Downward trend shown in public/private sector pay differential for the period 2015 to 2018
- Depending on the model applied, the pay differential for employees in the public sector ranged from -3.8% to 7.1% in the period 2015 to 2018, when compared to their counterparts in the private sector.
- When compared to the private sector, and depending on the model applied, the pay differential for male employees in the public sector ranged from -10.8% to 1.0% in the period 2015 to 2018.
- The corresponding differential for females showed that female workers in the public sector had a differential ranging from 3.3% to 15.8%, depending on the model applied, when compared to their counterparts in the private sector.
The Central Statistics Office (CSO) has today (15 November, 2019) issued a research paper which presents an econometric analysis of the public/private sector pay differential for the period 2015 to 2018. It has been prepared in response to user needs to inform discussions relating to the composition of earnings. Models including and excluding size of enterprise as a wage determining characteristic are presented and gross weekly earnings as well as weekly earnings after the deduction of the pension levy are considered.
Commenting on the release of the research paper, Morgan O’Donnell, Statistician, said: “Comparing pay in the public and private sectors is not a straightforward task. Complexity arises as the two sectors comprise of a variety of different industries, occupations and workers with differing education, experience and skill sets”.
This analysis takes into account the differences in characteristics of both employees (e.g. length of service, occupation, education, etc.) and their employers (e.g. organisation size etc.) to explore the wage differential between the public and private sector. As no one agreed method exists to measure the public private sector pay differential, the research paper details a comprehensive range of results where several estimates of the wage differential are presented depending on the specification of methods used.
The methods used in these analyses are: Ordinary Least Squares Regression (OLS); and Quantile Regression. For each of these methods, results based on a range of specifications are presented.
For further information contact:
Morgan O’Donnell (+353) 21 453 5269
or email email@example.com