Educational Attainment

Abstract

In today’s globalized economy, universities serve as economic growth hubs and as facilitators of higher education. However, the perils of the most recent economic crisis have caused these institutions and their surrounding regional communities to experience an array of challenges. An abundance of the economic development literature consistently illustrates the vital role that human capital can have on a region’s economic prosperity. Thus, this research explores the role that human capital theoretical perspectives have in the production of the long-term stability of a region’s economic growth and development efforts. This research seeks to determine if the level of educational attainment affects the economic growth and development efforts of nonmetropolitan areas with or without a research university. The results of this research provide marginal empirical support for the human capital and institutional intellectual capital theoretical perspectives as promoting economic growth and development.

Keywords

research university rural development human capital institutional intellectual capital

In a recent issue of The Chronicle of Higher Education, Selingo (2011) posited that universities serve their surrounding regional communities in dual capacities—as economic growth hubs and as facilitators of higher education. Likewise, Florida (2002) asserts that universities play a powerful role in generating innovation, and attracting and mobilizing talented individuals into a community. He also suggests that communities with the presence of a research university have higher economic growth and development measurements because the research universities serve as magnets, encouraging people and businesses to locate nearby (Florida, 2002).

The problem today, however, as noted by the Committee on Research Universities, Board on Higher Education and Workforce Policy and Global Affairs, National Research Council of the National Academies (2012), is that research universities and their surrounding communities are faced with an array of challenges. For example, as a result of the 2008 economic crisis, many of these institutions are now faced with a declining public tax base that has led to unstable revenue streams (Committee on Research Universities, Board on Higher Education and Workforce Policy and Global Affairs, National Research Council of the National Academies, 2012). Many of these institutions are also challenged by the promotion of policies intended to increase their competition with universities abroad (i.e., transitioning to a globalized economy). Thus, the lack of financial capital for their surrounding communities has caused state and local public administrators to develop alternative methods needed to stimulate and sustain their economies (Lamore, Link, & Blackmond, 2006).

Ultimately, these difficulties have caused unique concerns for colleges and universities in small, rural, isolated communities. These communities in particular are struggling because of the lack of financial capital and thus their ability to attract prospective students, faculty members, and new businesses (Selingo, 2011). Winter (2011) also notes that nonmetropolitan areas without the presence of higher education institutions tend to experience greater difficulty attracting highly educated persons into their communities. Consequently, economic development strategies have escalated to the top of the agenda for local governments across America, particularly in rural isolated communities. Considering the increasingly vital role that research universities can have on their local economy, this research explores these assertions by Florida (2002) and Selingo (2011) of the impact research universities and educational attainment have on communities nationwide in rural, isolated areas, but why is an examination of universities in rural isolated communities necessary?

The Significance of Universities in Nonmetropolitan Communities

As previously stated, the recent era of economic decline has profoundly affected small, rural, isolated, nonmetropolitan communities. According to the U.S. Department of Agriculture Economic Research Service (Kusmin, 2008), nearly 50 million or approximately 17% of Americans reside in nonmetropolitan areas. However, the population growth percentage gaps that exist among metropolitan and nonmetropolitan communities in America have intensified. The U.S. Department of Agriculture Economic Research Service reported that the annual growth rate for metropolitan areas was 1.1%, but merely 0.4% in nonmetropolitan areas (Kusmin, 2008).

The employment growth rates in nonmetropolitan and metropolitan areas also vary drastically, indicating the necessity to explore the trends associated within these unique jurisdictions. For example, the unemployment rates in nonmetropolitan areas rose in 2008 to its highest rate in almost 3 years, to 5.3%, then in 2009 to a dramatic 9.2% (Kusmin, 2008). These statistics illustrate the necessity to further explore ways in which nonmetropolitan areas can increase their attractiveness and their economic growth opportunities during this period of economic distress.

What Impact Do Research Universities Have on Local Economies?

According to Florida, Gates, Knudsen, and Stolarick (2006) and Florida (Florida, 2002; Florida & Cohen, 1999) the university’s role in sustaining a region’s economic development and growth has become increasingly important. Stewart (1997) and Edvinsson and Malone (1997) also assert that knowledge is a vital component of economic development for a community to possess. Miner, Eesley, Devaughn, and Rura-Polley (2001) confirm that universities are one of the contributing factors to successful local economic development because they provide skilled workers for a community. Goldstein and Drucker (2006) find that the presence of a university increases average annual earnings for a community. Similarly, Mansfield (1991) found that academic research investments yield significant returns to the economy and society.

Thus, in today’s knowledge-based economy, an examination of the factors that can aid local communities in understanding the impact that research universities and educational attainment have on a region’s economic growth is imperative for their survival. In an effort to further address these issues of concern, this research seeks to expand the discussion of the economic role that human capital theoretical perspectives have on today’s economy.

Economic Development Tools/Strategies

So what can be done to aid these communities? One tool identified in the economic development literature as useful for communities seeking to sustain their economies are concentrations of highly educated persons (i.e., high concentrations of human capital in an area). Building on this theoretical assertion, the objective of this research is to examine the role that human capital theoretical variables have on their surrounding community’s economic growth and prosperity. The goal of this assessment is to provide local communities in rural isolated areas with the presence of a research university with insight as to how they could survive the perils of their diminished economy.

