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The Economic Determinants of In-State Undergraduate Enrollment for Public Universities within the United States

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The Economic Determinants of In-State Undergraduate Enrollment for Public Universities within the United States
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Felegi, Brianna Nicole
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This thesis is a research paper with the intention of determining whether certain economic factors within a state contribute to higher in-state undergraduate enrollment among all of that state's universities. Specifically the variables analyzed included scholarship availability, state unemployment rate, average in-state tuition and percentage of those aged 18-24 residing in the state. By running a linear regression on the four variables, the effects of tuition price, scholarship availability and percentage of those 18-24 were shown to be significant. Many other variables could have been included into the model, but limited access and data restricted availability to do so. ( en )
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Awarded Bachelor of Arts, summa cum laude, on May 8, 2018. Major: Economics
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College or School: College of Liberal Arts and Sciences
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Advisor: Michelle Phillips. Advisor Department or School: Economics

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University of Florida
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University of Florida
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Copyright Brianna Nicole Felegi. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

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Brianna Felegi Honors Thesis Spring 2018 1 T he Economic Determinants of In State Undergraduate Enrollment for Public Universities within the United States I. INTRODUCTION In 1910, only 2.7 percent of the United States population had attained 4 or more years of college educat ion 1 In 2015, that number had increased to over 32 percent for those 25 years and older 2 The value of a B s degree has also become quite obvious through out the last century. According to the Bureau of Labor Statistics, the median usual weekly earnings degree a mounts to 1,156 dollars, while those wit h only a high school degree expect to earn a median weekly earning of only 692 doll ars two thirds of the American population still do not to attend a ny 4 s degree is a huge financial undertaking $38,500 on average according to Colle geBoard, and there are many other determinants that go into the decision of enrolling in an institution. This paper will attempt to determine the economic factors within a state that contribute to the enrollment of in state undergraduates at public univer sities in the United States Data regarding the total amount of undergraduates within a state for the years 2011through 2015 will be utilized in order to analyze the effects of a number of economic variables to dete rmine those significant factors. 1 Census Bureau, Section 31 20 th Century Statistics 2 Census Bureau, Educational attainment in the United States: 2015

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Brianna Felegi Honors Thesis Spring 2018 2 II. SAMPLE The sam ple used for this study include s the total amount of undergraduates within each state. Each variable has data from the years 2011 2016 for a total of 300 observations. III. DEPENDENT VARIABLE Total In state Undergraduate Enrollment The dependent variabl e being analyzed within this paper is the total number of in state undergraduates per 1,000 population. The number of in state undergraduates is obtained through the Term Enrollment Estimate report done by the National Student Clearinghouse Research Center and for each year, the fall report numbers were analyzed. Each value is then divided by dependent variable. It has been decided to use in state undergraduate enrollment per 1,000 population in order to control for the issues that would arise when comparing well populated states (e.g. California) to less populated states (e.g. Alaska). Without doing so, well populated sta tes would receive an automatic bias IV. INDEPENDENT VARIABLE S Average In State Tuition Price for the State In s tate tuition measures the amount an undergraduate would have to pay for a full academic year at the institution, so long that they meet the in state requirements. These requirements vary per state, but typically include a durational residency requirement and the intent to remain domicile. The intent to remain domicile simply implies that an individual is willing to treat the

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Brianna Felegi Honors Thesis Spring 2018 3 United States as their permanent home. These data were obtai ned through a report done by CollegeBoard 3 Following economic theory, as the price of a good rises it is expected that the quanti ty demanded would decrease. If i n state tuition is treated as the price of attending an undergraduate i nstitution it expecte d that as this value rises, fewer individuals would attend the university. This suggests a negative coefficient against the independent variable. Economic Outlook of the State The economic outlook of a state greatly influences the factors used in considering enrollment in an institution. Income, opportunity costs, confidence, etc. are directly affected by the state of the economy. In economic research today, t here are copious i ndicators for economic outlook, this paper will examine the state unemployment r ate. State Unemployment Rate Unemployment is defined as the percentage of individuals in the labor force (those over the age of 16 that are willing and able to work) that are not employed, yet seeking employment. The data for this independent variable was obtained from the Bureau of Labor Statistics and used on a state level. Intuitively, it makes sense that as the state unemployment rate rises undergradu ate enrollment will i ncrease as individuals seek opportunities to increase their human capital to become more attractive in the labor market. A study to support this claim was done by Gehring(2013 ) 4 3 Tuition and Fees by Sector and State over T ime CollegeBoard (2017) 4 Using Unemployment Rates to Predict Post Secondary Enrollment Gehring(2013)

