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Workers, Workplaces and Working Hours. Mark L Bryan ISER, University of Essex Presented at DTI/PSI workshop on linked employer-employee data, 16 th September 2005. Introduction. UK has high and (still?) rising employment rate (75%). Govt aspiration is 80%.
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Workers, Workplaces and Working Hours Mark L Bryan ISER, University of Essex Presented at DTI/PSI workshop on linked employer-employee data, 16th September 2005
Introduction • UK has high and (still?) rising employment rate (75%). Govt aspiration is 80%. • Policy debate about working hours and work-life balance. Govt campaign + new rights. • Both employers and employees care (to some extent) about working time. • To design effective working time polices, need to understand determinants of working hours, to identify best areas for intervention.
Why use linked employer-employee data? (1) • With perfect job mobility, not a problem analytically. Can explain all with, e.g., supply side data. • But perfect mobility seems implausible (due to geography, specific skills etc). • Hours then depend on both worker and firm preferences. • Unlike trad data, LEED can show: • differences in working hours between observably identical workers across workplaces • ‘sorting’ of workers into workplaces with different working hours.
Why use linked employer-employee data? (2) • Can separate within-firm from between-firm variation. • Can also enrich a standard one-sided analysis with characs from other side of market. • Can sometimes compare management and worker responses to ‘same’ question (e.g. Budd and Mumford, 2005, using WERS).
Questions • How important are firm-level factors (hours ‘policies’ or norms) in determining weekly hours of work? Do differences across firms depend on industry etc? • Do hours vary within firms? If so, how do they vary for different workers? How much to do with job/skills and how much to do with ‘preferences’? • Are workers sorted non-randomly into firms based on working hours?
Data (1) • Workplace Employee Relations Survey 1998 (WERS 98) cross section • 2191 workplaces • Management respondent • Worker representative where possible • Up to 25 workers in each workplace. • After excluding non-responses and invalid data: 1740 workplaces with on average 13 workers per workplace. • Assume workplace = firm.
Data (2) Working time question: “How many hours do you usually work each week, including any overtime or extra hours?” Median = 39 hours Lower quartile = 29 hours Upper quartile = 44 hours (Weighted figures)
Empirical framework (1) • Consider equation to explain working hours: hij= si + xi + Dj + εij hijis hours (individual i in workplacej) siis skill / occupation xiis labour supply preferences Djis workplace effect
Empirical framework (2) • The sicharacteristics reflect particular job within workplace: age and age squared, job tenure, highest educational qualification, receipt of training in the last 12 months, employment on fixed term or temporary contract, health problems affecting daily activities, 1 digit occupation, gender (allow different occupation and age effects for men and women).
Empirical framework (3) • The xiare standard labour supply variables: marital status and presence of children less than 5, 5-11 and 12-18 years old (allow separate effects for men and women). Assume mainly capture value of domestic time rather than market time (after controlling for skill & occupation).
Empirical framework (4) • Decomposition of hours variance into parts due to each set of factors, and their joint effects. Do for whole economy and then by sector. • Examine Dj and variables correlated with Dj: • Industry etc to capture, e.g., fixed costs of employment, fatigue at long hours. • Average levels within each workplace of siand xito see sorting effects. • Examine effects associated with family characteristics within workplace (and compare with sorting effects).
Decomposition of hours by sector (1) • Disaggregate by goods (manufacturing, electricity, gas and water, and construction), private services and public services. • Aggregate analysis hides differences between sectors (due to capital intensity and use, differing needs to suit customers’ time schedules, position of public sector relative to market and govt ?). • In goods, relatively tightly bunched hours (variance=74) with important role for workplace affiliation (50% of explained variance). Consistent with hours coordination in capital-intensive industries (e.g. production line).
Decomposition of hours by sector (2) • In private service sector, very wide variation in hours (variance=204), due to: • widely differing workplaces: absolute variance of workplace effects is 3-4 times bigger than in other sectors. • skills and occupation also have large effect • especially, workers sorted on skills (24% of explained variance). We do not see sorting on skills to this degree in either of the other sectors. • In public sector, wide hours variation (variance=164) • but workplace effects relatively unimportant (19% of explained variance) • instead, skills and preference characteristics account for large variance shares (46% and 13% of explained variance).
Correlates of workplace effects • Industry strongly affects workplace-level hours. • For example, in private services, expect a worker in property to work 3.5 hours/week more than a comparable worker in retail (5.5 hours/week more for transport worker). • Workplace/organisation size has some association, but less than industry. • Many factors unobserved.
Conclusions (1) • Workplace-level hours ‘policies’ or norms are strong drivers of the employees’ hours (nearly a third of explained variation), especially in private services. • Differences across workplaces unrelated to the observed worker characteristics, so would not be identified in unlinked employee-level data. Demonstrates values of linked data. • Hours also vary within firms (another third of explained variance), according to skill/occupation and family characteristics. E.g. effect of children on women’s hours. Effect combines worker prefs and firm response.
Conclusions (2) • Sorting process (final third of explained variation). Especially based on skill/occupation. Weaker evidence that workers who prefer longer (or shorter) hours also sort into long-hours (or short-hours) workplaces. • Policy implications? • Hours likely to become increasingly diverse with expansion of private services (contains largest spread of hours across workplaces). • If job mobility restricted, could be difficult to achieve policy goal of matching workers to jobs with hours that suit. • Encourage job mobility? Or, given substantial within-firm variation, dual-pronged approach: promote of job mobility and build on existing within-firm flexibility?
