This paper seeks to provide new insight into how school and post school training investments are linked to employer workplace practices and outcomes using a unique nationally representative survey of establishments in the U.S., the Educational Quality of the Workforce National Employers Survey (EQW-NES). We go beyond simply measuring the incidence of formal or informal training to examine the determinants of the types employers invest in, the relationship between formal school and employer provided training, who is receiving training, the links between investments in physical and human capital, and the impact that human capital investments have on the productivity of establishments. We find that the smallest employers are much less likely to provide formal training programs than employers from larger establishments. Regardless of size, those employers who have adapted some of the practices associated with what have been called "high performance work systems" are more likely to have formal training programs. Employers who have made large investments in physical capital or who have hired workers with higher average education are also more likely to invest in formal training programs and to train a higher proportion of their workers, especially in the manufacturing sector. There are significant and positive effects on establishment productivity associated with investments in human capital. Those employers who hire better educated workers have appreciably higher productivity. The impact of employer provided training differs according to the nature, timing, and location of the employer investments.
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Within and Between Firm Changes in Human Capital, Technology, and Productivity Preliminary and incomplete
December 2001
Working Paper Number:
tp-2001-03
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Gross Job Creation and Destruction: Microeconomic Evidence and Macroeconomic Implications
September 1990
Working Paper Number:
CES-90-10
This paper investigates the connection between the heterogeneity of establishment-level employment changes and aggregate fluctuations at business cycle frequencies. The empirical work exploits a rich data set with approximately 860,000 annual observations and 3.4 million quarterly observations on 160,000 manufacturing establishments to calculate rates of gross job creation, gross job destruction, and their sum, gross job reallocation. The central messages that emerge from the research in this paper are: (1) Establishment-level employment changes exhibit tremendous heterogeneity, even within narrowly defined sectors of the economy. This heterogeneity manifests itself in terms of high rates of gross job creation, destruction, and reallocation. Further, the magnitude of this heterogeneity varies significantly over time, most of the variation is due to time variation in the idiosyncratic component of establishment growth rates, and the variation is significantly countercyclical. (2) The theoretical model of employment reallocation and business cycles is suggestive of how both aggregate and allocative disturbances can drive fluctuations in job creation, job destruction, unemployment, productivity, and output. (3) The empirical analysis of the joint dynamics of job creation and job destruction supports the view that allocative disturbances were a major driving force behind movements in jobs creation, job destruction, job reallocation and net employment growth in the U.S. manufacturing sector during the 1972 to 1986 period.
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An Examination of the Informational Value of Self-Reported Innovation Questions
October 2022
Working Paper Number:
CES-22-46
Self-reported innovation measures provide an alternative means for examining the economic performance of firms or regions. While European researchers have been exploiting the data from the Community Innovation Survey for over two decades, uptake of US innovation data has been much slower. This paper uses a restricted innovation survey designed to differentiate incremental innovators from more far-ranging innovators and compares it to responses in the Annual Survey of Entrepreneurs (ASE) and the Business R&D and Innovation Survey (BRDIS) to examine the informational value of these positive innovation measures. The analysis begins by examining the association between the incremental innovation measure in the Rural Establishment Innovation Survey (REIS) and a measure of the inter-industry buying and selling complexity. A parallel analysis using BRDIS and ASE reveals such an association may vary among surveys, providing additional insight on the informational value of various innovation profiles available in self-reported innovation surveys.
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The Industry R&D Survey: Patent Database Link Project
November 2006
Working Paper Number:
CES-06-28
This paper details the construction of a firm-year panel dataset combining the NBER Patent Dataset with the Industry R&D Survey conducted by the Census Bureau and National Science Foundation. The developed platform offers an unprecedented view of the R&D-to-patenting innovation process and a close analysis of the strengths and limitations of the Industry R&D Survey. The files are linked through a name-matching algorithm customized for uniting the firm names to which patents are assigned with the firm names in Census Bureau's SSEL business registry. Through the Census Bureau's file structure, this R&D platform can be linked to the operating performances of each firm's establishments, further facilitating innovation-to-productivity studies.
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Firm Heterogeneity, Misallocation, and Trade
May 2025
Working Paper Number:
CES-25-33
To what extent do domestic distortions influence the gains from trade? Using data from Chinese manufacturing surveys and U.S. census records, I document two novel stylized facts: (1) Larger producers in China exhibit lower revenue productivity, whereas larger producers in the U.S. exhibit higher revenue productivity. (2) Larger exporters in China exhibit lower export intensity, whereas larger exporters in the U.S. exhibit higher export intensity. A model of heterogeneous producers shows that only the U.S. patterns are consistent with an efficient allocation. To reconcile the observed patterns in China, I introduce producer- and destination-specific subsidies and estimate the model without imposing functional form assumptions on the joint distribution of productivity and subsidy rates. Accounting for distortions in China leads to substantially smaller estimated gains from trade.
