Higher education in Minnesota

The Effect of Public Institutions on Local Economies: How MnSCU Campuses Reduce Unemployment in Minnesota
Abstract
There are 54 Minnesota State Colleges and University campuses spread across 87 counties in Minnesota. Localities competitively bid for the construction of MnSCU campuses; each campus creates jobs in the surrounding areas, either directly through employment or indirectly by attracting industry and development. The January 2011 Bureau of Labor Statistics publication displays a growing disparity in unemployment rates in Minnesota, ranging from Rock County with 5.4% to Clearwater County with 16.9%. In an effort to understand the disparities, research is conducted to ask why counties with multiple publicly funded institutions, such as Winona County, tend to have lower than national average unemployment rates (6.8% versus 9.8%)? To answer this question, the unadjusted unemployment rate will be compared to the number of institutions per county. It is hypothesized that the more institutions a county has (0-7), the lower the unemployment rate will be.
Introduction
There are 54 Minnesota State Colleges and University campuses spread across 87 counties in Minnesota. Localities competitively bid for the construction of MnSCU campuses; each campus creates jobs in the surrounding areas, either directly through employment or indirectly by attracting industry and development. The January 2011 Bureau of Labor and Statistics publication displays a growing disparity in unemployment rates in Minnesota, ranging from Rock County with 5.4% to Clearwater County with 16.9%. In an effort to understand the disparities, research is conducted to ask why counties with multiple publicly funded institutions, such as Winona County, tend to have lower than national average unemployment rates (6.8% versus 9.8%)? To further understand the differences in employment rates, the causes and determining factors of unemployment need to be defined. Unemployment is determined to be regional (Overman, Puga, Vandenbussche 2002) and not restricted to local boundaries or policies.
Many of the key indicators of unemployment can be linked to a higher education institution in the area- industrial diversity (Simon 1988), low crime rates (Edmark 2005), and average education level (Ashenfelter, Hamm 1979).
Based on the lack of literature available regarding public institutions and unemployment, it is clear that there is a need for a correlation study between unemployment and public institutions. MnSCU officials will attempt to make their case to the legislature this budget cycle to justify funding needs, and having a study that relates a hot button issue—such as unemployment—to the existence of a MnSCU campus will be greatly beneficial.
Literature Review
Do public college campuses impact local economies? The correlation between local economies and college campuses has been widely studied, yet there remains non-consensus in the findings. Much of the research previously done focuses on the economic impact of colleges and universities in regards to direct funding, relating the direct dollars provided by higher education as an indicator of success. Discrepancies remain on the spillover effects of public education institutions- attracting industry and reducing short-term unemployment. This paper is a review of findings previously done in the field of unemployment.
Blake Grumprecht’s article is a landmark in determining the cost-benefit analysis of colleges in their surrounding communities. The work focuses on geographical data and acknowledges a relationship between the college’s presence and an increase in key economic factors- an increase in median family income and a reduction in unemployment rates. The findings also determined college communities to be highly transient locations, which Terry Fitzgerald researched as an explanation for the “brain drain” in rural Minnesota communities. Both of the articles observed long-term growth rates, but Fitzgerald’s study focused on the macro-level cycles in Minnesota. Fitzgerald’s article was more of an overview of unemployment cycles in Minnesota—it was not an in-depth analysis of unemployment.
One of the major problems in determining the effect of a public college on the local economy is linking the causality of the spill over effects and explaining the anomalies in the findings. When observing unemployment, it is noted that there are two different types or stages of unemployment. First, there is the classic long-term unemployment that is attributable to a poor economy. Secondly, there is frictional unemployment, which is where a professional is temporarily unemployed after graduating or while they are in between jobs. Many MnSCU schools reside in rural areas where the school is the major employer in the region. If an employee in these regions quits or is laid off it is difficult to find a new job due to a lack of industrial diversity. Curtis Simons article tackles the issue of frictional unemployment and offers an explanation for this temporary form of unemployment that may affect the researches results. The article studies frictional unemployment and the level of industrial diversity in the surrounding communities. The research leaves room for expounding upon and provides opportunities in studying industrial diversity and how higher education provides a diversified supply of employment in Minnesota. Even with a diversified supply of employment, however, unemployment data can be skewed. Henry Overman and Diego Puga’s article examines unemployment as a regional phenomenon—one that is not restricted to government policies and boundary lines. This research is an innovative approach to unemployment in the sense that location is an incredibly important indicator of the unemployment rate.
The majority of the literature on higher education’s impact on local economies is not scientific and focuses on qualitative observations. There has been little research done in the areas of a college’s positive financial externalities, but studies on the causes of unemployment and the benefits of local colleges can be linked through common themes in an attempt to answer if public colleges impact local economies.
Research and Methodology
Research Question: Do public college campuses impact local economies? How do colleges, directly and indirectly, affect local unemployment rates? Does having a campus in a county reduce unemployment rates? And, if so, what is the marginal benefit of having more than one university? How do MnSCU campuses influence local economies during a recession?
H1: If a Minnesota county houses a MnSCU campus, it will see a reduction in unemployment rates.
AND
H1a: Assuming this is correct, the more campuses a county has the lower the relative unemployment rate will be.
AND
H1b: MnSCU campuses will have a stronger correlation with unemployment during an economic boom than a recession.
H0: The presence of a MnSCU campus has no effect on a counties unemployment rate.
AND
H0a: The number of MnSCU campuses has no relation to unemployment rates
AND
H0b: If there is a relationship between MnSCU campuses and county unemployment rates, there is no difference between the relationships in 2007 and 2011.
In regards to methodology, the unemployment rate by county was arranged for December 2007 (economies peak) and January 2011. Each county was analyzed to determine the number of MnSCU campuses within the counties boundaries. A few of the campuses, such as Mankato State, resided in multiple counties. In these instances, each county was selected as the colleges residing county. Next, it was observed whether a county had an institution at all. A dummy variable was assigned to determine if the county had an institution, a “0” assigned for “no” and a “1” assigned for “yes”.

