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Concording U.S. Harmonized System Categories Over Time
May 2009
Working Paper Number:
CES-09-11
This paper: outlines an algorithm for concording U.S. ten-digit Harmonized System export and import codes over time; describes the concordances we construct for 1989 to 2004; and provides Stata code that can be used to construct similar concordances for arbitrary beginning and ending years from 1989 to 2007.
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Complex Survey Questions and the Impact of Enumeration Procedures: Census/American Community Survey Disability Questions
April 2009
Working Paper Number:
CES-09-10
This paper explores challenges relating to the identification of the population with disabilities,focusing on Census Bureau efforts using the 2000 Decennial Census Long-Form (Census 2000) and 2000-2005 American Community Survey (ACS). In particular, the analyses explore the impact of survey methods on responses to the work limitation (i.e., employment disability) question in these two Census products. Building on the research of Stern (2003) and Stern and Brault (2005), we look for further evidence of misreporting of an employment disability by specific sub-populations using the participation in the Supplemental Security Income program as an exogenous employment disability status indicator along with a subset of ACS disability questions. We expand upon these earlier studies by examining both false-positive and falsenegative reports of employment disability by implementing logit estimations to examine the role of respondent/enumerator error on the accuracy of the employment disability response. In this manner, we enhance our understanding of Census 2000 and ACS responses to employment disability questions through an exploration of the role of enumeration procedures in two types of misclassifications, as well as by evaluating existing data and estimates to uncover characteristics that might make an individual more likely to misreport an employment disability.
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Exploring Differences in Employment between Household and Establishment Data
April 2009
Working Paper Number:
CES-09-09
Using a large data set that links individual Current Population Survey (CPS) records to employer-reported administrative data, we document substantial discrepancies in basic measures of employment status that persist even after controlling for known definitional differences between the two data sources. We hypothesize that reporting discrepancies should be most prevalent for marginal workers and marginal jobs, and find systematic associations between the incidence of reporting discrepancies and observable person and job characteristics that are consistent with this hypothesis. The paper discusses the implications of the reported findings for both micro and macro labor market analysis
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An Analysis of Key Differences in Micro Data: Results from the Business List Comparison Project
September 2008
Working Paper Number:
CES-08-28
The Bureau of Labor Statistics and the Bureau of the Census each maintain a business register, a universe of all U.S. business establishments and their characteristics, created from independent sources. Both registers serve critical functions such as supplying aggregate data inputs for certain national statistics generated by the Bureau of Economic Analysis. This paper examines key micro-level differences across these two business registers.
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Using Internal Current Population Survey Data to Reevaluate Trends in Labor Earnings Gaps by Gender, Race, and Education Level
July 2008
Working Paper Number:
CES-08-18
Most empirical studies of trends in labor earnings gaps by gender, race or education level are based on data from the public use March Current Population Survey (CPS). Using the internal March CPS, we show that inconsistent topcoding in the public use data will understate these gaps and inaccurately capture their trends. We create a cell mean series beginning in 1975 that provides the mean of all values above the topcode for each income source in the public use March CPS and better approximate earnings gaps found in the internal March CPS than was previously possible using publically available data.
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Consistent Cell Means for Topcoded Incomes in the Public Use March CPS (1976-2007)
March 2008
Working Paper Number:
CES-08-06
Using the internal March CPS, we create and in this paper distribute to the larger research community a cell mean series that provides the mean of all income values above the topcode for any income source of any individual in the public use March CPS that has been topcoded since 1976. We also describe our construction of this series. When we use this series together with the public use March CPS, we closely match the yearly mean income levels and income inequalities of the U.S. population found using the internal March CPS data.
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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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Confidentiality Protection in the Census Bureau Quarterly Workforce Indicators
February 2006
Working Paper Number:
tp-2006-02
The QuarterlyWorkforce Indicators are new estimates developed by the Census Bureau's Longitudinal
Employer-Household Dynamics Program as a part of its Local Employment Dynamics
partnership with 37 state Labor Market Information offices. These data provide detailed quarterly
statistics on employment, accessions, layoffs, hires, separations, full-quarter employment
(and related flows), job creations, job destructions, and earnings (for flow and stock categories of
workers). The data are released for NAICS industries (and 4-digit SICs) at the county, workforce
investment board, and metropolitan area levels of geography. The confidential microdata - unemployment
insurance wage records, ES-202 establishment employment, and Title 13 demographic
and economic information - are protected using a permanent multiplicative noise distortion factor.
This factor distorts all input sums, counts, differences and ratios. The released statistics are analytically
valid - measures are unbiased and time series properties are preserved. The confidentiality
protection is manifested in the release of some statistics that are flagged as "significantly distorted
to preserve confidentiality." These statistics differ from the undistorted statistics by a significant
proportion. Even for the significantly distorted statistics, the data remain analytically valid for
time series properties. The released data can be aggregated; however, published aggregates are
less distorted than custom postrelease aggregates. In addition to the multiplicative noise distortion,
confidentiality protection is provided by the estimation process for the QWIs, which multiply imputes
all missing data (including missing establishment, given UI account, in the UI wage record
data) and dynamically re-weights the establishment data to provide state-level comparability with
the BLS's Quarterly Census of Employment and Wages.
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Micro and Macro Data Integration: The Case of Capital
May 2005
Working Paper Number:
CES-05-02
Micro and macro data integration should be an objective of economic measurement as it is clearly advantageous to have internally consistent measurement at all levels of aggregation ' firm, industry and aggregate. In spite of the apparently compelling arguments, there are few measures of business activity that achieve anything close to micro/macro data internal consistency. The measures of business activity that are arguably the worst on this dimension are capital stocks and flows. In this paper, we document, quantify and analyze the widely different approaches to the measurement of capital from the aggregate (top down) and micro (bottom up) perspectives. We find that recent developments in data collection permit improved integration of the top down and bottom up approaches. We develop a prototype hybrid method that exploits these data to improve micro/macro data internal consistency in a manner that could potentially lead to substantially improved measures of capital stocks and flows at the industry level. We also explore the properties of the micro distribution of investment. In spite of substantial data and associated measurement limitations, we show that the micro distributions of investment exhibit properties that are of interest to both micro and macro analysts of investment behavior. These findings help highlight some of the potential benefits of micro/macro data integration.
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Employer-Provided Benefit Plans, Workforce Composition and Firm Outcomes
January 2005
Working Paper Number:
tp-2005-01
What do firms gain by offering benefits? Economists have proposed two payoffs: (i) benefits
may be a more cost-effective form of compensation than wages for employees facing high
marginal tax rates, and (ii) benefits may attract a more stable, skilled workforce. Both should
improve firm outcomes, but we have little evidence on this matter. This paper exploits a rich
new dataset to examine how firm productivity and survival are related to benefit offering, and
finds that benefit-offering firms have higher productivity and higher survival rates. Differences
in firm and workforce characteristics explain some but not all of the differences in outcomes.
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