Using data from a unique nationally representative sample of businesses, the Educational Quality of the Workforce National Employers Survey (EQW-NES), matched with the Bureau of the Census' Longitudinal Research Database (LRD), we examine the impact of workplace practices, information technology, and human capital investments on productivity. We estimate an augmented Cobb Douglas production function with both cross section and panel data covering the period of 1987-1993 using both within and GMM estimators. We find that what is associated with higher productivity is not so much whether or not an employer adopts a particular work practice but rather how that work practice is actually implemented within the establishment. We also find that those unionized establishments that have adopted what have been called new or "transformed" industrial relations practices that promote joint decision making coupled with incentive based compensation have higher productivty than other similar non-union plants, while those businesses that are unionized but maintain more traditional labor management relations have lower productivity. We also find that the higher the average educational level of production workers or the greater the proportion of non-managerial workers who use computers, the higher is plant productivity.
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Productivity Races I: Are Some Productivuty Measures Better Than Others?
January 1997
Working Paper Number:
CES-97-02
In this study we construct twelve different measures of productivity at the plant level and test which measures of productivity are most closely associated with direct measures of economic performance. We first examine how closely correlated these measures are with various measures of profits. We then evaluate the extent to which each productivity measure is associated with lower rates of plant closure and faster plant growth (growth in employment, output, and capital). All measures of productivity considered are credible in the sense that highly productive plants, regardless of measure, are clearly more profitable, less likely to close, and grow faster. Nevertheless, labor productivity and measures of total factor productivity that are based on regression estimates of production functions are better predictors of plant growth and survival than factor share-based measures of total factor productivity (TFP). Measures of productivity that are based on several years of data appear to outperform measures of productivity that are based solely on data from the most recent year.
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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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Learning by Doing and Plant Characteristics
August 1996
Working Paper Number:
CES-96-05
Learning by doing, especially spillover learning, has received much attention lately in models of industry evolution and economic growth. The predictions of these models depend on the distribution of learning abilities and knowledge flows across firms and countries. However, the empirical literature provides little guidance on these issues. In this paper, I use plant level data on a sample of entrants in SIC 38, Instruments, to examine the characteristics associated with both proprietary and spillover learning by doing. The plant level data permit tests for the relative importance of within and between firm spillovers. I include both formal knowledge, obtained through R&D expenditures, and informal knowledge, obtained through learning by doing, in a production function framework. I allow the speed of learning to vary across plants according to characteristics such as R&D intensity, wages, and the skill mix. The results suggest that (a) Ainformal@ knowledge, accumulated through production experience at the plant, is a much more important source of productivity growth for these plants than is Aformal@ knowledge gained via research and development expenditures, (b) interfirm spillovers are stronger than intrafirm spillovers, (c) the slope of the own learning curve is positively related to worker quality, (d) the slope of the spillover learning curve is positively related to the skill mix at plants, (e) neither own nor spillover learning curve slopes are related to R&D intensities. These results imply that learning by doing may be, to some extent, an endogenous phenomenon at these plants. Thus, models of industry evolution that incorporate learning by doing may need to be revised. The results are also broadly consistent with the recent growth models.
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Does Firms' Financial Status Affect Plant-Level Investment and Exit Decisions?
January 1999
Working Paper Number:
CES-99-03
This paper investigates the influence of a firm's financial status on the within-firm allocation of funds, reflected in its plant-level investment and exit decisions. In the empirical analysis, financial status is measured by both standard measures and an indicator variable recently suggested by Kaplan and Zingales. Based on these firm-level financial variables and on planet-level investment and production data from the U.S. Census Bureau's Longitudinal Research Database(LRD), econometric models of plant operating regimes are estimated which summarize investment and exit decisions. The empirical evidence supports the view that firm-level financial status affects investments and market exit decisions observed at the plant level.
