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Workplace job satisfaction: a multilevel analysis

Workplace job satisfaction: a multilevel analysis. WERS 2004 Users Group Meeting, NIESR March 16, 2007. Outline of presentation. Introduction Data Methodology Results Summary of findings Further work. Introduction. Job satisfaction as a theme of research in economics

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Workplace job satisfaction: a multilevel analysis

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  1. Workplace job satisfaction: a multilevel analysis WERS 2004 Users Group Meeting, NIESR March 16, 2007

  2. Outline of presentation • Introduction • Data • Methodology • Results • Summary of findings • Further work

  3. Introduction • Job satisfaction as a theme of research in economics • The link between JS and economic outcomes • Determinants of JS: The evidence thus far • Should we explore JS and its determinants further? • How is this study different, is it?

  4. Data • The data used is WERS 2004 • The most comprehensive of the WERS series of surveys • Nationally representative survey of British workplaces • Use is made of data from the management and employee surveys • SEQ: 22,451 (61% response rate) • MQ: 2,295 workplaces (64% response)

  5. Data (cont’d) • Eight different facets of JS have been monitored in wers2004 • An ‘overall’ JS indicator has also been generated • Each of the five scale JS indicators have been collapsed into a dummy (1 if ‘very satisfied’ or ‘satisfied’ & 0 otherwise • A range of exogenous variables (employee & establishment) has been used

  6. Data (Descriptive stat)

  7. Data (corr. Matrix)

  8. Methodology • The methodology employed exploits the data structure • No account has been made for possible endogeneity problems yet • Accounts for unobserved heterogeneity, unlike most in the literature • This version, focuses on unmeasured heterogeneity in overall response

  9. Methodology (cont’d) • Following Hammermesh (1977) & Freeman (1978), utility from work or aspects of work is given as • This is modelled using the basic 2-level ML model that is specified as

  10. Methodology (cont’d) • Unobserved heterogeneity component is modelled as • so that • We’ve binary JS indicators » need for a link function given by • with

  11. Results • Please see results in the handout!

  12. Summary of results • That firm-level unobserved heterogeneity is important for the most part • Important/significant employee & employer effects, particularly • availability of training opportunity (+) • Union membership (-) • Flexible work arrangement (+) • Skills mismatch (-) • Industry of employment (education (+), health (+))

  13. Further work • Refining/reducing the correlates • Investigate whether different results if using the ordinal indicators of satisfaction • Introducing higher levels (‘astatus’ for eg) • Random coefficient models • Account for possible endogeneity ~~~~

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