JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.Wiley, The Econometric Society are collaborating with JSTOR to digitize, preserve and extend access to Econometrica This content downloaded from 131.172.The fine structure of earnings is defined by a theoretically meaningful decomposition of the covariance matrix of earnings (or log earnings) time series. A three-element variance components model is proposed for analyzing earnings of young workers. These components are interpreted as the effects of differential on-the-job training (OJT) and differential economic ability. Several properties of these components and relationships between them are deduced from the OJT model. Background noise generated by a nonstationary first-order autoregressive process, with heteroscedastic innovations and time-varying AR parameters is also assumed present in observed earnings. ML estimates are obtained for all parameters of the model for a sample of Swedish males. The results are consistent with the view that the OJT mechanism is an empirically significant phenomenon in determining individual earnings profiles.i This study is a major extension of work reported in Hause [13]. I am particularly indebted to Chris Sims for early discussions, and to Dag Sorbom of the Statistics Institute at Uppsala for important discussions on statistical specifications with LISREL III and MLCOST, and for carrying out all but the final computations. The last calculations were completed with the generous cooperation of the Stanford Research Institute and its computing facilities. K. G. Joreskog first made me aware of the usefulness of the LISREL program for the statistical work. Seminars at Goteborg, Yale, UCLA, Columbia, Stanford, and at an SSRC conference on panel data patiently reacted to previous versions; Lee Lillard and Anders Klevmarken provided especially useful comments at and subsequent to them. The Econometrica referees offered productive suggestions and comments, materially improving the final study. The primary white collar (SAF) worker data were generously made available by Siv Gustafsson, and were converted to empirical covariance matrices by Kerstin Wennberg. The random samples of folkskola and gymnasium graduates mentioned in the Appendix were financed by Grant
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