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The Slow Growth of New Plants: Learning about Demand?
March 2012
Working Paper Number:
CES-12-06
It is well known that new businesses are typically much smaller than their established industry competitors, and that this size gap closes slowly. We show that even in commodity-like product markets, these patterns do not reflect productivity gaps, but rather differences in demand-side fundamentals. We document and explore patterns in plants' idiosyncratic demand levels by estimating a dynamic model of plant expansion in the presence of a demand accumulation process (e.g., building a customer base). We find active accumulation driven by plants' past production decisions quantitatively dominates passive demand accumulation, and that within-firm spillovers affect demand levels but not growth.
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Micro Data and the Macro Elasticity of Substitution
March 2012
Working Paper Number:
CES-12-05
We estimate the aggregate elasticity of substitution between capital and labor in the US manufacturing sector. We show that the aggregate elasticity of substitution can be expressed as a simple function of plant level structural parameters and sufficient statistics of the distribution of plant input cost shares. We then use plant level data from the Census of Manufactures to construct a local elasticity of substitution at various levels of aggregation. Our approach does not assume the existence of a stable aggregate production function, as we build up our estimate from the cross section of plants at a point in time. Accounting for substitution within and across plants, we find that the aggregate elasticity is substantially below unity at approximately 0.7. Lastly we assess the sources of the bias of aggregate technical change from 1987 to 1997. We find that the labor augmenting character of aggregate technical change is due almost exclusively to labor augmenting productivity growth at the plant level rather than relative growth in capital intensive plants.
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Firm Market Power and the Earnings Distribution
December 2011
Working Paper Number:
CES-11-41
Using the Longitudinal Employer Household Dynamics (LEHD) data from the United States Census Bureau, I compute firm-level measures of labor market (monopsony) power. To generate these measures, I extend the dynamic model proposed by Manning (2003) and estimate the labor supply elasticity facing each private non-farm firm in the US. While a link between monopsony power and earnings has traditionally been assumed, I provide the first direct evidence of the positive relationship between a firm\'s labor supply elasticity and the earnings of its workers. I also contrast the semistructural method with the more traditional use of concentration ratios to measure a firm\'s labor market power. In addition, I provide several alternative measures of labor market power which account for potential threats to identification such as endogenous mobility. Finally, I construct a counterfactual earnings distribution which allows the effects of firm market power to vary across the earnings distribution. I estimate the average firm\'s labor supply elasticity to be 1.08, however my findings suggest there to be significant variability in the distribution of firm market power across US firms, and that dynamic monopsony models are superior to the use of concentration ratios in evaluating a firm\'s labor market power. I find that a one-unit increase in the labor supply elasticity to the firm is associated with wage gains of between 5 and 18 percent. While nontrivial, these estimates imply that firms do not fully exercise their labor market power over their workers. Furthermore, I find that the negative earnings impact of a firm\'s market power is strongest in the lower half of the earnings distribution, and that a one standard deviation increase in firms\' labor supply elasticities reduces the variance of the earnings distribution by 9 percent.
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Entry Costs and Increasing Trade
December 2011
Working Paper Number:
CES-11-38R
Using confidential microdata from the US Census, we find that the fraction of manufacturing plants that export rose from 21% in 1987 to 39% in 2006. It has been suggested that similar trends in other countries may have been caused by declining costs of entering foreign markets. Our study tests this hypothesis for the first time. Both reduced form and structural estimation approaches find little evidence that entry costs declined significantly for US firms over this period. Despite the large literature on changes in variable costs to trade such as tariffs, our estimations represent the first analysis of how the costs of entering foreign markets have changed over time.
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Modeling Single Establishment Firm Returns to the 2007 Economic Census
September 2011
Working Paper Number:
CES-11-28
The Economic Census is one of the most important activities that the U.S. Census Bureau performs. It is critical for updating firm ownership/structure and industry information for a large number of businesses in the Census Bureau's Business Register, impacting most other economic programs. Also, it feeds into Bureau of Economic Analysis products, such as benchmark inputoutput accounts and Gross Domestic Product. The overall check-in rate for the 2007 Economic Census was just over 86%. Establishments owned by multi-location companies returned over 90% of their forms, as compared to the roughly two million single-establishment firms sampled in the Census that returned just over 80%. We model the check-in rate for single-establishment firms by using a large number of variables that might be correlated with whether or not a firm returns a form in the Economic Census. These variables are broadly categorized as the characteristics of firms, measures of external factors, and features of the survey design. We use the model for two purposes. First, by including many of the factors that may be correlated with returns we aim to focus limited advertising and outreach resources to low-return segments of the population. Second, we use the model to investigate the efficacy of an unplanned intervention expected to increase return rates: using certified mailing for one of the form follow-ups.
