Who hires older workers? A first look at industry variations in the recruitment of mature workers

Transcription

1 Who hires older workers? A first look at industry variations in the recruitment of mature workers Sylvia Dixon Statistics New Zealand and Department of Labour March 2009

2 Acknowledgements and disclaimers Who Hires Older Workers? This report was funded by the Department of Labour and was undertaken while the author was on secondment to Statistics New Zealand. Any views expressed are those of the author and do not purport to represent those of Statistics NZ or the Department of Labour. Any errors are the sole responsibility of the author. Access to the data used in this study was provided by Statistics NZ under conditions designed to give effect to the security and confidentiality provisions of the Statistics Act Only people authorised by the Statistics Act 1975 are allowed to see data about a particular person or firm. The tables in this paper contain information about groups of people so that the confidentiality of individuals is protected. The results are based in part on tax data supplied by Inland Revenue to Statistics NZ under the Tax Administration Act. These tax data must be used only for statistical purposes, and no individual information is published or disclosed in any other form, or provided back to Inland Revenue for administrative or regulatory purposes. Any discussion of data limitations or weaknesses is in the context of using the Linked Employer- Employee Data (LEED) for statistical purposes, and is not related to the ability of the data to support Inland Revenue's core operational requirements. Careful consideration has been given to the privacy, security, and confidentiality issues associated with using tax data in this project. Any person who had access to the unit record data has certified that they have been shown, have read, and have understood Section 81 (Privacy and Confidentiality) of the Tax Administration Act. A full discussion can be found in the Linked Employer-Employee Data Project: Privacy Impact Assessment paper (Statistics NZ, 2003). Published in March 2009 by Statistics New Zealand Tatauranga Aotearoa Wellington, New Zealand ISBN (online) 2

3 Abstract This research note is concerned with age structure variations in firms hiring patterns. It uses data from the Linked Employer-Employee Dataset (LEED) to document variations in the recruitment of older workers across industries, in the 2004/05 to 2006/07 period. It was motivated by concerns that the range of job opportunities that are open to job seekers aged 55 years and over may be more limited than for younger or prime-aged job seekers. The paper defines older as persons aged years. It distinguishes new hires (newlyrecruited employees who did not work for the firm in the previous two years) from rehires (people who were re-employed by a firm after a gap of up to two years), and focuses largely on the former. For each industry, the paper provides measures of the proportion of employees in the workforce who were older, the proportion of new hires who were older, and the ratio of the two, also known as the Hutchens index of hiring opportunity. The Hutchens index has been used to identify firms, occupations, or industries where older workers are under-represented in recruitment intakes, suggesting the possible existence of recruitment barriers. Across industries, we find a strong positive correlation between the proportion of existing employees who were older and the proportion of new hires who were older. In other words, industries that employ a high/low proportion of older workers tend to recruit a similar proportion of older workers within their new employee intakes. However, some variations do exist. The industries that recruited a low fraction of older workers relative to the fraction of their existing workers in this age group (leading to a low value of the Hutchens index), included many of the manufacturing industries, forestry and logging, food and beverage services, library and other information services, broadcasting, sport and recreation activities, air transport, and telecommunication services. While most of these industries are small, food and beverage services is a substantial employer. The information in LEED does not allow us to tell whether low values of the Hutchens index are due to recruitment barriers faced by older job seekers, or to other factors. Alternative explanations for low values of the Hutchens index are discussed in the paper. 3

4 Contents 1. Introduction The recruitment of older workers: a literature review Data sources and methods Use of LEED to study hiring patterns Measures of occupational openness to older recruits Age variations in recruitment patterns Older worker recruitment rates by type of firm and industry Firm type and industry variations in the recruitment of older workers What explains the high and low values of the hiring opportunity index? Recruitment patterns of the younger old and the older old Summary Appendix References Tables 1. Age composition of job matches, hires and separations, 2006/ Recruitment measures by age group, 2006/ Recruitment of older workers by size of establishment, 2006/ Recruitment of older workers by business type, 2006/ Top and bottom 20 Industries, ranked by fraction of new hires that were older and the hiring opportunity index Industry composition of new hires in 2006/07 by one-digit industry and detailed age group Appendix tables A1. Rehires as a percentage of all hires during 2006/ A2. Age composition of job matches, hires and separations in 2006/ A3. Recruitment of older workers by two-digit industry, annual average, 2004/05 to 2006/ A4. Two-digit industries ranked by the proportion of new hires who were aged 55+, annual average, 2004/05 to 2006/ A5. Two-digit industries ranked by the hiring opportunity index, annual average, 2004/05 to 2006/ A6. New hires in 2006/07 by one-digit industry and age group Figures 1. Recruitment measures by age group, 2006/ older workers aged 55+ in employment in 2005/06 and within new hires in 2006/

