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Monthly Labor Review Online

May, 2000, Vol. 123, No. 5

Précis

ArrowUncertainty and labor contracts
ArrowTales of total factor production
ArrowChanges in job satisfaction

Précis from past issues


Uncertainty and labor contracts

Theoretical analyses of the duration of labor contracts identify two factors that determine contract length: the cost of negotiation and uncertainty. Negotiation costs have a positive relationship with duration—the more costly the negotiation, the longer the parties want the agreement to last. "The effect of uncertainty in the economic environment on contract length, on the other hand, depends on the type of uncertainty involved, with nominal uncertainty predicted to be associated with contracts of shorter duration and real shocks associated with longer contracts," writes Kevin J. Murphy in the March issue of Labour Economics.

Murphy goes on to estimate a generalized-probit, simultaneous equation model using data derived from contacts in the Bureau of Labor Statistics collective bargaining agreement file. His dependent variables are contract length, indexation, and rate of wage change specified in the contract. In his analysis of the results pertaining to contract duration, Murphy notes that of these endogenous variables, wage change has a small but statistically significant negative impact on contract duration and on that of the exogenous variables not related to uncertainty; regional and industry-specific standards have the strongest influence.

Murphy then examines the impact of four uncertainty variables: a measure of nominal uncertainty based on the mean square errors of a regression of the inflation rate at the time the contract is signed, a measure of real uncertainty based on the mean square errors of a regression of the unemployment rate, a measure of the uncertainty of the local labor market, and a measure of the uncertainty surrounding the ratio of consumer prices (which matter to workers) and producer prices (which matter to firms). Of these variables, all but the measure of local area uncertainty had significant coefficients of the expected sign. (The local uncertainty coefficient was not significant and perverse.)

These results, Murphy concludes, provide empirical support for the hypotheses that uncertainty about real shocks to the aggregate economy leads to contracts of longer duration, the degree of uncertainty about relative price shocks is inversely related to contract duration, and that greater nominal uncertainty reduces contract duration.

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Tales of total factor productivity

Have you ever read a biography of an economic concept? Charles R. Hulten of the University of Maryland has written one about total factor productivity. In National Bureau of Economic Research Working Paper No. 7471, "Total Factor Productivity: A Short Biography," Hulten discusses the origins of the concept of total factor productivity and takes us through the decades of its development.

Simply put, total factor productivity, or TFP, relates output to the inputs used in its production. (The more familiar measure of productivity, labor productivity, compares output with only one input, the labor of workers.) TFP is measured as a "residual," using index number techniques. There has been much controversy over the years about how to measure TFP and about how important it is. A number of leading economists have played important roles in the evolution of TFP, among them Nobel Prize winner Robert M. Solow, Dale W. Jorgensen, and the late Zvi Griliches. Hulten discusses their roles in the development of TFP and those of others, including himself, over the course of this 75-page biography.

Hulten mentions that the 1980s were the "high-water mark" for the measurement of the TFP residual. This was when the Bureau of Labor Statistics first published its measures of productivity that take into account more than one factor; BLS uses the term "multifactor productivity" rather than "total factor productivity" for these measures to acknowledge that there might be relevant factors of production that one is not currently measuring or even able to measure.

In wrapping up his biography of TFP, Hulten writes: "The residual is still, after more than forty years, the work horse of empirical growth analysis. For all its flaws, real and imagined, many researchers have used it to gain valuable insights into the process of economic growth. Thousands of pages of research have been published, and the residual has become a closely watched government statistic."

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Changes in job satisfaction

Job satisfaction can be viewed as a measure that reflects how workers react as individuals to all of the characteristics of their jobs. However, economists have not often dealt with this kind of subjective measure. In a recent NBER Working Paper No. 7332, "The Changing Distribution of Job Satisfaction," economist Daniel Hamermesh of the University of Texas examines how the distribution of subjective job satisfaction—particularly men’s—has changed over the years in the United States and Germany.

Hamermesh’s source of data for the United States is the BLS National Longitudinal Survey program. Specifically, he analyzed data from the 1978 National Longitudinal Survey of Young Men (NLSYM) and the 1988 National Longitudinal Survey of Youth (NLSY). The question asked in the surveys was: "How do you feel about your job?" There were four possible responses: 1) like it very much, 2) like it fairly well, 3) dislike it somewhat, and 4) dislike it very much.

Hamermesh found that the distribution of work satisfaction of young men in the United States widened and that this widening was correlated with changes in wage inequality. The job satisfaction of workers at upper earnings levels rose compared with workers at lower levels. In a separate analysis of data on male workers in Germany, Hamermesh observed similar results.

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We are interested in your feedback on this column. Please let us know what you have found most interesting and what essential reading we may have missed. Write to: Executive Editor, Monthly Labor Review, Bureau of Labor Statistics, Washington, DC. 20212, or e-mail MLR@bls.gov



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