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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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.
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.
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.
An alternative estimate, the average treatment effect (ATE), shifts our attention to average effects across all observations. These results are available upon request.
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.
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.
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.
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.
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.
Aschauer, D. A. (1989). Is public expenditure productive? Journal of Monetary Economics. https://doi.org/10.1016/0304-3932(89)90047-0.
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.
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.
Audretsch, D., Lehmann, E., & Warning, S. (2004). University spillovers: Does the kind of science matter. Industry and Innovation. https://doi.org/10.1080/1366271042000265375.
Becker, H. S. (2008). Art worlds (25th anniversary ed.). Berkeley, CA: University of California Press.
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.
Breznitz, S. M., & Feldman, M. P. (2012). The engaged university. The Journal of Technology Transfer. https://doi.org/10.1007/s10961-010-9183-6.
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.
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.
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.
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.
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.
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.
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.
Currid, E. (2007). The Warhol economy: How fashion, art, and music drive New York City. Princeton, NJ: Princeton University Press.
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.
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.
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.
Etzkowitz, H., & Leydesdorff, L. (Eds.). (1997). Universities and the global knowledge economy: A triple helix of university-industry relations. London: Pinter.
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.
Feldman, M. P. (1994). The geography of innovation. Dordrecht: Kluwer Academic Publishers.
Florida, R. (2002). The rise of the creative class. New York: Basic Books.
Florida, R. (2003). Cities and the creative class. City & Community. https://doi.org/10.1111/1540-6040.00034.
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.
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.
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.
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.
Gumprecht, B. (2003a). The American college town. Geographical Review. https://doi.org/10.1111/j.1931-0846.2003.tb00020.x.
Gumprecht, B. (2003b). The American college town. Geographical Review, 93(1), 51–80.
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.
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.
Iowa Community Indicators Program, Iowa State University. (2018). Annual unemployment rates by state” [data file]. https://www.icip.iastate.edu/tables/employment/unemployment-states.
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.
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.
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.
Markusen, A. (2013). Artists work everywhere. Work and Occupations. https://doi.org/10.1177/0730888413504418.
Markusen, A., & Schrock, G. (2006). The artistic dividend: Urban artistic specialisation and economic development implications. Urban Studies. https://doi.org/10.1080/00420980600888478.
McGraw-Hill Construction, Inc. (2017). Dodge data and analytics. Population data [Data file]. https://www.construction.com.
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.
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.
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.
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.
National Association of State Budget Officers (NASBO). (2019). State expenditure report data [data file]. https://www.nasbo.org/reports-data/state-expenditure-report.
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.
National Center for Education Statistics (NCES). (2017). Digest of education statistics [data tables]. https://nces.ed.gov/programs/digest/.
National Center for Education Statistics (NCES). (2018). Integrated postsecondary education data system [data set]. https://nces.ed.gov/ipeds/datacenter/DataFiles.aspx?goToReportId=7.
Nelson, R. R. (Ed.). (1993). National innovation systems: A comparative analysis. New York, NY: Oxford University Press.
Nelson, R. R., & Nelson, K. (2002). Technology, institutions, and innovation systems. Research Policy. https://doi.org/10.1016/S0048-7333(01)00140-8.
Noonan, D. S. (2013). How US cultural districts reshape neighborhoods. Cultural Trends. https://doi.org/10.1080/09548963.2013.817652.
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.
Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. New York, NY: Simon & Schuster.
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.
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.
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.
Schroeder, J.P. (2016). Historical population estimates [data file]. https://conservancy.umn.edu/handle/11299/181605.
Scott, P. (1979). What future for higher education?. London: Fabian Society.
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.
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.
State Higher Education Executive Officers Association (SHEEO). (2018). State higher education finance: FY 2018 [data file]. https://sheeo.org/project/state-higher-education-finance/.
Strom, E. (2002). Converting pork into porcelain: Cultural institutions and downtown development. Urban Affairs Review. https://doi.org/10.1177/107808702401097763.
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.
U.S. Bureau of Labor Statistics. (2020). Local area unemployment statistics [data set]. https://www.bls.gov/lau/.
U.S. Census Bureau. (2010). Centers of population [data set]. https://www.census.gov/geographies/reference-files/time-series/geo/centers-population.html.
U.S. Census Bureau. (2016). County and ZIP code business patterns [data set]. https://www.census.gov/programs-surveys/cbp.html.
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.
U.S. Census Bureau. (2020). American Community Survey educational attainment and school enrollment [data set]. https://data.census.gov/cedsci/.
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.
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.
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.
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.
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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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
- Higher education institutions
- Cultural infrastructure