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The Draft E-SEC as a measure of employment relations Eric Harrison.

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Presentation on theme: "The Draft E-SEC as a measure of employment relations Eric Harrison."— Presentation transcript:

1 The Draft E-SEC as a measure of employment relations Eric Harrison

2 ESeC: Matters arising from its construction and critical comment Making ESeC with varying levels of information (mentioned by Italy, Ireland, Spain) Capturing a measure of ‘employment relations’ (see in particular Goldthorpe) Problematic groups within and between classes (of concern to everyone and largely dealt with in paper 1.1.)

3 On the returns from diminishing information ‘Mapping’ from national occupational classifications (slot tomorrow on this) Measurement errors in datasets (see 1.1 and 1.2) ‘Churning’ as ESeC declines from four to three to two digit versions Side-effects of simplified ESeC peculiar to individual countries

4 Mapping Issues The right people end up in the wrong places (nursing, associate professionals, trailing zeros, ‘nes’ etc) ISCO Size rule issue (paper 1.1) ISCO created from multiple but distinct national codes

5 Losing Digits from ESeC Beta

6 The Effects of Simplification

7 Measuring Employment relations Criterion validation – does it measure what it claims to measure? Goldthorpe on employment relations: –Forms of remuneration –Promotion opportunities –Autonomy (esp. with regard to time) 96/97 LFS asked eight additional questions, five of which used for NS-SEC

8 ERS scores Questions asked only of employees: –So classes 4 and 5 taken for granted, but self- employed professionals/managers dropped –Five item scale has alpha of.66 –Are contractual issues and worker autonomy contributory to ERs or could they be cross- cutting? –Potential to explore question profiles as well as scale scores

9 Some notes on Managers Size rule for different crosswalks (GB =25 employees, ISCO =10) Conceptual implications of organisation size – do larger organisations have more senior managers or just more of every manager? LFS data – most of 10+ are in fact 50+ ER data shows org size consistent predictor of higher scores – institutional reasons? Supervisor cells – can be opened but n are few


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