Research

Research Statement

Research Papers

Voting for Opportunity: County-Level Economic Mobility and Voting Patterns in US Presidential Elections (Job Market Paper)

Latest Draft

Abstract: Data shows that income is strongly related to voting behavior; in the US, rich people vote more Republican, and poor people vote more Democrat. However, this relationship has weakened over the last few decades as class issues have become less relevant and social issues have become more relevant. In this paper, I aim to see how mobility mediates the relationship between income and voting behavior, and to assess how much people react to economic mobility in their area. I expect that if mobility is high, then people will be less likely to identify with their class and vote based on it. This hypothesis applies to both the choice of whether to vote and the choice of who to vote for. Poor people are less likely to vote, but when mobility is higher voter turnout for the poor increases, partly because they are more likely to be around people who vote because they came from voting backgrounds, and partly because they feel less hopeless in their position. Support for redistributive policies will decline is people believe the economy is fair. In effect, I hypothesize that if mobility is high, rich people will be more likely to vote Democrat, poor people will be more likely to show up at the polling booth, and more likely to vote Republican when they do. To test this hypothesis, I use individual-level from the American National Election Survey (ANES), county-level intergenerational mobility data from The Opportunity Atlas, and other county-level data. I find that at the individual- and county-level, higher mobility is associated with higher turnout. At the county level, I find that higher mobility is associated with more Republican votes. These results show that people do have an intuitive understanding of economic mobility in their area and change their behavior to reflect it.

Urban Sprawl and Upward Mobility: How Sprawling Neighborhoods Decrease Opportunity (w. Yehua Dennis Wei) (Submitted for Publication)

Latest Draft

Abstract: The effect of urban sprawl on economic mobility has been contested. However, the effect is largely examined at the metropolitan or county level. There is evidence that characteristics of sprawl, like lower accessibility to jobs and services and decreased social cohesion in the neighborhood, have a negative effect on individual economic outcomes. In this study, we use Census tract-level mobility data and county-level and tract-level sprawl indexes to develop a richer understanding of the relationship between sprawl and mobility. Particularly, we use different scales of sprawl measures to determine whether the effect of sprawl is at the neighborhood level or a broader level. Our findings are that city-level sprawl has a negative effect on upward economic mobility for low-income people, while tract-level sprawl has a slightly positive effect. A one standard deviation increase in sprawl at the tract level is associated with a 0.04 standard deviation increase in upward mobility, and a one standard deviation increase in MSA-level sprawl is associated with a 0.18 standard deviation decrease in upward mobility. For comparison, the coefficient for the percent of households with single parents, an important predictor of mobility, is -0.34 at the MSA level and -0.12 at the tract level. Our findings confirm that sprawl does have a negative effect on economic mobility for low-income people at the metropolitan level, but indicate that the effect of sub-city level sprawl is more nuanced.

Neighborhoods, Race/Ethnicity, and Intergenerational Mobility in the United States (w. Yehua Dennis Wei and Ning Xiong)

This study examines two causes of spatial variation in intergenerational mobility (IM) in U.S. counties: race/ethnicity and four aspects of urban sprawl—density, mix of uses, centering, and accessibility—and their interaction with socioeconomic factors. We use spatial error regression, geographically weighted regression, and path analysis. By using these spatial techniques, we get a clearer understanding of how these geographic relationships occur. We find that Black share has the largest indirect and total effect on IM, and the effect is mediated by racial segregation, social capital, unemployment rates, test scores, and single parent share. We find that urban form variables do not all affect IM in the same direction, and the magnitude of the effect of each urban form variable depends on other variables. Employment centering negatively affects IM, while population centering enhances IM. Walkability, mixed-use development, and a jobs-housing balance could improve IM. Urban form variables could indirectly influence IM through inequality, segregation, social capital, and unemployment rates; these indirect effects are larger than the direct effects. This study shows that the relationship between IM and urban form variables is not straightforward, and that building economically just cities requires more than reducing segregation and increasing density. By understanding the paths through which urban form and racial patterns affect IM, we can create more directed policy.

