Influence of R&D Expenditures

This paper describes research designed to determine whether university R&D activity affects the local rate of new firm formations and economic growth. We created a file of university R&D expenditures by Labor Market Area (LMA) in the U.S. and combined this with data on new business formations by LMA. The hypothesized relationships were tested~~analyzed~~ using multiple regression analysis,s while controlling for other relevant exogenous socio-economic variables ~~by LMA.~~ The results show that university R&D expenditures are significantly related to new firm formations in the same LMA.

In addition, we tested for a relationship between R&D expenditures and local economic growth, measured as employment growth, by LMA. ~~This relationship was tested with multiple regressions while controlling for other variables.~~ The results show that university R&D expenditures are not significantly related to economic growth, once one has controlled for the birth rate in the previous period. However, the variations in the birth rates, which are affected by R&D spending, are strongly associated with the growth rates at the LMA level.

These findings lend strength to the argument that government and private sector R&D expenditures made through research universities contribute to economic growth. Although this argument has traditionally been made with the expectation of long term lags in the R&D to growth relationship, our findings are that this lag is relatively small – as little as one year and the effect seems to decrease slowly, but steadily, after the first year but lasts for at least five years. University R&D spending is also associated with localities with ~~much~~ higher levels of human capital, which also contributes substantially toward generating new firms., Thus, research universities and ~~those who~~ investment in R&D at these universities are major factors contributing toin economic growth in the labor market areas in which the universities are situated.

Introduction

The relationship between innovation and economic growth has been the topic of numerous theoretical and empirical research efforts over the past decades. Beginning with Schumpeter’s (1934) classic theoretical work, the notion that innovation or the commercialization of invention, leads to economic growth has continued to gain acceptance among scholars. Specifically, Schumpeter argues that newly formed independently owned firms commercialize inventions that increase overall demand thereby causing economic growth, destroying the existing market structures, and redistributing wealth among the remaining firms in the market. Schumpeter calls the process “creative destruction.”

Small Firms’ Superior Innovation Ability

In technology intensive industries, inventions often originate with research and development. Private firms in capitalist economies have long believed in research and development as an important process to produce new inventions to be commercialized. This is done with the hope that the invention will become an innovation that will yield a competitive advantage and thus firm growth. However, Scherer (1984) notes that scale economies do not seem to benefit large firms when it comes to innovation. In fact, from his analysis of 448 firms on the 1955 Fortune 500 list, he concludes that although large firms may spend more than small firms on research and development (R&D), the process of creating inventions that are the foundation for innovation, there is “little evidence of disproportionately great R&D input or output associated with the largest corporations” (p. 170). Additionally, in a report prepared for the U.S. Small Business Administration, The Futures Group (1984), replicating an earlier study by Gellman (1976), analyzed a database that consists of 8,074 product innovations introduced in the US market during 1982. The Futures Group finds evidence that small firms actually innovate at a rate of 1.24 to 2.38 times that of large firms. Furthermore, in his analyses of the Gellman (1976) product innovation database, Audretsch (1991, 1995) initially concludes that small firms are not necessarily at an innovative disadvantage in capital-intensive industries, and later adds that the ability to innovate may allow small firms to offset the scale disadvantages relative to large firms and may ultimately lead to greater growth and survival rates. Lastly, in a study of small and medium sized U.S. firms, Chakrabarti (1991) finds that small firms actually generate more innovations per R&D dollar than large firms. In summary, it seems a paradox exists; how are small firms able to innovate so successfully despite their limited investment in R&D?

Spillover Effects

A major link has been made that explains this paradox. The answer to this question can be found in studies examining the relationship between new technical knowledge and the spatial distribution of innovative activity (Acs, Audretsch and Feldman, 1994; Anselin, Varga and Acs, 1997; Audretsch & Feldman, 1996; and Jaffe, 1986, 1989). Jaffe finds empirical evidence to suggest that the tacit knowledge generated by the R&D process at one firm may spill over to and be subsequently exploited for economic gain by other firms. Moreover, in their retrospective analyses of the Gellman (1976) database, Acs et al. (1994) conclude that this flow of knowledge is unidirectional (specifically, that knowledge seems to flow from large to small firms) and that geographic proximity acts as a catalyst for this knowledge transfer (especially for small firms). Audretsch & Feldman (1996) add that these spillovers lead to innovative clustering and are most prevalent in R&D intensive industries. These findings suggest that while small firms may invest less in R&D than large firms, they are able to gain access to much of the same new knowledge upon which “innovation and technological change depend” as their larger counterparts through knowledge spillovers, especially in those economic sectors in which this knowledge is considered~~ably~~ particularly valuable (Audretsch & Feldman, 1996: 630).

