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Dive into the research topics where Kevin L. McKinney is active.

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Featured researches published by Kevin L. McKinney.


Journal of Business & Economic Statistics | 2007

Using Worker Flows to Measure Firm Dynamics

Gary Benedetto; John Haltiwanger; Julia Lane; Kevin L. McKinney

Information on firm dynamics is critical to understanding economic activity, yet is fundamentally difficult to measure. In this article we introduce a new way of capturing dynamics: following clusters of workers as they move across administrative entities. We show that a worker flow approach improves linkages across firms in longitudinal business databases. The approach also provides conceptual insights into the changing structure of businesses and employer–employee relationships. Many worker–cluster flows involve changes in industry particularly movements into and out of personnel supply firms. Another finding, that a nontrivial fraction of firm entry is associated with such flows, suggests that a path for firm entry is a group of workers at an existing firm starting a new firm.


IZA Journal of Labor Economics | 2012

Persistent inter‐industry wage differences: rent sharing and opportunity costs

John M. Abowd; Francis Kramarz; Paul A. Lengermann; Kevin L. McKinney; Sébastien Roux

AbstractWe reconsider the potential for explaining inter‐industry wage differences by decomposing those differences into parts due to individual and employer heterogeneity, respectively. Using longitudinally linked employer‐employee data, we estimate the model for the United States and France. The part arising from individual heterogeneity can be theoretically and empirically related to the worker’s opportunity wage rate. The part arising from employer heterogeneity can similarly be related to product market quasi‐rents and relative bargaining power. We find that these two variables are highly correlated with both parts of the differential in France. Although the U.S. inter‐industry wage differentials are strongly correlated with those in France, the decomposition is more nuanced in the American data, where the opportunity wage rate and the product market conditions are related to both the personal and employer heterogeneity.JEL codesJ31, J50, L10


Longitudinal Employer-Household Dynamics Technical Papers | 2005

Using Worker Flows in the Analysis of the Firm

Gary Benedetto; John Haltiwanger; Julia Lane; Kevin L. McKinney

This paper uses a novel approach to measure firm entry and exit, mergers and acquisition. It uses information about the flows of clusters of workers across business units to identify longitudinal linkage relationships in longitudinal business data. These longitudinal relationships may be the result of either administrative or economic changes and we explore both types of newly identified longitudinal relationships. In particular, we develop a set of criteria based on worker flows to identify changes in firm relationships ? such as mergers and acquisitions, administrative identifier changes and outsourcing. We demonstrate how this new data infrastructure and this cluster flow methodology can be used to better differentiate true firm entry/exit and simple changes in administrative identifiers. We explore the role of outsourcing in a variety of ways but in particular the outsourcing of workers to the temporary help industry. While the primary focus is on developing the data infrastructure and the methodology to identify and interpret these clustered flows of workers, we conclude the paper with an analysis of the impact of these changes on the earnings of workers.


Archive | 2014

Job-to-Job (J2J) Flows: New Labor Market Statistics from Linked Employer-Employee Data

Henry R. Hyatt; Erika McEntarfer; Kevin L. McKinney; Stephen Tibbets; Doug Walton

Flows of workers across jobs are a principal mechanism by which labor markets allocate workers to optimize productivity. While these job flows are both large and economically important, they represent a significant gap in available economic statistics. A soon to be released data product from the U.S. Census Bureau will fill this gap. The Job-to-Job (J2J) flow statistics provide estimates of worker flows across jobs, across different geographic labor markets, by worker and firm characteristics, including direct job-to-job flows as well as job changes with intervening nonemployment. In this paper, we describe the creation of the public-use data product on job-to-job flows. The data underlying the statistics are the matched employer-employee data from the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics program. We describe definitional issues and the identification strategy for tracing worker movements between employers in administrative data. We then compare our data with related series and discuss similarities and differences. Lastly, we describe disclosure avoidance techniques for the public use file, and our methodology for estimating national statistics when there is partially missing geography.


