Papers Containing Keywords(s): 'estimating'
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Viewing papers 61 through 70 of 170
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Working PaperResponse Error & the Medicaid undercount in the CPS
December 2016
Working Paper Number:
carra-2016-11
The Current Population Survey Annual Social and Economic Supplement (CPS ASEC) is an important source for estimates of the uninsured population. Previous research has shown that survey estimates produce an undercount of beneficiaries compared to Medicaid enrollment records. We extend past work by examining the Medicaid undercount in the 2007-2011 CPS ASEC compared to enrollment data from the Medicaid Statistical Information System for calendar years 2006-2010. By linking individuals across datasets, we analyze two types of response error regarding Medicaid enrollment - false negative error and false positive error. We use regression analysis to identify factors associated with these two types of response error in the 2011 CPS ASEC. We find that the Medicaid undercount was between 22 and 31 percent from 2007 to 2011. In 2011, the false negative rate was 40 percent, and 27 percent of Medicaid reports in CPS ASEC were false positives. False negative error is associated with the duration of enrollment in Medicaid, enrollment in Medicare and private insurance, and Medicaid enrollment in the survey year. False positive error is associated with enrollment in Medicare and shared Medicaid coverage in the household. We discuss implications for survey reports of health insurance coverage and for estimating the uninsured population.View Full Paper PDF
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Working PaperPlaying with Matches: An Assessment of Accuracy in Linked Historical Data
June 2016
Working Paper Number:
carra-2016-05
This paper evaluates linkage quality achieved by various record linkage techniques used in historical demography. I create benchmark, or truth, data by linking the 2005 Current Population Survey Annual Social and Economic Supplement to the Social Security Administration's Numeric Identification System by Social Security Number. By comparing simulated linkages to the benchmark data, I examine the value added (in terms of number and quality of links) from incorporating text-string comparators, adjusting age, and using a probabilistic matching algorithm. I find that text-string comparators and probabilistic approaches are useful for increasing the linkage rate, but use of text-string comparators may decrease accuracy in some cases. Overall, probabilistic matching offers the best balance between linkage rates and accuracy.View Full Paper PDF
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Working PaperRevisiting the Effects of Unemployment Insurance Extensions on Unemployment: A Measurement Error-Corrected Regression Discontinuity Approach
March 2016
Working Paper Number:
carra-2016-01
The extension of Unemployment Insurance (UI) benefits was a key policy response to the Great Recession. However, these benefit extensions may have had detrimental labor market effects. While evidence on the individual labor supply response indicates small effects on unemployment, recent work by Hagedorn et al. (2015) uses a county border pair identification strategy to find that the total effects inclusive of effects on labor demand are substantially larger. By focusing on variation within border county pairs, this identification strategy requires counties in the pairs to be similar in terms of unobservable factors. We explore this assumption using an alternative regression discontinuity approach that controls for changes in unobservables by distance to the border. To do so, we must account for measurement error induced by using county-level aggregates. These new results provide no evidence of a large change in unemployment induced by differences in UI generosity across state boundaries. Further analysis suggests that individuals respond to UI benefit differences across boundaries by targeting job search in high-benefit states, thereby raising concerns of treatment spillovers in this setting. Taken together, these two results suggest that the effect of UI benefit extensions on unemployment remains an open question.View Full Paper PDF
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Working PaperMeasuring Plant Level Energy Efficiency and Technical Change in the U.S. Metal-Based Durable Manufacturing Sector Using Stochastic Frontier Analysis
January 2016
Working Paper Number:
CES-16-52
This study analyzes the electric and thermal energy efficiency for five different metal-based durable manufacturing industries in the United States from 1987-2012 at the 3 digit North American Industry Classification System (NAICS) level. Using confidential plant-level data on energy use and production from the quinquennial U.S. Economic Census, a stochastic frontier regression analysis (SFA) is applied in six repeated cross sections for each five year census. The SFA controls for energy prices and climate-driven energy demand (heating degree days - HDD - and cooling degree days - CDD) due to differences in plant level locations, as well as 6-digit NAICS industry effects. A Malmquist index is used to decompose aggregate plant technical change in energy use into indices of efficiency and frontier (best practice) change. Own energy price elasticities range from -.7 to -1.0, with electricity tending to have slightly higher elasticity than fuel. Mean efficiency estimates (100 percent equals best practice level) range from a low of 32 percent (thermal 334 - Computer and Electronic Products) to a high of 86 percent (electricity 332 - Fabricated Metal Products). Electric efficiency is consistently better than thermal efficiency for all NAICS. There is no clear pattern to the decomposition of aggregate technical Thermal change. In some years efficiency improvement dominates; in other years aggregate technical change is driven by improvement in best practice.View Full Paper PDF
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Working PaperMeasuring Cross-Country Differences in Misallocation
January 2016
Working Paper Number:
CES-16-50R