Small Firms and Economic Growth

This knowledge spillover effect provides a logical rationale for the empirical research findings about the role of small, and especially newly formed small, firms. Many researchers, in a variety of different nations, have produced empirical research that shows small firms make a significant contribution to economic growth as measured by net new job creation (Birch, 1979, 1987; Kirchhoff, 1994; Storey, 1994; Baldwin, 1995; Wennekers and Thurik, 1999; Stel, et al., 2002; Almus and Nerlinger, 1999). Moreover, among small firms, newly formed firms create the largest share of net new jobs (Kirchhoff, 1994; Baldwin, 1995; Wennekers and Thurik, 1999). And, among newly formed firms, highly innovative new firms create a disproportionately greater share of net new jobs than those new firms with lesser innovation intensity (Kirchhoff, 1994). This suggests that highly innovative new firms are a major source of economic growth. This last sentence is, of course, Schumpeter’s (1934) original hypothesis.

University R&D and New Firm Formations

But spillover effects need not be limited to corporate R&D spillover to new small firms. University research laboratories are equally likely to exhibit the same spillover effects. To date, the research on such effects have focused on spin-off new firms typically started by one or more faculty from university R&D labs. Business incubators have been constructed at many research universities to accommodate such spin-offs. Tesfaye (1997) reviews the extensive literature on university spin-offs in Europe and North America. He then identifies and describes 21 successful new firms in the Stockholm area that spun-off from Stockholm University and the Royal Institute of Technology. Given the existence of university R&D spillovers, one can expect that new firm births would be correlated with the extent of R&D activity at research universities. As with private firm R&D, such effects would appear as clustering in the local area of the university R&D activity.

The above logic leads to two research questions: (1) Do R&D activities at research universities have a significant effect on local new firm formations? And: (2) do R&D activities at research universities have a significant effect on local economic growth? It is these questions that we seek to answer with the research reported here.

Hypotheses

Previous Research

Two major prior studies have attempted to explain the possible link between regional innovative activity, or knowledge, and new firm formation. The first was conducted by Reynolds, Miller and Maki (1995). In their research on new firm births and deaths in the U.S., the authors identify fifteen factors that may influence the rate of new firm formation, one of which was “access to research and development, information, and innovation.” The authors hypothesize that “where information is readily available and innovation and creativity flourish, the formation rate of new firms is enhanced” (Reynolds et al. 1995: 391). While they find no evidence to support this hypothesis, Reynolds et al. (1995) acknowledge that this lack of statistical support may have been in part due to invalid measurement. Specifically, they suggest that their operationalization of the independent knowledge/innovation variable (quantified as the density of post-college adults, professional and technical employees, patents granted, or doctorates earned in a given metropolitan area) may not have been appropriate.

The second was conducted by Armington & Acs (2001). They find some evidence in support of Reynolds et al.’s (1995) hypothesis in their analysis of the factors underlying new firm formation using the Longitudinal Establishment and Enterprise Microdata (LEEM). Armington & Acs (2001) conclude that firms are more likely to form in labor market areas (LMAs) that have a high percentage of college graduates than in those LMAs with high concentrations of less skilled workers, suggesting that a positive relationship may actually exist between the “size” of a region’s knowledge base and new firm formation rate.

There is little other research on this topic that deals specifically with local effects, in great part because of the earlier lack of appropriate data. The relatively recent development of the “local” measure defined as Labor Market Areas has provided a basis for aggregating county level data to construct local economic units. LMAs within the U.S. (50 states plus DC) are defined according to the 1990 specification of Tolbert and Sizer (1996) for the Department of Agriculture. There are 394 LMA’s, all based on aggregations of counties, many of them cutting across state boundaries. We use the most recently specified state and county for each establishment in the LEEM, assuming that most of the few location coding changes are corrections. Businesses that report operating statewide (county =999) have been placed into the largest LMA in each state. LMAs are defined not only by the place of work but also by where the workers live – i.e., commuting routes linking work to workers (Tolbert and Sizer, 1996).