Prior research of these rural isolated communities and the economic growth and development strategies they should pursue to stimulate their economies during these difficult times is limited. Migration trends illustrate that research exploring the impact that various economic growth and development trends have on isolated communities is vital since the annual growth rate for nonmetropolitan and metropolitan areas vary drastically. However, the abundance of scholarship regarding the impact of research universities on local communities in large metropolitan areas is vast and leads us to believe that, all else being equal, the presence of a university and a more highly educated population in a nonmetropolitan area should also result in greater economic growth. Therefore, the central question guiding this research is as outlined below:

Research Question: Does human capital account for positive economic results in nonmetropolitan areas?

In this research, as similarly defined by Hoyman and Faricy’s (2009) research examining the factors that affect urban growth, human capital is measured in two ways. First, human capital is measured as concentrations of educational attainment in a region. Second, this research also explores the impact that the human capital variable, institutional intellectual capital defined as densities and concentrations of higher education institutions, has on its surrounding local community’s economic growth and prosperity.

The remaining study continues with four sections. The next section discusses the theoretical frameworks examined. Specifically, this section provides background information on the human capital and institutional intellectual capital frameworks—the two dominant economic growth and development theoretical frameworks—to construct a multidimensional and robust model of the human capital-based factors that influence a community’s potential for economic growth. This section also presents a discussion of the role that research universities and various demographic factors have on the economic growth and development of a region in today’s economy. The section that follows introduces the methodology and data used in this research. The findings of the regression analysis are then presented, and finally conclusions, discussions, and policy implications are drawn.

Theoretical Frameworks

Human Capital

Human capital theory posits that economic growth trends can be explained by high-density patterns of highly educated people located in an area (Storper & Scott, 2009). More specifically, human capital theorists assert that areas with high concentrations of educated individuals produce high levels of long-term economic growth (Barron, Black, & Loewenstein, 1987; Becker, 1964; Glaeser, 2005). Human capital research conducted by Hoyman and Faricy (2009) of metropolitan cities and Ullman’s (1958) examination of concentrations of regional development support the argument that human capital is a vital explanation factor for economic growth. Furthermore, national and regional economic growth studies have concluded clear connections between a nation and a region’s high density of educational people (the level of human capital) and higher economic growth and development trends (Florida, 2002; Glaeser, 1998).

Human capital research examining the impact of highly skilled and educated people illustrates that these individuals have the ability to generate knowledge. In turn, these individuals’ knowledge has been found to lead to greater economic productivity. Research confirms that firms locate in areas with high stocks of human capital concentrations to gain competitive advantages (Florida, Mellander, & Stolarick, 2008).

Mathur (1999) argues that viable strategies for regional economic development are the result of the accumulation and the promotion of human capital. He refers to the concept as an accumulated stock of skills and talents of the educated and skilled workforce of a region. He asserts that a region will grow (employment and per capita income) if it saves and invests in human-based resources that accrue human capital. Similarly, Lucas (1988) examines the concept of human capital measured as the accumulation of education obtained through schooling. His research highlights the clustering effect of human capital and reveals that cities with high concentrations of human capital create knowledge spillovers that result in more economic productivity for a region. Thus, these regions become engines of economic growth (Lucas, 1988).

Berry and Glaeser (2005) examine trends of human capital migration and their research concludes that economic growth is a function of human capital. Koven and Lyons (2003) also note that the development of human capital enhances productivity and economic growth.

Additionally, Gottlieb and Fogarty (2003) explore the relationship between human capital and economic growth in metropolitan areas. They compare economic performance of highly educated and less educated areas by ranking large metropolitan areas by their educational attainment from 1980 and 2000. Their findings reveal that educational attainment is significantly related to employment growth. Goetz (1997) conducts a comprehensive study to identify state- and county-level determinants of economic growth and development. The findings of his research indicate that higher educational attainment levels were associated with statistically significant growth.

The human capital literature is vast, and its relationship to economic growth and development trends in metropolitan areas has been clearly articulated in the literature. One concern explored by this study is the impact that human capital has on rural isolated communities. This area of research has remained underexplored in the literature and its implications for such communities remains unclear. Thus, this research seeks to address this gap by solely examining these communities and the impact that human capital has on its communities. The data for the human capital variable as define in this study was obtained from the U.S. Census Bureau Fact Finder for Educational attainment.

Institutional Intellectual Capital

Recently interest has increased in understanding the impact of higher education institutions and their impact on the economic growth and development of an area. In 1998, Nahapiet and Ghoshal’s research extended the human capital theory by constructing the concept of “intellectual capital.” This concept refers to a density of higher education institutions and asserts that universities possess the ability to attract educated people, which thus led to increases in human capital (Nahapiet & Ghoshal, 1998).

Goldstein and Drucker’s (2006) research explores the extent to which institutions of higher education classified as teaching, research, and technology-based institutions influence regional economic development. Examining metropolitan statistical areas (MSAs) from 1986 to 2001, their findings reveal that research universities have substantial positive effects on a region’s average annual earnings. Hoyman and Faricy (2009) examine the relationship between intellectual capital measured by the density of universities and colleges in an area and their impact on the economic growth on a region. They found that those communities with high intellectual capital experienced growth in average wages for the region (Hoyman & Faricy, 2009).

Research University Presence

According to Feldman and Desrochers (2003), universities have been recognized as an important factor in economic development. Goldstein and Drucker’s (2006) research also supports this theory that the presence of a research university matters to the local economy. Their research illustrates specifically that the impact of a research university can vary by the size of the community that it surrounds. They found that in larger MSAs the universities are a less critical ingredient to a region’s economic growth. In the larger MSA regions they examined, the average earnings for those areas were more dependent on factors not related to the university (Goldstein & Drucker, 2006).

