COMPUTERIZATION AND RISING UNEMPLOYMENT DURATION
Edward N. Wolff New York University INTRODUCTION It is widely recognized that in the industrialized economies there has been a dramatic rise in the average duration of unemployment during recent decades. Though it has been most severe during periods of high unemployment rates, even at other times the length of time between jobs of an average unemployed worker has increased substantially. Here I offer one such hypothesis, ascribing at least part of the phenomenon to the information technology “revolution,” and provide empirical evidence for this proposed explanation. I will argue here that when the rate of technical transformation is high, the average duration of unemployment is likely to rise. Moreover, the duration of unemployment is likely to increase relatively more for older workers than younger ones and for the poorly educated than those with more schooling. While there is a voluminous literature on causes of unemployment and the unemployment rate, there is a much smaller literature on technological factors that influence unemployment duration. For example, Richard Layard and Stephen Nickell, who have worked extensively on unemployment issues, argued in a 1991 paper that the persistence of unemployment depends on the benefit and wage determination systems, and also on the degree of employment flexibility. However, there are a couple of papers related to this subject. Aaronson and Housinger  looked at the effects of new technology on the reemployment of displaced workers. They found that increases in new technology, as measured by R&D intensity and computer usage, decreased the likelihood of displaced workers finding new employment after being laid off. Their results also indicated that both older and less skilled workers had greater difficulty finding a new job after displacement. Friedberg , using data on individual workers from the Current Population Survey, concluded that impending retirement reduces the incentive of older workers to acquire new skills, particularly with regard to computer usage. Then using data from the Health and Retirement Survey, Friedberg  found that computer users retire later than nonusers. On the basis of Instrumental Variables regression analysis, Friedberg  estimated that computer use directly lowered the probability of retiring. Section 1 will review the basic data on unemployment duration for the United States. In Section 2, I will provide a rather elementary discussion, arguing that the introduction of a new technological “regime” might increase search time for displaced workers. Section 3 shows time trends and provides descriptive statistics on the key Edward N. Wolff: Department of Economics, New York University, 269 Mercer Street, 7th Floor, New York, New York 10003. E-mail: firstname.lastname@example.org.
Eastern Economic Journal, Vol. 31, No. 4, Fall 2005
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variables of interest in the analysis. The fourth section provides an econometric investigation of the relation between computerization and the duration of unemployment. Concluding remarks are provided in the fifth and final section. It should be stressed at the outset that in saying that duration of unemployment can be increased by a technology revolution as occasioned by the diffusion of information technology, I am not asserting that this is the only source of that development. Clearly, duration is affected by many other influences—the structure of the unemployment insurance system, other elements of public policy, union power and behavior, international trade developments, and a profusion of others. The econometric study seeks to take account of such variables, as well as measures of the speed of technical change. Its results shed light on the role of these other variables and provide support for my hypothesis. TRENDS IN THE DURATION OF UNEMPLOYMENT With a given unemployment rate, duration of joblessness can vary...
References: as well as a summary of the evidence are provided in Mallinckrodt and Fretz  (see especially p. 281). More ambiguous evidence on the relationship between unemployment and crime is discussed in Britt . 2. Similar time trends exist for most other industrialized countries as well. 3. It would be preferable to use the gross capital stock to measure TFP but this series was discontinued by the Bureau of Economic Analysis in 1994. TFP growth shows a smaller decline between the period before and after 1970 with the use of net stock than of gross stock. 4. Calculations of TFP and both equipment and OCA investment per worker using FTEE instead of PEP yield very similar time trends and correlation coefficients with unemployment duration. 5. See Marston , Ehrenberg and Oaxaca , Hamermesh , Welch , Classen , Solon , Barron and Mellow , Moffitt and Nicholson , Feldstein and Poterba , Meyer , Katz and Meyer [1990a; b], and Devine and Kiefer [1991, Ch. 5] for a fairly complete review of the literature. 6. An alternative formulation of the replacement rate is the ratio of UI average weekly benefits to the average weekly earnings for total private nonagricultural employees. It has a lower correlation with the mean duration of unemployment, 0.36. 7. Unfortunately, for the purposes of this analysis, unemployment duration by educational group is not available. Moreover, these series were discontinued in 1993. 8. The Phillips-Perron Unit Root Test Statistic is –3.213 for LNMEANDUR, –3.361 for UNEMPL15, and –3.206 for UNEMPL27, compared to a 10 percent critical value of –3.1816. The X variables are the dependent variable lagged one period, a constant term, and a trend term. 9. The coefficient of the percentage of workers aged 55 and over is negative but not statistically significant. The result does suggest that members of this age group may tend to drop out of the labor force when they lose their job. 10. The fourth parameter, UIINSCOV, the percent of unemployed workers receiving benefits, is excluded from the regression, since, as noted above, it is endogenous—a rise in unemployment duration will cause more unemployed workers to exhaust their UI benefits. 11. Regressions run by gender and race group do not show very sizable differences in results. The coefficient of TFP growth, for example, varies from 3.7 for black females to 3.9 for black males, 4.0 for white females, and 4.3 for white males. Differences in results among marital groups are also not very substantial. 12. Of course, in a period when the unemployment rate is rising, the decomposition results look quite different. In the period from 1951 to 1992, when the unemployment rate more than doubled, from 3.3 to 7.4 percent, the increase in the unemployment rate explains two-thirds of the increase in the mean duration of unemployment, while investment in OCA per worker accounts for only 10 percent.
Aaronson, D. and Housinger, K. The Impact of Technology on Displacement and Reemployment. Federal Reserve Bank of Chicago Economic Perspectives, 2nd Quarter 1999, 14-30.
COMPUTERIZATION AND RISING UNEMPLOYMENT DURATION
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