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

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

Notes

  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.

References

  1. Arikan, Y., Clark, T. N., Noonan, D. S., & Tolley, G. (2019). The arts, Bohemian scenes and income. Cultural Trends, 1, 1. https://doi.org/10.1080/09548963.2019.1680013.

    Article  Google Scholar 

  2. Aschauer, D. A. (1989). Is public expenditure productive? Journal of Monetary Economics. https://doi.org/10.1016/0304-3932(89)90047-0.

    Article  Google Scholar 

  3. Ashley, A. J. (2015). Beyond the aesthetic: The historical pursuit of local arts economic development. Journal of Planning History. https://doi.org/10.1177/1538513214541616.

    Article  Google Scholar 

  4. Audretsch, D. B., Lehman, E. E., & Warning, S. (2005). University spillover and new firm location. Research Policy. https://doi.org/10.1016/j.respol.2005.05.009.

    Article  Google Scholar 

  5. Audretsch, D., Lehmann, E., & Warning, S. (2004). University spillovers: Does the kind of science matter. Industry and Innovation. https://doi.org/10.1080/1366271042000265375.

    Article  Google Scholar 

  6. Becker, H. S. (2008). Art worlds (25th anniversary ed.). Berkeley, CA: University of California Press.

    Google Scholar 

  7. Bourdieu, P. (1977). Cultural reproduction and social reproduction. In J. Karabel & A. H. Halsey (Eds.), Power and ideology in education (pp. 487–511). New York: Oxford University Press.

    Google Scholar 

  8. Breznitz, S. M., & Feldman, M. P. (2012). The engaged university. The Journal of Technology Transfer. https://doi.org/10.1007/s10961-010-9183-6.

    Article  Google Scholar 

  9. Breznitz, S. M., & Noonan, D. S. (2014). Arts districts, universities, and the rise of digital media. The Journal of Technology Transfer. https://doi.org/10.1007/s10961-013-9315-x.

    Article  Google Scholar 

  10. Breznitz, S. M., & Noonan, D. S. (2020). Crowdfunding in a not-so-flat world. Journal of Economic Geography. https://doi.org/10.1093/jeg/lbaa008.

    Article  Google Scholar 

  11. Camagni, R. (1991). Technological change, uncertainty and innovation networks: Towards a dynamic theory of economic space. In D. Boyce, P. Nijkamp, & D. Shefer (Eds.), Regional science. Berlin: Springer.

    Google Scholar 

  12. Carboni, O. A. (2013). Spatial and industry proximity in collaborative research: Evidence from Italian manufacturing firms. The Journal of Technology Transfer. https://doi.org/10.1007/s10961-012-9279-2.

    Article  Google Scholar 

  13. Cardamone, P., Pupo, V., & Ricotta, F. (2012). University and firm performance in the Italian manufacturing sector. Working Paper 07-2012. Dipartimento di Economia e Statistica, Università Della Calabria. http://www.ecostat.unical.it/repec/workingpapers/WP07_2012.pdf. Accessed July 11, 2020.

  14. Cardamone, P., Pupo, V., & Ricotta, F. (2014). Assessing the impact of university technology transfer on firms’ innovation. Working Paper 03-2014. Dipartimento di Economia e Statistica, Università Della Calabria. http://www.ecostat.unical.it/repec/workingpapers/wp03_2014.pdf. Accessed July 11, 2020.

  15. Carnegie Foundation for the Advancement of Teaching. (2012). A classification of institutions of higher education, 1994 edition. Data file. http://carnegieclassifications.iu.edu/downloads/1994_edition_data.xls. Accessed July 11, 2020.

  16. Currid, E. (2007). The Warhol economy: How fashion, art, and music drive New York City. Princeton, NJ: Princeton University Press.

    Google Scholar 

  17. Easterly, W., & Rebelo, S. (1993). Fiscal policy and economic growth: An empirical investigation. Journal of Monetary Economics. https://doi.org/10.1016/0304-3932(93)90025-B.