Archive | 2012

Dynamically consistent noise infusion and partially synthetic data as confidentiality protection measures for related time-series

John M. Abowd; R. Kaj Kaj Gittings; Kevin L. McKinney; Bryce Stephens; Lars Vilhuber; Simon D. Woodcock

The Census Bureaus Quarterly Workforce Indicators (QWI) provide detailed quarterly statistics on employment measures such as worker and job flows, tabulated by worker characteristics in various combinations. The data are released for several levels of NAICS industries and geography, the lowest aggregation of the latter being counties. Disclosure avoidance methods are required to protect the information about individuals and businesses that contribute to the underlying data. The QWI disclosure avoidance mechanism we describe here relies heavily on the use of noise infusion through a permanent multiplicative noise distortion factor, used for magnitudes, counts, differences and ratios. There is minimal suppression and no complementary suppressions. To our knowledge, the release in 2003 of the QWI was the first large-scale use of noise infusion in any official statistical product. We show that the released statistics are analytically valid along several critical dimensions { measures are unbiased and time series properties are preserved. We provide an analysis of the degree to which confidentiality is protected. Furthermore, we show how the judicious use of synthetic data, injected into the tabulation process, can completely eliminate suppressions, maintain analytical validity, and increase the protection of the underlying confidential data.


Oxford Bulletin of Economics and Statistics | 2009

Matching, Reallocation and Changes in Earnings Dispersion

Simon Burgess; Julia Lane; Kevin L. McKinney

The ‘fractal’ nature of the rise in earnings dispersion is one of its key features. In this paper, we offer a new perspective on the causes of changes in earnings dispersion, focusing on the role of labour reallocation. We set out a framework showing that job and worker reallocation affects earnings dispersion. We quantify this using a data set comprising almost the universe of workers and employers in Maryland. The changing allocation of workers to jobs played a significant role in explaining movements in the dispersion of earnings.


Archive | 2015

Modeling Endogenous Mobility in Wage Determiniation

John M. Abowd; Kevin L. McKinney; Ian M. Schmutte

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.


Journal of Business & Economic Statistics | 2017

Modeling Endogenous Mobility in Earnings Determination

John M. Abowd; Kevin L. McKinney; Ian M. Schmutte

ABSTRACT 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 exogenous mobility by modeling the matched data as an evolving bipartite graph using a Bayesian latent-type framework. Our results suggest that allowing endogenous mobility increases the variation in earnings explained by individual heterogeneity and reduces the proportion due to employer and match effects. To assess external validity, we match our estimates of the wage components to out-of-sample estimates of revenue per worker. The mobility-bias-corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates. Supplementary materials for this article are available online.


Statistical journal of the IAOS | 2016

Noise infusion as a confidentiality protection measure for graph-based statistics

John M. Abowd; Kevin L. McKinney

We use the bipartite graph representation of longitudinally linked em-ployer-employee data, and the associated projections onto the employer and em-ployee nodes, respectively, to characterize the set of potential statistical summar-ies that the trusted custodian might produce. We consider noise infusion as the primary confidentiality protection method. We show that a relatively straightfor-ward extension of the dynamic noise-infusion method used in the U.S. Census Bureau’s Quarterly Workforce Indicators can be adapted to provide the same confidentiality guarantees for the graph-based statistics: all inputs have been modified by a minimum percentage deviation (i.e., no actual respondent data are used) and, as the number of entities contributing to a particular statistic increases, the accuracy of that statistic approaches the unprotected value. Our method also ensures that the protected statistics will be identical in all releases based on the same inputs.


Longitudinal Employer-Household Dynamics Technical Papers | 2009

The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators

John M. Abowd; Bryce Stephens; Lars Vilhuber; Fredrik Andersson; Kevin L. McKinney; Marc Roemer; Simon D. Woodcock

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Paul A. Lengermann

Federal Reserve Board of Governors

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John Haltiwanger

National Bureau of Economic Research

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Ron S. Jarmin

United States Census Bureau

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Fredrik Andersson

Office of the Comptroller of the Currency

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Gary Benedetto

United States Census Bureau

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