Dispersion in Dispersion: Measuring Establishment-Level Differences in Productivity
April 2018
Working Paper Number:
CES-18-25RR
Abstract
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.
Document Tags and Keywords
Keywords
Keywords are automatically generated using KeyBERT, a powerful and innovative
keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant
keywords.
By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the
text, highlighting the most significant topics and trends. This approach not only enhances searchability but
provides connections that go beyond potentially domain-specific author-defined keywords.
:
production,
productive,
quarterly,
manufacturing,
aggregate,
productivity growth,
growth,
earnings,
industry productivity,
productivity differences,
labor productivity,
labor,
measures productivity,
productivity measures,
sector,
efficiency,
growth productivity,
recession,
estimates productivity,
analysis productivity,
dispersion productivity,
revenue,
practices productivity,
gdp,
productivity dispersion,
productivity dynamics,
aggregate productivity
Tags
Tags are automatically generated using a pretrained language model from spaCy, which excels at
several tasks, including entity tagging.
The model is able to label words and phrases by part-of-speech,
including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are
identified to contain references to specific institutions, datasets, and other organizations.
:
Bureau of Labor Statistics,
Census of Manufactures,
Annual Survey of Manufactures,
Internal Revenue Service,
Center for Economic Studies,
National Bureau of Economic Research,
Bureau of Economic Analysis,
Current Population Survey,
Longitudinal Business Database,
IQR,
Economic Census,
Research Data Center,
North American Industry Classification System,
Business Register,
Census Bureau Business Register,
Current Employment Statistics,
Census Bureau Disclosure Review Board,
Disclosure Review Board,
Federal Statistical Research Data Center
Similar Working Papers
Similarity between working papers are determined by an unsupervised neural
network model
know as Doc2Vec.
Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the
capture of semantic meaning in a way that relates to the context of words within the document. The model learns to
associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as
document classification, clustering, and similarity detection by preserving the order and structure of words. The
document vectors are compared using cosine similarity/distance to determine the most similar working papers.
Papers identified with 🔥 are in the top 20% of similarity.
The 10 most similar working papers to the working paper 'Dispersion in Dispersion: Measuring Establishment-Level Differences in Productivity' are listed below in order of similarity.
-
Working PaperPunctuated Entrepreneurship (Among Women)🔥
May 2018
Working Paper Number:
CES-18-26
The gender gap in entrepreneurship may be explained in part by employee non-compete agreements. Exploiting exogenous state-level variation in non-compete policy, I find that women more strictly subject to non-competes are 11-17% more likely to start companies after their employers dissolve. This result is not explained by the incidence of non-competes or lawsuits; however, women face higher relative costs in defending against potential litigation and in returning to paid employment after abandoning their ventures. Thus entrepreneurship among women may be 'punctuated' in that would-be female founders are throttled by non-competes, their potential unleashed only by the failure of their employers.View Full Paper PDF
-
Working PaperProductivity 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.View Full Paper PDF
-
Working PaperInnovation, Productivity Dispersion, and Productivity Growth🔥
February 2018
Working Paper Number:
CES-18-08
We examine whether underlying industry innovation dynamics are an important driver of the large dispersion in productivity across firms within narrowly defined sectors. Our hypothesis is that periods of rapid innovation are accompanied by high rates of entry, significant experimentation and, in turn, a high degree of productivity dispersion. Following this experimentation phase, successful innovators and adopters grow while unsuccessful innovators contract and exit yielding productivity growth. We examine the dynamic relationship between entry, productivity dispersion, and productivity growth using a new comprehensive firm-level dataset for the U.S. We find a surge of entry within an industry yields an immediate increase in productivity dispersion and a lagged increase in productivity growth. These patterns are more pronounced for the High Tech sector where we expect there to be more innovative activities. These patterns change over time suggesting other forces are at work during the post-2000 slowdown in aggregate productivity.View Full Paper PDF
-
Working PaperCollaborative 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).View Full Paper PDF
-
Working PaperOpening the Black Box: Task and Skill Mix and Productivity Dispersion🔥
September 2022
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.View Full Paper PDF
-
Working PaperProductivity 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.View Full Paper PDF
-
Working PaperWhere Has All the Skewness Gone? The Decline in High-Growth (Young) Firms in the U.S.
