Empirical analysis of occupational hierarchies.

Using data on U.S. engineers and the position of engineering jobs within firms, this paper estimates a model of hierarchies within firms. The model extends Rosen's (1982) model of recursive production to two skills and multiple hierarchy levels. The model generates an empirical model that is neste...

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Publicado en:Journal of Human Resources Vol. 32; pp. 1 - 35
Autor principal: Ferrall, Christopher
Formato: Artículo
Publicado: University of Wisconsin Press Winter97
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Empirical analysis of occupational hierarchies.
      aug:
        au: Ferrall, Christopher
      su:
        Occupational prestige
        Maximum likelihood statistics
        Engineers -- United States
        Engineers' salaries
        Engineers
        Employment
        United States
      sug:
        subj:
          United States
          Occupational prestige
          Maximum likelihood statistics
          Engineers -- United States
          Engineers' salaries
          Engineers
          Employment
      ab: Using data on U.S. engineers and the position of engineering jobs within firms, this paper estimates a model of hierarchies within firms. The model extends Rosen's (1982) model of recursive production to two skills and multiple hierarchy levels. The model generates an empirical model that is nested within a general Roy (1951) model of self-selection. Maximum likelihood estimates of the parameters of the production technology and the skill-experience profiles are estimated. The results suggest that approximately two-thirds of changes in employment shares across hierarchy levels across time are explained by demographic shifts in the stock of engineering skills. Most of the returns to experience and to assignment to higher hierarchy levels within firms are caused by skill accumulation and self-selection rather than technological differences across hierarchy levels. Reprinted by permission of the publisher.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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