The findings of their research illustrate the need to further explore the impact that the presence of a research university has on a community, both large and small. Goldstein and Drucker (2006) concluded that the direct economic impact of a research university in a large MSA cannot be tied to the university itself. They assert that only in these communities the economic growth could be contributed to not only the presence of the university, but could also be the result of the other businesses in the community.

McGranahan and Wojan’s (2007) examination of both rural and urban counties examines the presence of higher education institutions as a measure of 2-year and 4-year public institutions and 4-year private institutions of higher education. Their findings reveal that the presence of a university in both rural and urban counties contributes to its economic growth.

Demographics (Controls)

A number of demographic factors have been identified in the economic development literature as vital for a region’s economic growth. This research includes as control variables race/ethnicity (U.S. Department of Commerce, U.S. Census Bureau, American Fact Finder. n.d.-b), median household income (U.S. Department of Commerce, U.S. Census Bureau, American Fact Finder. n.d.-a), employment status, population, region, and economic distress factors.

Race/Ethnicity

Generally, the racial composition of a community tends to have an impact on the economic prosperity of a community. Hoyman and Faricy’s (2009) study reveals that areas with high concentrations of African Americans are negatively correlated with growth, but Hispanics are positively correlated with growth. According to McGranahan and Wojan (2007), rural communities with high minority populations have historically been associated with declines in population and employment trends. These findings lead to the conclusion that communities with higher nonminority population concentrations will see higher economic growth and development. This research seeks to explore the impact that racial composition of slower growing nonmetropolitan areas will have on their communities’ economic growth efforts and the long-term policy implications.

Median Household Income

The median household income of a community has been scarcely explored as a factor affecting the economic development of a county. McGranhan and Wojan’s (2007) research of economic growth trends in rural and urban counties examined this variable, however. Their findings reveal, contrary to what is expected, that counties with lower median household incomes experienced higher economic growth. McGranahan, Wojan, and Lambert’s (2010) research also reveals the same findings for the percentage change in the number of jobs—that counties with lower median incomes lead to more jobs.

Population

Goldstein and Drucker (2006) use a population variable in their research examining the impact of universities on the economic growth of a region. Their findings reveal that university towns with higher population levels experience more rapid changes and more opportunities for economic development.

Region

According to Storper and Scott (2009), in the 1920s the Manufacturing Belt region of the United States flourished as the major concentration of industrialization. Since the 1980s, they confirm that the Sun Belt region has begun to see increased migration trends and more economic growth. They identified cities of the Northeast and Midwest as experiencing periods of stagnated levels of economic prosperity. Hoyman and Faricy’s (2009) research also reveals similar findings. They found that the Northeast, Midwest, and West regions lost jobs during their period of examination (Hoyman & Faricy, 2009).

Economic Distress Indicators

The 1965 Public Works and Economic Development Act outlines the requirements necessary for a community to be eligible for federal grants if it is an area under economic distress. The requirements include an examination of the market per capita income (U.S. Department of Commerce, U.S. Census Bureau. 2010), poverty rate, and unemployment rate (U.S. Department of Labor, Bureau of Labor Statistics. 2011). The per capita market income is calculated as the total personal income minus transfer payments divided by the population. Additionally, the poverty rate of an area is also used to determine the economic distress of an area, but it is not included in this research because it is highly collinear with some of the other variables. These factors combine to form an index that indicates whether a county is economically distressed (Public Works and Economic Development Act of 1965, 2004).

Data and Methodology

The human capital and institutional intellectual capital theories are tested to determine whether they serve as predictors of economic growth and development in nonmetropolitan isolated areas. The human capital variable is measured as the percentage of the population in a nonmetropolitan county 25 years or older with a bachelor’s degree or higher. The institutional intellectual capital variable that assesses the institutional human capital of a region was created by Hoyman and Faricy (2009). The institutional intellectual capital variable was modified here from their research, as a measure of the aggregate number and quality of university and college systems in a county. The 2000 Carnegie Classification is used in this research as follows: The Doctoral/Research Universities–Extensive in a county were assigned a 9 and Doctoral/Research Universities–Intensive an 8. The Master’s Colleges and Universities-I were assigned a 7 and Master’s Colleges and Universities-II a 6. The Baccalaureate Colleges-Liberal Arts institutions were assigned a 5, Baccalaureate Colleges-General a 4, and Baccalaureate/Associate’s College a 3. All Associate’s Colleges were assigned a 2 and all Tribal Colleges and Universities were assigned a 1.

Dependent Variables (Measures of Economic Development)

The dependent variable consists of a group of variables that is used to measure economic development. The three variables used in this research to measure economic development are new business establishments created, average annual pay, and job growth. New business establishments are defined as the percentage change in the number of new business establishments created from 2001 to 2009 according to the U.S. Department of Labor, Bureau of Labor Statistics, Quarterly Census of Employment and Wages (www.bls.gov). Average annual pay is measured as the percent change in average annual pay for 2001 and 2009 according to the Bureau of Labor Statistics, Quarterly Census of Employment and Wages. Job growth is measured as the percent change in the number of new jobs for 2000 and 2009 as reported by the U.S. Department of Commerce, Bureau of Economic Analysis, Local Area Personal Employment and Income (2011a, 2011b).

Unit of Analysis

This research uses data from 23 counties with the presence of a Carnegie-defined doctoral/research institution located in a nonmetropolitan statistical area. The selection of rural isolated communities with the presence of a research institution is the focus of this research to determine if the university has a direct impact on the economic growth of the community. As noted by Goldstein and Drucker (2006) in larger MSA regions, the presence of a research university was a less critical factor in determining the economic growth of a community. Thus, an examination of a rural isolated community will allow further exploration to understand if the university has directly contributed to the economic growth of an area.

