Synergistic Relationship Between Higher Education and the Economic Well-Being of a State - Essay Example

A review of the literature suggests that higher education is being evaluated within the context of a production function model (Garner 2000; Robst, 2001; Reich, 1992; Toffler and Toffler, 1995). Toward this end, the concept of an interdependent and intertwined synergistic relationship of an educational society, the economics of a community and the overall economy as proposed by Garner (2004) will be explored. This framework will be advanced with a foundation in human capital theory (Mincer, 1984) and by delving more deeply into a number of state level factors. In this study, the unit of analysis is institution level and the study population is all public four-or-more year institutions in the United States. To test the theory of an interdependent and intertwined synergistic relationship of an education society, the economics of a community and the overall economy, ANOVA, correlation analysis, and Regression Modeling analysis was applied. This paper is designed to frame the discussion about approaches states can use to build better models to connect public information about the economics of higher education along with state-level planning and public policy for higher education. INTRODUCTION Our study will focus on the effects of graduation rate on the Gross State Product (GSP). Over the past decade, accountability pressure in education is at the forefront on all levels. At the K-12 standards movement, buttressed by the No Child Left Behind legislation, which created a standards-assessment based paradigm to improve student academic performance to reduce achievement gaps between racial and economic groups. Despite many bumps in the road, the momentum from the K-12 accountability model has increased expectations for an analogous approached in higher education (Assessment, Accountability and Productivity: Building Models to Connect Learning Assessments with Public Accountability Structures in higher Education, p. 1; with Firestone Accountability Metrics question and answer). Burke and Modaressi1 (2000) present us one of these approaches in higher education through the example of performance funding which is believed to be only one aspect of the series of effort made by state to ensure accountability for campus performance. Though a confluence exists among political, economic, and demographic trends, a need is apparent for better state-level public information about higher education performance (http://www.aft.org/pubs-reports/higher_ed/accountability, Sept. 18, 2004). However, the relationship between states and public colleges and universities is symbiotic: each depends on the other for survival. State governments and policymakers play a critical role in financing higher education, while higher education institutions educate state residents and improve local economies (Weerts, 1999, p.1). This paper is a step toward determining an accountability metric for higher education appropriations. We will first present a literature review on the key issues of accountability and measurement of institutional performance in higher education and on the theories that will be used in our essay. Then, we will focus on introducing and defining variables and suggest existing relationships among them. Finally, we will offer an analysis through the facts, theories and variables presented. LITERATURE REVIEW Major Stakeholders: State Politics The major stakeholders in higher education reform during the 1990s have been state legislatures, state chancellors and state agencies, governors, and higher education institutions (Blackwell & Ciston, 1998). At the beginning of the 21st century, accountability of tax dollars provided to institutions of higher education is ever increasing within two perspectives: the proper use of the allocated funds and the follow up of the performance rate (Alexander2, 2000 and Burke and Modaressi, 2000) In the 1990s, higher education reform was guided by economic values – competition, productivity, and efficiency – at a level it had never reached before. This trend at the beginning of the 21st century had shown no signs of ceasing. This shift in priorities might be explained in part by the growing interest of business leaders in state higher education policy-making (Usdan, 2002; Zernike, 2002; Zumeta, 2000). Commonly-Used Indicators for measuring institutional performance Ruppert (1995) conducted an in-depth case study on performance indicators (PIs) and found that graduation outcome data was one measure that responded to policy concerns —rising college costs and the economic return to the state of college-educated citizens. In addition, the State Higher Education Executive Officers (SHEEO) conducted a survey between 1996 and 1997 and identified that graduation rate was used in 32 states as a common performance indicator by institutions (Christal, 1998). Human capital theory in American public education. The concept of human capital theory entered mainstream academic inquiry in the early 1960s through the work of Theodore Schultz and Gary Becker (Becker 1993; Schultz 1963). In the following decades, this concept fueled considerable and lasting debate among researchers. At the core of many of these debates are the assumptions spelled out in Becker’s 1964 Human Capital, where he formalized a theory presenting education as one of many investment alternatives individuals may choose to obtain future benefits. One assumption within human capital theory is that labor market earnings increase for individuals with more education because schools increase the productive skills of students. This assumption is the root of much debate over the role of education in society. Human capital theory has typically been applied to education in explaining investment decisions in higher education and on-the-job training. In choosing among various investment alternatives, individuals behave as if they perform a private calculus measuring the rates of return associated with each alternative. Investment in education occurs only if the expected returns compare favorably against existing alternatives, such as full-time employment. The key assumption of human capital theory is that “schooling raises earnings and productivity mainly by providing knowledge, skills and a way of analyzing problems” (Becker 1993). The impacts of this causal statement are many. By making schools responsible for the economic productivity students bring to the labor market; human capital theory joins investment in education, labor market earnings of students, and the very process of classroom learning. Human capital theory claims that high school and college education in the U.S. “greatly raise a person’s income, even after netting out direct and indirect costs of schooling, and after adjusting for the better family backgrounds and greater abilities of more educated people” (Becker 1993). Such a claim, together with the assumption that school changes