U.S. Census Bureau Center for Economic Studies research paper series最新文献

{"title":"Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data","authors":"J. Abowd, Martha Harrison Stinson","doi":"10.2139/ssrn.1894690","DOIUrl":"https://doi.org/10.2139/ssrn.1894690","url":null,"abstract":"We quantify sources of variation in annual job earnings data collected by the Survey of Income and Program Participation (SIPP) to determine how much of the variation is the result of measurement error. Jobs reported in the SIPP are linked to jobs reported in an administrative database, the Detailed Earnings Records (DER) drawn from the Social Security Administration’s Master Earnings File, a universe file of all earnings reported on W-2 tax forms. As a result of the match, each job potentially has two earnings observations per year: survey and administrative. Unlike previous validation studies, both of these earnings measures are viewed as noisy measures of some underlying true amount of annual earnings. While the existence of survey error resulting from respondent mistakes or misinterpretation is widely accepted, the idea that administrative data are also error-prone is new. Possible sources of employer reporting error, employee under-reporting of compensation such as tips, and general differences between how earnings may be reported on tax forms and in surveys, necessitates the discarding of the assumption that administrative data are a true measure of the quantity that the survey was designed to collect. In addition, errors in matching SIPP and DER jobs, a necessary task in any use of administrative data, also contribute to measurement error in both earnings variables. We begin by comparing SIPP and DER earnings for different demographic and education groups of SIPP respondents. We also calculate different measures of changes in earnings for individuals switching jobs. We estimate a standard earnings equation model using SIPP and DER earnings and compare the resulting coefficients. Finally exploiting the presence of individuals with multiple jobs and shared employers over time, we estimate an econometric model that includes random person and firm effects, a common error component shared by SIPP and DER earnings, and two independent error components that represent the variation unique to each earnings measure. We compare the variance components from this model and consider how the DER and SIPP differ across unobservable components.","PeriodicalId":92154,"journal":{"name":"U.S. Census Bureau Center for Economic Studies research paper series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81054961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LEHD Infrastructure Files in the Census RDC: Overview of S2004 Snapshot","authors":"Kevin McKinney, L. Vilhuber","doi":"10.2139/SSRN.1809948","DOIUrl":"https://doi.org/10.2139/SSRN.1809948","url":null,"abstract":"The Longitudinal Employer-Household Dynamics (LEHD) Program at the U.S. Census Bureau, with the support of several national research agencies, has built a set of infrastructure files using administrative data provided by state agencies, enhanced with information from other administrative data sources, demographic and economic (business) surveys and censuses. The LEHD Infrastructure Files provide a detailed and comprehensive picture of workers, employers, and their interaction in the U.S. economy. This document describes the structure and content of the 2004 Snapshot of the LEHD Infrastructure files as they are made available in the Census Bureau’s Research Data Center network.","PeriodicalId":92154,"journal":{"name":"U.S. Census Bureau Center for Economic Studies research paper series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84613901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Local Manufacturing Establishments and the Earnings of Manufacturing Workers: Insights from Matched Employer-Employee Data","authors":"C. Tolbert, Troy C. Blanchard","doi":"10.2139/ssrn.1754510","DOIUrl":"https://doi.org/10.2139/ssrn.1754510","url":null,"abstract":"We analyze the earnings determination process of more than 400,000 rural manufacturing workers in 12 selected U.S. states. Our theoretical motivation stems from an ongoing interest in the benefits of locally oriented business establishments. In this case, we distinguish manufacturing concerns that are single establishments in one rural place from branch plants that are part of larger multi-establishment enterprises. Our data permit us to introduce attributes of both workers and their employing firms into earnings determination models. For manufacturing workers in “micropolitan” rural counties, we find that working for a local (single) establishment has a positive impact on annual earnings. However, tenure with a firm returns more earnings for workers in non-local manufacturing facilities. Conversely, for manufacturing workers in “noncore” or rural areas without urban cores, we find that working for a local establishment has a negative effect on earnings. But, job tenure pays off more when working for a local establishment.","PeriodicalId":92154,"journal":{"name":"U.S. Census Bureau Center for Economic Studies research paper series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89880504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Migration Decisions in Arctic Alaska: Empirical Evidence of the Stepping Stones Hypothesis","authors":"L. Howe, L. Huskey","doi":"10.2139/ssrn.1729219","DOIUrl":"https://doi.org/10.2139/ssrn.1729219","url":null,"abstract":"This paper explores hypotheses of hierarchical migration using data from the Alaskan Arctic. We focus on migration of Inupiat people, who are indigenous to the region, and explore the role of income, harvests of subsistence resources, and other place characteristics in migration decisions. To test related hypotheses we use confidential micro-data from the US Census Bureau’s 2000 Decennial Census of Population and Income. Using predicted earnings and subsistence along with place invariant characteristics we generate migration probabilities using a mixed multinomial