Labor month in review
The August Review
Silicon valley employment
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Included in the multitude of information provided by the Bureau of Labor Statistics are data on earnings. One source of such data is the Current Population Survey (CPS), which is administered to a large, nationally representative sample of households and has been conducted each month since the 1940s. Over the years researchers and other interested parties have studied changes and trends in earnings over time by race, sex, and other demographic variables. In the first article of this issue, Professor Richard V. Burkhauser and Jeff Larrimore, both from Cornell University, look more deeply into CPS data to reevaluate trends in earnings gaps. The article analyzes internal, or non-public use, CPS data from 1975–2007, which, the authors find, show earnings gaps different from those calculated from public-use CPS data. The authors point out that public-use data, which are the data usually used by researchers, do not include suppressed—or topcoded—earnings. Topcoding is the replacement of a datum representing part or all of a person’s true income with a lower value and is done in order to protect the confidentiality of survey respondents. The article also finds that trends in and gaps between the earnings of men and women, Blacks and Whites, and people of various education levels are all sensitive to topcoding.
Another widely watched indicator produced by BLS is the Producer Price Index (PPI). The PPI is produced in the Office of Prices and Living Conditions (OPLC), and it measures the average change over time in the selling prices received by domestic producers for their output. Historically, this information has been collected and presented on an industry basis. However, beginning with the release of July data in August 2009, BLS introduced a new set of construction price indexes for wherever-provided goods and services. In contrast to industry-based price indexes, commodity-based indexes measure price change for a (wherever-provided) service or (wherever-made) good, regardless of the producer’s industry of origin. In this issue’s second article, Jonathan C. Weinhagen and Bonnie H. Murphy, both OPLC economists, introduce this new measure and explain in detail how it differs from the more traditional approach. The benefit of commodity-based indexes, the authors suggest, is that they allow data users to examine price movements for a specific service or construction-related product within a single price index that combines prices from all industries producing that product or service. In addition, detailed price indexes can be aggregated into many higher level indexes not found in the industry-based PPI aggregation structure. These wherever-provided aggregations give data users additional indexes to follow and analyze.
This month’s third article, by Bill Passero, a senior economist in the OPLC, discusses the impact that income imputations have had on the Consumer Expenditure (CE) Survey. Beginning in 2004, the CE Survey began imputing for missing responses to questions about income that survey respondents acknowledged receiving, but for which they had not provided values. The purpose of the article is to assess the impact and efficacy of imputation by comparing pre- and postimputation estimates of CE-reported income with estimates from the CPS, which has employed imputation for many years in the course of producing its income estimates. The conclusions are that, generally, imputation has brought CE estimates closer to CPS estimates and that further refinements to the CE income questions and imputation procedures are expected in the future.
For those who followed the news or their investment portfolios for most of 2000, the seemingly daily reports of downturns in the stock market are an all-too-painful memory. The "dot-com bubble" is the appellation usually used to refer to the financial fallout following the boom of investment and growth in certain kinds of information technology companies. But what did this dramatic fall in stocks and market capitalizations mean for workers and jobs in an area characterized by industries and occupations strongly associated with high-tech? A Regional Report by BLS economists Amar Mann and Tony Nunes looks at this issue from a regional perspective by analyzing Silicon Valley high-tech employment from 2001 to 2008. Silicon Valley refers geographically to six counties in northern California. The report shows that high-tech employment in the area remained relatively stable throughout early 2001, in spite of the 2000 stock market crash and the 2001 recession. However, by the end of 2001 the Silicon Valley unemployment rate had more than doubled, and it wasn’t until 2004 that high-tech employment began to increase. It continued to increase through 2008, although 2008 employment was still 17 percent lower than in 2001. The report is available online at http://www.bls.gov/opub/regional_reports/200908_silicon_valley_high_tech.pdf.
Communications regarding the Monthly Labor Review may be sent to the Editor-in-Chief by e-mail to mlr@bls.gov, by mail at 2 Massachusetts Avenue NE, Room 2850, Washington, DC, 20212, or by fax to (202) 691–7890.
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