Determinants of Quit Behavior Among Managerial and Professional Women

Abstract

This thesis examines the effects of personal, human capital and job related characteristics on the quit decision of managerial or professional level women. In addition, perceptual or equity factors, such as crowding within grade level and functional area, relative time to promotion and pay compared to others in the firm, were modeled. The micro-data are from the personnel files of a large manufacturing firm. Three types of analysis were conducted. The first was a logit analysis of a cross-sectional sample of the managerial/professional women in this firm. The second was a logit analysis of a pooled cohort sample of these women, during their second full year after hire. The third examination of the data used proportional hazard analysis, compensating for the selection bias, due to censored data, inherent in quit studies. The relative advantages and disadvantages of the three techniques are discussed. Empirical results of the proportional hazards model show that such job related factors as recent promotion, salary, grade level and favorable performance ratings significantly reduce quits, with promotion having the strongest effect. Personal factors such as marriage and children also reduce the managerial/professional woman's propensity to quit. Keywords: Quits, Turnover, Women logit, Proportional hazards, Performance ratings.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1986
Accession Number
ADA176929

Entities

People

  • Jacquelyn M. Arrowood

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Economics
  • Employment
  • Engineers
  • Enlisted Personnel
  • Factor Analysis
  • Families (Human)
  • Geographic Regions
  • Literature Surveys
  • Management Personnel
  • Manufacturing
  • Money
  • Organizational Structure
  • Personnel Management
  • Ratings
  • Regression Analysis
  • Surveys
  • United States

Fields of Study

  • Education

Readers

  • Naval Personnel Management
  • Regression Analysis.