and conditional logit model. Our results support stepwise migration patterns, both up and down an urban and rural hierarchy. At the same time, we also identify differences between men and women, and we find mixed effects of place amenities and predicted earnings.","PeriodicalId":92154,"journal":{"name":"U.S. Census Bureau Center for Economic Studies research paper series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72764219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Measures of Earnings Instability Based on Survey and Administrative Reports","authors":"Chinhui Juhn, Kristin McCue","doi":"10.2139/ssrn.1658489","DOIUrl":"https://doi.org/10.2139/ssrn.1658489","url":null,"abstract":"In Celik, Juhn, McCue, and Thompson (2009), we found that estimated levels of earnings instability based on data from the Current Population Survey (CPS) and the Survey of Income and Program Participation (SIPP) were reasonably close to each other and to others’ estimates from the Panel Study of Income Dynamics (PSID), but estimates from unemployment insurance (UI) earnings were much larger. Given that the UI data are from administrative records which are often posited to be more accurate than survey reports, this raises concerns that measures based on survey data understate true earnings instability. To address this, we use links between survey samples from the SIPP and UI earnings records in the LEHD database to identify sources of differences in work history and earnings information. Substantial work has been done comparing earnings levels from administrative records to those collected in the SIPP and CPS, but our understanding of earnings instability would benefit from further examination of differences across sources in the properties of changes in earnings. We first compare characteristics of the overall and matched samples to address issues of selection in the matching process. We then compare earnings levels and jobs in the SIPP and LEHD data to identify differences between them. Finally we begin to examine how such differences affect estimates of earnings instability. Our preliminary findings suggest that differences in earnings changes for those in the lower tail of the earnings distribution account for much of the difference in instability estimates.","PeriodicalId":92154,"journal":{"name":"U.S. Census Bureau Center for Economic Studies research paper series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87785806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concentration, Diversity, and Manufacturing Performance","authors":"J. Drucker","doi":"10.2139/ssrn.1649462","DOIUrl":"https://doi.org/10.2139/ssrn.1649462","url":null,"abstract":"Regional economist Benjamin Chinitz was one of the most successful proponents of the idea that regional industrial structure is an important determinant of economic performance. His influential article in the American Economic Review in 1961 prompted substantial research measuring industrial structure at the regional scale and examining its relationships to economic outcomes. A considerable portion of this work operationalized the concept of regional industrial structure as sectoral diversity, the degree to which the composition of an economy is spread across heterogeneous activities. Diversity is a relatively simple construct to measure and interpret, but does not capture the implications of Chinitz’s ideas fully. The structure within regional industries may also influence the performance of business enterprises. In particular, regional intra-industry concentration—the extent to which an industry is dominated by a few relatively large firms in a locality—has not appeared in empirical work studying economic performance apart from individual case studies, principally because accurately measuring concentration within a regional industry requires firm-level information. Multiple establishments of varying sizes in a given locality may be part of the same firm. Therefore, secondary data sources on establishment size distributions (such as County Business Patterns or aggregated information from the Census of Manufactures) can yield only deceptive portrayals of the level of regional industrial concentration. This paper uses the Longitudinal Research Database, a confidential establishment-level dataset compiled by the United States Census Bureau, to compare the influences of industrial diversity and intra-industry concentration upon regional and firm-level economic outcomes. Manufacturing establishments are aggregated into firms and several indicators of regional industrial concentration are calculated at multiple levels of industrial aggregation. These concentration indicators, along with a regional sectoral diversity measure, are related to employment change over time and incorporated into plant productivity estimations, in order to examine and distinguish the relationships between the differing aspects of regional industrial structure and economic performance. A better understanding of the particular links between regional industrial structure and economic performance can be used to improve economic development planning efforts. With continuing economic restructuring and associated workforce dislocation in the United States and worldwide, industrial concentration and over-specialization are separate mechanisms by which regions may “lock in” to particular competencies and limit the capacity to adjust quickly and efficiently to changing markets and technologies. The most appropriate and effective policies for improving economic adaptability should reflect the structural characteristics that limit flexibility. This paper gauges the consequences of distin","PeriodicalId":92154,"journal":{"name":"U.S. Census Bureau Center for Economic Studies research paper series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84844986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"National Estimates of Gross Employment and Job Flows from the Quarterly Workforce Indicators with Demographic and Industry Detail","authors":"J. Abowd, L. Vilhuber","doi":"10.2139/ssrn.1625260","DOIUrl":"https://doi.org/10.2139/ssrn.1625260","url":null,"abstract":"The