Statistical models for measuring job satisfaction
AbstractIn this paper we present two statistical approaches for discussing and modelling job satisfaction based on data collected in the Survey on Household Income and Wealth (SHIW) conducted by the Bank of Italy. In particular, we compare two different classes of model for ordinal data: the Ordinal Probit Model and the more recent CUB model. The aim is to establish common outcomes and differences in the estimated patterns of global job satisfaction, but also to stress the potential for curbing the effects of measurement errors on estimates by using CUB models, allowing us to control for the effect of uncertainty and shelter choices in the response process.
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Bibliographic InfoPaper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 852.
Date of creation: Feb 2012
Date of revision:
Job satisfaction; ordinal data Modelling; CUB models;
Find related papers by JEL classification:
- J28 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Safety; Job Satisfaction; Related Public Policy
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-03-21 (All new papers)
- NEP-HAP-2012-03-21 (Economics of Happiness)
- NEP-LMA-2012-03-21 (Labor Markets - Supply, Demand, & Wages)
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