The purpose of this project is to determine what affect a female’s level of educational attainment (DEGREE)has on her employment status (UNEMPLOYED) while holding constant the overall percentage of females(FEMALE), opportunities represented by her location/state’s gross domestic product (GDP), and the makeup of the workforce (WORKFORCE).
The independent variables DEGREE, FEMALE, GDP, and WORKFORCE determine the dependent variable UNEMPLOYED.
The most important independent variable in this relationship is DEGREE because of the effect educational attainment has on which employment opportunities are available to an individual. This effect of DEGREE works two ways: ...view middle of the document...
48). There is an expectation of lower unemployment rates relative to higher educational attainment (DePrince& Morris, 2008). There is a negative coefficient expected for this independent variable because these variables should adjust opposite each other. In other words, higher rates of educational attainment should correspond with lower unemployment rates for those persons.
FEMALE – This independent variable represents the percentage of females that make up the population by state.Knowing the percentage of females who make up each state’s population, particularly when coupled with how many females there are in the workforce, helps determine how many females there are available to be unemployed in each state. There is an expectation of higher females in the workforce relative to higher female population.For this reason, a positive coefficient is expected.
GDP – This independent variable represents the percentage of the overall U.S. gross domestic product each states contributes. This data helps determine how many employment opportunities there are per state. States that are worse off economically have fewer employment opportunities and impact UNEMPLOYED. Considerations of the GDP are each states population, size, and workforce, which all contribute to its level of output. There is an expectation of higher unemployment relative to lower GDP. For this reason, a negative coefficient is expected.
WORKFORCE – This independent variable represents the percentage of females in the workforce in each state. This data is important because it directly relates to the percentage of unemployed females. The number of females in the workforce relative to the female population and percentage of females with a degree impacts UNEMPLOYED. There is an expectation of higher female unemployment relative to the percentage of females who make up WORKFORCE. For this reason, a positive coefficient is expected.
This project uses cross-section data from 31 states from the years 2009 – 2011. This data was collected by the U.S. Census Bureau, Bureau of Labor Statistics, and the U.S. Department of Commerce Bureau of Economic Analysis through data compiled from the Current Population Survey. The cross-section data contains state unemployment rates, percentage of the U.S. GDP per state, the female population of each state, the percentage of persons in each state with a degree, and the female percentage of each state’s workforce.The female population by state, the state unemployment rates, and the number of persons with a degree were obtained from the U.S. Census Bureau through data collected in 2010. The GDP information was obtained from U.S. Department of Commerce Bureau of Economic Analysis through data collected in 2011. The percentage of females in each state’s workforce was obtained from the Bureau of Labor Statistics through data collected in 2011.
|State |State Unemployment |% with Degree ...