    Article  Google Scholar 

  18. Eaton, S. C., & Bailyn, L. (1999). Work and life strategies of professionals in biotechnology firms. The ANNALS of the American Academy of Political and Social Science. https://doi.org/10.1177/000271629956200111.

    Article  Google Scholar 

  19. Eckert, F., Fort, T. C., Schott, P. K., & Yang, N. J. (2020). Imputing missing values in the US Census Bureau’s County Business Patterns. NBER Working Paper 26632. National Bureau of Economic Research. http://www.nber.org/papers/w26632. Accessed July 11, 2020.

  20. Etzkowitz, H., & Leydesdorff, L. (Eds.). (1997). Universities and the global knowledge economy: A triple helix of university-industry relations. London: Pinter.

    Google Scholar 

  21. Fantino, D., Mori, A., & Scalise, D. (2015). Collaboration between firms and universities in Italy: The role of a firm’s proximity to top-rated departments. Italian Economic Journal. https://doi.org/10.1007/s40797-014-0003-2.

    Article  Google Scholar 

  22. Feldman, M. P. (1994). The geography of innovation. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  23. Florida, R. (2002). The rise of the creative class. New York: Basic Books.

    Google Scholar 

  24. Florida, R. (2003). Cities and the creative class. City & Community. https://doi.org/10.1111/1540-6040.00034.

    Article  Google Scholar 

  25. Garin, A. (2019). Putting America to work, where? The limits of infrastructure construction as a locally targeted employment policy. Journal of Urban Economics. https://doi.org/10.1016/j.jue.2019.04.003.

    Article  Google Scholar 

  26. Goddard, J. B., & Chatterton, P. (1999). Regional development agencies and the knowledge economy: Harnessing the potential of universities. Environment and Planning C Government and Policy. https://doi.org/10.1068/c170685.

    Article  Google Scholar 

  27. Grodach, C. (2010). Beyond Bilbao: Rethinking flagship cultural development and planning in three California cities. Journal of Planning Education and Research. https://doi.org/10.1177/0739456x09354452.

    Article  Google Scholar 

  28. Grodach, C., Currid-Halkett, E., Foster, N., & Murdoch, J. (2014). The location patterns of artistic cultures: A metro- and neighborhood-level analysis. Urban Studies, 51(13), 2822–2843. https://doi.org/10.1177/0042098013516523.

    Article  Google Scholar 

  29. Gumprecht, B. (2003a). The American college town. Geographical Review. https://doi.org/10.1111/j.1931-0846.2003.tb00020.x.

    Article  Google Scholar 

  30. Gumprecht, B. (2003b). The American college town. Geographical Review, 93(1), 51–80.

    Article  Google Scholar 

  31. Hale, J. S., & Woronkowicz, J. (2019). Evaluating a university’s investment in arts programming on student arts participation. Cultural Trends. https://doi.org/10.1080/09548963.2019.1679993.

    Article  Google Scholar 

  32. Hale, J. S., & Woronkowicz, J. (2020). Artists as public sector intrapreneurs: An experiment [Conference presentation]. Arts, Entrepreneurship, and Innovation Lab Symposium, Indianapolis, IN, United States. https://culturalaffairs.indiana.edu/resources/event-archive.html.

  33. Iowa Community Indicators Program, Iowa State University. (2018). Annual unemployment rates by state” [data file]. https://www.icip.iastate.edu/tables/employment/unemployment-states.

  34. Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. The Quarterly Journal of Economics. https://doi.org/10.2307/2118401.

    Article  Google Scholar 

  35. Ladry, C., Bianchini, F., Ebert, R., Gnad, F., & Kunzman, K. (1996). The creative city in Britain and Germany. London: Anglo-German Foundation for the Study of Industrial Society.

    Google Scholar 

  36. Leten, B., Landoni, P., & Van Looy, B. (2008). Developing technology in the vicinity of science: Do firms really benefit? An empirical assessment on the level of Italian provinces. MSI 0711. Faculty of Business and Economics, Katholieke Univrsiteit Leuven. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1101435. Accessed July 11, 2020.

