Author: Varun Prasad
Research Question: My research question for this project revolved around what historic trends in specific areas led to higher or lower rates of social mobility relative to surrounding regions. However, as I began digging deeper, I began to ask how the effects of economic downturns in certain communities and their ability to recover reflected the rates of relative social mobility that existed within them.
Key Social Mobility Finding:
- In both the cases of Gregg County, Texas and Fayette County, Georgia, the amount of impact that a recession had on a county as well as how quickly and strongly that county recovered from the downturn compared to surrounding regions was indicative of relative rates of social mobility.
- In Fayette County, a more isolated, educated, and diversely employed population has been able to avoid recessions and recover quicker than the poorer and more vulnerable counties that surround it, all of which have much lower mobility scores.
- In Gregg County, a lack of economic diversification and a heavy reliance on oil production after the Great Depression left the area undereducated and vulnerable to economic downturns and has a relatively low mobility score.
Background:
The basis for the research that I am conducting is in further analysis of the findings of Raj Chetty and Nathaniel Hendren about the effects of growing up in certain counties on one’s future income. Due to the fact that my analysis is based on multiple series of data collected by Raj Chetty and Nathaniel Hendren, understanding both how Chetty and Hendren themselves analyzed the data as well as what studies have followed the publishing of their findings is necessary in order to understand where there is missing research. This is essential to clarifying how best we can answer the question of what specifically is affecting the rates of mobility in certain areas. In their paper “The Impact of Neighborhoods on Intergenerational Mobility II”, Chetty and Hendren themselves devote time to analyze the individual cases of DuPage and Cook Counties in Illinois. Each of these counties offers a very different view of access to opportunity. While Cook County houses the actual city of Chicago, DuPage is home to the upper-class suburbs of the city, haven of the rich, mostly white, elite. Chetty and Hendren point to these specific demographic factors as the basis for why “moving from Chicago proper to the western suburbs of Chicago at birth would increase a child’s household income by $7,510 per year on average, a 28.8% increase”. However, apart from this brief insight into one specific community, there is no further investigation into what unique factors influenced levels of social mobility on a place by place basis.
This lack of scrutiny at a more local level is something that is continued in studies that have followed the publishing of their findings. Many have looked at the individual factors that I seek to study in a broader scale, but none of them pursues a specific case study as to why counties in certain regions perform relatively better or relatively worse than their neighbors. This is where the research that I have conducted fits in and I believe that it serves as a basis for developing theories as to what is necessary in certain locales to succeed. During both the literature review process as well as the initial data collection, indications that economic cycles may have unique effects on each county separately in ways that are correlated with their rates of social mobility began to arise. This trend guided the larger basis of my research, resulting in my interest to answer the question as to whether a county’s rate of relative social mobility is correlated with its ability to recover from economic downturns.
Methods:
My research this semester pulls strongly from past research and historical analyses, especially as a supplement for the raw data. For example, while numerical values were pulled for the percentages of county populations working in various industries, the data had to be supplemented with qualitative reasoning as to why one specific sector may employ more of the population than another. The specific data studied can be broken into the categories of economic standing, including differences in employment rates, employment categories, cost of living, as well as forecasted economic growth, social determinants, specifically crime rates, and educational attainment, and finally more general data on people and preferences.
Figure 1: Map of Fayette County in relation to neighboring Spalding, Coweta, and Clayton Counties
Figure 2: Map of Gregg County in relation to neighboring Panola and Upshur Counties
Preliminary Results:
My preliminary findings showed similarities in the demography in the cases of Fayette and Gregg counties, but stark contrasts in their development. Fayette County, a bastion of an elderly, right-wing, and largely white upper-class population, performs significantly better than the counties surrounding it, most notably the more urban Clayton County. Initially looking at the differences between the two, we see distinctions based on many of the categories that Chetty, Hendren, and many others had described: lower crime rates, better schools, higher levels of income, etc.
However, looking at the case of Gregg County in Texas, we find contradictions in all of those parameters. This is an area that has similar income levels, crime rates, as well as spending on education as neighboring Panola and Upshur counties. The population of Gregg County is also almost entirely white, and yet it performs significantly worse than either of the other two regions. When analyzing the differences in historical development between these regions, we find clear differences. All three areas are located in Eastern Texas, where the discovery of the East Texas Oilfield in the 1930s protected each one of the counties from the most brutal period of the Great Depression. Over the course of the 20 years that followed, each one of the counties developed largely on the growth of the oil industry. However, while Gregg County built the entire economy of the region around petroleum, Upshur and Panola counties diversified, pursuing manufacturing and agriculture and investing further in education.
When the oil recession of the 1980s hit East Texas, all three counties struggled, but Upshur and Panola were quick to bounce back as a result of their diversification. Gregg did not have the same fate. There was a significant decrease in new home construction in Gregg County following the recession of the 1980s and continued disinvestment in the area. A financial audit of the county in 2017 concluding that “its weaknesses include a negative spillover from a weak energy industry, low industrial diversity and above-average employment volatility, below-average educational attainment and high poverty, and above-average rate of uninsured residents” (Longview News-Journal).
Looking back at the example of Fayette and Clayton counties, we see that Clayton has historically fared worse in recession. The clearest and most recent example of this was the 2008 mortgage crisis. Just before the crisis, Clayton was rated second in the nation for the county with the largest proportion of homes bought with subprime mortgages. This meant that when the housing market collapsed, Clayton was devastated. Soon after, infighting within the Clayton County school board, caused in part by funding restrictions, led to the loss of accreditation of the school district. Average incomes in the area dropped significantly and have yet to see the type of recovery that characterized Fayette County’s residents.
The differences in recovery from the 2008 recession as well as similar patterns in those that preceded it are strongly correlated with the differences in rates of social mobility in the two areas. This pattern is echoed in the case of Gregg, Panola, and Upshur counties, showing that even in areas that are homogenous in the indicators most generally associated with social mobility, response to downturns are reflective of rates of mobility.
References
Changing Migration Patterns: A Review of Popular Press and Scholarly Analysis. Headwaters Economics, headwaterseconomics.org/wp-content/uploads/migration-literature-review.pdf.
Chetty, Raj, and Nathaniel Hendren. 2017. “The Impact of Neighborhoods on Intergenerational Mobility II: County-Level Estimates”.
“Federal Reserve Economic Data.” FRED, Federal Reserve Bank of St. Louis, fred.stlouisfed.org/.
U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table GCT0101; generated by John Smith; using American FactFinder; <http://factfinder2.census.gov>