5 1. Introduction The proportion of older New Zealanders who are in employment has risen rapidly during the past 15 years. 1 Despite substantial increases in the employment rates of men and women aged 55 years and over, there continue to be concerns about the availability and quality of job openings for mature workers. The recent publication of best practice guidelines for employers, intended to encourage them to consider new ways of recruiting and retaining older adults and using their skills (Human Rights Commission et al, 2008), reflects a concern that job opportunities for this age group are more limited than for prime-aged or younger workers, at least in some areas of the labour market. This research note explores the variations that exist in the recruitment of older workers across firms and industries, using data from the Linked Employer-Employee Data (LEED). It focuses on employees aged between 55 and 74 years, and analyses data for either the 2006/07 year or the three years from 2004/05 to 2006/07 (pooled). The paper begins with a brief review of published literature in section 2. These works give reasons for variations in the rate at which older adults are hired across firms, industries, and occupations. Focusing on job requirements and employer incentives, the literature has identified several reasons why older adults might face recruitment barriers in certain kinds of jobs. Section 3 describes the data source and the main indicator of hiring barriers that has been used in the literature, the Hutchens hiring opportunity index. The Hutchens index has been used to identify firms, occupations, or industries where older workers are under-represented in recruitment, suggesting the possible existence of recruitment barriers. Section 4 presents some initial results, showing how hiring and separation rates vary by age group and change over the life course. Section 5 presents data on the recruitment of older workers in different industries and firms of different sizes. The analysis identifies the particular industries that were relatively high or relatively low recruiters of older workers, and shows the extent of variation across industries. Drawing on the literature, it discusses some of the possible reasons for the patterns observed. Section 6 summarises the main findings. The statistics presented in this paper show that there are moderately large variations across two-digit industries in the share of older workers within an industry s new recruits. Overall, the proportion of older employees in an industry s workforce is strongly correlated with the proportion of older employees it hires. In other words, industries that employ a high/low proportion of older workers tend to recruit a similar proportion of older workers among their new hires. This correlation implies that the industries that employ older workers are generally fairly open to recruiting older workers from the external labour market. The analysis also shows that there are certain industries in which the ratio of older hires to older employees deviates from the overall pattern, and certain industries in which both the fraction of employees who are older and the fraction of new recruits who are older seem surprisingly low, given typical job characteristics in the industry. These are industries in which hiring barriers might exist, but the information in LEED is too limited for conclusions to be drawn. Statistical 1 The employment rate of year olds rose from 60 percent in 1992 to 79 percent in The employment rate of year olds rose from 23 percent to 65 percent over the same period. These figures are from the June 1992 and June 2007 quarters of the Household Labour Force Survey. 5

6 indicators calculated from data sources such as LEED could, however, be used to identify industries or occupations that could be targeted in future investigations of recruitment barriers. 2. The recruitment of older workers: a literature review It is well known that some firms, occupations and industries employ older workers to a greater extent than others. The hypotheses that have been put forward to explain the variations in the hiring of older workers refer to age-related differences in preferences for particular job characteristics; differences in the capabilities and skill sets of older and younger workers; and the impact of fixed training costs, seniority-based pay structures, and fringe benefits for older employees. These hypotheses are summarised below. Some focus on supply-side factors, such as older workers preferences for shorter or flexible working hours, and others focus on demandside factors, such as the training costs faced by employers. Some of the job characteristics that are identified in the literature, such as a high requirement for physical fitness, have the potential to influence both supply and demand (the willingness of older job seekers to apply and the willingness of employers to recruit applicants above a certain age). Mismatches between job requirements and worker preferences that develop as workers grow older. Job preferences can change as adults grow older. Although some people will be happy to continue doing the same tasks, others may prefer to change their occupation. On average, older workers are likely to apply for a somewhat different mix of jobs than prime-aged workers. The literature refers to age differences in preferences regarding hours of work, the flexibility of working time arrangements, the degree to which a job is physically demanding, and the level of stress associated with the job. o o o Hours of work. Older workers may be more likely to apply for jobs with shorter or more flexible hours, if they prefer a gradual transition from work to retirement, or if they have health problems that limit the hours they can comfortably work. Physical strength or fitness requirements. Due to fitness or health limitations, older workers may be less likely to apply for jobs with significant physical demands. Employers may also be less likely to recruit older workers for jobs requiring a high level of physical effort, if they believe that older workers are less likely to meet the fitness requirements. Job characteristics that make jobs stressful or demanding. Some older workers may choose to escape from stressful jobs as part of a transition from work to retirement. Jobs identified in the literature as stressful are those with management or supervisory responsibilities. Computer and numerical skills. Older workers acquired their education and training at an earlier time than younger workers, and this may affect the relevance of their education and training in occupations or industries that make considerable use of recent technology. Training. The returns to employers from training staff can decline as employees age, because there is a shorter period over which to recover costs and realise benefits. Workers who are near retirement age are less likely to be hired in jobs where training costs are high, or the time period needed to acquire the full range of skills and knowledge is a lengthy one. 6