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Brianna Felegi Honors Thesis Spring 2018 4 The goal of this study was to use the unemployment rate to predict postsecondary en rollment in the state of Min n esota Gehring(2013) measured the linear relationship between unemployment and new enrollment to be 0.462, but not significant at the 10 percent level. This occurs because the only variable that Gehring(2013) us ed to account for new enrollment was the unemployment rate. This paper will be accounting for the other intervening variables for und ergraduate enrollment; therefore it is suitable to expand the results of Gehring(2013) to all states in order to hypothesi ze that the coefficient on state unemployment rate will be positive. On the other hand, when unemployment rate is high individuals face a greater opportunity cost to attend an institution as they are taking the risk of exiting the labor force instead of searching for a new job. Similarly, f inancial pressures (along with several other factors) may not allow an individual to return to school. Combined, this reasoning suggests a negative coefficient against state unemployment rate. This paper will not assu me one effect outweighs anoth er and instead will place unknown prediction onto this variable. State unemployment rate will be used a proxy for economic outlook in the second model of the regression. The data for this variable was obtained through the RI De partment of Labor and Training. Percentage of Population aged 18 24 According to the National Center of Education, in 2015 those aged 18 24 accounted for 40.5 percent of undergraduate en rollment 5 This number has only increased f rom previous years. I n the year 2000 those aged 18 24 made up only 35.5 percent of the undergraduate population T his suggests that those aged 18 24 are increasingly dominating the population of undergraduate 5 Back to school statistics National Center of Education (2017)

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Brianna Felegi Honors Thesis Spring 2018 5 institutions ; thus the percentage of the population for each state that fall within this age range will have an effect on the number of in state undergraduates For each state, the percentage of the population that falls within the age range of 18 24 was gathered from the U.S. Census. As this group becomes more prominent it r easonable to hypothesize that the number of in state undergraduates will follow suit Therefore, there is an expected positive coefficient for this variable. Pre paid Tuition Plans and Hope T ype Scholarships The financial undertaking required to complete To date, there are several states that provide pre paid tuition plans 6 that allow for families to save sufficient funds for their children to attend college at lower rates. Pre paid plans allow for parents to pur s future costs. This essentially locks in c urrent tuition prices, while saving a family from any price hikes that could occur in the years Furthermore, there are currently 8 states that provide a hope type scholarship funded by the state lottery 7 These scholarships give automatic disbursements to students, so long as they meet the academic requirements and attend an in state institution. This paper will instill a dummy variable for the existence of either a pre paid tuition plan or hope type scholarship. Students that receive such scholarships or are in accordance with a pre paid tuition plan reduce their price of attendance dramatically. As previously no ted within this paper, as the price of good falls, the quantity demanded of that good is expected to increase. Therefore, it is 6 Prepaid Tuition Plans Listed by State Edvisors 7 Tennessee Higher Education Commission

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Brianna Felegi Honors Thesis Spring 2018 6 expected that there is a positive coefficient for the relationship between the existence of such programs and in state undergrad uate enrollment. Parental Education One factor that has become increasingly important in determining undergraduate enrollment is parental education. Even though a ccording to Smoke (2014) 8 the exact reasoning behind the relationship between highly educa ted parents and highly educated children cannot be interpreted (due to the differences between highly and low educated parents ) t he positive correlation between higher educated parents and higher educated children cannot be denied. This may be due either because children with higher educated parents tend to be placed in schools that exhibit higher academic demands or because of the increased expectations of children to attend and graduate from college since they are surrounded by a family that has done so in the past. The incomes of families with college educated parents an institution. Those who have graduated mes than those who have not. S imply put, with a higher income individuals are more able to afford Despite the apparent connection between parental education and undergraduate enrollment, there is no data at this time to control for this variable. Data is available on a national level for the year 2012, but this paper is analyzing the economic determinants on a state level with much of its data obtained from the year s 2011 through 2015. Unfortunately, with no data availability it is likely that the results derived from this paper will be biase d 8 Smoke (2014)

