Per capita income and farmers markets:
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1 Texas Tech University Per capita income and farmers markets: Searching for an environmental Kuznets curve for environmental attributes Ryan Blake Williams, Aaron Benson, Maria Mutuc, & Clinton L. Neill. Department of Agricultural & Applied Economics Texas Tech University Source of image: peyri/flickr
2 The Environmental Kuznets Curve Literature: Pollution Per Capita GDP Grossman & Krueger (1991, 1995) Theoretical Critiques Arrow et al. (1995) and Stern et al. (1996) Econometric Critiques Stern (2004) heteroskedasticity, simultaneity, omitted variable bias
3 Motivation Problems with estimating environmental Kuznets curve (EKC) across countries Evaluate at a regional level? See, for example, Paudel & Poudel, Kahn (1998), Wang et al. (1998), and Soytas et al. (2007). Can an EKC be found for reductions in environmental bads, or the purchase of environmental goods? Ghalwash (2007) evaluates recreational services in Sweden and finds support for the EKC
4 Farmers Market Prevalence
5 Farmers Markets and County PCI
6 Data farmers market data from USDA National Farmers Market Directory population and urban/urban cluster data from 2010 US Census per capita income (PCI) data from Bureau of Economic Analysis 2012 Presidential Election data from National Atlas of the United States
7 Initial evaluation of the data revealed the hypothesized results: count/ income >0 2 count/ 2 income >0 However, the first result is very small
8 The count data exhibits an excessive number of zero values. 1015/3088, or approximately 33% The distribution of counts is overdispersed
9 1200 Histogram of Counts 1000 Frequency Number of Farmers Markets per County
10 Estimation Results Zero-Inflated Negative Binomial (ZINB) Results for Farmers Market Counts in US Counties Negative Binomial Regression Variable Estimate Robust SE z P> z IRR Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Logistic Regression for Zero Inflation Variable Estimate Robust SE z P> z Odds Ratio Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Note: LR Test Against ZIP: chibar2(01) = , Pr>=chibar2 = Vuong's test: z = 5.036, Pr>z =
11 Estimation Results Zero-Inflated Negative Binomial (ZINB) Results for Farmers Market Counts in US Counties Negative Binomial Regression Variable Estimate Robust SE z P> z IRR Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Logistic Regression for Zero Inflation Variable Estimate Robust SE z P> z Odds Ratio Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Note: LR Test Against ZIP: chibar2(01) = , Pr>=chibar2 = Vuong's test: z = 5.036, Pr>z =
12 Estimation Results Zero-Inflated Negative Binomial (ZINB) Results for Farmers Market Counts in US Counties Negative Binomial Regression Variable Estimate Robust SE z P> z IRR Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Logistic Regression for Zero Inflation Variable Estimate Robust SE z P> z Odds Ratio Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Note: LR Test Against ZIP: chibar2(01) = , Pr>=chibar2 = Vuong's test: z = 5.036, Pr>z =
13 Estimation Results Zero-Inflated Negative Binomial (ZINB) Results for Farmers Market Counts in US Counties Negative Binomial Regression Variable Estimate Robust SE z P> z IRR Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Logistic Regression for Zero Inflation Variable Estimate Robust SE z P> z Odds Ratio Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Note: LR Test Against ZIP: chibar2(01) = , Pr>=chibar2 = Vuong's test: z = 5.036, Pr>z =
14 Estimation Results Zero-Inflated Negative Binomial (ZINB) Results for Farmers Market Counts in US Counties Negative Binomial Regression Variable Estimate Robust SE z P> z IRR Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Logistic Regression for Zero Inflation Variable Estimate Robust SE z P> z Odds Ratio Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Note: LR Test Against ZIP: chibar2(01) = , Pr>=chibar2 = Vuong's test: z = 5.036, Pr>z =
15 Estimation Results Zero-Inflated Negative Binomial (ZINB) Results for Farmers Market Counts in US Counties Negative Binomial Regression Variable Estimate Robust SE z P> z IRR Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Logistic Regression for Zero Inflation Variable Estimate Robust SE z P> z Odds Ratio Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Note: LR Test Against ZIP: chibar2(01) = , Pr>=chibar2 = Vuong's test: z = 5.036, Pr>z =
16 Estimation Results Zero-Inflated Negative Binomial (ZINB) Results for Farmers Market Counts in US Counties Negative Binomial Regression Variable Estimate Robust SE z P> z IRR Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Logistic Regression for Zero Inflation Variable Estimate Robust SE z P> z Odds Ratio Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Note: LR Test Against ZIP: chibar2(01) = , Pr>=chibar2 = Vuong's test: z = 5.036, Pr>z =
17 Estimation Results Zero-Inflated Negative Binomial (ZINB) Results for Farmers Market Counts in US Counties Negative Binomial Regression Variable Estimate Robust SE z P> z IRR Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Logistic Regression for Zero Inflation Variable Estimate Robust SE z P> z Odds Ratio Per Capita Income (2010) Percent Urban Percent Vote Democrat Population (2010) Constant Note: LR Test Against ZIP: chibar2(01) = , Pr>=chibar2 = Vuong's test: z = 5.036, Pr>z =
18 Conclusions The current model specification suggests that the EKC isn t supported by the number of markets county PCI relationship. Income elasticity of demand for fresh fruits and vegetables impacting results? Expenditures instead of counts Households instead of aggregated to county level The larger the share of a county that is urban, the greater the number of farmers markets, even when controlling for county population. Voting patterns are correlated with the markets, suggesting that the type of average household in a county matters.
19 Future Directions Panel data EKC at the household level for environmentally attributed goods? Non-parametric approach to more accurately reflect income
20 Comments?
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