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Collaborative Micro-productivity Project: Establishment-Level Productivity Dataset, 1972-2020
December 2023
Working Paper Number:
CES-23-65
We describe the process for building the Collaborative Micro-productivity Project (CMP) microdata and calculating establishment-level productivity numbers. The documentation is for version 7 and the data cover the years 1972-2020. These data have been used in numerous research papers and are used to create the experimental public-use data product Dispersion Statistics on Productivity (DiSP).
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Productivity Dispersion and Structural Change in Retail Trade
December 2023
Working Paper Number:
CES-23-60R
The retail sector has changed from a sector full of small firms to one dominated by large, national firms. We study how this transformation has impacted productivity levels, growth, and dispersion between 1987 and 2017. We describe this transformation using three overlapping phases: expansion (1980s and 1990s), consolidation (2000s), and stagnation (2010s). We document five findings that help us understand these phases. First, productivity growth was high during the consolidation phase but has fallen more recently. Second, entering establishments drove productivity growth during the expansion phase, but continuing establishments have increased in importance more recently. Third, national chains have more productive establishments than single-unit firms on average, but some single-unit establishments are highly productive. Fourth, productivity dispersion is significant and increasing over time. Finally, more productive firms pay higher wages and grow more quickly. Together, these results suggest that the increasing importance of large national retail firms has been an important driver of productivity and wage growth in the retail sector.
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The Impact of Industrial Opt-Out from Utility Sponsored Energy Efficiency Programs
October 2023
Working Paper Number:
CES-23-52
Industry accounts for one-third of energy consumption in the US. Studies suggest that energy efficiency opportunities represent a potential energy resource for regulated utilities and have resulted in rate of return regulated demand-side management (DSM) and energy efficiency (EE) programs. However, many large customers are allowed to self-direct or opt-out. In the Carolinas (NC and SC), over half of industrial and large commercial customers have selected to opt out. Although these customers claim they invest in EE improvements when it is economic and cost-effective to do so, there is no mechanism to validate whether they actually achieved energy savings. This project examines the industrial energy efficiency between the program participants and non participants in the Carolinas by utilizing the non-public Census of Manufacturing data and the public list of firms that have chosen to opt out. We compare the relative energy efficiency between the stay-in and opt-out plants. The t-test results suggest opt-out plants are less efficient. However, the opt-out decisions are not random; large plants or plants belonging to large firms are more likely to opt out, possibly because they have more information and resources. We conduct a propensity score matching method to account for factors that could affect the opt-out decisions. We find that the opt-out plants perform at least as well or slightly better than the stay-in plants. The relative performance of the opt-out firms suggest that they may not need utility program resources to obtain similar levels of efficiency from the stay-in group.
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Opening the Black Box: Task and Skill Mix and Productivity Dispersion
September 2022
Authors:
John Haltiwanger,
Lucia Foster,
Cheryl Grim,
Zoltan Wolf,
Cindy Cunningham,
Sabrina Wulff Pabilonia,
Jay Stewart,
Cody Tuttle,
G. Jacob Blackwood,
Matthew Dey,
Rachel Nesbit
Working Paper Number:
CES-22-44
An important gap in most empirical studies of establishment-level productivity is the limited information about workers' characteristics and their tasks. Skill-adjusted labor input measures have been shown to be important for aggregate productivity measurement. Moreover, the theoretical literature on differences in production technologies across businesses increasingly emphasizes the task content of production. Our ultimate objective is to open this black box of tasks and skills at the establishment-level by combining establishment-level data on occupations from the Bureau of Labor Statistics (BLS) with a restricted-access establishment-level productivity dataset created by the BLS-Census Bureau Collaborative Micro-productivity Project. We take a first step toward this objective by exploring the conceptual, specification, and measurement issues to be confronted. We provide suggestive empirical analysis of the relationship between within-industry dispersion in productivity and tasks and skills. We find that within-industry productivity dispersion is strongly positively related to within-industry task/skill dispersion.
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Decomposing Aggregate Productivity
July 2022
Working Paper Number:
CES-22-25
In this note, we evaluate the sensitivity of commonly-used decompositions for aggregate productivity. Our analysis spans the universe of U.S. manufacturers from 1977 to 2012 and we find that, even holding the data and form of the production function fixed, results on aggregate productivity are extremely sensitive to how productivity at the firm level is measured. Even qualitative statements about the levels of aggregate productivity and the sign of the covariance between productivity and size are highly dependent on how production function parameters are estimated. Despite these difficulties, we uncover some consistent facts about productivity growth: (1) labor productivity is consistently higher and less error-prone than measures of multi-factor productivity; (2) most productivity growth comes from growth within firms, rather than from reallocation across firms; (3) what growth does come from reallocation appears to be driven by net entry, primarily from the exit of relatively less-productive firms.