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Brianna Felegi Honors Thesis Spring 2018 7 V. RESULTS Descriptive Statistics of Data Total In State Undergraduate Enrollment per 1,000 Average In State Tuition Price State Unemployment Rate Mean 43.16 Mean 9236.45 Mean 6.33 Standard Deviation 20.95 Standard Deviation 2 1 54.79 Standard Deviation 1.92 Minimum 5.19 Minimum 2003.86 Minimum 2.7 Maximum 78.61 Maximum 15969.56 Maximum 13 Observations 300 Observations 300 Observations 300 Dummy for either pre paid option or automatic scholarship Percentage of those aged 18 24 Mean 0.44 Mean 9.48 Standard Deviation 0.50 Standard Deviation 0. 97 Minimum 0 Minimum 5.21 Maximum 1 Maximum 12.19 Observations 300 Observations 300 Regression with all variables Model : In State Undergraduate Enrollment = + ( Average In State Tuition) + ( Prepaid Option or Hope Type Scholarship ) + ( Percentage of those aged 18 24 ) + ( State Unemployment Rate ) + Error

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Brianna Felegi Honors Thesis Spring 2018 8 Excel Regression Output Regression Statistics Multiple R 0. 3993 R Square 0. 1594 Adjusted R Square 0. 1480 Standard Error 19.34 Observations 300 ANOVA df SS MS F Significance F Regression 4 20924.19 5231.05 13.99 1.84E 10 Residual 295 110326.80 373.99 Total 299 131250.98 Coefficients Standard Error t Stat P value Lower 95% Upper 95% Intercept 90.60 12.14 7.46 0.00 66.70 114.49 Average In State Tuition Price 0.0012 0.0004 2.68 1 0.0078 0.0021 0.0003 Dummy for either pre paid option or automatic scholarship 12.48 2.27 5.49 8.65E 08 8.01 16.96 Percentage of those aged 18 24 3.798 1.173 3.239 0.001 6.105 1.490 State Unemployment Rate 0.931 0.586 1.588 0.113 2.085 0.223 Variable Coefficients P value In State Tuition 0.0012 0.0078 State Unemployment Rate 0.931 0.113 Dummy for either pre paid option or automatic scholarship 12.48 8.65E 08 Percentage of those aged 18 24 3.798 0.001

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Brianna Felegi Honors Thesis Spring 2018 9 Interpretation of the Results With an a djusted R squared value of .1480, the data the proposed model captures approximately 14.80 % of the variance within the dependent variable. In State Tuition Con gruent with the hypothesis, there is a negative coefficient placed on this variable. The coefficient value of 0.0012 is next to zero but this is not outlandish sin ce a one dollar increase in tuition is not expected to drastically change in state undergraduate enrollment. T his variable is deemed statistically significant. With a p value of 0 .0078 (0.0078<.01), this coefficient value is significant to the 99% percenti le. The upper and lower 95% intervals are also negative, supporting the claim that this variable is negative. Pre paid tuition or Hope Type Scholarship Con gruent with the hypothesis, there is a positive coefficient placed on this variable. The coeffici ent value of 12.28 suggests that the existence of a pre paid tuition plan or hope type scholarship leads to a 12.28 increase in total undergraduate enrollment per 1,000 population. The p value associated with these results is also statistically significant (8.65E 08<.01 ), implying that these findings are significant to the 99 th percentile Also, the direction of this variable is supported by the fact that both the upper and lower 95% intervals are positive values. Percentage of those aged 18 24 Contrary to th e hypothesis, there is a negativ e coefficient placed on this variable. The coefficient value of 3.798 ages of 18 24 there is a decrease in the in state undergraduate enro llment by 3.789 per 1,000 population The p value asso ciated with this variable is 0.001, implying these results are significant beyond the 99 th percentile (.001<.01). The upper and lower bounds of this variable also bolster the idea that the direction of this variable is indeed negative. The exact reasoning behind this finding is unclear and requires further attention. State Unemployment Rate This variable did not have an assigned hypothesis, since there are strong arguments for both sides and with thes e results, the true direction of this variable still remains a mystery. A coefficient value of 0.931, suggests that there is a negative relationship, but with a p value of 0.113(>0.05), these results are deemed statistically insignificant and likely due t o chance. Secondly, the lower and upper 95% interval for this variable switches from a negative value to a positive value, making the true direction unclear.

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Brianna Felegi Honors Thesis Spring 2018 10 VI. CONCLUSION The proposed model reported an adjusted R squared of 0.1480. This implies that 14.80 % of the variance in the dependent variable can be explained through the model Despite a low percentage of the variance being captured, there are three statistically significant variabl es: average in state t uition, the existence of a pre paid tuition opti on or hope type scholarship, and percentage of those aged 18 24. While this paper was able to identify statistically significant factors in in state undergraduate enrollment, the low adjusted R squared valued suggests that there are many other factors that affect in state undergraduate enrollment. Along with parental education (as discussed as an omitted independent variable) some other variable s to be considered include: geographical location of the top school s, ranking of the schools within the st ate nationally, sports team performance s and job placement percentage s This in conjunction with the reasoning behind the contradictory coefficient placed on percentage of those aged 18 24, provides ample room for improvement for this paper. With a l ack of data availability in combination with a short time constraint, none of these items can be considered. On a positive note, these omitted items do give insights into possible opportunities for future research.