7 Deferred compensation structures. Implicit wage contracts, by which younger workers are underpaid and older workers are overpaid relative to their productivity, discourage the hiring of older workers. Implicit wage contracts are used to incentivise work effort in jobs where monitoring work effort is difficult, and to encourage job retention in jobs where turnover is costly. These compensation systems make older workers more costly to the firm, and are likely to be less effective in motivating older workers than younger ones. Fringe benefits. Some types of fringe benefit, such as defined benefit pensions and health insurance, are more costly to employers if the employee is older. Separation rates. Another hypothesis that does not seem to have been discussed in the literature is the role of separation patterns, and in particular, the age structure of separations. Suppose worker quit rates decline with age, while firms prefer to maintain a particular age and experience profile within their workforce. Firms and industries that have relatively low quit rates on the part of older employees will have less need for the replacement hiring of experienced workers, leading to an age differential in hiring patterns. The literature provides some empirical evidence on most of the hypotheses. On hours of work, survey evidence from New Zealand and other countries indicates that a high proportion of working adults say they would prefer to work on a part-time basis, or have more flexible hours of work, before completing retirement (EEO Trust, 2006). The implication is that the demand for jobs with part-time or flexible hours is likely to rise when workers get close to retirement. An occupational analysis undertaken by Hirsch et al using United States data showed that occupations with higher proportions of part-time workers tended to have higher proportions of older workers and older hires. They interpreted this as a supply rather than a demand phenomenon. Johnson and Kawachi (2007) found that many older workers in the US who changed jobs moved into part-time work, self-employment, and jobs offering flexible work arrangements. Forty-three percent of older workers who left long-term jobs worked part time in their new jobs, 25 percent were self-employed, and 50 percent worked in jobs with flexible work arrangements. By comparison, only 15 percent worked part-time, 15 percent were selfemployed, and 28 percent had flexible work arrangements in their pre-separation jobs. (However, it was not possible to tell what proportion of the moves were motivated by the desire for shorter hours or more flexible work arrangements, and in what proportion these benefits were gained incidentally.) 2 Although many writers argue that fitness and strength requirements matter, Hirsch et al (2000, p.12) report that required strength was not related to the age structure of occupations for men or women in the US. Johnson and Kawachi (2007) also found that when older workers in their longitudinal sample changed their jobs, there was little change in the proportion of jobs that required physical effort. Johnson and Kawachi (2007) found that older workers in the US tended to move into jobs that they perceived as being less stressful but more satisfying than their previous jobs. However, their data did not show that the desire for a less stressful job was the driving force behind the job change. 2 The sample consisted of job changes between 1986 and 2004 made by adults aged 45 to 75 who were respondents to the Health and Retirement Survey. 7

8 Hirsch et al (2000) found that occupations requiring computer use and high numerical aptitude in the US have relatively fewer older workers. They find that occupations requiring computer use are also less open to older workers, in terms of the ratio of new hires to existing employees. Adams and Heywood (2007) and Daniel and Heywood (2007) found evidence that workers who are near to retirement age are less likely to be hired in jobs where training costs are high or the time period needed to acquire the full range of skills and knowledge is a lengthy one. According to Hirsch et al (2000), occupations in which the volume of firm-provided training is significant have fewer older male workers. Adams and Heywood (2007), Daniel and Heywood (2007), and Hirsch et al (2000) find evidence of a negative relationship between deferred compensation structures (proxied in their studies by the steepness of the wage/age profile) and the openness of occupations and/or firms to older workers. Hirsch et al (2000) also reports that pension coverage is negatively related to an occupation s openness to older hires. In summary, the literature provides evidence that a variety of different supply-side and demandside factors can contribute or lead to age differentials in hiring rates across different types of jobs reflected in the fact that older adults are more likely to be hired in some types of jobs than others, and older adults make up a larger share of new hires in some types of jobs than others. 3. Data source and methods 3.1 Use of LEED to study hiring patterns LEED is an administrative dataset that brings together information from the tax system on individuals employment activity and government income support, and information from the Statistics NZ business register on businesses. The dataset includes monthly records of the gross earnings of each wage and salary earner in each calendar month, derived from the pay as you earn (PAYE) income tax system. The records for each individual are linked longitudinally, providing a complete picture of their taxed employment earnings and government income support payments during the period covered by LEED (April 1999 to the present). All residents of New Zealand will appear in LEED, provided they have an IRD number and have received at least one payment since April 1999 that was subject to income taxes, such as earnings from wage or salary employment; earnings from self-employment; a benefit, student allowance, or paid parental leave payment; earnings-related compensation from the Accident Compensation Corporation; or New Zealand Superannuation. 3 LEED contains monthly observations on whether or not an individual received wage or salary earnings, and the gross amount of those earnings, linked to firm identifiers. These data can be used to calculate monthly employment rates and measure new job starts (hires) and separations. Information is available on the characteristics of the firm including its size, business type, and detailed industry. 3 The coverage of LEED is expected to be comprehensive because of the comprehensive coverage of the personal income tax system in New Zealand. All earnings from employment and the main types of government income support are subject to income tax. Non-residents with income that was taxed in New Zealand are also included in LEED. 8

