My dependent variable will be drop developed in Han and Goetz (2015). It is reciprocal for resistance, which is calculated as the amount of impulse that a county experiences from a shock (the percentage of deviation of the actual employment from the expected employment during the Great Recession).
I want to find what factors affect drop. Then I run an OLS regression using drop as the dependent variables and following independent varaibles:
Income inequality: the Gini coefficient
Income distribution: poverty rate and the share of aggregate income held by households earning $200,000 or more
Control variables: Population growth rate from 2001-2005, % Black or African & American (2000), % Hispanic or Latino (2000)
Capital Stock variables:
Human Capital: % population with Bachelor’s degree or higher, age group (20-29, 30-49), female civilian labor force participation rate (2000)
Natural Capital: natural amenity scale (1999)
Social Capital: social capital index (2005)
Built Capital(2000): median housing value (2000)
Financial Capital (2000): share of dividends, interest and rent(2000)
Economic structure:
Employment share of 20 two-digit NAICS industries (manufacturing, construction, etc. other services (except public administration) omitted)
Significant variables and diagnostic tests results are shown in the following table.
| Ind. Var | Coefficient | Ind. Var | Coefficient |
| Population growth rate 2001-2005 | 0.205*** | Transportation and warehousing | -0.337*** |
| %Black or African American | 0.0542* | Information | -0.590** |
| %Hispanic or Latino | -0.113*** | Finance and insurance | -0.710*** |
| % pop with Bachelor’s degree or higher | -0.132* | Educational services | -0.894*** |
| Natural amenity scale | 0.00728*** | Health care and social assistance | -0.215*** |
| Manufacturing | -0.112* | Accommodation and food services | -0.244** |
| Wholesale trade | -0.580*** | Government and government enterprises | -0.231*** |
| Retail trade | -0.620*** | Adjusted R-squared: 0.199 | AICc: -4693.152798 |
| #obs:2770 | Koenker BP Statistics *** | Joint Wald Statistic*** | Jarque-Bera Statistic *** |
Among the significant variables, I want to explore population growth rate from 2001 to 2005 because it has the larges coefficient in size among control variables.
Moreover, since the Konker BP statistics are significant, the results are non-stationary. Then I run the GWR for population growth rate 2001-2005.
GWR
The following maps are the distribution and GWR coefficients of population growth rate 01-05
For the distribution of population growth rate 01-05, blue regions show negative population growth while the rest counties are all growing in population. Orange and red counties are high in population growth rate.
For the GWR coefficients, negative relationship between population growth rate and drop exist in blue, grey and yellow regions – higher population growth, lower drop (employment loss)
Positive relationship exist in orange and red regions – higher population growth, higher drop (employment loss)
My explanation are :
In an expanding economy (regions high in population growth), there are more people marginally attached to the labor market. They are easily fired in the Great Recession.
In regions with negative population growth, there more deaths than births and population aging rises.
- Older people have higher accumulated savings per head than younger people, but spend less on consumer goods.
- Less available active labor
- An increase in population growth will decrease employment loss.
However, my local R-squared, range from 0-0.620 with the mean 0.0247. Only in Orange County and San Deigo, CA, local R-squared is higher than 0.5, 0.603 and 0.620 separately. This situation (low local R-squared) happened to all my other significant control and capital stock variables () There might be misspecification problems in my model, I will try adding new explanatory variables.
Population growth rate
- Local R-squared, 0-0.620, mean 0.0247
- Only in 06059 Orange County and 06073 San Deigo, CA, local R-squared is higher than 0.5, 0.603 and 0.620 separately
Natural amenity scale
- Local R-squared, 0-0.519, mean 0.0216
- 12087 Monroe County, Florida 0.519
% Black or African American
- Local R-squared, 0-0.347, mean 0.0326
- 26095 Luce county, Michigan, 0.347
% Hispanic or Latino
- Local R-squared, 0-0.379, mean 0.0276
- 35029, Luna County, New Mexico, 0.379
% pop with Bachelor’s degree or higher
- Local R-squared, 0-0.391, mean 0.055
- 06073 San Deigo, CA, 0.391