The 23 institutions were identified by examining the 2000 Carnegie Classification of Higher Education Institutions category listing. This listing includes public and private colleges and universities in the United States that are degree-granting accredited agencies recognized by the U.S. Department of Education (Carnegie Foundation for the Advancement of Teaching, 2001).

Each university’s host county metropolitan status was determined by the 2000 Office of Management and Budget’s definition for metropolitan and nonmetropolitan statistical areas. Once the 2000 Carnegie listing of doctoral/research institutions was identified based on these criteria, a match/comparable county from the same state was identified. There are 23 counties with the presence of Carnegie-defined doctoral/research institutions, and for each research university an additional 23 match/comparable counties are included in the unit of analysis. The comparable counties with the absence of a Carnegie (2000) doctoral/research institution were identified and included in this study based on two criteria: similar populations and similar economic status. More specifically, the match/comparable counties were identified based on similar populations with the university county from 2000 and a similar per capita income with the university county from 1999. This information was obtained from the U.S. Department of Commerce, U.S. Census Bureau. (n.d.).

The inclusion of these match/comparable counties is used to determine the relationship that a research university has on the economic growth and development of an area in comparison with those areas that lack the presence of a research university. Table 1 below summarizes the universities classified by the Carnegie Foundation (2000) as research institutions. Additionally, the host city and the county in which it is located are presented below.

Table 1.

Unit of Analysis: Research University Counties & Match/Comparable Counties.

Institution	University county population 2000	University county per capita income 1999 ($)	University county	Match county	Match county population 2000	Match county per capita income 1999 ($)
Antioch University New England	73,825	20,685	Cheshire. NH	Carroll, NH	43,666	21,931
Central Michigan University	63,351	16,242	Isabella, MI	Montcalm, MI	61,266	16,183
Clarkson University	111,931	15,728	St. Lawrence, NY	Jefferson, NY	111,738	16,202
Dartmouth College	81,743	22,227	Grafton, NH	Belknap, NH	56,325	22,758
Georgia Southern University	55,983	16,080	Bulloch, GA	Troup, GA	58,779	17,626
Indiana University of Pennsylvania	89,605	15,312	Indiana, PA	Crawford, PA	90,366	16,870
Louisiana Tech University	42,509	15,876	Lincoln, LA	Webster, LA	41,831	15,203
Michigan Technological University	36,016	15,078	Houghton, MI	Chippewa, MI	38,543	15,858
Mississippi State University	42,902	14,998	Oktibbeha, MS	Warren, MS	49,644	17,527
Missouri University of Science and Technology	39,825	16,084	Phelps, MO	Pettis, MO	39,403	16,251
Montana State University	67,831	19,074	Gallatin, MT	Flathead, MT	74,471	18,112
New Mexico Institute of Mining & Technology	18,078	12,826	Socorro, NM	Roosevelt, NM	18,018	14,185
Ohio University	62,223	14,171	Athens, OH	Huron, OH	59,487	18,133
Oklahoma State University	68,190	15,983	Payne, OK	Muskogee, OK	69,451	14,828
South Carolina State University	91,582	15,057	Orangeburg, SC	Lancaster, SC	61,351	16,276
South Dakota State University	28,220	17,586	Brookings, SD	Codington, SD	25,897	18,761
Southern Illinois University Carbondale	59,612	15,755	Jackson, IL	Coles, IL	53,196	17,370
Texas A & M University–Kingsville	31,549	13,542	Kleberg, TX	Howard, TX	33,627	15,027
University of Idaho	34,935	16,690	Latah, ID	Bonner, ID	36,835	17,263
University of Mississippi	38,744	16,406	Lafayette, MS	Pike, MS	38,940	14,040
University of South Dakota	13,537	14,452	Clay, SD	Lake, SD	11,276	16,446
University of Wyoming	32,014	16,706	Albany, WY	Fremont, WY	35,804	16,519
Washington State University	40,740	15,298	Whitman, WA	Stevens, WA	40,066	15,895

Findings and Analysis

Regression Equation Full Model

\begin{array}{l} Economic Growth & Development = (Pay, Businesses, and Jobs) \\ f (research university presence, \\ human capital, and institutional capital) . \end{array}

\begin{array}{l} Y1 (Average Annual Pay Percentage Change) = f (research university presence, human capital, \\ and institutional capital) . \end{array}

\begin{array}{l} Y2 (Number of Businesses Created Percentage Change) = f (research university presence, \\ human capital, and institutional capital) . \end{array}

\begin{array}{l} Y3 (Number of Jobs Percentage Change) = f (research university presence, human capital, and \\ institutional capital) . \end{array}

Analysis and Results for Demographics Models

In this section, the ordinary least squares regression analysis results are presented. These models are critical because the demographics (control variables) establish a standardized series of factors identified by Hoyman and Faricy (2009), McGranahan and Wojan (2007), Goldstein and Drucker (2006), Storper and Scott (2009), and Feldman and Desrocher (2003) as vital for understanding the context in which the dependent variables can be best explained and understood.

Table 2 provides an assessment of the control variables and their impact on the number of businesses established (Model 1), average annual pay (Model 2), average annual pay with dummied Payne County, Oklahoma (Model 3), and the number of jobs (Model 4). Payne County, Oklahoma was identified as an outlier following Fox’s (1991) recommendations. However, due to the small sample size, the outlier remained a part of the study because the analysis results with its inclusion were not dramatically changed.

Table 2.

Results for the Demographics Models.