  37. Markusen, A. (2013). Artists work everywhere. Work and Occupations. https://doi.org/10.1177/0730888413504418.

    Article  Google Scholar 

  38. Markusen, A., & Schrock, G. (2006). The artistic dividend: Urban artistic specialisation and economic development implications. Urban Studies. https://doi.org/10.1080/00420980600888478.

    Article  Google Scholar 

  39. McGraw-Hill Construction, Inc. (2017). Dodge data and analytics. Population data [Data file]. https://www.construction.com.

  40. Minshall, T., Druilhe, C., & Probert, D. (2006). The evolution of ‘third mission’ activities at the university of Cambridge: Balancing strategic and operational considerations. In A. Groen, R. Oakey, P. C. Van der Siide, & S. Kauser (Eds.), New technology-based firms in the new millennium (Vol. V, pp. 7–21). Oxford: Elsevier.

    Google Scholar 

  41. Moretti, E. (2004). Estimating the social return to higher education: Evidence from longitudinal and repeated cross-sectional data. Journal of Econometrics. https://doi.org/10.1016/j.jeconom.2003.10.015.

    Article  Google Scholar 

  42. Muscio, A. (2013). University-industry linkages: What are the determinants of distance in collaborations? Papers in Regional Science. https://doi.org/10.1111/j.1435-5957.2012.00442.x.

    Article  Google Scholar 

  43. Muscio, A., & Pozzali, A. (2013). The effects of cognitive distance in university-industry collaborations: Some evidence from Italian universities. The Journal of Technology Transfer. https://doi.org/10.1007/s10961-012-9262-y.

    Article  Google Scholar 

  44. National Association of State Budget Officers (NASBO). (2019). State expenditure report data [data file]. https://www.nasbo.org/reports-data/state-expenditure-report.

  45. National Bureau of Economic Research (NBER). (2016). Census U.S. intercensal county population data [data set]. https://data.nber.org/data/census-intercensal-county-population.html.

  46. National Center for Education Statistics (NCES). (2017). Digest of education statistics [data tables]. https://nces.ed.gov/programs/digest/.

  47. National Center for Education Statistics (NCES). (2018). Integrated postsecondary education data system [data set]. https://nces.ed.gov/ipeds/datacenter/DataFiles.aspx?goToReportId=7.

  48. Nelson, R. R. (Ed.). (1993). National innovation systems: A comparative analysis. New York, NY: Oxford University Press.

    Google Scholar 

  49. Nelson, R. R., & Nelson, K. (2002). Technology, institutions, and innovation systems. Research Policy. https://doi.org/10.1016/S0048-7333(01)00140-8.

    Article  Google Scholar 

  50. Noonan, D. S. (2013). How US cultural districts reshape neighborhoods. Cultural Trends. https://doi.org/10.1080/09548963.2013.817652.

    Article  Google Scholar 

  51. Patterson, M., & Silver, D. (2015). The place of art: Local area characteristics and arts growth in Canada, 2001–2011. Poetics. https://doi.org/10.1016/j.poetic.2015.05.003.

    Article  Google Scholar 

  52. Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. New York, NY: Simon & Schuster.

    Google Scholar 

  53. Putnam, R. D. (2003). Better together: The arts and social capital. Saguaro Seminar on Civic Engagement in America, John F. Kennedy School of Government, Harvard University. https://www.creativecity.ca/database/files/library/better_together.pdf. Accessed July 11, 2020.

  54. Reynolds, G. L. (2007). The impact of facilities on recruitment and retention of students. New Directions for Institutional Research. https://doi.org/10.1002/ir.223.

    Article  Google Scholar 

  55. Sanchez-Robles, B. (1998). Infrastructure investment and growth: Some empirical evidence. Contemporary Economic Policy. https://doi.org/10.1111/j.1465-7287.1998.tb00504.x.

