Papers Containing Tag(s): 'University of Michigan'
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Viewing papers 11 through 20 of 44
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Working PaperWhy are employer-sponsored health insurance premiums higher in the public sector than in the private sector?
February 2019
Working Paper Number:
CES-19-03
In this article, we examine the factors explaining differences in public and private sector health insurance premiums for enrollees with single coverage. We use data from the 2000 and 2014 Medical Expenditure Panel Survey-Insurance Component, along with decomposition methods, to explore the relative explanatory importance of plan features and benefit generosity, such as deductibles and other forms of cost sharing, basic employee characteristics (e.g., age, gender, and education), and unionization. While there was little difference in public and private sector premiums in 2000, by 2014, public premiums had exceeded private premiums by 14 to 19 percent. We find that differences in plan characteristics played a substantial role in explaining premium differences in 2014, but they were not the only, or even the most important, factor. Differences in worker age, gender, marital status, and educational attainment were also important factors, as was workforce unionization.View Full Paper PDF
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Working PaperPredictive Analytics and Organizational Architecture: Plant-Level Evidence from Census Data
January 2019
Working Paper Number:
CES-19-02
We examine trends in the use of predictive analytics for a sample of more than 25,000 manufacturing plants using proprietary data from the US Census Bureau. Comparing 2010 and 2015, we find that use of predictive analytics has increased markedly, with the greatest use in younger plants, professionally-managed firms, more educated workforces, and stable industries. Decisions on data to be gathered originate from headquarters and are associated with less delegation of decision-making and more widespread awareness of quantitative targets among plant employees. Performance targets become more accurate, long-term oriented, and linked to company-wide performance, and management incentives strengthen, both in terms of monetary bonuses and career outcomes. Plants increasing predictive analytics become more efficient, with lower inventory, increased volume of shipments, narrower product mix, reduced management payroll and increased use of flexible and temporary employees. Results are robust to a specification based on increased government demand for data.View Full Paper PDF
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Working PaperSqueezing More Out of Your Data: Business Record Linkage with Python
November 2018
Working Paper Number:
CES-18-46
Integrating data from different sources has become a fundamental component of modern data analytics. Record linkage methods represent an important class of tools for accomplishing such integration. In the absence of common disambiguated identifiers, researchers often must resort to ''fuzzy" matching, which allows imprecision in the characteristics used to identify common entities across dfferent datasets. While the record linkage literature has identified numerous individually useful fuzzy matching techniques, there is little consensus on a way to integrate those techniques within a single framework. To this end, we introduce the Multiple Algorithm Matching for Better Analytics (MAMBA), an easy-to-use, flexible, scalable, and transparent software platform for business record linkage applications using Census microdata. MAMBA leverages multiple string comparators to assess the similarity of records using a machine learning algorithm to disambiguate matches. This software represents a transparent tool for researchers seeking to link external business data to the Census Business Register files.View Full Paper PDF
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Working PaperOccupational Classifications: A Machine Learning Approach
August 2018
Working Paper Number:
CES-18-37
Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations.View Full Paper PDF
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Working PaperDisclosure Limitation and Confidentiality Protection in Linked Data
January 2018
Working Paper Number:
CES-18-07
Confidentiality protection for linked administrative data is a combination of access modalities and statistical disclosure limitation. We review traditional statistical disclosure limitation methods and newer methods based on synthetic data, input noise infusion and formal privacy. We discuss how these methods are integrated with access modalities by providing three detailed examples. The first example is the linkages in the Health and Retirement Study to Social Security Administration data. The second example is the linkage of the Survey of Income and Program Participation to administrative data from the Internal Revenue Service and the Social Security Administration. The third example is the Longitudinal Employer-Household Dynamics data, which links state unemployment insurance records for workers and firms to a wide variety of censuses and surveys at the U.S. Census Bureau. For examples, we discuss access modalities, disclosure limitation methods, the effectiveness of those methods, and the resulting analytical validity. The final sections discuss recent advances in access modalities for linked administrative data.View Full Paper PDF
