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%. 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: h ij = s i  + x i  + D j  + ε ij h ij is hours (individual i in workplacej) s i is skill / occupation x i is labour supply preferences D j is workplace effect

Empirical framework (2) The s i characteristics 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 x i are standard labour supply variables: marital status and presence of children less than 5, 5-11 and 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 D j and variables correlated with D j : –Industry etc to capture, e.g., fixed costs of employment, fatigue at long hours. –Average levels within each workplace of s i and x i to see sorting effects. Examine effects associated with family characteristics within workplace (and compare with sorting effects).

The (un)importance of workplace affiliation (1) Individual variableMeanVarianceProportion of variation due to workplace affiliation Total hours Log total hours Part time incidence

The (un)importance of workplace affiliation (2) Individual variableMeanVarianceProportion of variation due to workplace affiliation Degree level qualified High-skilled non- manual Less-skilled non- manual Manual Age Tenure (months) Female

The (un)importance of workplace affiliation (3) Individual variableMeanVarianceProportion of variation due to workplace affiliation Married or cohabiting Children under Children Children

Decomposition of total weekly hours VarianceShare of total Share of explained Total explained56.5 %100 % Skill / occupation chars16.1 %28.4 % Preference chars3.9 %6.9 % Workplace effects17.8 %31.5 % Joint skill / occupation – workplace11.2 %19.7 % Joint preference – workplace3.7 %6.6 % Joint skill / occupation – preference3.9 %6.8 %

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).

Workplace effects – goods sector

Workplace effects – private services

Workplace effects – public services

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.

Effect of labour supply characteristics – men Goods Private services Public services Variable Individual level W/place mean Individual level W/place mean Individual level W/place mean Married1.220*** * ** (4.00)(0.55)(1.63)(1.70)(0.87)(2.41) Children < (0.81)(0.98)(0.41)(0.20)(0.49)(1.15)

Effect of labour supply characteristics – women Goods Private services Public services Variable Individual level W/place mean Individual level W/place mean Individual level W/place mean Married ***0.828 (1.33)(0.80)(0.99)(1.33)(5.92)(0.49) Child < *** ***-3.273*-6.167***-3.530** (8.59)(0.88)(18.93)(1.73)(17.38)(2.06) Child * ***-3.932*-0.934**-4.422*** (1.82)(0.46)(4.96)(1.80)(2.56)(2.63)

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?