elaborate on the picture of job tenure by relating patterns of job tenure to firm
characteristics. Interviews with managers and staff of specific firms are then used to
identify some of the conditions under which the different patterns of job tenure emerge.
^Government provisions may well affect the shape of the labour market in indirect ways. This is illustrated by the Education Acts of the late ninteenth and early twentieth centuries in Australia (C.F. Maclaine, 1974: 19-34). These are likely to have affected the structure of jobs in retailing considerably, but this was an entirely unintended consequence of the Acts. A large number of juniors were employed in the late nineteenth century as 'cash-runners' delivery boys and in other functions (c.f. Farmer and Co. Ltd. 'blue cards'; Grace Bros Archives). Employment of all staff involved a low degree of de jure tenure. For example, at Farmer and Co. Ltd. (a Department Store operating in NSW) employees signed a 'blue card' stating : "the said engagement and salary shall terminate at a moment's notice given by either party". However the period of employment of many staff was quite long. In a document on duration of employment lodged in the Farmer and Co. Ltd. archives it is noted that "most staff have worked for the company for more than twenty years". Thus de facto tenure was probably quite high.
Prior to 11 October 1882, juniors were employed in the company on a yearly salary and were paid monthly. This was typical of most employees. After 11 October 1882, the juniors were paid a weekly wage. This may have been due to the NSW Education Act of 1880 (c.f. Maclaine, 1974: 27), which required children to attend primary school. The numbers of cash runners and other juniors declined in the 1880s. While the archives are far from complete, the reduction in junior's jobs and change in system of payment indicates that the Act may have resulted in a restructuring of jobs and a severing of junior's jobs from the internal labour market of the firm. In this indirect way the Education Acts are implicated
in the emergence of the first casual jobs in the industry.
^ Indeed McDonalds has a policy of not hiring anyone who is a member of a union, or anyone whose close kin (such as parents) are active members of unions (Macken, 1989).
3. While it is formally the case that penalty rates are intended to compensate employees for lost holiday and sickness benefits, a deterence component has been built into some awards in practice. In the retail industry in Australia this has not happened largely because the Unions have not been sufficiently strong to obtain such penalty rates.
Chapter 5
J o b Tenure in Retail Firms in Australia
This chapter uses data from Australian retail firms to identify the factors which are associated with patterns of job tenure within the retail industry. Once these factors are identified the chapter discusses how and why these factors are related to patterns of job tenure. Data enabling identification of salient factors was obtained from public reports submitted by employers to the Affirmative Action Agency. This is the only source of data on rates of casual employment by firm. Data is available from the Retail Census 1985-6 (ABS 8622.0) on casual employment in each 'trade' and this is used to evaluate the reliability of the AAA data where possible. However this attempt at triangulation of data is limited in that ABS statistics are not available on employment patterns in specific firms. The following discussion of how and why the identified factors are important in shaping patterns of job tenure is based on interviews with store managers and employees.
Identifying some Firm Characteristics which Shape Job Tenure in the Retail Industry: Methodology
Profiles of job tenure in retail firms in Australia were constructed from the public reports held by the Affirmative Action Agency (AAA). The Affirmative Action Agency was set up after the Affirmative Action Act was introduced by the Commonwealth Government in October 1986. The agency's aim is to facilitate the removal of barriers to women in employment and promotion. The legislation requires that companies and organisations with 100 employees or more develop an affirmative action program and that they report on it annually. In the retail sector this covers the employers of nearly 20% of the retail workforce (ABS 8622.0: Table 2, 1979-80). In 1987 all private sector organisations with more than 1,000 employees were required to develop programs and report on them. In 1988 private sector organisations of between 500 and 999 employees were required to report; and in 1989 private sector organisations with between 100 and 499 employees reported for the first time. The short history of the Agency means that trend data is not yet available from this source.
To assist employers in completing the required reports, the AAA sends reporting units blank schedules which can be filled in by the employers. Two forms are sent to each employer. The first is a public report schedule which asks for background information such as details of numbers of employees, their occupations, employment status and sex. The second is a confidential report schedule for employers who feel that public release of
details of their affirmative action program would disadvantage them. The public reports are the source of the A A A data discussed below'.
The public report schedule includes items on firm structure (independent or part of a larger organisanon); nationality of ownership of parent company; the proportion of the firm's staff employed in major occupations; the proportion of staff employed in casual, part-time, full-time weekly, contract and other employment relationships. It also includes items on the position of women in the company and the attempts by the firms to construct and implement affirmative action strategies.
