Hammond Econ Presentation Fisher

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OSU Econ 539 presentation of Fisher: "Why is U.S. Poverty Higher in Nonmetropolitan than in Metropolitan Areas?” in In Growth and Change,
(March 2007)
Vol. 38 No. 1 pp. 56-76

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Hammond Econ Presentation Fisher

  1. 1. “ Why is U.S. Poverty Higher in Nonmetropolitan than in Metropolitan Areas?” by Monica Fisher, OSU AREC In Growth and Change, (March 2007) Vol. 38 No. 1 pp. 56-76 Presented by D’Anne Hammond
  2. 2. Rural/Urban Poverty, Fisher <ul><li>Examines whether the difference in poverty levels reflects personal choice in addition to structural explanations of limited economic and social opportunities. </li></ul><ul><li>i.e. structural condition and sorting hypothesis? </li></ul>
  3. 3. The question: <ul><li>“ ...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.” </li></ul>
  4. 4. Observable differences in poverty rates <ul><li>Other research shows the odds of being poor are 1.2 to 2.3 times higher in rural areas </li></ul><ul><li>1/20 metro counties poverty rate 20% or higher </li></ul><ul><li>1/5 non-metro counties poverty rate 20% or higher </li></ul>
  5. 5. Theoretical model <ul><li>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) </li></ul><ul><li>where y = adj. income-needs ratio </li></ul>
  6. 6. Method <ul><li>Series of multivariate regression models using OLS to model poverty across place </li></ul><ul><li>All models are variations of </li></ul><ul><li>y 1 = a 0 + a 1 x i + a 2 n i + a 3 s i + e i </li></ul><ul><li>Where y = pretax income-to-need; need is census-based poverty threshold </li></ul><ul><li>x = individual factors: race, age, gender, presence of children, etc. </li></ul><ul><li>n = binary variable indicating non-metro </li></ul><ul><li>s = fixed-effects controlling for state-level expenditures, tax structure, etc. </li></ul><ul><li>e = error term, assumed to be i.i.d. </li></ul>
  7. 7. Variable y <ul><li>Dependent variable y (income-to-need) is adjusted for housing cost differences using fair market rent values (FMR) </li></ul><ul><ul><li>Persistent differences in housing costs between metro and non-metro </li></ul></ul>
  8. 8. Data source <ul><li>Panel Study of Income Dynamics (PSID) </li></ul><ul><ul><li>Nationally representative sample </li></ul></ul><ul><ul><li>Longitudinal data </li></ul></ul><ul><ul><li>Survey following apx. 5,000 families since 1968 </li></ul></ul><ul><ul><li>This research uses the nine panels between 1985 and 1993 that include non-metro variable </li></ul></ul>
  9. 9. Data subsample <ul><li>Household min. 2 yrs. observations in study </li></ul><ul><li>Restricted to lower income distribution </li></ul><ul><li>Records must have complete data for all variables </li></ul><ul><li>Head of household is a proxy for entire household </li></ul><ul><li>Result is sample of 2,007 household heads in poverty in 1993 and at least one other year between 1985 and 1993 </li></ul><ul><li>Average number of years in sample =7 </li></ul>
  10. 10. Final data set characteristics <ul><li>Differences (statistically significant) between whole sample and selected sub-sample </li></ul><ul><ul><li>Female </li></ul></ul><ul><ul><li>With young child </li></ul></ul><ul><ul><li>Non-metro area </li></ul></ul><ul><ul><li>More likely non-white and unemployed </li></ul></ul><ul><ul><li>Lower education levels </li></ul></ul>
  11. 11. Results <ul><li>F-stat indicates joint significance of explanatory variables (95% ci) </li></ul><ul><li>Indirect evidence supporting both structural condition hypothesis and self-sorting to non-metro </li></ul><ul><li>Metro to non-metro movers have increased income-needs ratio of 25% </li></ul><ul><li>Non-metro households economically worse off (ceteris parabus) </li></ul>
  12. 12. Policy Implications <ul><li>Further empirical studies on place-level and individual-level variables over time </li></ul><ul><li>“ ...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) </li></ul>

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