Quarterly Workforce Indicators (QWI) are local labor market data produced and released every quarter by the United States Census Bureau. Unlike any other local labor market series produced in the U.S. or the rest of the world, QWI measure employment flows for workers (accession and separations), jobs (creations and destructions) and earnings for demographic subgroups (age and gender), economic industry (NAICS industry groups), detailed geography (block (experimental), county, Core-Based Statistical Area, and Workforce Investment Area), and ownership (private, all) with fully interacted publication tables. The current QWI data cover 47 states, about 98% of the private workforce in those states, and about 92% of all private employment in the entire economy. State participation is sufficiently extensive to permit us to present the first national estimates constructed from these data. We focus on worker, job, and excess (churning) reallocation rates, rather than on levels of the basic variables. This permits comparison to existing series from the Job Openings and Labor Turnover Survey and the Business Employment Dynamics Series from the Bureau of Labor Statistics (BLS). The national estimates from the QWI are an important enhancement to existing series because they include demographic and industry detail for both worker and job flow data compiled from underlying micro-data that have been integrated at the job and establishment levels by the Longitudinal Employer-Household Dynamics Program at the Census Bureau. The estimates presented herein were compiled exclusively from public-use data series and are available for download.","PeriodicalId":92154,"journal":{"name":"U.S. Census Bureau Center for Economic Studies research paper series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79530935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Euler-Equation Estimation for Discrete Choice Models: A Capital Accumulation Appplication","authors":"Russell Cooper, J. Haltiwanger, Jonathan L. Willis","doi":"10.2139/ssrn.1543239","DOIUrl":"https://doi.org/10.2139/ssrn.1543239","url":null,"abstract":"This paper studies capital adjustment at the establishment level. Our goal is to characterize capital adjustment costs, which are important for understanding both the dynamics of aggregate investment and the impact of various policies on capital accumulation. Our estimation strategy searches for parameters that minimize ex post errors in an Euler equation. This strategy is quite common in models for which adjustment occurs in each period. Here, we extend that logic to the estimation of parameters of dynamic optimization problems in which non-convexities lead to extended periods of investment inactivity. In doing so, we create a method to take into account censored observations stemming from intermittent investment. This methodology allows us to take the structural model directly to the data, avoiding time-consuming simulation based methods. To study the effectiveness of this methodology, we first undertake several Monte Carlo exercises using data generated by the structural model. We then estimate capital adjustment costs for U.S. manufacturing establishments in two sectors.","PeriodicalId":92154,"journal":{"name":"U.S. Census Bureau Center for Economic Studies research paper series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88177168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Formal Test of Assortative Matching in the Labor Market","authors":"J. Abowd, F. Kramarz, S. Pérez-Duarte, Ian M. Schmutte","doi":"10.2139/ssrn.1515695","DOIUrl":"https://doi.org/10.2139/ssrn.1515695","url":null,"abstract":"We estimate a structural model of job assignment in the presence of coordination frictions due to Shimer (2005). The coordination friction model places restrictions on the joint distribution of worker and firm effects from a linear decomposition of log labor earnings. These restrictions permit estimation of the unobservable ability and productivity differences between workers and their employers as well as the way workers sort into jobs on the basis of these unobservable factors. The estimation is performed on matched employer-employee data from the LEHD program of the U.S. Census Bureau. The estimated correlation between worker and firm effects from the earnings decomposition is close to zero, a finding that is often interpreted as evidence that there is no sorting by comparative advantage in the labor market. Our estimates suggest that this finding actually results from a lack of sufficient heterogeneity in the workforce and available jobs. Workers do sort into jobs on the basis of productive differences, but the effects of sorting are not visible because of the composition of workers and employers.","PeriodicalId":92154,"journal":{"name":"U.S. Census Bureau Center for Economic Studies research paper series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75897130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effect of Wage Insurance on Labor Supply: A Test for Income Effects","authors":"Henry R. Hyatt","doi":"10.2139/SSRN.1491533","DOIUrl":"https://doi.org/10.2139/SSRN.1491533","url":null,"abstract":"Studies of moral hazard in wage insurance programs such as Unemployment Insurance (UI) or Workers Compensation (WC) have demonstrated that higher benefits discourage work, emphasizing the price distortion inherent in benefit provision. Utilizing administrative data linking WC claim records to wage records from a UI payroll tax database, I find that the effect of WC benefits on the duration of benefit receipt cannot fully account for the effect of these benefits on post-injury unemployment. This indicates that a significant fraction of the effect of WC benefits on employment is due to an income effect rather than a price distortion.","PeriodicalId":92154,"journal":{"name":"U.S. Census Bureau Center for Economic Studies research paper series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78449336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}