To explain the variation in the salaries of specialized workers in São Paulo's industries of transformation, we have used a model made up of five variables: the person's occupational preparation, the influence he may exert within the company because of his occupation, his age, his seniority in the company, and his time on the job.

The data obtained for the total sample show clearly that the status of the worker within the company (occupational influence) as well as his occupational preparation and age, are powerful partial determinants of salary levels in São Paulo. On the whole, training is the most powerful of these variables because it has a strong direct effect on wages and because it has an indirect effect on wages through its impact on occupational influence level. Variables indicating experience in the company (seniority) and in the present job are almost negligible. The results suggest the presence of a modern industrial structure where one's technical preparation and position in the company are closely related and where these factors weigh far more heavily than experience on the job and in the company.

Except for age, the viable variables used here are special cases of major status dimension: wealth (wages); power (occupational influence); informational status (occupatibnal preparation or education). Occupational prestige was also investigated and, in a stepwise regression, was found useless as a determinant of wages. In this research we explore, possibly for the first time, the use of a power variable, occupational influence, as a determinant of a reward variable, hourly wages. Though theoretically promising, power has previously been remarkably resistant to empirical analysis. Although our use of occupational influence has been successful, the introduction of new variables is always risky. We hope that others will conduct studies leading either to refinements in the use of this and similar indicators or to their rejection.

Also, recent publications report only a small effect of most known variables on individual income differentials in the United States. Perhaps adding occupational influence might help. It is worth repeating that in the present data-set, this variable alone explains just about as much variance in hourly wages (23 per cent) as a set of 13 repressors does on job income (27 per cent) in data analyzed by Spaeth. The whole set of five variables is, of course, more effective here, with 36 per cent of the variance explained. These differences may be due to many factors. It would seem that education may be more influential in Brazil—or at least in this sample —than in the United States. Clearly, educated personnel are in shorter supply than in the United States, and the relative rewards may be greater. If this is true, the rewards for education should decrease as Brazil's education system improves.

In any case, by its clear elimination of job experience and seniority, and its strong support for occupational training, occupational influence level, and age, we hope the present work may add to the growing body of evidence regarding the determinants of wage differentials, especially in Brazil and perhaps in other dynamic third world sectors.