9 Occupation is not measured in LEED. Previous studies of older worker recruitment patterns have used data on the occupation, the occupation by industry cell, or the enterprise, as their basic unit of analysis. Many of the factors that are believed to influence age differentials in employment patterns and hiring patterns are likely to operate at the job or occupational level. A few, such as compensation policies, fringe benefits, and possibly training, are likely to operate at the firm level. Classifying jobs by industry may make it more difficult to observe clear statistical patterns in the fraction of new hires that are older, and certainly makes it more difficult to attribute the differences that are observed to job characteristics. In this analysis, job matches are defined as matches between a particular employee and a particular establishment in which earnings were paid during the reference year (eg 2006/07, the year ending in March 2007). This is closer to the concept of an employment relationship than the concept of a job spell. Hires are job matches in which a new job spell began during the reference year, after a break in the sequence of earnings of at least one month. New hires are the subset of these hires in which the employee had not worked for the establishment in the 24 months prior to the start of their first recorded job spell in the reference year. Rehires are hires of employees who had worked for the establishment in the 24 months prior to the first job spell that began in the reference year. The distinction between new hires and rehires is a significant one, as employers who are rehiring a former employee already have information on that person s skills and likely performance, and do not have to screen them. This paper focuses mainly on new hires. Separations are defined as job matches that existed in the reference year but had ended (at least temporarily) by the end of the reference year, and were not in operation in the first three months of the following year. A measure of separations was calculated simply to test the hypothesis that sectors or industries with relatively low rates of older worker separations also have lower rates of older worker recruitment. Because the dataset used for this analysis ended in June 2007, we did not calculate a more robust measure of separations that took into account the possibility of subsequent job spells with the same establishment. Although our measure of separations is not a particularly good one, a simple measure of the separation rate is likely to be adequate for the limited objective of exploring the correlation between older worker separations and older worker hires. The age of each employee is measured as at the first month during the reference year in which the job match was in operation and earnings were paid. In LEED, date of birth is imputed for around 4 percent of employees. Individuals with imputed birth dates have been retained in this analysis. A small number of individuals in LEED have implausibly low or high ages due to errors in the date of birth field. The samples used in this analysis are restricted to the records of people aged 15 74, thus excluding people with improbable ages. 3.2 Measures of occupational openness to older recruits The most widely cited measure of the openness of an occupation to older workers was developed by Hutchens (1986). This measure comprises the fraction of all hires that are older divided by the fraction of all the employed that are older, and can be computed for specific industry, occupation, or firm cells. I(i) = (% of recently hired employees in cell i aged over k years)/(% of all employees in cell i aged over k years) 9

