The nature of occupational unemployment rates in the United States: hysteresis or structural?

Abstract

This article provides new evidence on the nature of occupational differences in unemployment dynamics, which is relevant for the debate between the structural or hysteresis hypotheses. We develop a procedure that permits us to test for the presence of a structural break at unknown date. Our approach allows the investigation of a broader range of persistence than the 0/1 paradigm about the order of integration, usually implemented for testing the hypothesis of hysteresis in occupational unemployment. In almost all occupations, we find support for both the structuralist and the hysteresis hypotheses, but stress the importance of estimating the degree of persistence of seasonal shocks along with the degree of long-run persistence on raw data without applying seasonal filters. Indeed hysteresis appears to be underestimated when data are initially adjusted using traditional seasonal filters.

Acknowledgements

We thank participants at a seminar at Maastricht University. Luis Gil-Alana thanks the financial support from the Ministerio de Ciencia y Tecnologia (SEJ2005-07657/ECON, Spain).

Notes

¹ Other studies have focused on demographic differences (Clark and Summers (1981), Vedder and Gallaway (1992) and Tolvi (2003) among others) and differences between skill groups (Teulings and Koopmanschap (1989) and Fabiani et al. (2001) among others). These studies indicate that minorities and less-skilled workers have experienced persistently higher unemployment rates and greater cyclical variations.

² See Okun (1962).

³ Although remarked long ago (Gjermoe, 1931; Kuznets, 1932), it is only recently that economists have explicitly allowed seasonality to change over time.

⁴ However, van Dijck et al. (2003) conclude that cyclical changes in seasonality are unimportant.

⁵ See also Arestis and Biefang-Frisancho Mariscal (2000) for a similar analysis on 22 OECD countries but allowing for a single break only.

⁶ Note that for the US, Papell et al. (2000) found two breaks: one in 1974 and the other in 1986.

⁷ The most famous methods used to removed seasonality are the Holt-Winter, Census X12, Tramo-Seat and Band Pass Filters.

⁸ For example, Gil-Alana (2002) in his post WWII data, considers a break occurring in the third quarter of 1973.

⁹ The description of this test statistic is available on a web appendix at http://www.roa.unimaas.nl/cv/Dupuy/Dupuy.htm or from the authors upon request.

¹⁰ These conditions are very mild and concern technical assumptions to be satisfied by the model in (3)–(5).

¹¹ The derivation of the asymptotic properties of the test as well as a Monte Carlo simulation study of the small sample properties of the test are available on a web appendix at http://www.roa.unimaas.nl/cv/Dupuy/Dupuy.htm or from the authors upon request.

¹² Herewith seasonality is removed using the multiplicative Holt-Winter method. Similar results have been found using different methods to remove seasonality. Results are available from the authors upon request.

¹³ Seasonal dummy variables can also be included in the regression model (8), though its inclusion would imply that at least part of the seasonality has a deterministic component. In this article, however, we believe that the seasonal component in the unemployment rates is purely stochastic, which may be stationary (d ₁ < 0.5) or nonstationary (d ₁ > 0.5).