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Estimating the Distribution of Plant-Level Manufacturing Energy Efficiency with Stochastic Frontier Regression
March 2007
Working Paper Number:
CES-07-07
A feature commonly used to distinguish between parametric/statistical models and engineering models is that engineering models explicitly represent best practice technologies while the parametric/statistical models are typically based on average practice. Measures of energy intensity based on average practice are less useful in the corporate management of energy or for public policy goal setting. In the context of company or plant level energy management, it is more useful to have a measure of energy intensity capable of representing where a company or plant lies within a distribution of performance. In other words, is the performance close (or far) from the industry best practice? This paper presents a parametric/statistical approach that can be used to measure best practice, thereby providing a measure of the difference, or 'efficiency gap' at a plant, company or overall industry level. The approach requires plant level data and applies a stochastic frontier regression analysis to energy use. Stochastic frontier regression analysis separates the energy intensity into three components, systematic effects, inefficiency, and statistical (random) error. The stochastic frontier can be viewed as a sub-vector input distance function. One advantage of this approach is that physical product mix can be included in the distance function, avoiding the problem of aggregating output to define a single energy/output ratio to measure energy intensity. The paper outlines the methods and gives an example of the analysis conducted for a non-public micro-dataset of wet corn refining plants.
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Identifying Individual and Group Effects in the Presence of Sorting: A Neighborhood Effects Application
January 2007
Working Paper Number:
CES-07-03
Researchers have long recognized that the non-random sorting of individuals into groups generates correlation between individual and group attributes that is likely to bias naive estimates of both individual and group effects. This paper proposes a non-parametric strategy for identifying these effects in a model that allows for both individual and group unobservables, applying this strategy to the estimation of neighborhood effects on labor market outcomes. The first part of this strategy is guided by a robust feature of the equilibrium in the canonical vertical sorting model of Epple and Platt (1998), that there is a monotonic relationship between neighborhood housing prices and neighborhood quality. This implies that under certain conditions a non-parametric function of neighborhood housing prices serves as a suitable control function for the neighborhood unobservable in the labor market outcome regression. This control function converts the problem to a model with one unobservable so that traditional instrumental variables solutions may be applied. In our application, we instrument for each individual.s observed neighborhood attributes with the average neighborhood attributes of a set of observationally identical individuals. The neighborhood effects model is estimated using confidential microdata from the 1990 Decennial Census for the Boston MSA. The results imply that the direct effects of geographic proximity to jobs, neighborhood poverty rates, and average neighborhood education are substantially larger than the conditional correlations identified using OLS, although the net effect of neighborhood quality on labor market outcomes remains small. These findings are robust across a wide variety of specifications and robustness checks.
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Distribution Preserving Statistical Disclosure Limitation
September 2006
Working Paper Number:
tp-2006-04
One approach to limiting disclosure risk in public-use microdata is to release multiply-imputed,
partially synthetic data sets. These are data on actual respondents, but with confidential data
replaced by multiply-imputed synthetic values. A mis-specified imputation model can invalidate
inferences because the distribution of synthetic data is completely determined by the model used
to generate them. We present two practical methods of generating synthetic values when the imputer
has only limited information about the true data generating process. One is applicable when
the true likelihood is known up to a monotone transformation. The second requires only limited
knowledge of the true likelihood, but nevertheless preserves the conditional distribution of the confidential
data, up to sampling error, on arbitrary subdomains. Our method maximizes data utility
and minimizes incremental disclosure risk up to posterior uncertainty in the imputation model and
sampling error in the estimated transformation. We validate the approach with a simulation and
application to a large linked employer-employee database.
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The Impact of Hurricanes Katrina, Rita and Wilma on Business Establishments: A GIS Approach
August 2006
Working Paper Number:
CES-06-23
We use Geographic Information System tools to develop estimates of the economic impact of disaster events such as Hurricane Katrina. Our methodology relies on mapping establishments from the Census Bureau's Business Register into damage zones defined by remote sensing information provided by FEMA. The identification of damaged establishments by precisely locating them on a map provides a far more accurate characterization of affected businesses than those typically reported from readily available county level data. The need for prompt estimates is critical since they are more valuable the sooner they are released after a catastrophic event. Our methodology is based on pre-storm data. Therefore, estimates can be made available very quickly to inform the public as well as policy makers. Robustness tests using data from after the storms indicate our GIS estimates, while much smaller than those based on publicly available county-level data, still overstate actual observed losses. We discuss ways to refine and augment the GIS approach to provide even more accurate estimates of the impact of disasters on businesses.
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The Dynamics of Plant-Level Productivity in U.S. Manufacturing
July 2006
Working Paper Number:
CES-06-20
Using a unique database that covers the entire U.S. manufacturing sector from 1976 until 1999, we estimate plant-level total factor productivity for a large number of plants. We characterize time series properties of plant-level idiosyncratic shocks to productivity, taking into account aggregate manufacturing-sector shocks and industry-level shocks. Plant-level heterogeneity and shocks are a key determinant of the cross-sectional variations in output. We compare the persistence and volatility of the idiosyncratic plant-level shocks to those of aggregate productivity shocks estimated from aggregate data. We find that the persistence of plant level shocks is surprisingly low-we estimate an average autocorrelation of the plantspecific productivity shock of only 0.37 to 0.41 on an annual basis. Finally, we find that estimates of the persistence of productivity shocks from aggregate data have a large upward bias. Estimates of the persistence of productivity shocks in the same data aggregated to the industry level produce autocorrelation estimates ranging from 0.80 to 0.91 on an annual basis. The results are robust to the inclusion of various measures of lumpiness in investment and job flows, different weighting methods, and different measures of the plants' capital stocks.