10 where i is the occupation, occupation and industry cell, or firm, and k is some threshold such as 50 or 55 years. The index represents an attempt to separate the effects of demand-side factors from supply-side factors in the recruitment of older workers. It is assumed that supply-side factors will influence both the proportion of employed workers who are older and the proportion of new hires who are older, while demand-side factors will influence the age composition of new hires disproportionately. For example, occupations requiring strength may have low levels of existing older workers and low levels of older new hires (because both supply and demand are low), but need not have a low ratio of older hires to older employment. Similarly, occupations in which working long hours is the norm may have both low levels of new hires and low proportions of older employees, and so the ratio of the two need not be low. In contrast, occupations in which compensation is deferred to the later stages of the working career are likely to employ many older workers but hire few new older workers, and therefore have a low value of the index. In the latter situation, the demand for new recruits in older age groups is likely to be lower than the potential supply. Several recent studies (Adams and Heywood, 2007; Daniel and Heywood, 2007; and Hirsch et al, 2000) have examined age variations in hiring opportunity, based on evidence that some firms and occupations hire relatively low proportions of older workers compared with the share of older workers in their existing workforces. Low values of the Hutchens index have been interpreted as evidence that some types of jobs are suitable for and attractive for older adults to work in, but are relatively closed to new recruits who are older. The Hutchens index is open to more than one interpretation, however. Without additional information on vacancies and applications, it is impossible to tell whether certain firms and occupations hire low proportions of older workers because there are few suitable vacancies for relatively experienced employees, or because of age-biased recruitment practices. If employees are sorted across different types of jobs according to their level of experience, and older employees have relatively low quit rates, the lesser need for replacement hiring into the high experience jobs could lead indirectly to lower levels of recruitment of older employees. Firms would then be relatively closed to older workers in the sense that fewer vacancies matching the skills of older workers are available for external applicants, but not in the sense that recruitment policies and practices are age biased. Adjustment barriers or delays could also lead to disparities between the fraction of older people who are employed in particular occupations and the fraction of new hires that are older. For example, workers might remain in existing jobs as they age, even if their skills are no longer well suited to the job requirements, or the hours are no longer the ones they would prefer to work, simply because searching for a new job is costly and entails risk. A hiring constraint could be said to exist if there was a mismatch between the rate of applications by older adults and the rate of hiring, which couldn t be attributed to differences in the quality of applicants. Unfortunately, it is difficult to measure the volume and quality of vacancies and job applications in surveys or administrative data collections. Statistical datasets generally contain only measures of the age composition of new hires and the age composition of existing employees. Although the hiring opportunity or openness index can be used to identify occupations or industries in which demand constraints might exist, a low value of the index does not, by itself, provide sufficient evidence that there is a barrier to the recruitment of older workers. 10

11 4. Age variations in recruitment patterns Who Hires Older Workers? Before examining the recruitment patterns of older workers, we present some preliminary measures of recruitment and separation rates by five-year age group, in order to show how hiring probabilities change with age across the life course. This is relevant for understanding the measures presented later. Table 1 Age Composition of Job Matches, Hires and Separations, 2006/07 Age group Employees (total who worked during the year) Job matches Hires New hires Rehires Separations Percent Total N 2,180,230 3,435,500 1,881,960 1,489, ,990 1,589,290 Table 1 shows the age distribution of the employees, jobs, hires, and separations that were recorded in LEED during 2006/07. The first column gives the age distribution of employees (the total number of persons who worked in waged jobs during the year) and the second gives the age distribution of all jobs (ie all unique person-firm matches that were recorded in LEED during the year). The third column shows the age distribution of all hires, defined as all person-firm job matches that started during the reference year, after a break of at least one month. These are further divided into genuinely new hires in column 4 (job matches that never existed before or were not operating in the previous 24 months) and rehires in column 5 (job matches that were resuming after a break of up to 24 months). The final column shows the age distribution of separations (defined simply as the 2006/07 job matches that were no longer in operation during the first three months of 2007/08). Comparison of the columns shows that younger workers those in the 15 19, 20 24, and age groups make up a larger percentage of both hires and separations than their relative share of employment (whether employment is measured in terms of the number of employees or 11

12 in terms of the number of job matches). Presumably, younger workers are over-represented within the counts of hires and separations because of their relatively high job mobility. For all mature age groups from years upwards, the age group s share of hires and separations is below its share of employment. For example, year olds were 6.6 percent of all employees during the 2006/07 year and 5.8 percent of job incumbents, but only 4.3 percent of hires and 4.2 percent of separations. Table 2 gives age-specific measures of the new hire rate, the rehire rate, the percentage of hires that were rehires, and the separation rate. In each case, the rate is calculated by dividing the number of hires or separations by the stock of job matches for that age group. 4 The final column shows the hiring opportunity index for each age group, that is, the age group s percentage of all new hires over its percentage of all job matches. These measures are also plotted in figure 1. Table 2 Recruitment Measures by Age Group, 2006/07 Age group New hires / jobs Rehires / jobs Rehires as a percent of all hires Age group % of all new hires / age Separations / jobs all group % of jobs If the alternative measure of employment, the number of employees, is used as the base the patterns are very similar. 12

