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Food and Agricultural Industries: Opportunities for Improving Measurement and Reporting

January 2016

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  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, the 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. 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.
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• Working Paper

  
  
  Outsourcing Dynamism
  

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  Authors: Chen Yeh, Andrea Atencio De Leon, Claudia Macaluso

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  CES-23-64

  This paper investigates the increasing importance of domestic outsourcing in U.S. manufacturing. Under domestic outsourcing, the agency is the employer of record for temporary workers, though they perform their tasks at the client business' premises. On a yearly basis, one in two manufacturing plants hires at least some of its workers through a temporary help agency. Furthermore, domestic outsourcing is becoming increasingly more important: the average share of revenue spent on such arrangements has gone up by 85 percent since 2006. We develop a methodology to transform reported expenses on temporary and leased workers into plant-level outsourced employment counts, using administrative data on the U.S. manufacturing sector. We find that domestic outsourcing is an important margin of adjustment that plants use to modify their workforce in response to productivity shocks. Plant-level outsourced employment adjusts more quickly and is twice as responsive as payroll employment. These micro implications have significant aggregate consequences. Without taking reallocations in outsourced employment into account, the measured pace at which jobs reallocate across workplaces is underestimated. On average, we omit the equivalent of 15 percent of payroll employment reallocations in each year. However, outsourced employment churns at a much higher rate compared to its payroll counterpart. Therefore, the omission of outsourced reallocations can rationalize 37 percent of the secular decline in the aggregate job reallocation rate. Lastly, the extent of mismeasurement varies with the business cycle; falling in downturns and increasing in upturns implying that the speed of economic recovery is underestimated.
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• Working Paper

  
  
  Multiple Classification Systems For Economic Data: Can A Thousand Flowers Bloom? And Should They?
  

  December 1991

  Authors: Robert H Mcguckin

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  CES-91-08

  The principle that the statistical system should provide flexibility-- possibilities for generating multiple groupings of data to satisfy multiple objectives--if it is to satisfy users is universally accepted. Yet in practice, this goal has not been achieved. This paper discusses the feasibility of providing flexibility in the statistical system to accommodate multiple uses of the industrial data now primarily examined within the Standard Industrial Classification (SIC) system. In one sense, the question of feasibility is almost trivial. With today's computer technology, vast amounts of data can be manipulated and stored at very low cost. Reconfigurations of the basic data are very inexpensive compared to the cost of collecting the data. Flexibility in the statistical system implies more than the technical ability to regroup data. It requires that the basic data are sufficiently detailed to support user needs and are processed and maintained in a fashion that makes the use of a variety of aggregation rules possible. For this to happen, statistical agencies must recognize the need for high quality microdata and build this into their planning processes. Agencies need to view their missions from a multiple use perspective and move away from use of a primary reporting and collection vehicle. Although the categories used to report data must be flexible, practical considerations dictate that data collection proceed within a fixed classification system. It is simply too expensive for both respondents and statistical agencies to process survey responses in the absence of standardized forms, data entry programs, etc. I argue for a basic classification centered on commodities--products, services, raw materials and labor inputs--as the focus of data collection. The idea is to make the principle variables of interest--the commodities--the vehicle for the collection and processing of the data. For completeness, the basic classification should include labor usage through some form of occupational classification. In most economic surveys at the Census Bureau, the reporting unit and the classified unit have been the establishment. But there is no need for this to be so. The basic principle to be followed in data collection is that the data should be collected in the most efficient way--efficiency being defined jointly in terms of statistical agency collection costs and respondent burdens.
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