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Brianna Felegi Honors Thesis Spring 2018 11 References "A Comparison of Stat es' Lottery Scholarship Program." Thec.ppr.tn.gov Policy, Planning, and Research Division of the Tennessee Higher Education Commission, July 2012. Web. 25 Oct. 2017. https://thec.ppr.tn.gov/THECSIS/Lottery/pdfs/SpecialReports/A%20Comparison%20of%2 0States'%20Lottery%20Scholarship%20Programs%20120717.pdf "Back to School Statistics." Nces.ed.gov National C enter for Education Statistics, 2017. Web. 25 Oct. 2017. https://nces.ed.gov/fastfacts/display.asp?id=372 Bls.gov. (2017). Unemployment rates and earnings by educational attainment [online] Available at: https://www.bls.gov/emp/ep_chart_001.htm [Accessed 28 Sep. 2017]. Bls.gov. (2017). Unemployment Rates for States, Annual Averages [online] Available at: https://www.bls.gov/lau/lastrk15.htm [Accessed 28 Sep. 2017]. Census gov. (2017). State Population by characteristics 2010 2017 [online] Available at: https://www.census.gov/data/tables/2017/demo/popest/state detail.html [Accessed 12 March 2018 ]. Pew Research Center Pew Research Center, 15 Jan. 2014, www.pewresearch.org/fact tank/2014/01/15/college enrollment among low income stude nts still trails richer groups/ Factfinder.census.gov. (2015). American FactFinder Results [online] Available at: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk [Accessed 12 March 2018]. Gehring, A. (2013 ). Using Unemployment Rates to Predict Post Secondary Enrollment [online ] Minnesota Employment and Economic Development Ava ilable at: https://mn.gov/deed/newscenter/publications/review/february 2013/unemployment college enrollment.jsp [Accessed 3 Oct. 2017]. N ational Student Clearinghouse Research Center (2016) Current Term Enrollment Estimates [online ] Available at: https://nscresearchcenter.org/category/reports/current term/ [Accesse d 12 March 2018]. "Prepaid Tuition Plans Listed by State." Www.edvisors.com Edvisors, n.d. Web. 25 Oct. 2017. https://www.edvisors.com/plan for college/saving for college/prepaid tuition plans/state list/

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Brianna Felegi Honors Thesis Spring 2018 12 RI Department of Labor and Training (2017) Unemployment Rates for States Annual Average Rankings [online ] Available at: http://www.dlt.ri.gov/lmi/laus/us/annavg.htm [Accessed 12 March 2018]. Ryan, C. and Bauman, K. (2016). Educational Attainment in the United States: 2015 [pdf] U.S Census Bureau, p.2. Available at: https://www.census.gov/content/dam/Census/library/publications/2016/demo/p20 578.pdf [Accessed 28 Sep. 2017]. Section 31 20th Century Statistics. (2017). [pdf] U.S. Census Bureau, p.12. Available at: https://www.census.gov/prod/99pubs/99statab/sec31.pdf [Accessed 28 Sep. 2017]. Smoke, K. (2014). ge Students [pdf] Henderson State University, pp.2 3. Available at: http://www.hsu.edu/academicforum/2013 2014/smoke.pdf [Accessed 2 Oct. 2017]. "The Economics of Higher Education." Www.treasury.gov Department of Treasury with the Department of Education, Dec. 2012. Web. 26 Oct. 2017. https://www.treasury.gov/connect/blog/Documents/20121212_Economics%20of%20Highe r%20Ed_vFINAL.pdf Trends.collegeboard.org (2017). Tuition and Fees by Sector and State Over Time [online] Available at: https://trends.collegeboard.org/college pricing/figures tables/tuition fees sector state over time [Accessed 12 March 2018 ]. "Trends in Public Higher Education: Enrollment, Prices, Student Aid, Revenues, and Expenditures." Trends.collegeboard.org CollegeBoard, May 2012. Web. 26 Oct. 2017. https://trends.collegeboard.org/ sites/default/files/trends 2012 public higher education expenditures brief.pdf