Public support of research typically relies on the notion that universities are engines of economic development, and that university research is a primary driver of high wage localized economic activity. Yet the evidence supporting that notion is based on aggregate descriptive data, rather than detailed links at the level of individual transactions. Here we use new micro-data from three countries - France, Spain and the United States - to examine one mechanism whereby such economic activity is generated, namely purchases from regional businesses. We show that grant funds are more likely to be expended at businesses physically closer to universities than at those farther away. In addition, if a vendor has been a supplier to a grant once, that vendor is subsequently more likely to be a vendor on the same or related grants. Firms behave in a way that is consistent with the notion that propinquity is good for business; if a firm supplies a research grant at a university in a given year it is more likely to open an establishment near that university in subsequent years than other firms.
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Tracing the Sources of Local External Economies
August 2004
Working Paper Number:
CES-04-13
In a cross-sectional establishment-level analysis using confidential secondary data, I evaluate the influence of commonly postulated sources of localized external economies'supplier access, labor pools, and knowledge spillovers'on the productivity of two U.S. manufacturing sectors (farm and garden machinery and measuring and controlling devices). Measures incorporating different distance decay specifications provide evidence of the spatial extent of the various externality sources. Chinitz's (1961) hypothesis of the link between local industrial organization and agglomeration economies is also investigated. The results show evidence of labor pooling economies and university-linked knowledge spillovers in the case of the higher technology measuring and controlling devices sector, while access to input supplies and location near centers of applied innovation positively influence efficiency in the farm and garden machinery industry. Both sectors benefit from proximity to producer services, though primarily at a regional rather than highly localized scale.
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AI Adoption in America: Who, What, and Where
September 2023
Working Paper Number:
CES-23-48R
We study the early adoption and diffusion of five AI-related technologies (automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition) as documented in the 2018 Annual Business Survey of 850,000 firms across the United States. We find that fewer than 6% of firms used any of the AI-related technologies we measure, though most very large firms reported at least some AI use. Weighted by employment, average adoption was just over 18%. AI use in production, while varying considerably by industry, nevertheless was found in every sector of the economy and clustered with emerging technologies such as cloud computing and robotics. Among dynamic young firms, AI use was highest alongside more educated, more-experienced, and younger owners, including owners motivated by bringing new ideas to market or helping the community. AI adoption was also more common alongside indicators of high-growth entrepreneurship, including venture capital funding, recent product and process innovation, and growth-oriented business strategies. Early adoption was far from evenly distributed: a handful of 'superstar' cities and emerging hubs led startups' adoption of AI. These patterns of early AI use foreshadow economic and social impacts far beyond this limited initial diffusion, with the possibility of a growing 'AI divide' if early patterns persist.
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Occupational Classifications: A Machine Learning Approach
August 2018
Working Paper Number:
CES-18-37
Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations.
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What Drives Differences in Management?
January 2017
Working Paper Number:
CES-17-32
Partnering with the Census we implement a new survey of 'structured' management practices in 32,000 US manufacturing plants. We find an enormous dispersion of management practices across plants, with 40% of this variation across plants within the same firm. This management variation accounts for about a fifth of the spread of productivity, a similar fraction as that accounted for by R&D and twice as much as explained by IT. We find evidence for four 'drivers' of management: competition, business environment, learning spillovers and human capital. Collectively, these drivers account for about a third of the dispersion of structured management practices.
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R&D or R vs. D?
Firm Innovation Strategy and Equity Ownership
April 2020
Working Paper Number:
CES-20-14
We analyze a unique dataset that separately reports research and development expenditures
for a large panel of public and private firms. Definitions of 'research' and 'development' in this dataset, respectively, correspond to definitions of knowledge 'exploration' and 'exploitation' in the innovation theory literature. We can thus test theories of how equity ownership status relates to innovation strategy. We find that public firms have greater research intensity than private firms, inconsistent with theories asserting private ownership is more conducive to exploration. We also find public firms invest more intensely in innovation of all sorts. These results suggest relaxed financing constraints enjoyed by public firms, as well as their diversified shareholder bases, make them more conducive to investing in all types of innovation. Reconciling several seemingly conflicting results in prior research, we find private-equity-owned firms, though not less innovative overall than other private firms, skew their innovation strategies toward development and away from research.