The data was analyzed using crosstabs to determine relations. A regression analysis was performed to determine if there was a significant relationship between each dependent variable (January 2011 Unadjusted Unemployment Rate by Minnesota County and December 2007 Unadjusted Unemployment Rate by Minnesota County) and each independent variable, which were the number of MnSCU Campuses (0-7, 3-7 recoded to 3+) and if the county has a MnSCU Campus (0No, 1Yes).

The sample used for the study is N87. The number is based on the total number of counties in Minnesota. There was a total N61 for the number of MnSCU campuses. The results from the study should be cautiously applied to a larger scale—as predetermined in previous literature, unemployment is regional and relations in Minnesota may be very different from state to state.
Findings
The regression analysis discovered interesting results. The first regression output compared the dependent variable of January 2011 unemployment with the independent variable: the number of MnSCU campuses (recoded) the county has. A statistically significant relationship was found, as P equals.028. The confidence interval was relatively strong—97.2%.

The next regression was comparing the pre-recession unemployment rates with the number of MnSCU campuses (recoded). A statistically significant relationship was found, as P equals.009. This does not mean that MnSCU campuses have more of an effect on unemployment during an economic boom; it just means that the confidence interval is higher.

The third regression compares Jan2011 unemployment with the dummy variable of whether the county has a MnSCU campus or not. The result was not statistically significant, as P equals .061. This was an interesting surprise—the null hypothesis was not able to be rejected.

The final regression output compares Dec2007 unemployment rates with the dummy variable of whether a county has a MnSCU campus or not. The result was a statistically significant relationship, as P equals.018. This is interesting, because a relationship was not found during the recession.

Overall, most of the relationships that were discovered were similar to the hypotheses. Two of the three null hypotheses were able to be rejected—whether a campus has an institution during a recession was not statistically significant.
Discussion and Conclusion: Suggestions for Further Research
There appears to be a significant relationship between public institutions and the local unemployment rate. Counties with a public higher education institution in Minnesota tended to have lower aggregate unemployment rates than counties without such an institution. Similarly, there was an inverse relationship between unemployment and the number of MnSCU campuses a county has. The research presented is not able to determine whether or not MnSCU campuses is the cause of low unemployment—all that can be done is three of the four null hypotheses can be rejected. The notion that if there is a relationship between MnSCU campuses and county unemployment rates, there is no significant difference between the relationships in December 2007 (boom) and January 2011 (recession) was not able to be rejected. The findings in this article should be used as a basis for understanding causes of unemployment. While higher educations may not be directly related to lower unemployment rates, the research and data presented in this article make evident the fact that public higher education should not be ignored as a key indicator.

Further studies are needed to determine a public institutions relationship with unemployment. Researchers should consider using larger sample sizes—a multistate analysis would be greatly beneficial. Future studies may also want to create a measurement that determines the size and influence of the institution as a factor to consider the economic impact of the colleges and universities. As determined by Overman and Puga (2002, p. 141), unemployment is a regional phenomenon that is not subject to borders. With this in mind, the most accurate way to recreate this study would be with the use of GIS mapping data. Determining unemployment rate in a 50-mile radius would be more telling that using county boundaries to determine relationships. By using both GIS mapping and a larger sample size, further research could determine a more conclusive correlation between public institutions and unemployment rates.

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