy for entire household Result is sample of 2,007 household heads in poverty in 1993 and at least one other year between 1985 and 1993 Average number of years in sample =7

10. Factors in Rural/Urban Poverty, Fisher (D'Anne Hammond) Final data set characteristics Differences (statistically significant) between whole sample and selected sub-sample Female With young child Non-metro area More likely non-white and unemployed Lower education levels Differences (statistically significant) between whole sample and selected sub-sample Female With young child Non-metro area More likely non-white and unemployed Lower education levels

11. Factors in Rural/Urban Poverty, Fisher (D'Anne Hammond) Results F-stat indicates joint significance of explanatory variables (95% ci) Indirect evidence supporting both structural condition hypothesis and self-sorting to non-metro Metro to non-metro movers have increased income- needs ratio of 25% Non-metro households economically worse off (ceteris parabus) F-stat indicates joint significance of explanatory variables (95% ci) Indirect evidence supporting both structural condition hypothesis and self-sorting to non-metro Metro to non-metro movers have increased income- needs ratio of 25% Non-metro households economically worse off (ceteris parabus)

12. Factors in Rural/Urban Poverty, Fisher (D'Anne Hammond) Policy Implications Further empirical studies on place-level and individual-level variables over time “...can improve the design of anti-poverty policy, providing insights on what combinations of human-capital and community-strengthening policies are most likely to reduce non-metro poverty and its unfavorable consequences.” (p. 73) Further empirical studies on place-level and individual-level variables over time “...can improve the design of anti-poverty policy, providing insights on what combinations of human-capital and community-strengthening policies are most likely to reduce non-metro poverty and its unfavorable consequences.” (p. 73)