More than STEM: spillovers from higher education institution infrastructure investments in the arts


Higher education institutions (HEIs) represent an enormous density of investment and resources, concentrating infrastructure spending and creating high human capital citizens and knowledge spillovers increasingly seen as critical to advancing regional quality of life. While HEIs’ prominent role in promoting regional economic growth, innovation, and attractiveness receives considerable research attention, most of that attention is paid to aspects of HEIs that are directly related to STEM activity. There are various theories that suggest spillovers from non-STEM activity at HEIs as well, specifically in the arts. This study examines whether spillovers occur for HEIs’ large capital investments in the arts. Specifically, we focus on HEI investments in arts physical infrastructure and whether these investments have any effects on regional-level business activity, including jobs and firms. To analyze HEI spillovers of physical arts infrastructure on regional jobs and firms, we use construction starts data on building projects from Dodge Analytics, Inc. and data from the Integrated Postsecondary Education Data System, which include administrative data for every college, university, and technical/vocational institution that participates in the federal student financial aid programs. We couple these data with public data on regional-level socioeconomic indicators from the U.S. Census Bureau’s Zip-code Business Patterns data. We employ a quasi-experimental propensity-score matching design in order to control for a host of HEI and regional-level characteristics in examining the impact of infrastructure investments. The results suggest strong and consistent spillover effects (i.e. overall and specifically in the arts industry) for regions with HEIs that make these investments.

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Fig. 1
Fig. 2


  1. 1.

    In the very few instances where an institution records multiple investment projects in the same year, we consider this as a single ‘treatment’ and sum the value of those projects as if it were one single, large project. We use the zip-code of the largest project as the location of the treatment in that year.

  2. 2.

    We also perform the same analysis using the County Business Patterns (CBP) data from the US Census Bureau. Our analysis at the county level demonstrates this diluted effect over larger geographies. We find that county-level trends for all establishments can run counter to the zip-code level trends. The county-level results are available upon request.

  3. 3.

    An alternate approach would be to estimate an ordinary least squares (OLS) regression for each of our outcome measures, using our rich set of controls, to identify the effect of the treatment. A limitation of this approach, however, is that the vast majority of the institution-year observations might reasonably not be considered ‘comparable’ to the observations that received treatments. The assumed linear parameters in OLS to control for differences between treatment and control observations can be quite limiting, especially when a large portion of the data points have minimal likelihood of receiving a treatment. Our OLS results indicate insignificant effects of the treatment across the board—we cannot reject zero effect for each of our outcome variables measured at various time lags. These results are available upon request.

  4. 4.

    An alternative estimate, the average treatment effect (ATE), shifts our attention to average effects across all observations. These results are available upon request.

  5. 5.

    The ATT can be expressed as E[(y1i − y0i) | Di = 1] for outcome y for observation i receiving a treatment (y1i) compared to if had not received the treatment (y0i) restricted to observations receiving treatments of Di = 1.

  6. 6.

    Both the wide and narrow arts categories have been used elsewhere (Arikan, Clark, Noonan and Tolley 2019, Patterson and Silver 2015), which helps connect to the prior work on arts impacts on regional economic development. This prior work informs the crosswalk to identify comparable categories using the Standard Industrial Classification (SIC) system for pre-1997, and the North American Industrial Classification System (NAICS) post-1997. To further mitigate inconsistencies between outcome measures in the SIC and the NAICS eras, we drop 1997 observations from the analysis. As 1997 is the first year of NAICS-based jobs and establishments measures, controls for the prior year’s level of jobs or establishments may not be comparable in 1997 as it would in other years. See Table 6 for a list of industry codes included in each category.

  7. 7.

    In some cases, an HEI invested in an “off campus” building project in a different zip code from the school itself. We also estimated effects using only the firms and jobs in the school’s zip code, regardless of where the project is located. We expect to see weaker or no effects using this approach, because the investments occurred elsewhere, although we might still see effects if the off-campus investment displaced on-campus business activity. The results, available on request, show only insignificant effects when ignoring the location of the project itself. This provides some validation of our estimator and suggests that campus-related displacement may not be a major factor.

  8. 8.

    Eckert et al. (2020) offer an advanced approach to improving job-count imputations in the business patterns at the county level, although their work is not available at the zip-code level. Applying our midpoint-based imputation technique to county-level data yields correlations with the Eckert et al. estimates of 0.994, 0.979, and 0.987 levels for all jobs, wide arts jobs, and narrow arts jobs, respectively. We expect that our imputations for zip-code level data thus reasonably proxies for business activity at that level.

  9. 9.

    Details on the full set of variables, including definitions, data sources, and descriptive statistics, can be found in Tables 7 and 8.


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The authors gratefully acknowledge the research assistance of Michael Weigel.


This project was supported in part or in whole by an award from the Research: Art Works program at the National Endowment for the Arts: Grant# 17-3800-7015. The opinions expressed in this paper are those of the author(s) and do not represent the views of the Office of Research & Analysis or the National Endowment for the Arts. The NEA does not guarantee the accuracy or completeness of the information included in this paper and is not responsible for any consequence of its use.

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See Tables 6, 7 and 8.

Table 6 North American Industry Classification System (NAICS), Standard Industrial Classification (SIC) codes included in wide, narrow arts definitions
Table 7 Variable definitions
Table 8 Descriptive statistics

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Noonan, D.S., Woronkowicz, J. & Hale, J.S. More than STEM: spillovers from higher education institution infrastructure investments in the arts. J Technol Transf (2020).

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  • Spillovers
  • Higher education institutions
  • Cultural infrastructure
  • Arts

JEL Classification

  • I25
  • Z11