	Model 1: Business establishments	Model 2: Average annual pay	Model 3: Average annual pay and Payne County	Model 4: Number of jobs
Constant	b	b	b	b
	65.488 (3.51)	136.565 (8.82)	138.838 (9.85)	129.246 (5.69)
% white	.36739** (2.39)	.05823 (0.46)	−.05980 (–0.49)	.11871 (0.64)
Median household income	−.00126*** (–2.86)	−.00071* (–1.97)	−.00053 (–1.60)	−.00151*** (–2.84)
Unemployment	1.967 (1.46)	−.76856 (–0.69)	.03522 (–0.03)	−.01664 (–0.10)
Population	−.00001 (–0.24)	.00001 (0.19)	−.00003 (–0.54)	−.00009 (–1.04)
Northeast	−10.896* (–1.99)	−5.206 (–1.16)	−2.818 (–0.68)	.45922 (0.07)
Midwest	−7.264* (–1.91)	−6.605** (–2.11)	−5.227* (–1.81)	−12.803*** (–2.79)
South	5.549 (1.12)	−.89564 (–0.22)	−4.074 (–1.06)	−5.954 (–1.00)
Market per capita income	.00242*** (3.46)	.00094 (1.63)	.00095* (1.83)	.00138 (1.63)
Payne County, OK	—	—	22.076*** (2.97)	—
Adjusted R²	.3083	.1146	.2695	.2571
F statistic	3.51*	1.73	2.84*	2.95*
N	46	46	46	46

Note. Unstandardized coefficients with t-scores in parentheses reported.

Significant at the .10 level, two-tailed test.

Significant at the .05 level, two-tailed test.

***

Significant at the .01 level, two-tailed test.

The regression estimates for Model 1 reveal that five variables are statistically significant: percent white, median household income, northeast, midwest, and market per capita income. The positive coefficient for the percent white indicates that a higher White population percentage of a county leads to a positive percent change in business establishments. This finding provides support for Hoyman and Faricy (2009) and McGranahan and Wojan’s (2007) studies, which reveal that minorities typically experience more hardships regarding economic opportunities. The negative coefficient for median household income reveals that a county’s decline in median household income leads to increases in the number of business establishments. Median household income is a measurement of the amount of wealth in a county. One explanation for this negative relationship between median household income and business establishments could be that the less wealthy counties (lower median household incomes) had more room for growth opportunities for new businesses in their jurisdiction. This expected finding provides support for the hypothesis that counties with lower median household income have higher economic growth. This finding also confirms the findings from McGranahan and Wojan (2007), whose research found that in nonmetropolitan areas, counties with lower median household incomes experienced higher economic growth. As expected, this research also provides support for McGranahan et al.’s (2010) research, which also reveals similar findings for counties with lower median household incomes.

The negative coefficients for the Northeast and the Midwest reveal that the Northeast and Midwest regions in comparison with the West experienced lower growth in the number of businesses. These results are consistent with Storper and Scott (2009), who found that cities in the northwest and midwest experienced periods of stagnated economic growth. These findings also confirm the results from Hoyman and Faricy’s (2009) research in which they found that the Northeast and Midwest regions lost jobs during their time period of examination.

The last significant variable in this model is per capita market income. The per capita market income of a county is a measurement of the quality of jobs in a county. This variable, which is defined as the personal per capita income less transfer payments to individuals, provides an indication of the worth of jobs in a county. The positive coefficient for market per capita income reveals that increases in a county’s market per capita income leads to an increase in the number of business establishments.

Model 2 presents the results for the average annual pay dependent variable. The result for the midwest variable is again significant and provides support for the expected negative relationship between counties in the midwest and measures of economic growth. Additionally, the median household income variable has an expected negative relationship with average annual pay model.

Model 3 presents the results for the average annual pay and Payne County, Oklahoma. Payne County was identified as an outlier for the percentage change in the average annual pay model as the county with the largest average annual pay percent change. This county was identified as an outlier based on its Cooks D value, leverage value, and studentized deleted residual statistics exceeding the critical value times two for the Cooks D and leverage value, and exceeding 2 for the studentized deleted residuals (Fox, 1991). A dummy variable for Payne County was used in the model to correct for this problem. From this point forward, each average annual pay model is run with the dummy variable created for Payne County, Oklahoma.

In Model 3, there is a negative and significant relationship with the midwest regional variable and average annual pay. In this model, the market per capita income variable also has a significant and positive relationship with average annual pay. Also as similarly identified from the other demographics models, the midwest is a significant variable with a negative relationship. These findings illustrate that in comparison with the West region, which was omitted from the model as the reference category, the Midwest region lost jobs. These findings confirm the works of Storper and Scott (2009) and Hoyman and Faricy (2009).

In Model 4, there is also a negative and significant relationship with the midwest region variable and the number of jobs. Median household income similarly has a negative and significant relationship with the number of jobs as identified in the business establishments and average annual pay and Payne County models.

Analysis and Results for Research University Presence Models

The results in Table 3 present the demographics model with the inclusion of the research university dummy variable. According to Florida’s research (Florida, 2002, 2003; Florida & Cohen, 1999; Florida et al., 2006; Florida et al., 2008), research universities are key contributors to regional economic growth and development. Research universities possess the capacity and ability to generate innovation and thus economic prosperity for surrounding communities. The research university presence variable is positive and significant for the business establishments in Model 6 and for the number of jobs in Models 8 as expected. The models for business establishments and for jobs show that counties with the presence of a research university are more likely to experience higher increases in the number of business establishments and jobs created.

Table 3.

Results for the Research University Presence Models.