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Do Market Leaders Lead in Business Process Innovation? The Case(s) of E-Business Adoption
April 2011
Working Paper Number:
CES-11-10
This paper investigates the relationship between market position and the adoption of IT-enabled process innovations. Prior research has focused overwhelmingly on product innovation and garnered mixed empirical support. I extend the literature into the understudied area of business process innovation, developing a framework for classifying innovations based on the complexity, interdependence, and customer impact of the underlying business process. I test the framework's predictions in the context of ebuying and e-selling adoption. Leveraging detailed U.S. Census data, I find robust evidence that market leaders were significantly more likely to adopt the incremental innovation of e-buying but commensurately less likely to adopt the more radical practice of e-selling. The findings highlight the strategic significance of adjustment costs and co-invention capabilities in technology adoption, particularly as businesses grow more dependent on new technologies for their operational and competitive performance.
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Financing, Ownership, and Performance: A Novel, Longitudinal Firm-Level Database
December 2024
Working Paper Number:
CES-24-73
The Census Bureau's Longitudinal Business Database (LBD) underpins many studies of firm-level behavior. It tracks longitudinally all employers in the nonfarm private sector but lacks information about business financing and owner characteristics. We address this shortcoming by linking LBD observations to firm-level data drawn from several large Census Bureau surveys. The resulting Longitudinal Employer, Owner, and Financing (LEOF) database contains more than 3 million observations at the firm-year level with information about start-up financing, current financing, owner demographics, ownership structure, profitability, and owner aspirations ' all linked to annual firm-level employment data since the firm hired its first employee. Using the LEOF database, we document trends in owner demographics and financing patterns and investigate how these business characteristics relate to firm-level employment outcomes.
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The Span of the Effect of R&D in the Firm and Industry
May 1994
Working Paper Number:
CES-94-07
Previous studies have found that the firm's own research and spillovers of research by related firms increase firm productivity. In contrast, in this paper we explore the impact of firm R&D on the productivity of its individual plants. We carry out this investigation of within firm R&D effects using a unique set of Census data. The data, which are from the chemicals industry, are a match of plant level productivity and other characteristics with firm level data on R&D of the parent company, cross-classified by location and applied product field. We explore three aspects of the span of effect of the firm's R&D: (i), the degree to which its R&D is "public" across plants; (ii), the extent of its localization in geographic space, and (iii), the breadth of its relevance outside the applied product area in which it is classified. We find that (i), firm R&D acts more like a private input which is strongly amortized by the number of plants in the firm; (ii), firm R&D is geographically localized, and exerts greater influence on productivity when it is conducted nearer to the plant; and (iii), firm R&D in a given applied product area is of limited relevance to plants producing outside that product area. Moreover, we find that while geographic localization remains significant, it diminishes over time. This trend is consistent with the effect of improved telecommunications on increased information flows within organizations. Finally, we consider spillovers of R&D from the rest of industry, finding that the marginal product of industry R&D on plant productivity, though positive and significant, is far smaller than the marginal product of parent firm's R&D.
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What Causes Industry Agglomeration? Evidence from Coagglomeration Patterns
April 2007
Working Paper Number:
CES-07-13
Many industries are geographically concentrated. Many mechanisms that could account for such agglomeration have been proposed. We note that these theories make different predictions about which pairs of industries should be coagglomerated. We discuss the measurement of coagglomeration and use data from the Census Bureau's Longitudinal Research Database from 1972 to 1997 to compute pairwise coagglomeration measurements for U.S. manufacturing industries. Industry attributes are used to construct measures of the relevance of each of Marshall's three theories of industry agglomeration to each industry pair: (1) agglomeration saves transport costs by proximity to input suppliers or final consumers, (2) agglomeration allows for labor market pooling, and (3) agglomeration facilitates intellectual spillovers. We assess the importance of the theories via regressions of coagglomeration indices on these measures. Data on characteristics of corresponding industries in the United Kingdom are used as instruments. We find evidence to support each mechanism. Our results suggest that input-output dependencies are the most important factor, followed by labor pooling.
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The Color of Money: Federal vs. Industry Funding of University Research
September 2021
Working Paper Number:
CES-21-26
U.S. universities, which are important producers of new knowledge, have experienced a shift in research funding away from federal and towards private industry sources. This paper compares the effects of federal and private university research funding, using data from 22 universities that include individual-level payments for everyone employed on all grants for each university year and that are linked to patent and Census data, including IRS W-2 records. We instrument for an individual's source of funding with government-wide R&D expenditure shocks within a narrow field of study. We find that a higher share of federal funding causes fewer but more general patents, more high-tech entrepreneurship, a higher likelihood of remaining employed in academia, and a lower likelihood of joining an incumbent firm. Increasing the private share of funding has opposite effects for most outcomes. It appears that private funding leads to greater appropriation of intellectual property by incumbent firms.
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