    Article  Google Scholar 

  56. Schroeder, J.P. (2016). Historical population estimates [data file]. https://conservancy.umn.edu/handle/11299/181605.

  57. Scott, P. (1979). What future for higher education?. London: Fabian Society.

    Google Scholar 

  58. Seaman, B. A. (2011). Economic impact of the arts. In R. Towse (Ed.), A handbook of cultural economics (2nd ed., pp. 201–210). Cheltenham: Edward Elgar Publishing Inc.

    Google Scholar 

  59. Seaman, B. A. (2020). Economic impact of the arts. In R. Towse & T. N. Hernández (Eds.), Handbook of cultural economics (3rd ed., pp. 241–253). Cheltenham: Edward Elgar Publishing Inc.

    Google Scholar 

  60. State Higher Education Executive Officers Association (SHEEO). (2018). State higher education finance: FY 2018 [data file]. https://sheeo.org/project/state-higher-education-finance/.

  61. Strom, E. (2002). Converting pork into porcelain: Cultural institutions and downtown development. Urban Affairs Review. https://doi.org/10.1177/107808702401097763.

    Article  Google Scholar 

  62. Tomusk, V. (2011). The micropolitics of knowledge in England and Europe: The Cambridge University IPRs controversy and its macropolitical lessons. In D. Rhoten & C. Calhoun (Eds.), Knowledge matters: The public mission of the research university (pp. 377–396). New York: Columbia University Press.

    Google Scholar 

  63. U.S. Bureau of Labor Statistics. (2020). Local area unemployment statistics [data set]. https://www.bls.gov/lau/.

  64. U.S. Census Bureau. (2010). Centers of population [data set]. https://www.census.gov/geographies/reference-files/time-series/geo/centers-population.html.

  65. U.S. Census Bureau. (2016). County and ZIP code business patterns [data set]. https://www.census.gov/programs-surveys/cbp.html.

  66. U.S. Census Bureau. (2018). Median household income by state [data set]. https://www2.census.gov/programs-surveys/cps/tables/time-series/historical-income-households/h08.xls.

  67. U.S. Census Bureau. (2020). American Community Survey educational attainment and school enrollment [data set]. https://data.census.gov/cedsci/.

  68. van der Zwan, P., Hessels, J., & Burger, M. (2020). Happy Free Willies? Investigating the relationship between freelancing and subjective well-being. Small Business Economics. https://doi.org/10.1007/s11187-019-00246-6.

    Article  Google Scholar 

  69. von Hippel, P., & Powers, D. (2019). RPME: Stata module to compute Robust Pareto midpoint estimator. Statistical Software Components S457962. Boston College Department of Economics. https://EconPapers.repec.org/RePEc:boc:bocode:s457962. Accessed July 11, 2020.

  70. Woronkowicz, J. (2015). Art-making or place-making? The relationship between open-air performance venues and neighborhood change. Journal of Planning Education and Research. https://doi.org/10.1177/0739456X15597759.

    Article  Google Scholar 

  71. Woronkowicz, J., Bradburn, N. M., Frumkin, P., Gertner, R., Joynes, D. C., Kolendo, A., & Seaman, B. (2012). Set in stone: Building America’s new generation of arts facilities, 1994–2008. Cultural Policy Center, University of Chicago. https://www.norc.org/PDFs/setinstone%20FINAL%20REPORT.pdf. Accessed July 11, 2020.

Download references

Acknowledgements

The authors gratefully acknowledge the research assistance of Michael Weigel.

Funding

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Douglas S. Noonan.

Ethics declarations

Conflict of interest

There are no conflicts of interest related to the conduct of this research to report.

Availability of data and material

The datasets generated during the current study are not publicly available because they include proprietary data. The data are available from the corresponding author on reasonable request.

Code availability

Stata code is available from the corresponding author upon request.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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). https://doi.org/10.1007/s10961-020-09825-2

Download citation

Keywords

  • Spillovers
  • Higher education institutions
  • Cultural infrastructure
  • Arts

JEL Classification

  • I25
  • Z11