13 Figure 1 Recruitment Measures by Age Group, 2006/ Percent Age group New hires / jobs Separations / jobs Rehires / jobs Percentage of new hires / percentage of jobs The ratio of new hires to all jobs (shown in the first column of table 2) declines with age to reach a low in the year age group, and then increases a little in the and age groups. The ratio of rehires to jobs (column 2) fall slightly from young to prime ages, but then increases substantially in the older age groups. Employees in the and age groups were more likely to return to an employer they had previously worked for, compared with employees in younger age groups. The percentage of all hires that were rehires is shown in the third column. This percentage is around 20 to 25 percent for prime-aged workers (30 49 years) but increases to 29 percent for year-olds, 33 percent for year-olds, and 44 percent for year olds. Potentially, there are several possible reasons for the greater importance of rehiring in older age groups. Employees aged over 60 may be more likely than younger people to return to work at the invitation of a past employer, rather than initiating the job match themselves. Employers may be more willing to hire people in the oldest age groups if they have prior information on the job seeker s abilities. Another possible explanation is that employees aged over 60 are more likely to work in jobs where casual and seasonal employment are common, leading to episodic rather than continuous employment, and a higher frequency of rehires. The first two explanations are speculative but there is some evidence to support the third. A Statistics NZ survey that gathered information on job characteristics in the March 2008 quarter found that older employees, and especially employees aged 65 and over, were more likely to be working in temporary jobs than the prime aged. The proportion of employees who were in temporary jobs was 6.9 percent for 13

14 45 54-year olds, 7.5 percent for year olds, and 16.6 percent for the 65 and over age group. The percentage of employees who identified their job as being a casual job also rose with age, from 2.8 percent at ages 45 54, to 4.0 percent at years, and 11.5 percent at ages 65 and over. 5 Using our current data, an analysis of the proportion of all hires that were rehires in 2006/07, showed that rehiring was more likely in industries were temporary employment tended to be more common: education and training, agriculture, arts and recreation services, health care and social assistance, and other services (see table A1 in the appendix). The ratio of separations to the stock of jobs (see the fourth column of table 2) declines with age until years, but increases again in the and age groups. This is broadly similar to the age profile of the hiring rate. The Hutchens hiring opportunity index, representing the percentage of all new hires who were in a given age group divided by the percentage of all jobs in that same age group, is shown in the final column of table 2. This proportion declines over all ages from to 60 64, before rising slightly again in the and age groups. The index is below 100 for all age groups from ages upwards. This pattern shows that it is not very meaningful to interpret a hiring opportunity index value below 100 as an indicator of hiring constraints in any general sense. The measure is likely to be influenced by job mobility rates, which generally decline with age (but start to increase again after about 65 years). Note that the results presented in this section have treated each job match, hire, or separation equally, regardless of their duration or other characteristics. In fact, longer jobs and jobs with longer hours make a larger contribution to employment and economic activity than short jobs and jobs involving few hours. Unfortunately, there is no straightforward method of weighting jobs in LEED to account for differences in their volume. Hours of work are not measured in LEED and any job duration measure will be censored at each end of the panel dataset. However, to explore the impact of weighting, the figures in table 1 were recalculated using duration weights, based on the number of months in which earnings were paid, from one to a maximum of 12. (Jobs with earnings for 12 months or more all have the same weight. A ceiling is needed because there are many jobs in LEED whose completed duration cannot be observed.) The results are given in table A2. Weighting by duration reduces the relative share of the jobs, job starts, and separations held by young and old employees, and increases the relative share held by people in the prime age groups. Because there is no satisfactory method of weighting jobs in LEED to account for differences in their volume, we continue to report unweighted data in the rest of the paper. 5. Older worker recruitment rates by type of firm and industry We now turn to focus on the recruitment of older employees specifically those in the age group. Section 5.1 presents and discusses statistics on hiring rates by size of firm, business type, and two-digit industry. Section 5.2 considers two potential explanations for the crossindustry variation in the hiring of older workers. Section 5.3 looks at the question of whether recruitment patterns change much within the older age groups that is, those in the to age groups. It is worth noting that the majority of employees and job matches in the age range are aged below 65 years. The figures in table 1 show that 81.5 percent of employees and These figures are from the Survey of Working Life, March 2008 quarter. 14

