Papers Containing Keywords(s): 'estimating'
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Viewing papers 71 through 80 of 167
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Working PaperWithin and Across County Variation in SNAP Misreporting: Evidence from Linked ACS and Administrative Records
July 2014
Working Paper Number:
carra-2014-05
This paper examines sub-state spatial and temporal variation in misreporting of participation in the Supplemental Nutrition Assistance Program (SNAP) using several years of the American Community Survey linked to SNAP administrative records from New York (2008-2010) and Texas (2006-2009). I calculate county false-negative (FN) and false-positive (FP) rates for each year of observation and find that, within a given state and year, there is substantial heterogeneity in FN rates across counties. In addition, I find evidence that FN rates (but not FP rates) persist over time within counties. This persistence in FN rates is strongest among more populous counties, suggesting that when noise from sampling variation is not an issue, some counties have consistently high FN rates while others have consistently low FN rates. This finding is important for understanding how misreporting might bias estimates of sub-state SNAP participation rates, changes in those participation rates, and effects of program participation. This presentation was given at the CARRA Seminar, June 27, 2013View Full Paper PDF
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Working PaperEstimating Record Linkage False Match Rate for the Person Identification Validation System
July 2014
Working Paper Number:
carra-2014-02
The Census Bureau Person Identification Validation System (PVS) assigns unique person identifiers to federal, commercial, census, and survey data to facilitate linkages across files. PVS uses probabilistic matching to assign a unique Census Bureau identifier for each person. This paper presents a method to measure the false match rate in PVS following the approach of Belin and Rubin (1995). The Belin and Rubin methodology requires truth data to estimate a mixture model. The parameters from the mixture model are used to obtain point estimates of the false match rate for each of the PVS search modules. The truth data requirement is satisfied by the unique access the Census Bureau has to high quality name, date of birth, address and Social Security (SSN) data. Truth data are quickly created for the Belin and Rubin model and do not involve a clerical review process. These truth data are used to create estimates for the Belin and Rubin parameters, making the approach more feasible. Both observed and modeled false match rates are computed for all search modules in federal administrative records data and commercial data.View Full Paper PDF
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Working PaperUSING THE PARETO DISTRIBUTION TO IMPROVE ESTIMATES OF TOPCODED EARNINGS
April 2014
Working Paper Number:
CES-14-21
Inconsistent censoring in the public-use March Current Population Survey (CPS) limits its usefulness in measuring labor earnings trends. Using Pareto estimation methods with less-censored internal CPS data, we create an enhanced cell-mean series to capture top earnings in the public-use CPS. We find that previous approaches for imputing topcoded earnings systematically understate top earnings. Annual earnings inequality trends since 1963 using our series closely approximate those found by Kopczuk, Saez, & Song (2010) using Social Security Administration data for commerce and industry workers. However, when we consider all workers, earnings inequality levels are higher but earnings growth is more modestView Full Paper PDF
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Working PaperEARNINGS ADJUSTMENT FRICTIONS: EVIDENCE FROM SOCIAL SECURITY EARNINGS TEST
September 2013
Working Paper Number:
CES-13-50
We study frictions in adjusting earnings to changes in the Social Security Annual Earnings Test (AET) using a panel of Social Security Administration microdata on one percent of the U.S. population from 1961 to 2006. Individuals continue to "bunch" at the convex kink the AET creates even when they are no longer subject to the AET, consistent with the existence of earnings adjustment frictions in the U.S. We develop a novel framework for estimating an earnings elasticity and an adjustment cost using information on the amount of bunching at kinks before and after policy changes in earnings incentives around the kinks. We apply this method in settings in which individuals face changes in the AET bene.t reduction rate, and we estimate in a baseline case that the earnings elasticity with respect to the implicit net-of-tax share is 0.23, and the .xed cost of adjustment is $152.08.View Full Paper PDF
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Working PaperCOMPARING METHODS FOR IMPUTING EMPLOYER HEALTH INSURANCE CONTRIBUTIONS IN THE CURRENT POPULATION SURVEY
August 2013
Working Paper Number:
CES-13-41
The degree to which firms contribute to the payment of workers' health insurance premiums is an important consideration in the measurement of income and for understanding the potential impact of the 2010 Affordable Care Act on employment-based health insurance participation. Currently the U.S. Census Bureau imputes employer contributions in the Annual Social and Economic Supplement of the Current Population Survey based on data from the 1977 National Medical Care Expenditure Survey. The goal of this paper is to assess the extent to which this imputation methodology produces estimates reflective of the current distribution of employer contributions. The paper uses recent contributions data from the Medical Expenditure Panel Survey-Insurance Component to estimate a new model to inform the imputation procedure and to compare the resulting distribution of contributions. These new estimates are compared with those produced under current production methods across employee and employer characteristics.View Full Paper PDF
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Working PaperENVIRONMENTAL REGULATION AND INDUSTRY EMPLOYMENT: A REASSESSMENT
July 2013
Working Paper Number:
CES-13-36
