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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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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Growth is Getting Harder to Find, Not Ideas
April 2025
Working Paper Number:
CES-25-21
Relatively flat US output growth versus rising numbers of US researchers is often interpreted as evidence that "ideas are getting harder to find." We build a new 46-year panel tracking the universe of U.S. firms' patenting to investigate the micro underpinnings of this claim, separately examining the relationships between research inputs and ideas (patents) versus ideas and growth. Over our sample period, we find that researchers' patenting productivity is increasing, there is little evidence of any secular decline in high-quality patenting common to all firms, and the link between patents and growth is present, differs by type of idea, and is fairly stable. On the other hand, we find strong evidence of secular decreases in output unrelated to patenting, suggesting an important role for other factors. Together, these results invite renewed empirical and theoretical attention to the impact of ideas on growth. To that end, our patent-firm bridge, which will be available to researchers with approved access, is used to produce new, public-use statistics on the Business Dynamics of Patenting Firms (BDS-PF).
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USING LINKED CENSUS R&D-LRD DATA TO ANALYZE THE EFFECT OF R&D INVESTMENT ON TOTAL FACTOR PRODUCTIVITY GROWTH
January 1989
Working Paper Number:
CES-89-02
Previous studies have demonstrated that productivity growth is positively correlated with the intensity of R&D investment. However, existing studies of this relationship at the micro (firm or line of business) level have been subject to some important limitations. The most serious of these has been an inability to adequately control for the diversified activities of corporations. This study makes use of linked Census R&D - LRD data, which provides comprehensive information on each firms' operations at the 4-digit SIC level. A marked improvement in explaining the association between R&D and TFP occurs when we make appropriate use of the data by firm by industry. Significant relationships between the intensities of investment in total, basic, and company-funded R&D, and TFP growth are confirmed.
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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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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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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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Business Dynamics of Innovating Firms: Linking U.S. Patents with Administrative Data on Workers and Firms
July 2015
Working Paper Number:
CES-15-19
This paper discusses the construction of a new longitudinal database tracking inventors and patent-owning firms over time. We match granted patents between 2000 and 2011 to administrative databases of firms and workers housed at the U.S. Census Bureau. We use inventor information in addition to the patent assignee firm name to and improve on previous efforts linking patents to firms. The triangulated database allows us to maximize match rates and provide validation for a large fraction of matches. In this paper, we describe the construction of the database and explore basic features of the data. We find patenting firms, particularly young patenting firms, disproportionally contribute jobs to the U.S. economy. We find patenting is a relatively rare event among small firms but that most patenting firms are nevertheless small, and that patenting is not as rare an event for the youngest firms compared to the oldest firms. While manufacturing firms are more likely to patent than firms in other sectors, we find most patenting firms are in the services and wholesale sectors. These new data are a product of collaboration within the U.S. Department of Commerce, between the U.S. Census Bureau and the U.S. Patent and Trademark Office.
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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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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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