15 percent of jobs within this broad age group represented, or were held by, people aged under 65.This means that the employment patterns of people aged below 65 will tend to shape the results for the age group as a whole. 5.1 Firm type and industry variations in the recruitment of older workers Variations in the share of jobs and new hires represented by older workers, across firms of different sizes, are shown in table 3. Firms are defined as establishments (geographical units). Firm size is defined in terms of the number of employees. The variation across establishment size groups in the share of older workers is relatively small, and does not show a clear pattern. The only significant variation is that the hiring opportunity index is slightly lower for the largest group (100 plus employees). Table 3 Recruitment of Older Workers by Size of Establishment, 2006/07 Establishment size jobs held by employees aged 55+ (%) hires aged 55+ (%) new hires aged 55+ (%) rehires aged 55+ (%) separations aged 55+ (%) Hiring opportunity index < Total Table 4 Recruitment of Older Workers by Business Type, 2006/07 Business type jobs held by employees aged 55+ (%) hires aged 55+ (%) new hires aged 55+ (%) rehires aged 55+ (%) separations aged 55+ (%) Hiring opportunity index Private Public Not-for-profit Total Hiring proportions calculated for private firms, public organisations, and not-for-profit organisations are shown in table 4. Public sector and not-for-profit organisations employ and recruit significantly larger shares of workers in the age range than firms in the private sector. For example, in 2006/07, older employees contributed 9.6 percent of private sector employment, 17 percent of public sector employment, and 18 percent of employment in non- 15

16 profit organisations. The hiring opportunity index is around 72 for public sector employers, compared with 58 for private sector employers. Differences in industry composition are likely to be contributing to these patterns. Further analysis could decompose the business-type patterns to see if any significant differences remain after industry composition is controlled for. There are wide variations across industries in the hiring and employment of older workers. We use the Australian and New Zealand Standard Industrial Classification 2006 (ANZSIC06) at twodigit level and present results for the 72 industries that each accounted for at least 0.1 percent of total employment in 2006/07. 6 (Recruitment statistics were not calculated for the very smallest industries because of the risk of large fluctuations in the age profile of recruitment caused by essentially random events.) We also present averages of estimates that were calculated separately for 2004/05, 2005/06, and 2006/07, so that the results for specific industries are not unduly influenced by any variations in hiring patterns that occur from year to year. When calculating the hiring opportunity index we divide the fraction of new hires that were older in year t by the fraction of job matches that were older in year t-1, so that the denominator is independent of the numerator. The full results are given in tables A3 (industries in the usual order), A4 (industries by the share of their new hires that were aged 55 or over), and A5 (industries by the value of the hiring opportunity index). In addition to showing the share of jobs held by older employees, the share of new hires who were older, and the hiring opportunity index, we show the relative size of each industry, the separation rate among older employees, an estimate of the share of part-time jobs that were part-time or part-month in nature, and the absolute number of new hires in the older age group. All figures in the tables are averages of the results for the 2004/05, 2005/06, and 2006/07 years. The separation rate for older employees, and the share of industry jobs that were part-time or part-month in nature, were calculated to explore the associations between these factors and the recruitment of older persons (see section 5.2). Across the 72 largest two-digit industries, the percentage of jobs that were held by persons aged 55 plus ranged from a low of 4.3 percent in computer systems design, to a high of 20.6 percent in residential care services. The proportion of new hires that were aged 55 plus ranged from a low of 2.0 percent in telecommunication services, to a high of 17.1 percent in residential care services a range that is fairly similar. Figure 2 shows the correlation between an industry s older worker share of employment in 2005/06 and its older worker share of new hires in 2006/07, where there is a strong positive relationship (the correlation coefficient is 0.94). This suggests that the share of older workers within the workforce of an industry is likely to be a fairly good predictor of the share of older workers that it will recruit, when new staff are hired. The vertical distance between each observation and the trend line in figure 2 indicates whether that industry will have a relatively low or a relatively high hiring opportunity index. Industries that are above the trend line will have higher than average index values and vice versa. The spread 6 Twelve industries were excluded because their employment share in 2006/07 was less than 0.1 percent of the total. These are aquaculture, fishing hunting and trapping, coal mining, oil and gas extraction, metal ore mining, exploration and other mining support services, petroleum and coal product manufacturing, gas supply, water supply sewage and drainage services, non-store retailing and commission-based buying and selling, rail transport, and Internet publishing. Defence was also excluded because of data coding issues. 16

17 of industries along the horizontal axis represents the variation in the fraction of older employees who work in different industries. This spread is greater, and perhaps more interesting, than the vertical variation around the trend line. Figure 2 Older Workers Aged 55+ in Employment in 2005/06 and Within New Hires in 2006/07 20 Percentage of new hires aged 55+ in Percentage of jobs held by persons aged 55+ in