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Plant Turnover and Demand Fluctuations in the Ready-Mix Concrete Industry
March 2006
Working Paper Number:
CES-06-08
Fluctuations in demand cause some plants to exit a market and other to enter. Would eliminating these 'uctuations reduce plant turnover? A structural model of entry and exit in concentrated markets is estimated for the ready-mix concrete industry, using plant level data from the U.S. Census. The Nested Pseudo-Likelihood algorithm is used to 'nd parameters which rationalize behavior of 'rms involved in repeated competition. Due to high sunk costs, turnover rates would only be reduced by 3% by eliminating demand 'uctuations at the county level, saving around 20 million dollars a year in scrapped capital. However, demand 'uctuations blunt 'rms'incentive to invest, reducing the number of large plants by more than 50%.
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Soft and Hard Within- and Between-Industry Changes of U.S. Skill Intensity: Shedding Light on Worker's Inequality
January 2006
Working Paper Number:
CES-06-01
In order to examine the worsening of inequality between workers of different skill levels over the past three decades and to further motivate the theoretical discussion on this issue, we use the decomposition methodology to focus on the interaction of within- and between-industry changes of the relative skill intensity in U.S. manufacturing. Unlike previous work, we use more detailed levels of industry classification (5-digit SIC product codes), and we analyze the impact of plants switching industries as well as of plant births and deaths on these changes. Internal, plant-level data from the U.S. Census Bureau's Longitudinal Research Database and the new Longitudinal Business Database provide us with the requisite information to conduct these studies. Finally, our empirical conclusions are discussed in relation to the inspired theoretical inference, as they enrich the debate concerning the sources of the inequality by justifying the skill-biased character of technical change.
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The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators
January 2006
Working Paper Number:
tp-2006-01
The Longitudinal Employer-Household Dynamics (LEHD) Program at the U.S. Census Bureau,
with the support of several national research agencies, has built a set of infrastructure files
using administrative data provided by state agencies, enhanced with information from other administrative
data sources, demographic and economic (business) surveys and censuses. The LEHD
Infrastructure Files provide a detailed and comprehensive picture of workers, employers, and their
interaction in the U.S. economy. Beginning in 2003 and building on this infrastructure, the Census
Bureau has published the Quarterly Workforce Indicators (QWI), a new collection of data series
that offers unprecedented detail on the local dynamics of labor markets. Despite the fine detail,
confidentiality is maintained due to the application of state-of-the-art confidentiality protection
methods. This article describes how the input files are compiled and combined to create the infrastructure
files. We describe the multiple imputation methods used to impute in missing data and
the statistical matching techniques used to combine and edit data when a direct identifier match
requires improvement. Both of these innovations are crucial to the success of the final product. Finally,
we pay special attention to the details of the confidentiality protection system used to protect
the identity and micro data values of the underlying entities used to form the published estimates.
We provide a brief description of public-use and restricted-access data files with pointers to further
documentation for researchers interested in using these data.
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Wage Dispersion, Compensation Policy and the Role of Firms
November 2005
Working Paper Number:
tp-2005-04
Empirical work in economics stresses the importance of unobserved firm- and person-level characteristics
in the determination of wages, finding that these unobserved components account for the overwhelming
majority of variation in wages. However, little is known about the mechanisms sustaining these wage di'er-
entials. This paper attempts to demystify the firm-side of the puzzle by developing a statistical model that
enriches the role that firms play in wage determination, allowing firms to influence both average wages as
well as the returns to observable worker characteristics.
I exploit the hierarchical nature of a unique employer-employee linked dataset for the United States,
estimating a multilevel statistical model of earnings that accounts for firm-specific deviations in average
wages as well as the returns to components of human capital - race, gender, education, and experience -
while also controlling for person-level heterogeneity in earnings. These idiosyncratic prices reflect one aspect
of firm compensation policy; another, and more novel aspect, is the unstructured characterization of the
covariance of these prices across firms.
I estimate the model's variance parameters using Restricted (or Residual) Maximum Likelihood tech-
niques. Results suggest that there is significant variation in the returns to worker characteristics across
firms. First, estimates of the parameters of the covariance matrix of firm-specific returns are statistically
significant. Firms that tend to pay higher average wages also tend to pay higher than average returns to
worker characteristics; firms that tend to reward highly the human capital of men also highly reward the
human capital of women. For instance, the correlation between the firm-specific returns to education for
men and women is 0.57. Second, the firm-specific returns account for roughly 9% of the variation in wages
- approximately 50% of the variation in wages explained by firm-specific intercepts alone. The inclusion of
firm-specific returns ties variation in wages, otherwise attributable to firm-specific intercepts, to observable
components of human capital.
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Networking Off Madison Avenue
October 2005
Working Paper Number:
CES-05-15
This paper examines the effect on productivity of having more near advertising agency neighbors and hence better opportunities for meetings and exchange within Manhattan. We will show that there is extremely rapid spatial decay in the benefits of having more near neighbors even in the close quarters of southern Manhattan, a finding that is new to the empirical literature and indicates our understanding of scale externalities is still very limited. The finding indicates that having a high density of commercial establishments is important in enhancing local productivity, an issue in Lucas and Rossi-Hansberg (2002), where within business district spatial decay of spillovers plays a key role. We will argue also that in Manhattan advertising agencies trade-off the higher rent costs of being in bigger clusters nearer 'centers of action', against the lower rent costs of operating on the 'fringes' away from high concentrations of other agencies. Introducing the idea of trade-offs immediately suggests heterogeneity is involved. We will show that higher quality agencies are the ones willing to pay more rent to locate in greater size clusters, specifically because they benefit more from networking. While all this is an exploration of neighborhood and networking externalities, the findings relate to the economic anatomy of large metro areas like New Yorkthe nature of their buzz.
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