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Measuring the Electronic Economy: Current Status and Next Steps
June 2000
Working Paper Number:
CES-00-10
The recent growth of consumer retailing over the Internet draws attention to the electronic economy. However, businesses also conduct other business processes over computer networks, and many have been doing so for some time. Uses of computer networks attract attention because of assertions that they lead to new products and services, new delivery methods, streamlined or re-engineered business processes, new business structures, and enhanced business performance. These changes, in turn, potentially affect the performance of the entire economy, including economic growth, productivity, prices, employment, trade, and the structures of businesses, regions, and markets. Evaluating these assertions, and their effects on economic performance, requires solid statistical information about the electronic economy. This paper develops principles for identifying information critical to measuring the size and evaluating the potential effects of the electronic economy, relates that information to current data collection programs, and notes relevant measurement issues. Some of the required information about the electronic economy can be collected by adding questions to existing surveys, making the scope of existing surveys consistent, or developing new surveys. However, many key pieces of information pose significant challenges to economic measurement. While some of those challenges are specific to the electronic economy, others are long-standing ones. Interest in the electronic economy highlights the importance of continuing attempts to address these challenges. Improving and enhancing the statistical system to provide information about the electronic economy, therefore, would also substantially improve the baseline information available for evaluating the performance of the entire economy.
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Concentration, Diversity, and Manufacturing Performance
July 2010
Working Paper Number:
CES-10-14
Regional economist Benjamin Chinitz was one of the most successful proponents of the idea that regional industrial structure is an important determinant of economic performance. His influential article in the American Economic Review in 1961 prompted substantial research measuring industrial structure at the regional scale and examining its relationships to economic outcomes. A considerable portion of this work operationalized the concept of regional industrial structure as sectoral diversity, the degree to which the composition of an economy is spread across heterogeneous activities. Diversity is a relatively simple construct to measure and interpret, but does not capture the implications of Chinitz's ideas fully. The structure within regional industries may also influence the performance of business enterprises. In particular, regional intra-industry concentration'the extent to which an industry is dominated by a few relatively large firms in a locality'has not appeared in empirical work studying economic performance apart from individual case studies, principally because accurately measuring concentration within a regional industry requires firm-level information. Multiple establishments of varying sizes in a given locality may be part of the same firm. Therefore, secondary data sources on establishment size distributions (such as County Business Patterns or aggregated information from the Census of Manufactures) can yield only deceptive portrayals of the level of regional industrial concentration. This paper uses the Longitudinal Research Database, a confidential establishment-level dataset compiled by the United States Census Bureau, to compare the influences of industrial diversity and intra-industry concentration upon regional and firm-level economic outcomes. Manufacturing establishments are aggregated into firms and several indicators of regional industrial concentration are calculated at multiple levels of industrial aggregation. These concentration indicators, along with a regional sectoral diversity measure, are related to employment change over time and incorporated into plant productivity estimations, in order to examine and distinguish the relationships between the differing aspects of regional industrial structure and economic performance. A better understanding of the particular links between regional industrial structure and economic performance can be used to improve economic development planning efforts. With continuing economic restructuring and associated workforce dislocation in the United States and worldwide, industrial concentration and over-specialization are separate mechanisms by which regions may 'lock in' to particular competencies and limit the capacity to adjust quickly and efficiently to changing markets and technologies. The most appropriate and effective policies for improving economic adaptability should reflect the structural characteristics that limit flexibility. This paper gauges the consequences of distinct facets of regional industrial structure, adding new depth to the study of regional industries by economic development planners and researchers.
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Downsizing and Productivity Growth: Myth or Reality?
April 1994
Working Paper Number:
CES-94-04
The conventional wisdom is that the rising productivity in the U.S. manufacturing sector in the 1980s has been driven by the apparently pervasive downsizing over this period. Aggregate evidence clearly shows falling employment accompanying the rise in productivity. In this paper, we examine the microeconomic evidence using the plant level data from the Longitudinal Research Database (LRD). In contrast to the conventional wisdom, we find that plants that increased employment as well as productivity contribute almost as much to overall productivity growth in the 1980s as the plants that increased productivity at the expense of employment. Further, there are striking differences by sector (defined by industry, size, region, wages, and ownership type) in the allocation of plants in terms of whether they upsize or downsize and whether they increase or decrease productivity. Nevertheless, in spite of the striking differences across sectors defined in a variety of ways, most of the variance of productivity and employment growth is accounted for by idiosyncratic factors.
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The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)
April 2025
Working Paper Number:
CES-25-27
We examine the prevalence and productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the Census Bureau to collect detailed large-scale data for 2017 and 2021, we focus on AI-related technologies with industrial applications. We find causal evidence of J-curve-shaped returns, where short-term performance losses precede longer-term gains. Consistent with costly adjustment taking place within core production processes, industrial AI use increases work-in-progress inventory, investment in industrial robots, and labor shedding, while harming productivity and profitability in the short run. These losses are unevenly distributed, concentrating among older businesses while being mitigated by growth-oriented business strategies and within-firm spillovers. Dynamics, however, matter: earlier (pre-2017) adopters exhibit stronger growth over time, conditional on survival. Notably, among older establishments, abandonment of structured production-management practices accounts for roughly one-third of these losses, revealing a specific channel through which intangible factors shape AI's impact. Taken together, these results provide novel evidence on the microfoundations of technology J-curves, identifying mechanisms and illuminating how and why they differ across firm types. These findings extend our understanding of modern General Purpose Technologies, explaining why their economic impact'exemplified here by AI'may initially disappoint, particularly in contexts dominated by older, established firms.
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Management Challenges of the 2010 U.S. Census
August 2011
Working Paper Number:
CES-11-22
This paper gives an insider's perspective on the management approaches used to manage the 2010 Census during its operational phase. The approaches used, the challenges faced (in particular, difficulties faced in automating data collection), and the solutions applied to meet those challenges are described. Finally, six management lessons learned are presented.
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