	Model 5: Business establishments	Model 6: Business establishments and Orangeburg County	Model 7: Average annual pay	Model 8: Number of jobs
Constant	b	b	b	b
	53.499 (2.62)	60.025 (2.99)	135.514 (8.67)	109.882 (4.56)
% white	.35766** (2.35)	.18517 (1.06)	−.06327 (–0.51)	.10300 (0.57)
Median household income	−.00097** (–2.03)	−.00082* (–1.75)	−.00045 (–1.22)	−.00104* (–1.85)
Unemployment	2.654* (1.87)	2.673* (1.95)	.16083 (0.14)	.94305 (0.56)
Population	−.00004 (–0.61)	−1.10 × 10^-07 (–0.00)	−.00003 (–0.65)	−.00013 (–1.55)
Northeast	−10.613* (–1.96)	−12.069** (–2.28)	−2.724 (–0.65)	.91595 (–0.14)
Midwest	−6.726* (–1.78)	−6.005 (–1.63)	−5.069* (–1.73)	−11.934** (–2.68)
South	6.273 (1.28)	3.651 (0.74)	−3.893 (–1.00)	−4.784 (–0.83)
Market per capita income	.00239*** (3.46)	.00245*** (3.67)	.00095* (1.80)	.00133* (1.64)
Research university	4.221 (1.41)	5.399* (1.82)	1.175 (0.51)	6.818* (1.93)
Orangeburg County, SC	—	−19.565* (–1.84)	—	—
Payne County, OK	—	—	22.219*** (2.96)	—
Adjusted R²	.3265	.3684	.2543	.3083
F statistic	3.42*	3.62*	2.53*	3.23*
N	46	46	46	46

Note. Unstandardized coefficients with t-scores in parentheses reported.

Significant at the .10 level, two-tailed test.

Significant at the .05 level, two-tailed test.

***

Significant at the .01 level, two-tailed test.

The findings for business establishments and the number of jobs provide support for Florida’s (Florida, 2002, 2003; Florida & Cohen, 1999; Florida et al., 2006; Florida et al., 2008) assertion that the presence of a research university is a key contributor to a region’s economic growth. The findings from these models provide support for Miner et al.’s (2001) research, which posits that universities are one of the conditions that contribute to successful local economic development. These results also confirm the findings of Goldstein and Drucker’s (2006) research, which found that universities have a significant contribution to a region’s economic growth.

Analysis and Results for the Human Capital Models

The findings for the human capital theoretical framework are presented in Table 4. The models found in Table 4 are simply the original demographics models from Table 2 with the addition of the human capital variable. The findings from these models reveal that the human capital variable is not initially significant across the three dependent variables. The human capital variable is not statistically significant until the median household income and research university presence variables are removed from the business establishments and number of jobs models. These findings illustrate that the median household income and research university presence variables appear to be better predictors of explaining increases in the percentage change trends for the number of jobs created. These findings illustrate that for nonmetropolitan counties, the context in which a variable is examined can affect the relationship and significance that they have with other variables. Interestingly, however, the human capital variable is not significant for the average annual pay model, even with the removal of variables.

Table 4.

Results for the Human Capital Models.

	Model 1: Business establishments	Model 2: Business establishments with modifications	Model 3: Average annual pay	Model 4: Number of jobs	Model 5: Number of jobs with modifications
Constant	b	b	b	b	b
	63.018 (3.00)	55.641 (2.61)	137.487 (8.50)	111.410 (4.86)	108.361 (4.48)
% white	.18441 (1.05)	.14083 (0.79)	−.05545 (–0.44)	.14887 (0.88)	.04105 (0.23)
Median household income	−.00096* (–1.80)	—	−.00056 (–1.33)	−.00111* (–1.86)	—
Unemployment	2.551* (1.82)	2.359 (1.62)	.07461 (0.07)	.41383 (0.26)	.08262 (0.05)
Population	2.13 × 10^-06 (0.03)	−.00001 (–0.23)	−.00004 (–0.67)	−.00020** (–2.41)	−.00018** (–2.13)
Northeast	−13.998** (–2.20)	−7.918 (–1.33)	−4.111 (–0.84)	−5.930 (0.84)	3.828 (0.55)
Midwest	−7.311 (–1.67)	−3.703 (–0.87)	−6.109* (–1.75)	−15.851*** (–3.19)	−9.634* (–1.92)
South	2.435 (0.44)	5.765 (1.06)	−4.738 (–1.12)	−6.020 (–1.05)	−1.976 (–0.32)
Market per capita income	.00278*** (3.11)	.00111** (2.06)	.00120* (1.73)	.00197* (2.01)	−.00033 (–0.53)
Research university	7.366* (1.60)	—	2.642 (0.76)	13.798** (2.67)	—
Human capital	−.17960 (–0.56)	.41684** (2.39)	−.13976 (–0.57)	−.45598 (–1.28)	.49975** (2.42)
Orangeburg County, SC	−21.012* (–1.90)	−16.809 (–1.54)	—	—	—
Payne County, OK	—	—	22.503*** (2.96)	—	—
Jefferson County, NY	—	—	—	29.074** (2.69)	22.095* (1.96)
Adjusted R²	.3557	.2964	.2395	.4041	.2729
F statistic	3.26*	3.11*	2.29*	3.77*	2.88*
N	46	46	46	46	46

Note. Unstandardized coefficients with t-scores in parentheses reported.

Significant at the .10 level, two-tailed test.

Significant at the .05 level, two-tailed test.

***

Significant at the .01 level, two-tailed test.