This paper examines the impact of environmental regulation on industry employment, using a structural model based on data from the Census Bureau's Pollution Abatement Costs and Expenditures Survey. This model was developed in an earlier paper (Morgenstern, Pizer, and Shih (2002) - MPS). We extend MPS by examining additional industries and additional years. We find widely varying estimates across industries, including many implausibly large positive employment effects. We explore several possible explanations for these results, without reaching a satisfactory conclusion. Our results call into question the frequent use of the average impacts estimated by MPS as a basis for calculating the quantitative impacts of new environmental regulations on employment.View Full Paper PDF
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Working PaperA METHOD OF CORRECTING FOR MISREPORTING APPLIED TO THE FOOD STAMP PROGRAM
May 2013
Working Paper Number:
CES-13-28
Survey misreporting is known to be pervasive and bias common statistical analyses. In this paper, I first use administrative data on SNAP receipt and amounts linked to American Community Survey data from New York State to show that survey data can misrepresent the program in important ways. For example, more than 1.4 billion dollars received are not reported in New York State alone. 46 percent of dollars received by house- holds with annual income above the poverty line are not reported in the survey data, while only 19 percent are missing below the poverty line. Standard corrections for measurement error cannot remove these biases. I then develop a method to obtain consistent estimates by combining parameter estimates from the linked data with publicly available data. This conditional density method recovers the correct estimates using public use data only, which solves the problem that access to linked administrative data is usually restricted. I examine the degree to which this approach can be used to extrapolate across time and geography, in order to solve the problem that validation data is often based on a convenience sample. I present evidence from within New York State that the extent of heterogeneity is small enough to make extrapolation work well across both time and geography. Extrapolation to the entire U.S. yields substantive differences to survey data and reduces deviations from official aggregates by a factor of 4 to 9 compared to survey aggregates.View Full Paper PDF
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Working PaperMISCLASSIFICATION IN BINARY CHOICE MODELS
May 2013
Working Paper Number:
CES-13-27
We derive the asymptotic bias from misclassification of the dependent variable in binary choice models. Measurement error is necessarily non-classical in this case, which leads to bias in linear and non-linear models even if only the dependent variable is mismeasured. A Monte Carlo study and an application to food stamp receipt show that the bias formulas are useful to analyze the sensitivity of substantive conclusions, to interpret biased coefficients and imply features of the estimates that are robust to misclassification. Using administrative records linked to survey data as validation data, we examine estimators that are consistent under misclassification. They can improve estimates if their assumptions hold, but can aggravate the problem if the assumptions are invalid. The estimators differ in their robustness to such violations, which can be improved by incorporating additional information. We propose tests for the presence and nature of misclassification that can help to choose an estimator.View Full Paper PDF
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Working PaperINNOVATION, REALLOCATION AND GROWTH
April 2013
Working Paper Number:
CES-13-23
We build a model of firm-level innovation, productivity growth and reallocation featuring endogenous entry and exit. A key feature is the selection between high- and low-type firms, which differ in terms of their innovative capacity. We estimate the parameters of the model using detailed US Census micro data on firm-level output, R&D and patenting. The model provides a good fit to the dynamics of firm entry and exit, output and R&D, and its implied elasticities are in the ballpark of a range of micro estimates. We find industrial policy subsidizing either the R&D or the continued operation of incumbents reduces growth and welfare. For example, a subsidy to incumbent R&D equivalent to 53 of GDP reduces welfare by about 1.53 because it deters entry of new high-type firms. On the contrary, substantial improvements (of the order of 53 improvement in welfare) are possible if the continued operation of incumbents is taxed while at the same time R&D by incumbents and new entrants is subsidized. This is because of a strong selection effect: R&D resources (skilled labor) are inefficiently used by low-type incumbent firms. Subsidies to incumbents encourage the survival and expansion of these firms at the expense of potential high-type entrants. We show that optimal policy encourages the exit of low-type firms and supports R&D by high-type incumbents and entry.View Full Paper PDF
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Working PaperSYNTHETIC DATA FOR SMALL AREA ESTIMATION IN THE AMERICAN COMMUNITY SURVEY
April 2013
Working Paper Number:
CES-13-19
Small area estimates provide a critical source of information used to study local populations. Statistical agencies regularly collect data from small areas but are prevented from releasing detailed geographical identifiers in public-use data sets due to disclosure concerns. Alternative data dissemination methods used in practice include releasing summary/aggregate tables, suppressing detailed geographic information in public-use data sets, and accessing restricted data via Research Data Centers. This research examines an alternative method for disseminating microdata that contains more geographical details than are currently being released in public-use data files. Specifically, the method replaces the observed survey values with imputed, or synthetic, values simulated from a hierarchical Bayesian model. Confidentiality protection is enhanced because no actual values are released. The method is demonstrated using restricted data from the 2005-2009 American Community Survey. The analytic validity of the synthetic data is assessed by comparing small area estimates obtained from the synthetic data with those obtained from the observed data.View Full Paper PDF