Research in Progress

Mining and Mobility: How Growing Up with Mining Fathers or In Mining Towns Affected Economic Outcomes of Children in the Early 1900s

Recently, mining employment has been in decline, and these jobs are usually located in remote areas with few other opportunities. Mining employment went through a similar decline almost 100 years ago as technological change improved labor productivity. This study is meant to determine what happened to the children of miners, or children in mining communities, at this critical juncture. Did they have better occupational outcomes than children of parents with other occupations or in other communities? Did different mining communities have different outcomes? I will compare outcomes in different areas, using covariates like average educational attainment, proximity to urban areas, and rate of unionization of the mine, to see why areas have different outcomes. I will use full count Census data for the years between 1880 and 1940, and use the Census Linking Project for linking across time. I expect to find that children of miners are not as well off as children of manufacturers, and that areas with strong institutions like education or unions and areas that are close to cities will have better outcomes.

Total Worker Health (w. Norman Waitzman, Ken Smith, Jaewhan Kim, and Matt Thiese)

The purpose of this project is to explore a broad category of workplace hazards. While physical hazards are well-documented in the workplace, mental and emotional hazards are less studied. We will use O*NET data on occupational characteristics and link it with several different health data sources to find out what characteristics lead to lower worker health. This research is related to the production of health literature.

Intergenerational and Intra-generational Occupational Mobility Among Women from the Late 1800s to the Late 1900s: Longitudinal Evidence on the Relationship between Family Health, Family Structure and Women’s Labor Market Outcomes (w. Thomas N. Maloney and Ken Smith)

Most historical analysis of time-series economic outcomes is unable to include women because most women marry and change their names. The Utah Population Database is a unique dataset that is able to track women in Utah through their life and connect them with their family characteristics. The goal of our research is to study women's occupational mobility, including how it is related to both origin characteristics, like family size, parent occupation, and parental health/early parental mortality, and lifetime characteristics, like spouse’s occupation, spouse’s health, and number of children.

Past Research Assistant Positions

Collect 2 Connect (w. Norman Waitzman and others)

This is a large interdisciplinary project that includes researchers from many areas, including the Department of Family and Consumer Studies, College of Health, and the School of Business. The idea of the project was to reduce poverty and increase the middle class by encouraging people who did not collect the Earned Income Tax Credit (EITC) to do so. Preliminary estimates showed that almost 15% of people who were eligible for the EITC did not take it, and these peoples' lives could be greatly benefitted with the extra money. The project tried to connect to people who did not collect the EITC through community health centers. I helped do some preliminary data analysis on who is not collecting the EITC, where they are located, and risk factors for not collecting the refund.

Utah Office for Victims of Crime Analysis (w. Sheena Yoon and Richard Fowles)

This research was a part of the Department of Economics Economic Evaluation Unit. The Utah Office for Victims of Crime (UOVC) is facing large budget cuts in 2022 and so they hired us to do some cost-benefit analysis on the various services they provide to victims of domestic violence and sexual assault. Unfortunately, there has been very few studies on outcomes for the funded interventions, so our analysis required interviewing Victims of Crime offices from other states and organizations within Utah to get an understanding of best practices in victims of crime assistance. My role was assisting in the interviews of the 30+ organizations that we talked to, analyzing the data from the UOVC on the organizations they fund, and helping to write a report. The report is forthcoming.

Genuine Progress Indicator (w. Gunseli Berik)

This was a one-semester partially-funded RA position in which I updated the Genuine Progress Indicator (GPI) for Utah for the 20 years between 1997 and 2017. The GPI is alternative measure to GDP for economic well-being of an area. The GPI includes several things excluded from GDP, including externalities from inequality and environmental degradation, unpaid work, and leisure time. Data came from over 15 sources at the state and federal level.

Sectoral Composition and Divisia Index for States (w. Codrina Rada)

This was a one-semester partially-funded RA position in which I gathered and cleaned government data on labor and output by sector for states for the period from 1963 to 2017.

Local Population Estimates in Utah (at the Kem C. Gardner Policy Institute)

In this 2-semester internship, I helped the Demography Team to estimate population at the Census-tract level for intercensal years in Salt Lake and Utah Counties, the most populous counties in Utah. We mainly did this by gathering information on new housing construction in propriety GIS files and in real estate market reports. These estimates help many people in local business and government with their planning.

Undergraduate Researcher with Dr. Djavad Salehi-Isfahani at Virginia Tech

I was a paid undergraduate researcher with Dr. Salehi-Isfahani for several semesters. The goal of the project was to study how Iran's development had been dependent on the increased position and education of women in Iran, and how these trends reacted to the many political upsets in Iran over the last 50 years. My job was to read and discuss Dr. Salehi's work, and to analyze health survey data in Afghanistan as a comparison to Iran.