The research by Reynolds et.al. (1995) may have failed to find significance because they did not use LMAs as their regional identifiers. To overcome this difficulty, we have assembled a broad range of data based on the 394 LMAs in the U.S. For this type of research, LMAs are far superior to Metropolitan Statistical Areas (MSAs) because LMAs cover all of the U.S., not just the cities, and there are research universities in rural areas (e.g., University of Iowa at Iowa City; University of Alabama at Tuscaloosa; University of Missouri at Rolla) that are not in MSAs but are in LMAs. Furthermore, LMAs link the workers to the work place so that measures of college educated adults are truly linked to the place where they work. MSAs include areas of population density but not necessarily suburbs where most college educated adults live. This may be why Armington and Acs (2001) were able to find a relationship between college educated adults and firm births while Reynolds (1995) was not.

In summary, our literature review develops the argument that university R&D creates inventions that spilll -over into commercialization by new firm formations. Intuitively, it is reasonable to expect that this spill-over does not happen simultaneously with the R&D expenditures. We expect that there will be a lag between the R&D activity and firm births.

Hypotheseis One and Two

Following Reynolds, et al. (1995) and Armington and Acs (2001) we hypothesize:

H1 LMA new firm formation rates will be positively related to (a) LMA university research and development expenditures and (b) LMA human capital.

However, we cannot ignore other social and economic factors that affect new firm formations. These must be included in our models to control for their effect.

Based upon the literature cited above showing the linkage between firm births and economic growth, we also hypothesize:

H2 LMA economic growth rates will be positively related to (a) LMA new firm formation rates, and (b) LMA university research and development expenditures, and (c) LMA human capital.

Research Methodology

There are a number of socio-economic variables, measuring other important differences in the environment for entrepreneurial activity. We chose ~~these~~ environmental factors that prior research has shown are likely to affect firm birth rates. These factors are included in our regressions to control for environmental effects so that the effect of R&D and knowledge base can be isolated and identified.

Our first step in testing these hypotheses was to create a database with the necessary “primary” variables. We define these as primary variables since many of the actual variables in the regression analysis are transformations of these variables, e.g. firm births are measured as firm births divided by number of persons in the labor force. As will be explained later in this section, such ratios have advantages over the primary numbers. In the following paragraphs, we will cite the literature on economic growth and firm births as a basis for selecting the exogenous variables that are most likely to be additional factors affecting firm birth rates.

Primary Variables Subject to Hypothesis

Firm Births

We obtained firm births by LMA for 1990 through 1996 from a data file prepared by Armington and Acs (2001), using an earlier version of the LEEM file at the Center for Economic Studies of the Bureau of the Census. The same procedures were followed to tabulate firm births for 1997 through 1999 from more recent LEEM files. These later tabulations were prepared by the Company Statistics Division at the Census Bureau, under contract to the Kauffman Center for Entrepreneurial Leadership, whicho also provided the funding for the earlier work.

Some additional explanation about this database and how firm births are identified is useful here. The current LEEM file facilitates tracking employment, payroll, and firm affiliation and (employment) size for the more than eleven million establishments that existed at some time during 1989 through 1999. This file was constructed by the Bureau of the Census from its annual Statistics of U.S. Business (SUSB) files,[1] which were developed from the economic microdata underlying Census’ County Business Patterns. These annual data were linked together using the Longitudinal Pointer File associated with the SUSB, which facilitates tracking establishments over time, even when they change ownership or identification numbers.

The basic unit of the LEEM data is a business establishment (location or plant). An establishment is a single physical location where business is conducted or where services or industrial operations are performed. The microdata describe each establishment for each year of its existence in terms of its employment, annual payroll, location (state, county, and metropolitan area), primary industry, and start year. Additional data for each establishment and year identify the firm (or enterprise) to which the establishment belongs, and the total employment of that firm.

A firm (or enterprise or company) is the largest aggregation (across all industries) of business legal entities under common ownership or control. Establishments are owned by legal entities, which are typically corporations, partnerships, or sole proprietorships. Most firms are composed of only a single legal entity that operates a single establishment—their establishment data and firm data are identical, and they are referred to as “single-unit” establishments or firms. The single-unit businesses are frequently owner-operated. Only 4 percent of firms have more than one establishment, and they and their establishments are both described as multi-location or multi-unit.