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Regional Industrial Dominance, Agglomeration Economies, and Manufacturing Plant Productivity
December 2007
Working Paper Number:
CES-07-31
In a seminal article, Benjamin Chinitz (1961) focused attention on the effects that industry size, structure, and economic diversification have on firm performance and regional economies. He also raised a related but conceptually distinct question that has been overlooked since: how does the extent to which a regional industry is concentrated in a single or small number of firms impact the performance of other local firms within that industry? He suggested that such regional industrial dominance may impact input prices, limit capital accessibility, deter entrepreneurial activity, and reduce the regional availability of agglomeration economies such as specialized labor and supply pools In this paper, we use an establishment-level production function to quantify the links between industrial dominance, agglomeration economies, and firm performance. We consider two questions. First, do greater levels of regional industrial dominance lead to lower economic performance by small, dominated manufacturing plants? Second, are small plants in dominated regional industries more limited in capturing regional agglomeration benefits and therefore do they face rigidities in deploying production factors to maximum advantage? Our results suggest that regional industrial organization does influence productivity but that the effect tends to be a direct one, rather than an indirect effect via its influence on agglomeration economies.
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Productivity Adjustments and Learning-by-Doing as Human Capital
November 1997
Working Paper Number:
CES-97-17
This paper measures plant-level productivity gains associated with learning curves across the entire manufacturing sector. We measure these gains at plant startups and also after major employment changes. We find: 1.) The gains are strongly associated with a variety of human capital measures implying that learning-by-doing is largely a firm-specific human capital investment. 2.) This implicit investment is large; many plants invest as much in learning-by-doing as they invest in physical capital and much more than they invest in formal job training. 3.) This investment differs persistently over industries and is higher with greater R&D. 4.) Consistent with a learning-by-doing interpretation, the human capital investment is much larger following employment decreases than increases. We conclude that learning-by-doing is a major factor in wage determination, technical progress and asymmetric employment adjustment costs.
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Learning By Doing And Competition In The Early Rayon Industry
February 1993
Working Paper Number:
CES-93-04
In this paper, I derive a structural econometric model of learning by doing from a dynamic oligopoly game. Unlike previous empirical models, this model is capable of testing hypotheses concerning both the technological nature and behavioral implications of learning. I estimate the model with firm level data from the early U.S. rayon industry. The empirical results show that there were considerable differences across firms in both proprietary and spillover learning. The results also indicate that two of the three firms took their rival's reactions into account when choosing their strategies.
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The Industry Life Cycle and Acquisitions and Investment: Does Firm Organization Matter?
October 2005
Working Paper Number:
CES-05-29
We examine the effect of financial dependence on the acquisition and investment of single segment and conglomerate firms for different long-run changes in industry conditions. Conglomerates and single-segment firms differ in the investments they make. The main differences are in the investment in acquisitions rather than in the level of capital expenditure. Financial dependence, a deficit in a segment's internal financing, decreases the likelihood of acquisitions and opening new plants, especially for single-segment firms. These effects are mitigated for conglomerates in growth industries and also for firms that are publicly traded. In declining industries, plants of segments that are financially dependent are less likely to be closed by conglomerate firms. These findings persist after controlling for firm size and segment productivity. We also find that plants acquired by conglomerate firms in growth industries increase in productivity post-acquisition. The results are consistent with the comparative advantages of different firm organizations differing across long-run industry conditions.
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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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Labor Reallocation, Employment, and Earnings: Vector Autoregression Evidence
January 2017
Working Paper Number:
CES-17-11R
Analysis of the labor market has given increasing attention to the reallocation of jobs across employers and workers across jobs. However, whether and how job reallocation and labor market 'churn' affects the health of the labor market remains an open question. In this paper, we present time series evidence for the U.S. 1993-2013 and consider the relationship between labor reallocation, employment, and earnings using a vector autoregression (VAR) framework. We find that an increase in labor market churn by 1 percentage point predicts that, in the next quarter, employment will increase by 100 to 560 thousand jobs, lowering the unemployment rate by 0.05 to 0.25 percentage points. Job destruction does not predict future changes in employment but a 1 percentage point increase in job destruction leads to an increase in future unemployment 0.14 to 0.42 percentage points. We find mixed results on the relationship between labor reallocation rates and earnings: we nd that, especially for earnings derived from administrative records data, a 1 percentage point increase to either job destruction or churn leads to increased earnings of less than 2 percent. Results vary substantially depending on the earnings measure we use, and so the evidence inconsistent on whether productivity-enhancing aspects of churn and job destruction provide earnings gains for workers in aggregate. Our findings on churn leading to increased employment and a lower unemployment rate are consistent with models of replacement hiring and vacancy chains.
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