Classification and Statistical Analysis of Employment Growth in United States Counties

Employment growth is an important economic indicator. It is used to judge the state of the economy and drive major policy decisions. Employment growth is typically examined by analyzing company characteristics and policy initiatives. In this thesis, we engage other types of data at the county level in pursuit of explaining this growth. Specifically, we investigate how social, economic, and demographic information can explain changes in levels of employment. Principal component analysis is employed across ten county-level data sets from the United States Census to describe trends in these variables. Linear regression is used to examine connections between principal component trends and growth in economy-wide employment, labor force participation, and employment in four main economic sectors. Clear structure is observed amongst the principal components, showing strong differences across regional and urban/rural divides. Regression analysis finds significant relationships with the employment and labor force growth rates investigated. These connections indicate potential new avenues of research for policy initiatives aimed at promoting economic growth.

Level thesis:degree_level

Master's Degree

Year dc:date.issued

2019

Author dc:creator

• Claassen, Benjamin H.

Advisor dc:contributor.advisor

• Schmidt, Deena R.

Committee members dc:contributor.committeemember

• Zaliapin, Ilya
• Hurtado, Paul
• Nichols, Mark

• Counties
• Employment growth
• Labor Force Participation
• Linear Regression
• Principal Component Analysis
• United States Census

Statement dc:rights

• Creative Commons Attribution 4.0 United States

Handle dc:identifier.uri

http://hdl.handle.net/11714/5778

OAI identifier oai:identifier

oai:scholarwolf.unr.edu:11714/5778