Working Paper
Did The China Shock Lower Covid Vaccination Rates?
(Job Market Paper)
This paper explores how recent economic shocks have effected uptake of the Covid 19 vaccine. Here I demonstrate that Trade Exposure to China, Growth Rates of Immigration, and Impact from The Great Recession have contributed to lower vaccination uptake at the county level. Recession Impact and Trade Exposure have effects larger or equal to more typical explanations of vaccination rates such as education, income, and social capital. These events both directly affect vaccination rates and indirectly through their effect on a counties political affiliation. To look at the channel by which these shocks affected vaccination rates I use a 2SLS with county trade exposure as an instrument to show that a 1 percentage point fall in a counties share of manufacturing employment results in a near .4 percentage point drop in counties vaccination rate.
National Origins of State Legislators:
Does Where Your State Legislators Come From Effect Economic Growth?
Cultural and “deep roots” history have been shown to have an affect on Economic growth (Putterman and Weil (2010), Comin Easterly Gong (2010) , and Fulford, Petkov & Schiantarelli (2020)). The mechanism of these effects is less studied. Here I create a unique data set of State legislators for each state in the United States from 1900-2000. This data set provides ancestral origin information on the legislators that I then use to assign cultural and deep roots values to. Then I run a Fixed Effects model that keeps the cultural and deep roots scores of counties within a state constant while allowing for changes in the deep roots and culture of its state of government. I do this by using by Fulford, Petkov, and Schiantarelli (2020) measures of counties deep roots and culture. This allows me to look at changes in the deep roots of the public sphere while keeping the private and social spheres the same. Doing this allows me to see how counties with similar cultural and deep roots histories are effected by changes in these characteristics among state law makers. My results show that cultural trust and early state history among that state legislators have an influence on a counties economic growth.
What About Technology?
Technological Adoption History and Economic Growth in United States Counties
Deep Roots literature has focused on how distant historical characteristics help explain cross country variations in economic growth. Scholars have also attempted to investigate how these traits affect economic growth in the United States. Recently, Fulford, Petkov, & Schiantarelli developed an ancestry data set for U.S. counties for purposes of assessing the effects of Deep Roots characteristics on economic growth in the U.S. Despite being the most powerful measure in the Deep Roots literature, their paper failed to make use of the measure of technological adoption developed by Comin, Easterly and Gong (2010). In this paper, I take the ancestral data of U.S. counties developed by Fulford, Petkov, & Schiantarelli and combine it with the technological adoption measure by Comin, Easterly and Gong (2010) to create a measure of each county’s ancestral history of technological adoption. I then run a series of fixed effects regressions and demonstrate that technological adoption appears to be the most important Deep Roots and ancestral characteristic effect on economic growth in the US
The Deep Roots of Voting Patterns in the United States
Recently there’s been a growing body of literature on how deep roots cultures effects economic growth and political outcomes in the United States. Ancestral culture appears to be more important for changing political outcomes then its direct effect on economic growth. However, most of these studies on political outcomes have been limited in terms of the time periods they cover. Here I gather data on the voting patterns of every US county from 1880 to 2018 for election of the presidency and house of representatives and see how changes in the ancestral deep roots culture of a county effects voting patterns. My results show that the state history, ancestral trust, and technological adoption rates of a countries population all have positive and highly significant effects on the share of a counties vote that goes to the republican. 1% increases in one of these measures increases republican vote share between .3% and .4%. For comparison, a lag in republican vote share only increases current republican vote share by .5%. This finding is robust to a host of controls including controls for racial categories. I then design a IV model using lagged ancestry and railroad exposure as a second robustness check. The size of the effect remains and is still statistically significant.