Papers Containing Keywords(s): 'estimating'
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Viewing papers 101 through 110 of 167
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Working PaperLong Term Effects of Military Service on the Distribution of Earnings
August 2009
Working Paper Number:
CES-09-17
I estimate the long term effect of military service on quantiles of earnings and education using the Vietnam draft lottery eligibility status as an instrument. I compare the local quantile treatment effect estimator studied by Abadie, Angrist, and Imbens (2002) to the instrumental variables quantile regression technique developed by Chernozhukov and Hansen (2008). Ordinary quantile regression shows a large negative association between service in Vietnam and earnings of white men, with the effect increasing in magnitude for the upper quantiles. Quantile treatment effects estimates show the opposite pattern, although much smaller in magnitude, with a small negative effect at the lower end of the distribution, and a small positive effect at the upper end. This suggests the ordinary quantile result is due to heterogeneous selection effects. The two methods of quantile treatment effects estimation give similar results.View Full Paper PDF
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Working PaperEstimating the "True" Cost of Job Loss: Evidence Using Matched Data from Califormia 1991-2000
June 2009
Working Paper Number:
CES-09-14
Estimates of the cost of job displacement from survey and administrative data differ markedly. This paper uses a unique match of data between the Displaced Worker Survey (DWS) and administrative wage records from California to examine the sources of this discrepancy. When we use similar estimation methods and account for measurement error in survey wages correlated with worker demographics, estimates of earnings losses at displacement are similar from both datasets and significantly larger than those based on the DWS alone. Also correcting for measurement errors in reported displacements suggests both sources of such estimates may yield lower bounds for the true cost of displacement.View Full Paper PDF
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Working PaperMeasuring Inequality Using Censored Data: A Multiple Imputation Approach
April 2009
Working Paper Number:
CES-09-05
To measure income inequality with right censored (topcoded) data, we propose multiple imputation for censored observations using draws from Generalized Beta of the Second Kind distributions to provide partially synthetic datasets analyzed using complete data methods. Estimation and inference uses Reiter's (Survey Methodology 2003) formulae. Using Current Population Survey (CPS) internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using CPS public use data with cell mean imputations may lead to incorrect inferences about inequality differences. Multiply-imputed public use data provide an intermediate solution.View Full Paper PDF
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Working PaperMeasuring Labor Earnings Inequality Using Public-Use March Current Population Survey Data: The Value of Including Variances and Cell Means When Imputing Topcoded Values
November 2008
Working Paper Number:
CES-08-38
Using the Census Bureau's internal March Current Population Surveys (CPS) file, we construct and make available variances and cell means for all topcoded income values in the publicuse version of these data. We then provide a procedure that allows researchers with access only to the public-use March CPS data to take advantage of this added information when imputing its topcoded income values. As an example of its value we show how our new procedure improves on existing imputation methods in the labor earnings inequality literature.View Full Paper PDF
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Working PaperLinking Investment Spikes and Productivity Growth: U.S. Food Manufacturing Industry
October 2008
Working Paper Number:
CES-08-36
We investigate the relationship between productivity growth and investment spikes using Census Bureau's plant-level data set for the U.S. food manufacturing industry. We find that productivity growth increases after investment spikes suggesting an efficiency gain or plants' learning effect. However, efficiency and the learning period associated with investment spikes differ among plants' productivity quartile ranks implying the differences in the plants' investment types such as expansionary, replacement or retooling. We find evidence of both convex and non-convex types of adjustment costs where lumpy plant-level investments suggest the possibility of non-convex adjustment costs and hazard estimation results suggest the possibility of convex adjustment costs. The downward sloping hazard can be due to the unobserved heterogeneity across plants such as plants' idiosyncratic obsolescence caused by different R&D capabilities and implies the existence of convex adjustment costs. Food plants frequently invest during their first few years of operation and high productivity plants postpone investing due to high fixed costs.View Full Paper PDF
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Working PaperProductivity Dispersion and Input Prices: The Case of Electricity
September 2008
Working Paper Number:
CES-08-33
We exploit a rich new database on Prices and Quantities of Electricity in Manufacturing (PQEM) to study electricity productivity in the U.S. manufacturing sector. The database contains nearly 2 million customer-level observations (i.e., manufacturing plants) from 1963 to 2000. It allows us to construct plant-level measures of price paid per kWh, output per kWh, output per dollar spent on electric power and labor productivity. Using this database, we first document tremendous dispersion among U.S. manufacturing plants in electricity productivity measures and a strong negative relationship between price per kWh and output per kWh hour within narrowly defined industries. Using an IV strategy to isolate exogenous price variation, we estimate that the average elasticity of output per kWh with respect to the price of electricity is about 0.6 during the period from 1985 to 2000. We also develop evidence that this price-physical efficiency tradeoff is stronger for industries with bigger electricity cost shares. Finally, we develop evidence that stronger competitive pressures in the output market lead to less dispersion among manufacturing plants in price per kWh and in electricity productivity measures. The strength of competition effects on dispersion is similar for electricity productivity and labor productivity.View Full Paper PDF
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Working PaperUsing the P90/P10 Index to Measure U.S. Inequality Trends with Current Population Survey Data: A View From Inside the Census Bureau Vaults
June 2007
Working Paper Number:
CES-07-17
The March Current Population Survey (CPS) is the primary data source for estimation of levels and trends in labor earnings and income inequality in the USA. Time-inconsistency problems related to top coding in theses data have led many researchers to use the ratio of the 90th and 10th percentiles of these distributions (P90/P10) rather than a more traditional summary measure of inequality. With access to public use and restricted-access internal CPS data, and bounding methods, we show that using P90/P10 does not completely obviate time inconsistency problems, especially for household income inequality trends. Using internal data, we create consistent cell mean values for all top-coded public use values that, when used with public use data, closely track inequality trends in labor earnings and household income using internal data. But estimates of longer-term inequality trends with these corrected data based on P90/P10 differ from those based on the Gini coefficient. The choice of inequality measure matters.View Full Paper PDF
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Working PaperEstimating 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.View Full Paper PDF
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Working PaperIdentifying 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.View Full Paper PDF
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Working PaperDistribution 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.View Full Paper PDF