students, suggests that independent of socioeconomic status, family dynamics, or the skills and knowledge students develop prior to schooling, it is largely what takes place inside the classroom that corresponds to increased earnings once students enter the labor market. At the national or state level, human capital can be considered both a condition and a consequence of economic growth. Human capital activities involve not merely the transmission and embodiment in people of available knowledge, but also the production of new knowledge (Mincer, 1984, p. 195). Investment decisions in institutions of higher education are made with an expected return by the parents and the graduates for full-time employment and the state policymakers by an increase in state-wide economic indicators, such as per capita income and GSP. Economic and demographic variables. The literature suggests that the forecast or status of a state’s economy significantly affects the rate of higher education funding in a particular state. Specifically, the unemployment rate, per capita income, availability of state revenues, and tax capacity help to determine the level at which the state will fund its public universities. Put simply, the overall wealth of a state is an important factor in determining the level of support for higher education (Layzell & Lyydon, 1990) (Weerts, p. 8). In addition, the composition of the population in a state is a factor that influences appropriations levels for colleges and universities. Changes in the overall population of the state, percentage of the population that are college age (18-24), and enrollment or participation rates are varying conditions that adjust the level of higher education funding over time (Layzell & Lyyddon, 1990). Demographic information is critical when planning for the future of higher education and education in general. Evidence of the power of economic and demographic factors as determinants of higher education appropriations is seen in the fact that support for higher education varied considerably among populous and wealthy regions during the mid-1980s. During that period, West Coast and New England states enjoyed a more prosperous economy, resulting in greater appropriations for higher education. Also, support for higher education increased in the sun-belt states due to a surge in population growth in these areas. In contrast, the Midwest experienced an “out-migration” of residents, and some states, such as Ohio and Indiana, struggled to make the transition from an industrial to service economy. These factors explained much of the variations in state higher education funding during that period (Layzell and Lyydon, 1990). (Weerts, p. 8). Beyond issues of state wealth and population, the symbiotic relationship between higher education and economic growth is known to be important. Linking higher education to a state’s economy positively affects the level of higher education funding in a particular state primarily because investing in universities is considered one means of improving a state’s economy. During the late 1980s, state with large increase in appropriations for higher education explicitly linked higher education with economic development in that state. These states invested more heavily in higher education because it was considered a means to improve tax capacity (Hines, Hickrod, Pruyne, 1989). (Weerts, p. 8). DEFINITIONS For the purpose of our analysis we will use the following four variables: Graduation rate, Gross State Product (GSP), Per capita income and State appropriations. We will define and explain these four variables before presenting the existing relationships among them. Graduation rate: this rate measures the percentage of students starting college in year one who graduate by the end of year four. The information on the annual graduation rate for all 4-year public institutions is available from the Integrated Postsecondary Education Data System (IPEDS).3 The unit of analysis for this study is the graduation rate of all 4-year public higher education institutions within a particular state because the target is to assess the impact on the economy within the state via the GSP. Gross State Product (GSP) is a state’s counterpart to the Gross Domestic Product (GDP). GSP measures the market value of goods and services produced by labor and property in a specific state. It is slightly different from the GDP because it does not include the market values of goods and services produced by persons coming from the specific state and working outside this state. The state Gross State Product (GSP) is obtained from the United States bureau of Economic Analysis (www.bea.gov/bea/rels.htm). The GSP metric that will be used is Million of dollars. Human capital theory suggests that personal income is related to education and the data show this to be true. Since a state consist of people who earn income and human capital theory supports that those people who earn a higher education (in this study this variable will be measured by the graduation rate from 4-year public institutions, aggregated) will earn a higher income then it follows that for a given state if a higher percentage of the population graduates from college then they will earn a higher income. (Garner, 2004, for definition, data, and references). Per capita income: The per capita income for a group of people may be defined as their total personal income divided by the total population. In our study per capita income will be considered as the amount of people within the state who have a higher education level (4 years) divided by the total state population. Information as to per capita income will be retrieved from the U.S. Census Bureau (http://www.census.gov/govs/statetax). Unrestricted State appropriations: Unrestricted appropriations are those state dollars used for higher education operating and research expenses. These expenses include, but are not limited to, faculty salaries, fringe benefits, wages, equipment, scholarships, and mandatory transfers. This definition excludes facilities budgets and exceptional units such as University Hospital Clinics (IPEDS, 1998) (Weerts, 1999, p. 6). The data source used in this essay for unrestricted State appropriations is the National Center for Educational Statistics, Integrated Postsecondary Education Data System (IPEDS) (http://nces.ed.gov/ipeds/data.html) ANALYSIS In the last part of our essay, we will analyze the interdependent and intertwined synergistic relationship between economics and education through the human capital theory. We will then offer a proposition of analysis through a regression model related to variables that we have described along our essay – per capita income, GSP, graduation rate, state appropriation. The interdependent and intertwined synergistic relationship between economics and education through the human capital theory. According to Garner (2004) quoting Reich (1992): Reich also recognizes that a person’s financial potential is related to the