1 Determinants of Agency Work: the Role of family-friendly Practices Paper to be presented at the WERS Study Group, NIESR, 16/3/07 Authors: John Heywood (Wisconsin), Stan Siebert (Birmingham), Xiangdong Wei (Hong Kong) Acknowledgements particularly to John Forth for advice on weighting

2 Background Agency work (AW) is an extreme form of flexible staffing, with little hiring/firing cost AW has nearly tripled in size over the past 15 years (currently about 300k AWs), yet other temporary work categories have been steady – see graph 10% of workplaces in WERS04 have AW, and 4% have prof/managerial AWs (2.5% in private sector) – table 1 Yet there is concern (Forde and Slater) about the quality of agency jobs, and the happiness of the workers At the same time, do policies to improve working conditions, e.g family-friendly policies, encourage AW?

3 Trends in Temporary Employment including AW (millions) Source: Labour Force Surveys

4 Table 1: Agency workers WERS 04 Note: statistics calculated using Stata’ s svy command to take into account the sample selection procedure.

5 Reasons for Hiring AW See Table 2 - we distinguish between professional/managerial and others – the main difference is the need to obtain specialist skills The most important reason for both groups is obtaining short-term cover for staff absence – around 50% of workplaces Linked to this motive is cover for maternity and annual leave – another 15% Saving on wage and benefit costs is not so important (“unable to fill vacancies” is mentioned by 20% of workplaces) Note, we will distinguish between public and private sectors (see below)

6 Table 2: Reasons for Hiring AW Source: WERS 2004 Notes: the figures show the proportion of workplaces agreeing with the given statement. More than one statement could be accepted, so the columns do not sum to 100%.

7 The demand for AW as a buffer AW as a buffer is most important from Table 2 Obviously we need control for turbulent, competitive conditions faced by workplaces Reduced numerical flexibility also increases buffering need, hence we control for workplace job security practices, and expect increased job security to increase demand for AW Workplaces with internal flexibility should also have reduced demand for AW: –part-timers and temps might be more easily re-assigned –variables for the “variety” of tasks (proxy for multiple skills), use of teams, use of an internal labour market (indicating existing workers can be redirected), low training requirements

8 Hypotheses for FF practices and AW AW gives firms flexibility to direct worker effort at time and place needed Basic hypothesis: where family-friendly (FF) work practices reduce flexibility the demand for AW rises, cet. par. “Paradoxically, generous parental leave is likely to generate a demand for non-standard arrangements such as temporary workers, to fill in for regular workers that are on leave” (Olsen and Kalleberg, WES ) However, FF practices are heterogeneous

9 FF practices FF practices are shown in Table 3 Heterogeneity is shown in last column: some practices bring earnings penalties – (leaves are linked with the “mommy track”), but others bring a premium e.g. workplace nurseries. Thus: if nurseries enable more attention to work, there will be less need to cover for absence, and less demand for AW If leaves enable less attention to work, there will be increased demand for AW

10 FF practices (cont) As for job-sharing, this should increase worker ability to cover for others’ absences, and so reduce the demand for AW As for flexitime, this moves the contract in the worker’s direction, and should reduce the absence propensity, and so the demand for AW In sum, the direction of influence of an FF practice depends on whether the practice makes worker effort more or less reliable Since leaves are more generous in the public sector effects might be greater here

11 Table 3: FF variables Note: statistics calculated using Stata’ s svy command to take into account the sample selection procedure. Earnings equation is from WERS68, using individual data, controls for usual variables, and instruments for the FF variable using workplace manager attitudes on work-family balance

12 Data Data are drawn from the management questionnaire, the manager being the “senior manager dealing with personnel” which is appropriate The WERS is a stratified random national sample of 2,295 workplaces in GB, with a cut- off at workplaces of size 5, excluding agriculture and mining It is representative of 37% of all GB workplaces and 91% of employees in employment Means are given in handout

13 Estimation Our main estimation technique is probit, for whether or not the workplace uses AWs We also present some estimates for numbers of AWs hired We also present separate estimates for professional/managerial AWs, and other AWs All our estimates correct for the complex sample design using the survey’s sample weights and stratification variables (Forth et al, 2006)

14 Equation specification In choosing the RHS variables, we emphasise use of AWs as a buffer, and focus on whether FF practices increase/decrease the buffering need Our several FF practices are entered separately to allow for heterogeneity In addition to controls already mentioned, we include: –unionism –whether the plant has a JCC (which might limit management power to direct workers, and increase demand for AWs) – workplace size (larger workplaces must be more likely to have some AWs) –gender, which might capture more absence, plus lower employer attachment and specific training, hence more AW –industry, and in particular the public sector – our assumptions of profit max and market clearing being less appropriate here

15 Results for FF variables Results are given for the full sample, then for the private sector only, distinguishing between professional/managerial and others Marginal effects are given, to show economic as well as statistical significance FF leaves, in particular paternity leave, and specific leaves for elderly care increase AW presumably due to increased absence On the other hand, nursery support and flexible hours reduce AW, presumably by making absence less likely However, for professional/managerial AWs, only nursery support has an effect

16 Marginal effects for FF variables Notes: also included are other controls in next table plus dummies for industry Marginal effects give change in probability of hiring AW for unit change in independent variable, evaluated at the mean. Thus, for matleave, marginal effect is for the total sample, so changing this variable from 0 to 1 lowers probability of hiring AW from.116 to.110.

17 Results for controls Again we see that the controls have less impact for professional/managerial AWs For ordinary AWs, the most important variables raising demand are guaranteed job security, having a jcc, and using paid consultants to assist recruitment (empagent) The public sector also has a large demand, ceteris paribus – the marginal effect is.09, on a mean of.11 Reducing demand are variables for task variety, and many part-timers, perhaps indicating workers can be easily re-assigned Also the long-training variable reduces demand, presumably because AWs are not then suitable

18 Marginal effects for controls

19 Further results We added an absence rate variable – this emerged as a significant positive determinant of AW (though several hundred observations lost due to missing values), and little else changed We also ran a Tobit equation with the number of AWs hired as the dependent variable (see handout) – again special leave increased AWs, while nursery support and the ability to work at home reduced AW

20 Conclusions Our hypothesis is that organisations with less exisint staff flexibility are more likely to use AWs Our focus is on FF practices, and we find that practices which increase the employers ability to direct work effort and reduce absence such as nursery provision reduce AW, while special leaves – and job security guarantees for incumbents - have the opposite effect Some FF practices help incumbents but increase the number of outsiders (AWs) – yet others, such as workplace nurseries, do not.