We describe differences between the commonly used version of the U.S. Census of Manufactures available at the RDCs and what establishments themselves report. The originally reported data has substantially more dispersion in measured establishment productivity. Measured allocative efficiency is substantially higher in the cleaned data than the raw data: 4x higher in 2002, 20x in 2007, and 80x in 2012. Many of the important editing strategies at the Census, including industry analysts' manual edits and edits using tax records, are infeasible in non-U.S. datasets. We describe a new Bayesian approach for editing and imputation that can be used across contexts.View Full Paper PDF
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Working PaperEvidence for the Effects of Mergers on Market Power and Efficiency
January 2016
Working Paper Number:
CES-16-43
Study of the impact of mergers and acquisitions (M&As) on productivity and market power has been complicated by the difficulty of separating these two effects. We use newly-developed techniques to separately estimate productivity and markups across a wide range of industries using confidential data from the U.S. Census Bureau. Employing a difference-in-differences framework, we find that M&As are associated with increases in average markups, but find little evidence for effects on plant-level productivity. We also examine whether M&As increase efficiency through reallocation of production to more efficient plants or through reductions in administrative operations, but again find little evidence for these channels, on average. The results are robust to a range of approaches to address the endogeneity of firms' merger decisions.View Full Paper PDF
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Working PaperSimultaneous Edit-Imputation for Continuous Microdata
December 2015
Working Paper Number:
CES-15-44
Many statistical organizations collect data that are expected to satisfy linear constraints; as examples, component variables should sum to total variables, and ratios of pairs of variables should be bounded by expert-specified constants. When reported data violate constraints, organizations identify and replace values potentially in error in a process known as edit-imputation. To date, most approaches separate the error localization and imputation steps, typically using optimization methods to identify the variables to change followed by hot deck imputation. We present an approach that fully integrates editing and imputation for continuous microdata under linear constraints. Our approach relies on a Bayesian hierarchical model that includes (i) a flexible joint probability model for the underlying true values of the data with support only on the set of values that satisfy all editing constraints, (ii) a model for latent indicators of the variables that are in error, and (iii) a model for the reported responses for variables in error. We illustrate the potential advantages of the Bayesian editing approach over existing approaches using simulation studies. We apply the model to edit faulty data from the 2007 U.S. Census of Manufactures. Supplementary materials for this article are available online.View Full Paper PDF
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Working PaperJob Creation, Small vs. Large vs. Young, and the SBA
September 2015
Working Paper Number:
CES-15-24
Analyzing a list of all Small Business Administration (SBA) loans in 1991 to 2009 linked with annual information on all U.S. employers from 1976 to 2012, we apply detailed matching and regression methods to estimate the variation in SBA loan effects on job creation and firm survival across firm age and size groups. The estimated number of jobs created per million dollars of loans within the small business sector generally increases with size and decreases in age. The results suggest that the growth of small, mature firms is least financially constrained, and that faster growing firms experience the greatest financial constraints to growth. The estimated association between survival and loan amount is larger for younger and smaller firms facing the 'valley of death.'View Full Paper PDF
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Working PaperEstimation and Inference in Regression Discontinuity Designs with Clustered Sampling
August 2015
Working Paper Number:
carra-2015-06
Regression Discontinuity (RD) designs have become popular in empirical studies due to their attractive properties for estimating causal effects under transparent assumptions. Nonetheless, most popular procedures assume i.i.d. data, which is not reasonable in many common applications. To relax this assumption, we derive the properties of traditional non-parametric estimators in a setting that incorporates potential clustering at the level of the running variable, and propose an accompanying optimal-MSE bandwidth selection rule. Simulation results demonstrate that falsely assuming data are i.i.d. when selecting the bandwidth may lead to the choice of bandwidths that are too small relative to the optimal-MSE bandwidth. Last, we apply our procedure using person-level microdata that exhibits clustering at the census tract level to analyze the impact of the Low-Income Housing Tax Credit program on neighborhood characteristics and low-income housing supply.View Full Paper PDF
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Working PaperModeling Endogenous Mobility in Wage Determiniation
June 2015
Working Paper Number:
CES-15-18
We evaluate the bias from endogenous job mobility in fixed-effects estimates of worker- and firm-specific earnings heterogeneity using longitudinally linked employer-employee data from the LEHD infrastructure file system of the U.S. Census Bureau. First, we propose two new residual diagnostic tests of the assumption that mobility is exogenous to unmodeled determinants of earnings. Both tests reject exogenous mobility. We relax the exogenous mobility assumptions by modeling the evolution of the matched data as an evolving bipartite graph using a Bayesian latent class framework. Our results suggest that endogenous mobility biases estimated firm effects toward zero. To assess validity, we match our estimates of the wage components to out-of-sample estimates of revenue per worker. The corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates.View Full Paper PDF
