1 Research Question
Counties reacted differently towards the Great Recession from Dec. 2007 to June 2009. Economic resilience is defined to measure the performance of counties. This research focused on waht contributes to economic resilience of a county. Especially, how does income inequality affect community economic resilience?
Definition:
Income inequality refers to the uneven manner of income distribution. Gini Coefficient is used to measure income inequality in this analysis. Gini Coefficient measures the ratio of area between lorenz curve and the 45 degree line (perfect equality).
Economic resilience refers to the regional ability to absorb and adjust to an external shock (recession, natural disaster, etc.) Martin (2012) defined dimensions of economic resilience, which two offer guidance for measurement, resistance and recovery(the other two is reorientation and renewal). Resistance means the sensitivity of reaction towards an exogenous shock and recovery shows the speed and degree of recovery.
Rising income inequality might pose an explanation to understand what is behind the great recession and why regions react differently. Economic growth theory shows income inequality is related to economic growth.
- Higher inequality retards growth in poor countries and encourages growth in richer places (Barro, 2000).
- Fallah and Patridge (2007) concludes positive inequality-growth link in the urban sample with the opposite in nonmetro case.
- Inequality leads to lower productivity, more instability, lower efficiency and lower growth (Stiglitz, 2012, Chapter 4).
The assumed relationship is
- Income inequality(pre-recession, 2000) affects economic resilience.
- Economic resilience affects income inequality later on (Five year average of 2007-2011 include the recession time). This relationship seems to be weak in rationale. It will not be tested in the analysis.
- Simultaneous relationship between income inequality and economic resilience. The instrumental variable is hard to find, hence this relationship will not be tested.
The relationships have not been determined yet.
Spatial autocorrelation is assumed to act in the relationship. The effect of income inequality or resilience or demographic or economic factors may not be limited within a region but attenuate with distance. Resilience in a county might be affected by its own characteristics as well as the surrounding counties. (Confused about the difference between GWR, spatial lag and spatial error model.)
1) Identify if counties will be affected by neighboring counties, i.e. spatial clustering of Income Inequality for year 1990, 2000, and ACS 2007-2011, and Economic Resilience/Drop/Rebound.
2) Identify the impact of income inequality on economic resilience or inverse relationship. The simultaneous relationship is hard to test because I haven’t found an IV which affect income inequality but not economic resilience and another which affect economic resilience but not income inequality.
3) How demographic, economic and industrial factors affect income inequality and economic resilience, especially the role of rural/urban?
4) Spatial Lags or Spatial Errors model
2 Dataset
The cross-sectional data covers 3141 counties in the U.S.. Data for pre-recession time period of 2000 and 2001, are used. Income inequality of year 2000 and ACS 5-Yr Esitmators are used. Details are listed below.
Key variable to measure economic resilience is drop, rebound and resilience, to measure income inequality is Gini coefficient.
Han and Goetz(2015) developed a one number measurement to measure economic resilience ,which was a ratio combined with drop (shows resistance) and rebound(recovery), and called economic resilience. Monthly employment data from Bureau of Labor statistics for 2003-2014 is used to calculate.
http://blogs.oregonstate.edu/geo599spatialstatistics/wp-admin/post.php?post=1605&action=edit
Formulations:
Gini Ceofficient is calculated using Household Income (group means) from 1990 and 2000 Decennial Census, and American Community Survey 2007-2011 via R using a package inequal. Gini Coefficient provided by American Community Survey for the first time using individual data is used. Income inequality calculate for year 2000 and provided by ACS for 2007-2011 are used in the research.http://blogs.oregonstate.edu/geo599spatialstatistics/wp-admin/post.php?post=1605&action=edit
Key explanatory variable: economic structure(all twenty 3-digit NAICS industry):
Economic Structure : Location Quotients for ten industries chosen by looking at the national level data of annual employment from 2000. They are high in growth
3 Hypotheses
1) There is spatial clustering in income inequality and economic resilience.
2) Demographic economic and industrial factors affect income inequality and economic resilience. The relationship differs across rural/urban.
3) Not sure if it is spatial lag or spatial error.
4 Approaches
1) Identify the spatial clusterings of Income Inequality and Economic Resilience: Hot-spot analysis, Global Moran’s I and Anselin Local Moran’s I
2) Identify the impact of income inequality on economic resilience or inverse relationship: OLS, GWR
3) How demographic, economic and industrial factors affect income inequality and economic resilience, especially the role of rural/urban places matter: OLS, GWR
4) Spatial Lags or Spatial Errors model: GeoDa
5 Expected outcome
1) Maps of hot spots, clustering of income inequality and economic resilience
2) Statistical relationships between income inequality and economic resilience
3) Statistical relationships between income inequality, economic resilience and other demographic variables.
4) Spatial lag model or spatial error model which fits the statistical relationship of income inequality and economic resilience
6 Significance
There are papers discussing income inequality and economic resilience, but little work is done to explore the relationship between income inequality and economic resilience. No spatial analysis so far.
7 Your level of preparation
(a) Arc-Info, medium
(b) Model builder and/or GIS programming in Python, none
(c) R, medium
Reference:
Barro, Robert J. “Inequality and Growth in a Panel of Countries.” Journal of economic growth 5, no. 1 (2000): 5-32.
Fallah, Belal N., and Mark Partridge. “The elusive inequality-economic growth relationship: are there differences between cities and the countryside?.” The Annals of Regional Science 41, no. 2 (2007): 375-400.
Stiglitz, Joseph E. “The price of inequality (London, Allen Lane).” (2012).
Peters, David J. “Income Inequality across Micro and Meso Geographic Scales in the Midwestern United States, 1979–20091.” Rural Sociology 77, no. 2 (2012): 171-202.
Xiurou,
This is a very good start. You might want to proof this for grammatical errors. The basic ideas are sound and very interesting. Some comments: in the introduction and research questions section, (1) please define economic resilience and income inequality, and (2) explain why you expect them to be related. Which is cause, which is effect? Can you determine? (3) Why is this a spatio-temporal problem – what is the role of spatial autocorrelation? In the data section, (1) please define your key response variable to measure economic resilience (the variable called “drop”) and how it is calculated, and your key explanatory variable to measure income inequality (Gini coefficient) and how it is measured. (2) please define the spatial and temporal scales over which these processes might be related – spatial scales of counties in the US, temporal scales of decades? In the hypotheses and expected outcome sections, please add what you expect to demonstrate or discover: Do you expect that counties with high income inequality will have lower economic resilience? What is the role of spatial autocorrelation: if a county has equitable distribution of income, but is surrounded by counties with high income inequality, do you expect it to have high or low economic resilience? What is the role of temporal autocorrelation: if a county has equitable distribution of income in the current time period, but had high income inequality in the previous time period, do you expect it to have high or low economic resilience? In the significance section, please explain how spatio-temporal analysis would contribute to the theory about income inequality and economic resilience: for example, when data are spatially autocorrelated they introduce bias into relationships. So do you expect that the relationship between these two variables has been overestimated because both variables are highly spatially autocorrelated?