These findings of the modified business establishments and number of jobs models confirm Storper and Scott’s (2009) research, which asserts that economic growth trends can be best explained by patterns of highly educated people in a location. The business establishments and number of jobs models for human capital with modifications also confirms other human capital research, which has proven that concentrations of highly educated individuals are significantly important to regional economic growth (Barron et al., 1987; Becker, 1964; Hoyman & Faricy, 2009; Ullman, 1958). This research also provides support for Glaeser’s (1998) examination of the impact that the human capital assertion has on regions. His research reveals that locations with greater numbers of highly educated people have higher economic growth trends. Furthermore, this research confirms the findings from Lucas’s (1988) research of the human capital theory in which he found that cities with higher concentrations of human capital become engines of economic growth.

Analysis and Results for the Institutional Intellectual Models

In Table 5, results for the institutional intellectual capital theoretical framework are shown. As similarly found in the human capital models, the institutional intellectual capital variable is only a significant predictor in explaining economic growth when some of the demographics variables are removed. More specifically, the institutional intellectual capital variable is only found significant for the business establishments and number of jobs models, with modifications to the original set of demographic variables used. In other words, institutional intellectual capital leads to more businesses and jobs but only with the removal of some of the control variables.

Table 5.

Results for the Institutional Intellectual Capital Models.

	Model 1: Business establishments	Model 2: Business establishments with modifications	Model 3: Average annual pay	Model 4: Number of jobs	Model 5: Number of jobs with modifications
Constant	b	b	b	b	b
	60.530 (2.85)	57.444 (2.52)	134.912 (8.10)	111.641 (4.66)	102.904 (6.04)
% white	.18228 (1.01)	.14692 (0.80)	−.05896 (–0.45)	.07409 (0.42)	.11784 (0.64)
Median household income	−.00082* (–1.69)	—	−.00046 (–1.21)	−.00071 (–1.30)	—
Unemployment	2.646* (1.85)	1.982 (1.31)	.19064 (0.16)	.46243 (0.29)	—
Population	−3.83 × 10^-07 (–0.00)	−.00001 (–0.23)	−.00003 (–0.64)	−.00019** (–2.25)	−.00021** (–2.46)
Northeast	−11.963* (–2.17)	−14.240** (–2.44)	−2.833 (–0.65)	.07317 (0.01)	−3.661 (–0.54)
Midwest	−5.990 (–1.60)	−7.560* (–1.90)	−5.090* (–1.71)	−12.128*** (–2.88)	−14.232*** (–3.23)
South	3.612 (0.71)	2.910 (0.54)	−3.832 (–0.96)	−4.182 (–0.76)	−3.835 (–0.65)
Market per capita income	.00244*** (3.53)	.00156** (2.66)	.00096* (1.77)	.00106 (1.37)	.00026 (0.49)
Research university	5.650 (1.34)	—	.88978 (0.27)	11.531** (2.41)	—
Institutional intellectual capital	−.03822 (–0.08)	.57156* (1.69)	.04243 (0.12)	−.41797 (–0.81)	.71452* (1.99)
Orangeburg County, SC	−19.455* (–1.79)	−22.173* (–1.92)	—	—	—
Payne County, OK	—	—	22.214** (2.92)	—	—
Jefferson County, NY	—	—	—	25.000** (2.31)	27.662** (2.33)
Adjusted R²	.3499	.2449	.2327	.3874	.2349
F statistic	3.20*	2.62*	2.24*	3.59*	2.73*
N	46	46	46	46	46

Note. Unstandardized coefficients with t-scores in parenthesis reported.

Significant at the .10 level, two-tailed test.

Significant at the .05 level, two-tailed test.

***

Significant at the .01 level, two-tailed test.

The findings from these models in Table 5 confirm that counties with higher densities of higher education institutions experienced higher business establishments and the number of jobs created growth trends as expected. The findings from these models provide support for Nahapiet and Ghoshal’s (1998) research, which explored the impact that densities of higher education institutions have on a region’s ability to attract educated people. Their research reveals that regions with higher densities of higher education institutions gain more human capital, which leads to more economic growth. The findings of this research also provide support for Hoyman and Faricy’s (2009) research in which they found that clusters of universities correlated highly with economic growth. The results for the institutional intellectual capital models reveal that institutions of higher education are more likely to contribute to higher measures of economic growth and development in increases in the percentage change of the number of businesses and jobs in a region.

Summary Analysis of the Findings

According to the regression models, this study does produce some different findings in comparison with other researchers. In comparison with Hoyman and Faricy’s (2009) research, where they found human capital to be a significant variable across their models, human capital was not found as a significant predictor of economic growth and development in any of the models initially. In this research, it was not until median household income and research university presence were removed from the business establishments and number of jobs models that human capital becomes significant. These findings illustrate that the economic stability of the community and the presence of a research university are more important predictors in explaining the economic growth trends of a community.

These results are interesting for this particular variable, considering that this factor has typically been found to be a strong and consistent predictor of economic growth and development in other research. Similarly, institutional intellectual capital was also found significant and positively correlated with the business establishments and number of jobs models. These models were found significant, but only with the removal of median household income and removal of research university presence for business establishments, and the removal of median household income, unemployment, and research university presence for the number of jobs.

Conclusion

Discussion

Although the human capital and institutional intellectual capital theories examined here were not supported in their entirety, such conclusions do not call for the full rejection of these perspectives as strategies for sustaining the economic growth and development efforts of a community. The significance of these variables was contingent on the removal of some control variables, yet such findings should not discount their overall value and validity as predictors of measurements of economic growth. These findings simply emphasize the assertion that there are multiple applications for these theories among different types of communities. Particularly in isolated smaller communities, an examination of the economic vitality of the community itself must be examined before considering the implications of the findings for all similarly situated communities. However, in response to the question guiding this research, “Does human capital account for positive economic development and growth results in nonmetropolitan areas?,” the following can be concluded. The findings from this study reveal that in this context, human capital measured as concentrations of highly educated people and higher education institutions does lead to positive economic growth and development for counties.