amount and quality of knowledge acquired. He proposes that civilization is moving from high-volume to high-value businesses. Within the high-value enterprises he suggests there are three major job categories ranging from routine production jobs to symbolic-analytic jobs? The higher job category requires more education, and correspondingly, these workers earn more money. To back up his claim, Reich presents data to show the widening income gap that has evolved between workers in the lower and higher job categories. As we have seen in the literature review of this essay, human capital considers education as an investment and that it ultimately raises a student’s income according to his academic level. If we relate this rule of human capital theory to Reich’s statement, we must obviously consider the truthfulness of the idea. Nevertheless, this relationship shall not be considered one way. As the earnings of the higher job categories workers increase so does the per capita income of this category and ultimately the taxes that this type of workers pay to the State. The interaction between economics and education is a circle: as the highly educated pay more tax, they assist the education system by paying more taxes. Education creates the opportunity for students to earn more money and in return these workers allow the education to keep creating and raising these opportunities by paying higher taxes. Even if, since 1997, data show that each year it is 4% of the average GSP that is allocated to education, we shall not conclude that the allocation remained the same as the average GSP increased each year. We have here the exact theory as described by Schultz (1961) (http://fcis.oise.utoronto.ca/~daniel_schugurensky/assignment1/1961schultz.html) “investment in human capital must focus on supporting individuals in acquiring an education, since it is skill and knowledge that affect ones ability to do productive work. He believes that an investment to enhance these capabilities leads to an increase in human productivity, which in turn leads to a positive rate of return.” Therefore, the need of sufficient and constant unrestricted state appropriation to higher education is clear. Nevertheless, we believe that the current research on the interdependent and intertwined synergistic relationship between economics and education do not reflect accurately the relationship between them. The need for testing this theory is obvious and this is why we will present in the following section different frameworks for deeper analysis and their relevance to prove our theory ANOVA, Correlation analysis and regression modeling to explain the interdependent and intertwined synergistic relationship between economics and education. ANOVA, Analysis of variance, is used to uncover the main and interaction effects of categorical independent variables (called "factors") on an interval dependent variable. A "main effect" is the direct effect of an independent variable on the dependent variable. An "interaction effect" is the joint effect of two or more independent variables on the dependent variable. Whereas regression models cannot handle interaction unless explicit crossproduct interaction terms are added, ANOVA uncovers interaction effects on a built-in basis. (http://www2.chass.ncsu.edu/garson/pa765/anova.htm) Correlation analysis is the statistical tool that is used to describe the degree to which one variable is linearly related to another. Frequently, correlation analysis is used in conjunction with regression analysis to measure how well the least squares line fits the data. Correlation analysis can also be used by itself, however, to measure the degree of association between two variables. (http://www.netnam.vn/unescocourse/statistics/11_6.htm) Regression modeling is any statistical method where the mean of one or more random variables is predicted conditioned on other (measured) random variables. In particular, there are linear regression, logistic regression, Poisson regression and supervised learning. Regression analysis is the statistical view of curve fitting: choosing a curve that best fits given data points. (http://en.wikipedia.org/wiki/Regression_analysis) We believe that the three previous methods of analysis can bring satisfactory results in regards of the theory but we will focus on regression modeling especially multiple regression because it can prove very useful in this context as it allows researchers to examine separately the relationships between a series of independent variables and the dependent variable. The 4 variables that will be used in the multiple regression are Unrestricted State Appropriations for public universities, Graduation rate, Per capita income and Gross State Product. The dependent variable in this study is Unrestricted State Appropriations for public universities. The independent variables will then be the Graduation rate, per capita income and the GSP because they are the multiple factors useful to examine and predict state appropriations for higher education. (The variables in this framework can be reviewed in Table 1.) The multiple regression model will help us in to consider the need for Unrestricted State Appropriation from the three other variables that compose the model because we believe that showing the relationship between economics will ultimately lead in determining the degree of Unrestricted State Appropriation for higher education within a state.. Though we do not possess the necessary means to build this model, we believe that it is a strong basis to proving our theory and furthermore it would be a great contribution to the knowledge base on public policy related to the financing of higher education. It is certainly an important tool for state government in this matter. CONCLUSION According to the human capital theory we have proved our theory regarding the interdependent and intertwined synergistic relationship between economics and education though it has shown its limits. To strengthen this theory we would need to run a multiple regression model which can also and mainly be used to determine Unrestricted State Appropriation for higher education. We believe that conducting this model would be a great contribution to the knowledge base on public policy related to the financing of higher education. Table 1. Dependent and Independent Variable used in the multiple regression model. Variable Metric Data Source Dependent Variable (Y) Unrestricted state appropriations Dollars National Center for Educational Statistics, Integrated Postsecondary Education Data System (IPEDS) http://nces.ed.gov/ipeds/data.html Independent Variable (X) Gross State Product (GSP) Dollars U.S. Census Bureau, United States Department of Commerce, Website: http://www.census.gov/ Graduation Rate % Integrated Postsecondary Education Data System (IPEDS) Website: http://nces.ed.gov/ipeds Per capita income Dollars Bureau of Economic Analysis: United States Department of Commerce. Website: http://bea.doc/gov/ Read More