November 2015
Working Paper Number:
CES-15-43
The pace of business dynamism and entrepreneurship in the U.S. has declined over recent decades. We show that the character of that decline changed around 2000. Since 2000 the decline in dynamism and entrepreneurship has been accompanied by a decline in high-growth young firms. Prior research has shown that the sustained contribution of business startups to job creation stems from a relatively small fraction of high-growth young firms. The presence of these high-growth young firms contributes to a highly (positively) skewed firm growth rate distribution. In 1999, a firm at the 90th percentile of the employment growth rate distribution grew about 31 percent faster than the median firm. Moreover, the 90-50 differential was 16 percent larger than the 50-10 differential reflecting the positive skewness of the employment growth rate distribution. We show that the shape of the firm employment growth distribution changes substantially in the post-2000 period. By 2007, the 90-50 differential was only 4 percent larger than the 50-10, and it continued to exhibit a trend decline through 2011. The reflects a sharp drop in the 90th percentile of the growth rate distribution accounted for by the declining share of young firms and the declining propensity for young firms to be high-growth firms.View Full Paper PDF
-
Working PaperThe Link Between Aggregate and Micro Productivity Growth: Evidence from Retail Trade
August 2002
Working Paper Number:
CES-02-18
Understanding the nature and magnitude of resource reallocation, particularly as it relates to productivity growth, is important both because it affects how we model and interpret aggregate productivity dynamics, and also because market structure and institutions may affect the reallocation's magnitude and efficiency. Most evidence to date on the connection between reallocation and productivity dynamics for the U.S. and other countries comes from a single industry: manufacturing. Building upon a unique establishment-level data set of U.S. retail trade businesses, we provide some of the first evidence on the connection between reallocation and productivity dynamics in a non-manufacturing sector. Retail trade is a particularly appropriate subject for such a study since this large industry lies at the heart of many recent technological advances, such as E-commerce and advanced inventory controls. Our results show that virtually all of the productivity growth in the U.S. retail trade sector over the 1990s is accounted for by more productive entering establishments displacing much less productive exiting establishments. Interestingly, much of the between-establishment reallocation is a within, rather than betweenfirm phenomenon.View Full Paper PDF
-
Working PaperRising Markups or Changing Technology?
September 2022
Working Paper Number:
CES-22-38R
Recent evidence suggests the U.S. business environment is changing, with rising market concentration and markups. The most prominent and extensive evidence backs out firm-level markups from the first-order conditions for variable factors. The markup is identified as the ratio of the variable factor's output elasticity to its cost share of revenue. Our analysis starts from this indirect approach, but we exploit a long panel of manufacturing establishments to permit output elasticities to vary to a much greater extent - relative to the existing literature - across establishments within the same industry over time. With our more detailed estimates of output elasticities, the measured increase in markups is substantially dampened, if not eliminated, for U.S. manufacturing. As supporting evidence, we relate differences in the markups' patterns to observable changes in technology (e.g., computer investment per worker, capital intensity, diversification to non-manufacturing) and find patterns in support of changing technology as the driver of those differences.View Full Paper PDF
-
Working PaperGross Job Flows for the U.S. Manufacturing Sector: Measurement from the Longitudinal Research Database
December 2006
Working Paper Number:
CES-06-30
Measures of job creation and destruction are now produced regularly by the U.S. statistical agencies. The Bureau of Labor Statistics releases via the Business Employment Dynamics (BED) on a quarterly basis measures of job creation and destruction for the U.S. nonfarm business sector and related disaggregation by industrial sector and size class. The U.S. Census Bureau has developed the Longitudinal Business Database (LBD) covering the nonfarm business sector that has been used to produce research analysis and special tabulations including tabulations of job creation and destruction. Both of these data programs build upon the measurement methods and data analysis of job creation and destruction measures from the Longitudinal Research Database (LRD) developed and published by Davis, Haltiwanger and Schuh (1996). In this paper, the LRD based estimates of job creation and destruction are updated and made available for consistent annual and quarterly series from 1972-1998. While the BED and LBD programs are more comprehensive in scope than the LRD, the extensive development of the LRD permits the construction of measures of job creation and destruction for a rich array of employer characteristics including industry, size, business age, ownership structure, location and wage structure. The updated series that are released with this working paper provide measures along each of these dimensions. The paper describes in detail the changes in the processing of the Annual Survey of Manufactures over the 1972-1998 period that are important to incorporate by users of the LRD at Census Research Data Centers as well as users of products from the LRD such as job creation and destruction.View Full Paper PDF