18 Table 5 Top and Bottom 20 Industries, Ranked by Fraction of New Hires That Were Older and The Hiring Opportunity Index older workers among new hires Hutchens Index of Hiring Opportunity Top 20 (ranked from highest to lowest) Preschool and School Education Public Administration Residential Care Services Preschool and School Education Adult, Community and Other Education Adult, Community and Other Education Non-Metallic Mineral Mining and Quarrying Administrative Services Medical and Other Health Care Services Social Assistance Services Public Administration Water Transport Road Transport Warehousing and Storage Services Other Transport Finance Social Assistance Services Insurance and Superannuation Funds Commission Based Wholesaling Residential Care Services Property Operators and Real Estate Services Other Transport Tertiary Education Non-Metallic Mineral Mining and Quarrying Personal and Other Services Medical and Other Health Care Services Library and Other Information Services Public Order, Safety and Regulatory Services Heavy and Civil Engineering Construction Rental and Hiring Services Water Transport Building Construction Postal and Courier Pick-up and Delivery Services Road Transport Non-Metallic Mineral Product Manufacturing Computer Systems Design and Related Services Publishing (except Internet and Music Publishing) Property Operators and Real Estate Services Public Order, Safety and Regulatory Services Agriculture Bottom 20 (ranked from highest to lowest) Artistic Activities Fabricated Metal Product Manufacturing Food Product Manufacturing Auxiliary Finance and Insurance Services Accommodation Non-Metallic Mineral Product Manufacturing Building Construction Food and Beverage Services Other Store-Based Retailing Forestry and Logging Beverage and Tobacco Product Manufacturing Textile, Leather, Clothing and Footwear Manufacturing Sport and Recreation Activities Motion Picture and Sound Recording Activities Construction Services Furniture and Other Manufacturing Air and Space Transport Internet Service Providers and Data Processing Services Agriculture, Forestry and Fishing Support Services Library and Other Information Services Fuel Retailing Transport Equipment Manufacturing Administrative Services Polymer Product and Rubber Product Manufacturing Forestry and Logging Pulp, Paper and Converted Paper Product Manufacturing Food Retailing Basic Chemical and Chemical Product Manufacturing Broadcasting Gambling Activities Internet Service Providers and Data Processing Services Broadcasting Computer Systems Design and Related Services Sport and Recreation Activities Motion Picture and Sound Recording Activities Primary Metal and Metal Product Manufacturing Food and Beverage Services Air and Space Transport Telecommunications Services Telecommunications Services 18

19 The industries with the highest and lowest proportions of older workers among their new hires are listed in the first column of table 5. The industries that recruited the highest shares of older adults included preschool and school education; residential care services; adult, community and other education; medical and other health care services; social assistance services; road transport; public administration; personal and other services; and building cleaning. Education, social services, and transport are prominent in this list. The top 20 list also includes some relatively small extraction and manufacturing industries (non-metallic mineral mining and nonmetallic mineral product manufacturing), heavy and civil engineering construction, and public administration. There are several industries with above average shares of part-time or partmonth jobs (residential care services, adult community and other education, social assistance services, tertiary education, personal and other services) and two industries with very high separation rates for older employees (public administration and adult community education). 7 High turnover could be associated with higher levels of hiring. In terms of absolute numbers, preschool and school education was by far the largest recruiter of older workers. Other large recruiters of older workers, in absolute numbers, were residential care services; professional, scientific and technical services; agriculture; administrative services; and the other store-based retailing industry. It is worth pointing out that agriculture, administrative services, and other store-based retailing were all large recruiters of older workers in absolute terms, even though they recruited a relatively low share of older workers. The industries with the lowest shares of older workers among their new hires included agriculture; building construction; sport and recreation activities; construction services; administrative services; insurance and superannuation funds; broadcasting; forestry and logging; food retailing; Internet service providers; computer systems design; food and beverage services; and telecommunication services. This group of industries includes several industries in which physically demanding jobs may be common (building construction, sport and recreation activities, and forestry and logging). It includes several industries in which many jobs may have high-technology skill requirements (Internet providers and data processers, computer systems design, and telecommunication services). Overall however, the industries that recruited the lowest shares of older workers are quite diverse. The second column of table 5 lists the industries with the highest and lowest values of the hiring opportunity index. The index ranged from 45.9 in telecommunication services to in public administration. 8 Industries with relatively high index values include preschool and school education; adult community and other education; social assistance services; administrative services; finance; residential care services; public order and safety services; and medical and other health care services. There is considerable overlap between this group and the group of industries that hired a relatively high share of older workers (shown in the left-hand column of table 5). The 20 industries with the lowest values of the index include nine different manufacturing industries; forestry and logging; food and beverage services; sport and recreation activities; air transport; and telecommunication services. The results shown in table 5 and table A5 suggest 7 The results for public administration are likely to be distorted by hiring patterns in election years, because a large number of short-term jobs are created in this industry for the administration of the election. Our data suggest that older people tend to fill these jobs. 8 See footnote 7. 19