Purpose 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: Meeting a minimum education requirement to hold a position, and being considered overqualified for a position due to educational attainment.
Model
UNEMPLOYED = DEGREE + FEMALE + GDP + WORKFORCE
Definition of variables: UNEMPLOYED – This dependent variable represents the percentage of unemployed persons per each of the 31 states listed. Different factors affect unemployment, and each independent variable listeddetermines UNEMPLOYED in this model. DEGREE –This is the primary independent variable that represents the number of persons in each state with a bachelor’s degree. This represents bachelor’s degree holders only, not those with advance degrees. This independent variable has the greatest impact on the dependent variable, UNEMPLOYED. Somoza (2012) asserts the unemployment rate for college graduates was only a third of the rate for those who only attained high school diplomas. Additionally, there is an important relationship between education and labor market success, with education serving as an umbrella during economic storms (Somoza, 2012). According to DePrince and Morris (2008), “the average educational level of the labor force ages 25 and older has increased over the years, and the unemployment rate has trended downward over the past two decades” (p. 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.
Data
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 |Female Population |State % of GDP |Female % of State Workforce |
|Alabama |9.5% |22.0% |52.0% |1.18% |49.0% |
|Arizona |10.0% |25.6% |50.3% |1.73% |50.0% |
|Arkansas |7.9% |18.9% |50.9% |0.71% |50.4% |
|California |12.4% |29.9% |50.4% |13.02% |49.7% |
|Colorado |8.9% |35.9% |49.9% |1.76% |58.5% |
|Connecticut |9.1% |35.6% |51.3% |1.54% |56.7% |
|Florida |11.5% |25.3% |51.1% |5.11% |50.8% |
|Georgia |10.2% |27.5% |51.2% |2.80% |52.7% |
|Hawaii |6.6% |29.6% |49.9% |0.46% |55.9% |
|Illinois |10.3% |30.6% |50.9% |4.49% |54.1% |
|Indiana |10.2% |22.5% |50.8% |4.49% |52.7% |
|Louisiana |7.5% |21.4% |51.0% |1.85% |47.9% |
|Maine |7.9% |26.9% |51.1% |0.35% |56.6% |
|Maryland |7.5% |35.7% |51.6% |2.03% |58.6% |
|Massachusetts |8.5% |38.2% |51.6% |2.62% |57.5% |
|Michigan |12.5% |24.6% |50.9% |2.56% |50.1% |
|Minnesota |7.3% |31.5% |50.4% |1.88% |62.8% |
|Mississippi |10.4% |19.6% |51.4% |0.66% |49.8% |
|Missouri |9.6% |25.2% |51.0% |1.69% |56.4% |
|Nevada |14.9% |21.8% |49.5% |0.88% |52.7% |
|New Jersey |9.5% |34.5% |51.3% |3.33% |54.4% |
|New Mexico |8.4% |25.3% |50.6% |0.53% |48.7% |
|New York |8.6% |32.4% |51.6% |7.83% |51.7% |
|N. Carolina |10.6% |26.5% |51.3% |2.94% |50.2% |
|Ohio |10.1% |24.1% |51.2% |3.24% |54.0% |
|Oklahoma |7.1% |22.7% |50.5% |1.28% |50.9% |
|Oregon |10.8% |29.2% |50.5% |1.02% |56.0% |
|Pennsylvania |8.7% |26.4% |51.3% |3.88% |52.9% |
|S. Carolina |11.2% |24.3% |51.4% |1.11% |49.3% |
|Tennessee |9.7% |23.0% |51.3% |1.78% |52.0% |
|Texas |8.2% |25.5% |50.4% |8.48% |53.0% |

Presentation and Interpretation of Results The model was regressed and yielded the following results:
|SUMMARY OUTPUT UNEMPLOYED &| | | | | |
|DEGREE | | | | | |
| | | | | | |
|Regression Statistics | | | | |
|Multiple R |0.234210273 | | | | |
|R Square |0.054854452 | | | | |
|Adjusted R Square |0.021099254 | | | | |
|Standard Error |0.018133071 | | | | |
|Observations |31 | | | | |
| | | | | | |
|ANOVA | | | | | |
| |df |SS |MS |F |Significance F |
|Regression |1 |0.000534335 |0.000534335 |1.6250668 |0.212864273 |
|Residual |28 |0.009206631 |0.000328808 | | |
|Total |29 |0.009740967 | | | |
| | | | | | |
| |Coefficients |Standard Error |t Stat |P-value | |
|Intercept |0.118313276 |0.018302344 |6.464378208 |5.29508E-07 | |
|0.22 |-0.08393054 |0.065839179 |-1.274781079 |0.212864273 | |

The focus of this analysis is on the impact of DEGREE on UNEMPLOYED. A multiple linear regression was performed on the data to determine if there is a relationship between unemployment rates and educational attainment. The results show a negative coefficient, or that one variable increases as the other decreases. The R2 is 0.054854452, and the adjusted R2 is 0.021099254.R2explains how much of UNEMPLOYED is explained by DEGREE, FEMALE, GDP, and WORKFORCE. The adjusted R2serves to penalize outside predictors with little to no relevance. Each of the independent variables were regressed, and the results are as follows:
|Regression Statistics for |
|UNEMPLOYED & FEMALE POPULATION |
|Multiple R |0.154520424 |
|R Square |0.023876561 |
|Adjusted R Square |-0.01098499 |
|Standard Error |0.018427838 |
|Observations |31 |

|Regression Statistics for |
|UNEMPLOYED & GDP |
|Multiple R |0.220135401 |
|R Square |0.048459595 |
|Adjusted R Square |0.014476009 |
|Standard Error |0.018194312 |
|Observations |31 |

|Regression Statistics for |
|UNEMPLOYED & WORKFORCE |
|Multiple R |0.342094425 |
|R Square |0.117028596 |
|Adjusted R Square |0.085493903 |
|Standard Error |0.017526505 |
|Observations |31 |

The coefficients for DEGREE, FEMALE, GDP, and WORKFORCE are -0.08393054, -0.522027808, 0.14624833, and -0.178046384 respectively. According to the coefficients, GDP has the most significant effect on UNEMPLOYED, followed by DEGREE, WORKFORCE, and POPULATION.

References
Bureau of Labor Statistics. (2011). Table 14: Employment status of the civilian noninstitutional population by sex, race, Hispanic or Latino ethnicity, marital status, and detailed age,2011 annual averages. Retrieved from http://www.bls.gov/lau/table14full11.pdf
DePrince, A. R., & Morris, P. D. (2008). The Effects of Education on the Natural Rate of Unemployment. Business Economics, 43(2), 45-54.
Somoza, L. (2012). Who is the most unemployed? Factors affecting joblessness. EconSouth, 6-11. Retrieved from http://www.frbatlanta.org/documents/pubs/econsouth/12q1_employment_recession.pdf
U.S. Census Bureau. (2012). Table 627: Unemployment rates by educational attainment, sex, race, and Hispanic origin. Retrieved from http://www.census.gov/compendia/statab/2012/tables/12s0627.pdf
U.S. Census Bureau. (2012).Table 22: Unemployed workers. Retrieved from http://www.census.gov/compendia/statab/2012/tables/12s0622.pdf
U.S. Department of Commerce Bureau of Economic Analysis. (2012). Gross domestic product by state. Retrieved from http://www.bea.gov/regional/index.htm