From a complete list of returns to the A A A I was able to compile a list of all the returns in the distributive 'trades'.^ There were 99 files from which I was able to obtain data. This represented all but 19 on the original list of firms in the distributive 'trades'. Seven of the 19 unavailable schedules had not yet been returned to the A A A , three were excluded because they had very limited retail or wholesale function, six were unavailable because A A A staff were processing or working with the files and three firms had not included the data I required on their returns. One of these had recorded casual and full- time employment as "full-time equivalents" rather than actual numbers of employees. This firm therefore appeared to have far fewer part-time and casual employees than other firms and was excluded to avoid confusion. Since the unavailable files appeared to be randomly distributed with respect to 'trade' I did not make extra-ordinary efforts to access these files at a later date.
The available files included 17 franchises of one fast food company. In analysing the data I aggregated these to one firm since I did not want the 17 to have a disproportionate impact on the analysis. Other firms with a similar numbers of outlets had aggregated the information in one return themselves and, while franchises can be seen as quite different from company owned outlets in some ways, for the purposes of analysis here they are treated in the same way.
I thus ended up with 82 firms on which I had comparable data on employment strategy, 'trade' and company structure (listed in Appendix B). I then categorised these firms into 'trades' for comparative purposes. The 'trades' are: wholesalers and importers/distributors (11 firms); department stores (7 firms); clothing retailers (6 firms); automotive sales, including earth-moving equipment (11 firms); electrical, scientific equipment and chemical products (11 firms); computers and software (8 firms); fast food (5 firms); music: equipment, sheet and record (2 firms); hardware (4 firms); books and stationary (3 firms); homewares (2 firms); supermarkets (4 firms) and other/ misc. (8 firms) including cosmetics, confectionery, liquor, duty free, handbags and jewellery.
These categories were generated by the respondents themselves who categorised their firms in an open-ended question asking the respondent to describe the main business of the company. It can be seen that there are some absences from the 'trade' list. Notable absences include pharmacists and butchers: presumably because there are none which employ more than 100 people and they are not organised into chains which are targeted by the reporting criteria.
The firms were divided into three groups: Bands 1,2,and 3. Firms with 1,000 or more employees are in Band 1; those with 500-999 are in Band 2 and those with 100-499 are in Band 3. This did not mean that the repordng unit employed this number of staff. In many cases the reporting units were smaller than Band 2 and 3 firms. For example, MacDonalds franchises each reported separately and were included as Band 1 when in total all 17 stores employed 3,071 people, an average of only 43 per outlet. Similariy, Automotive retailers were often part of large organisations such as Honda or Mitsubishi and hence categorised as Band 1 but each reporting unit (outlet) employed a small number of people.
The firms were also divided into those with links to overseas companies and those without. Again the disdnction was generally made on the basis of self-reports by the person who completed the quesdonnaire. The answers were cross checked against the Who's W h o of Australian Business lisdng to ensure that the self-reporting was accurate. While not all reporting units were listed in the Who's Who, of the 47 which were, all but two of the entries agreed with the self-reported categorisadon. O f these two both were Australian firms (and reported as such) which were linked to overseas companies through holding companies. I re-classified these as having overseas links.
With data on only 92 reporting units, the stadsdcal analysis which could be done was fairiy limited. Some firms had less than 10% of their staff in sales and some had no casual employees. Thus any attempt to cross tabulate rates of casual employment by 'trade' with other variables, such as proportion in sales, was likely to provide little useful informadon. The patterns which emerged from the analysis are thus more suggestive of possible relationships than a clear demonstradon of those relationships.
A Profile of Job Tenure in Eightv-two Retail Finns
A number of factors operating within industries were identified in Chapter 1 as likely to be significant in shaping patterns of j o b tenure. Factors include: firm size, social characteristics of the workforce (in particular sex), managers' attitudes, skill, the organisational structure of the f i n n , patterns of demand and occupations of workers. Where possible, each of these is considered in relation to the data available from the AAA. Managers' attitudes and skill cannot be detemiined from the AAA data and are treated in the subsequent section.
Patterns of demand are assumed, for the purposes of this analysis, to be similar in all 'trades'. There is no data concerning the fluctuation in demand for retail goods on a weekly or daily basis which distinguishes between the different 'trades'. Unpublished .•\BS data from the Labour Force Surx ey (Figure 5.1) indicates that the size and pattern of seasonal tluctuations in demand for labour does vary somewhat between different 'trades'. However, it has not been possible to detemiine the importance of this in the following analysis of factors affecting job tenure.
Figure 5.1
Seasonal Variation in Retail Employment by 'Trade'.
20 -r- % change in employment Dept & Ge CFF Hardware Auto Food Other
Feb May Aug
Month
Nov
Dept. and Ge: dcparuiicni and general stoa\^. CFF: clothing, fabric and furnishings. Hardu .ire: hanlware and household a{"ipli;inccs. •Auto: new and used motor vehicle dealers.
Food: c a v e r s confectioners and lotiacconisLs. fish shops, take away food and milk bars.