The findings from this relationship between the institutional intellectual capital variable and economic growth measurements are as expected but provide marginal support for Nahapiet and Ghoshal’s (1998) research, which reveals that regions with higher densities of higher education institutions have increases in human capital that leads to more economic growth. These findings also provide marginal support for Hoyman and Faricy’s (2009) research in which they found that clusters of universities correlated highly with economic growth.

The findings for the relationship between the human capital and economic development variables provide marginal support for the hypothesis that regions with concentrations of highly educated individuals grew more with regard to businesses and jobs, supporting the works of Storper and Scott (2009). Interestingly, the findings from this research reveal that the human capital variable is not a strong determinant in predicting average annual pay changes for counties. This research also confirms other human capital research, which has proven that concentrations of highly educated individuals are vital to the promotion of regional economic growth (Barron et al., 1987; Becker, 1964; Hoyman & Faricy, 2009; Ullman, 1958). The findings in this study also support Glaeser’s (1998) examination of the impact that the human capital assertion has on regions, an examination which asserts that locations with greater numbers of highly educated people have higher economic growth trends. Furthermore, this research confirms the findings from Lucas’s (1988) research of the human capital theory in which he found that cities with higher concentrations of human capital become engines of economic growth.

The goal of this study was to show how research examining the economic growth and development trends of rural counties needs to be further explored. Because of the limited research in this context, future studies should be conducted based on the findings from this study that will lead to the identification of other patterns and explanations for understanding trends in these unique communities. Of particular interest to future studies of rural, isolated, nonmetropolitan communities is to conduct a panel data set to further expand and identify other explanations of the economic growth and development trends of counties with and without the presence of a research university. Such studies will aid in the development of more robust models with identified factors that better explain the economic growth and development trends of rural counties.

Policy Implications

The recurring theme found from the results of this study is the important role that higher education has on improving the economic prosperity of a community. As posited by Mathur (1999), concentrations of human capital lead both directly and indirectly to economic growth and development in a community. Therefore, one recommendation for local public administrators with access to higher education institutions is to focus their efforts on improving the advanced (postsecondary) education of their workforce. Why? Because universities matter; they act as facilitators of economic growth by attracting people to locate in a community.

The findings of this study suggest to local public administrators the need to invest in workforce development and educational development training programs. High concentrations of highly educated individuals in a community lead to a positive impact on a region’s economic growth and development patterns, particularly in communities without the presence of a research university. Specifically, it can be concluded that investing in programs and initiatives that promote the educating of a workforce are in the best long-term interest for communities.

The findings of this study, which examined a total of 23 communities with a research university presence and 23 towns without a research university presence, illustrate the positive impact that a university has on the economic growth of a community. Specifically, the findings of this study consistently show that across the business establishments and number of jobs models the presence of a research university matters in explaining economic growth trends in nonmetropolitan areas. The research university presence results also illustrate the important roles that higher education and knowledge have in today’s economy.

Research universities play a vital role and are invaluable to their community’s regional economy. It was found consistently in this research that the presence of a research university led to increases in the development of new business establishments and jobs created in the surrounding region. The local communities and the research universities in small nonmetropolitan regions must begin to build collaborations with each other. Research universities must actively seek to facilitate connections with local government officials and community groups to identify strategies to address the challenges with which they are faced. Simultaneously, local government officials within the community must take an active approach to ensure this relationship is maintained and flourishes. For small nonmetropolitan communities to thrive and reach their fullest potential in fostering economic growth and development, active collaborations and partnerships must be made between the universities and the communities.

The findings also lead to a number of conclusions that can be drawn about public policy decisions in rural communities. The prominence of the demographic factors being statically significant illustrates the necessity for local communities to invest their efforts in projects to build more attractive communities. The amount of disposable income in nonmetropolitan communities is a strong determinant of the economic growth trends of these communities. Such communities must invest their efforts and resources into projects that will bring in/increase the amount of money that people have in these communities. These surprisingly marginal results highlight the unique context in which nonmetropolitan regions exist. The findings of this research illustrate that successful economic development must first start with successful community development efforts. Such efforts that pay attention to the physical infrastructure and the economic welfare of the residents are most critical to the survival of these communities. Affordable housing and providing the necessary resources to support individuals and families are the keys to success for a successful business environment in these communities. Sustained interaction among key stakeholders in the region will yield successful solutions. Isolated communities should invest their efforts in collaborating with government, nonprofit partners, private partners, and citizens (Federal Reserve Bank of San Francisco, 2012). The communities that are going to succeed are those that take an innovative entrepreneurial approach to economic development, involving everyone in the community. These jurisdictions must find ways to make their communities more desirable places to live. These communities need to become people based, focused on creating a better life for their residents.

Footnotes

Author’s Note

This article is based on data also used in my doctoral dissertation at Mississippi State University (Pink, 2011). Some of the data and content from this article were presented at the American Society for Public Administration Annual Meeting in Las Vegas, Nevada (March 2012).

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Author Biography

Stephanie A. Pink-Harper is an Assistant Proofessor in the Department of Political Science at Southern Illinois University Carbondale. Her primary research interest include rural regional economic development, and local government administration. She has also published in The Ralph Bunche Journal of Public Affairs.

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