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Working PaperEstimating Unequal Gains across U.S. Consumers with Supplier Trade Data
January 2018
Working Paper Number:
CES-18-04
Using supplier-level trade data, we estimate the effect on consumer welfare from changes in U.S. imports both in the aggregate and for different household income groups from 1998 to 2014. To do this, we use consumer preferences which feature non-homotheticity both within sectors and across sectors. After structurally estimating the parameters of the model, using the universe of U.S. goods imports, we construct import price indexes in which a variety is defined as a foreign establishment producing an HS10 product that is exported to the United States. We find that lower income households experienced the most import price inflation, while higher income households experienced the least import price inflation during our time period. Thus, we do not find evidence that the consumption channel has mitigated the distributional effects of trade that have occurred through the nominal income channel in the United States over the past two decades.View Full Paper PDF
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Working PaperDoes Federally-Funded Job Training Work? Nonexperimental Estimates of WIA Training Impacts Using Longitudinal Data on Workers and Firms
January 2018
Working Paper Number:
CES-18-02
We study the job training provided under the US Workforce Investment Act (WIA) to adults and dislocated workers in two states. Our substantive contributions center on impacts estimated non-experimentally using administrative data. These impacts compare WIA participants who do and do not receive training. In addition to the usual impacts on earnings and employment, we link our state data to the Longitudinal Employer-Household Dynamics (LEHD) data at the US Census Bureau, which allows us to estimate impacts on the characteristics of the firms at which participants find employment. We find moderate positive impacts on employment, earnings and desirable firm characteristics for adults, but not for dislocated workers. Our primary methodological contribution consists of assessing the value of the additional conditioning information provided by the LEHD relative to the data available in state Unemployment Insurance (UI) earnings records. We find that value to be zero.View Full Paper PDF
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Working PaperDOES PARENTS' ACCESS TO FAMILY PLANNING INCREASE CHILDREN'S OPPORTUNITIES? EVIDENCE FROM THE WAR ON POVERTY AND THE EARLY YEARS OF TITLE X
January 2017
Working Paper Number:
CES-17-67
This paper examines the relationship between parents' access to family planning and the economic resources of their children. Using the county-level introduction of U.S. family planning programs between 1964 and 1973, we find that children born after programs began had 2.8% higher household incomes. They were also 7% less likely to live in poverty and 12% less likely to live in households receiving public assistance. After accounting for selection, the direct effects of family planning programs on parents' incomes account for roughly two thirds of these gains.View Full Paper PDF
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Working PaperUpstream, Downstream: Diffusion and Impacts of the Universal Product Code
January 2017
Working Paper Number:
CES-17-66R
We study the adoption, diffusion, and impacts of the Universal Product Code (UPC) between 1975 and 1992, during the initial years of the barcode system. We find evidence of network effects in the diffusion process. Matched-sample difference-in-difference estimates show that firm size and trademark registrations increase following UPC adoption by manufacturers. Industry-level import penetration also increases with domestic UPC adoption. Our findings suggest that barcodes, scanning, and related technologies helped stimulate variety-enhancing product innovation and encourage the growth of international retail supply chains.View Full Paper PDF
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Working PaperRanking Firms Using Revealed Preference
January 2017
Working Paper Number:
CES-17-61
This paper estimates workers' preferences for firms by studying the structure of employer-toemployer transitions in U.S. administrative data. The paper uses a tool from numerical linear algebra to measure the central tendency of worker flows, which is closely related to the ranking of firms revealed by workers' choices. There is evidence for compensating differential when workers systematically move to lower-paying firms in a way that cannot be accounted for by layoffs or differences in recruiting intensity. The estimates suggest that compensating differentials account for over half of the firm component of the variance of earnings.View Full Paper PDF