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Wage Dynamics along the Life-Cycle of Manufacturing Plants
August 2011
Working Paper Number:
CES-11-24R
This paper explores the evolution of average wage paid to employees along the life-cycle of a manufacturing plant in U.S. Average wage starts out low for a new plant and increases along with labor productivity, as the plant survives and ages. As a plant experiences productivity decline and approaches exit, average wage falls, but more slowly than it rises in the case of surviving new plants. Moreover, average wage declines slower than productivity does in failing plants, while it rises relatively faster as productivity increases in surviving new plants. These empirical regularities are studied in a dynamic model of labor quality and quantity choice by plants, where labor quality is reflected in wages. The model's parameters are estimated to assess the costs a plant incurs as it alters its labor quality and quantity in response to changes in its productivity over its life-cycle.
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Using the Survey of Plant Capacity to Measure Capital Utilization
July 2011
Working Paper Number:
CES-11-19
Most capital in the United States is idle much of the time. By some measures, the average workweek of capital in U.S. manufacturing is as low as 55 hours per 168 hour week. The level and variability of capital utilization has important implications for understanding both the level of production and its cyclical fluctuations. This paper investigates a number of issues relating to aggregation of capital utilization measures from the Survey of Plant Capacity and makes recommendations on expanding and improving the published statistics deriving from the Survey of Plant Capacity. The paper documents a number of facts about properties of capital utilization. First, after growing for decades, capital utilization started to fall in mid 1990s. Second, capital utilization is a useful predictor of changes in capacity utilization and other factors of production. Third, adjustment of productivity measures for variable capital utilization improves statistical and economic properties of these measures. Fourth, the paper constructs weights to aggregate firm level capital utilization rates to industry and economy level, which is the major enhancement to available data.
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Plant-Level Productivity and Imputation of Missing Data in the Census of Manufactures
January 2011
Working Paper Number:
CES-11-02
In the U.S. Census of Manufactures, the Census Bureau imputes missing values using a combination of mean imputation, ratio imputation, and conditional mean imputation. It is wellknown that imputations based on these methods can result in underestimation of variability and potential bias in multivariate inferences. We show that this appears to be the case for the existing imputations in the Census of Manufactures. We then present an alternative strategy for handling the missing data based on multiple imputation. Specifically, we impute missing values via sequences of classification and regression trees, which offer a computationally straightforward and flexible approach for semi-automatic, large-scale multiple imputation. We also present an approach to evaluating these imputations based on posterior predictive checks. We use the multiple imputations, and the imputations currently employed by the Census Bureau, to estimate production function parameters and productivity dispersions. The results suggest that the two approaches provide quite different answers about productivity.
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National Estimates of Gross Employment and Job Flows from the Quarterly Workforce Indicators with Demographic and Industry Detail
June 2010
Working Paper Number:
CES-10-11
The Quarterly Workforce Indicators (QWI) are local labor market data produced and released every quarter by the United States Census Bureau. Unlike any other local labor market series produced in the U.S. or the rest of the world, the QWI measure employment flows for workers (accession and separations), jobs (creations and destructions) and earnings for demographic subgroups (age and gender), economic industry (NAICS industry groups), detailed geography (block (experimental), county, Core- Based Statistical Area, and Workforce Investment Area), and ownership (private, all) with fully interacted publication tables. The current QWI data cover 47 states, about 98% of the private workforce in those states, and about 92% of all private employment in the entire economy. State participation is sufficiently extensive to permit us to present the first national estimates constructed from these data. We focus on worker, job, and excess (churning) reallocation rates, rather than on levels of the basic variables. This permits comparison to existing series from the Job Openings and Labor Turnover Survey and the Business Employment Dynamics Series from the Bureau of Labor Statistics. The national estimates from the QWI are an important enhancement to existing series because they include demographic and industry detail for both worker and job flow data compiled from underlying micro-data that have been integrated at the job and establishment levels by the Longitudinal Employer-Household Dynamics Program at the Census Bureau. The estimates presented herein were compiled exclusively from public-use data series and are available for download.
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Euler-Equation Estimation for Discrete Choice Models: A Capital Accumulation Application
January 2010
Working Paper Number:
CES-10-02
This paper studies capital adjustment at the establishment level. Our goal is to characterize capital adjustment costs, which are important for understanding both the dynamics of aggregate investment and the impact of various policies on capital accumulation. Our estimation strategy searches for parameters that minimize ex post errors in an Euler equation. This strategy is quite common in models for which adjustment occurs in each period. Here, we extend that logic to the estimation of parameters of dynamic optimization problems in which non-convexities lead to extended periods of investment inactivity. In doing so, we create a method to take into account censored observations stemming from intermittent investment. This methodology allows us to take the structural model directly to the data, avoiding time-consuming simulation based methods. To study the effectiveness of this methodology, we first undertake several Monte Carlo exercises using data generated by the structural model. We then estimate capital adjustment costs for U.S. manufacturing establishments in two sectors.
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