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Productivity Dispersion, Entry, and Growth in U.S. Manufacturing Industries
August 2021
Working Paper Number:
CES-21-21
Within-industry productivity dispersion is pervasive and exhibits substantial variation across countries, industries, and time. We build on prior research that explores the hypothesis that periods of innovation are initially associated with a surge in business start-ups, followed by increased experimentation that leads to rising dispersion potentially with declining aggregate productivity growth, and then a shakeout process that results in higher productivity growth and declining productivity dispersion. Using novel detailed industry-level data on total factor productivity and labor productivity dispersion from the Dispersion Statistics on Productivity along with novel measures of entry rates from the Business Dynamics Statistics and productivity growth data from the Bureau of Labor Statistics for U.S. manufacturing industries, we find support for this hypothesis, especially for the high-tech industries.
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Cyclical Worker Flows: Cleansing vs. Sullying
May 2021
Working Paper Number:
CES-21-10
Do recessions speed up or impede productivity-enhancing reallocation? To investigate this question, we use U.S. linked employer-employee data to examine how worker flows contribute to productivity growth over the business cycle. We find that in expansions high-productivity firms grow faster primarily by hiring workers away from lower-productivity firms. The rate at which job-to-job flows move workers up the productivity ladder is highly procyclical. Productivity growth slows during recessions when this job ladder collapses. In contrast, flows into nonemployment from low productivity firms disproportionately increase in recessions, which leads to an increase in productivity growth. We thus find evidence of both sullying and cleansing effects of recessions, but the timing of these effects differs. The cleansing effect dominates early in downturns but the sullying effect lingers well into the economic recovery.
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Regulating Mismeasured Pollution: Implications of Firm Heterogeneity for Environmental Policy
August 2018
Working Paper Number:
CES-18-03R
This paper provides the first estimates of within-industry heterogeneity in energy and CO2 productivity for the entire U.S. manufacturing sector. We measure energy and CO2 productivity as output per dollar energy input or per ton CO2 emitted. Three findings emerge. First, within narrowly defined industries, heterogeneity in energy and CO2 productivity across plants is enormous. Second, heterogeneity in energy and CO2 productivity exceeds heterogeneity in most other productivity measures, like labor or total factor productivity. Third, heterogeneity in energy and CO2 productivity has important implications for environmental policies targeting industries rather than plants, including technology standards and carbon border adjustments.
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Dispersion in Dispersion: Measuring Establishment-Level Differences in Productivity
April 2018
Working Paper Number:
CES-18-25RR
We describe new experimental productivity statistics, Dispersion Statistics on Productivity (DiSP), jointly developed and published by the Bureau of Labor Statistics (BLS) and the Census Bureau. Productivity measures are critical for understanding economic performance. Official BLS productivity statistics, which are available for major sectors and detailed industries, provide information on the sources of aggregate productivity growth. A large body of research shows that within-industry variation in productivity provides important insights into productivity dynamics. This research reveals large and persistent productivity differences across businesses even within narrowly defined industries. These differences vary across industries and over time and are related to productivity-enhancing reallocation. Dispersion in productivity across businesses can provide information about the nature of competition and frictions within sectors, and about the sources of rising wage inequality across businesses. Because there were no official statistics providing this level of detail, BLS and the Census Bureau partnered to create measures of within-industry productivity dispersion. These measures complement official BLS aggregate and industry-level productivity growth statistics and thereby improve our understanding of the rich productivity dynamics in the U.S. economy. The underlying microdata for these measures are available for use by qualified researchers on approved projects in the Federal Statistical Research Data Center (FSRDC) network. These new statistics confirm the presence of large productivity differences and we hope that these new data products will encourage further research into understanding these differences.
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The Reallocation Myth
April 2018
Working Paper Number:
CES-18-19
There is a widely held view that much of growth in the U.S. can be attributed to reallocation from low to high productivity firms, including from exiting firms to entrants. Declining dynamism ' falling rates of reallocation and entry/exit in the U.S. ' have therefore been tied to the lackluster growth since 2005. We challenge this view. Gaps in the return to resources do not appear to have narrowed, suggesting that allocative efficiency has not improved in the U.S. in recent decades. Reallocation can also matter if it is a byproduct of innovation. However, we present evidence that most
innovation comes from existing firms improving their own products rather than from entrants or fast-growing firms displacing incumbent firms. Length: 26 pages
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