Firm births include both new single-unit firms with less than 500 employees, and the primary locations of new multi-unit firms with less than 500 employees, firm-wide. Those new firms that had 500 or more employees in their first year of activity appear to be primarily offshoots of existing companies. Annually, there were somewhat fewer than 150 such large apparent births of single-unit firms, with an average of about 1500 employees each. About a third of these larger single-unit firms were employee-leasing firms or employment agencies, while the remainder were widely distributed across industries. In contrast, examination of the new firms with 100-499 employees in their first year showed that most seemed credible startups, frequently in industries that are associated with large business units, such as hotels and hospitals. Since this study is not concerned with the direct employment impact of firm births, there is little risk of bias in the aggregate birth counts as a result of inclusion of a few larger startups that might actually be offshoots of existing businesses. Therefore, the startups with 100 to 499 employees were included, if they qualified otherwise.

Single-unit firm births in year t are identified on the LEEM as non-affiliated establishments with a start-year of “t” or “t” that had no employment in March of year t, and had positive employment below 500 in March of year t. This avoids inclusion of either new firms that have not yet actually hired an employee, or firms recovering from temporary inactivity. The ‘start-year’ is the year that the establishment entered the Census business register. About 400,000 new firms generally appear in the business register (with some positive annual payroll) the year before they have any March employment, and we postpone their ‘birth’ until their first year of reported employment. An average of 90,000 older firms each year reduce their March employment to zero and then recover the following year, and they will not be included as births.

We have also included most of the relatively few multi-unit firms (1500 to 6000 per year) that appeared to start up with less than 500 employees in multiple locations in their first year. We limited multi-unit firm births to those whose employment in their new primary location constituted at least a third of their total employment in the first year.[2] This rule effectively eliminated the 600 to 1000 new firms each year which were apparently set up to manage existing locations -- relatively small new headquarters supervising large numbers of employees in mainly older branch locations which were newly acquired, or perhaps contributed by joint venture partners.

University R&D Expenditures[3]

We selected R&D expenditures as the measurement of R&D activity since it is reasonable to assume that activity is proportional to expenditures, and data on expenditures was available by institution. The National Science Foundations uses an annual survey of research universities and colleges in the U.S. to collect data on separately budgeted R&D spending for science and engineering (including all sources of funding, about 60% of which is federal). They use a certainty sample of universities that award science and engineering PhD’s, and those that are traditionally Black and award Masters degrees, and of all 18 federally funded R&D Centers. Prior to 1992 they also surveyed about a 25% sample of other smaller colleges and universities that have had at least $50 million of separately funded science and engineering R&D. This latter group is fully covered in the annual surveys since 1992, which cover around 600 institutions, while around 500 were covered prior to 1992. The data for 1989 to 1991 for those few not previously covered, but with large expenditures already in 1992, have been set to their reported 1992 levels, but this approximation accounts for a very small portion of the total. NSF claims that their survey accounts for 95% of total US academic science and engineering R&D expenditures. The National Science Foundation survey data include Zip codes for the university laboratories, which we converted first to their corresponding state and county codes, and then to their LMA codes.

It is worthwhile to note at this point that university R&D expenditures include both federal government and private sector funding of research. However, little of the private sector funding comes from for-profit corporations. The National Science Board (2002, p. 10) reports corporate funding of 1.5 percent of academic R&D in 1994.[4] By 1999, this had risen to 8 percent. On the other hand, 58 percent was funded by the federal government. The remaining 34 percent was funded by private foundations, state governments, and by the academic institutions themselves (National Science Board, 2002, p. 13).

SBIR and STTR Grants

We also obtained data from the National Science Foundation on the Small Business Innovation Research grants and the Small Business Technology Transfer Research grants for the years 1994 through 1999. These grants provide funds for small businesses and universities for developing scientific information useful to the federal government. Some of the STTR grant money will also be reported in the university R&D data collected by NSF, but these are very small amounts compared to the total R&D expenditures. The grants made by these two programs were combined, by adding them together, since their functions seemed to be similar, and the separate series were pretty sparse.