Socio-Economic Patterns in UK Employees Sickness Absence:

Transcription

1 Socio-Economic Patterns in UK Employees Sickness Absence: Marco G. Ercolani Department of Economics, University of Birmingham, UK B15 2TT. December 2008 Abstract This article reports the proportion of usual work hours excluding overtime that are lost due to sickness absence in the UK. These rates of sickness absence are calculated for all employees across time and across various economic and demographic characteristics using the largest possible sample from the UK Labour Force Surveys. The total sample is over four million cases spanning 1984 to Quantifying sickness absence is of interest to practitioners and researchers because it represents lost work hours for employees and employers, it also highlights issues relating to the health of the workforce. JEL codes: J28, J32, M12

2 Contents 1 Introduction. 4 2 Quantifying Rates of Sickness Absence Statistical Procedures Data Extraction Procedures Estimated Rates of Sickness Absence Sickness absence across time Sickness absence across economic characteristics Sickness absence across demographic characteristics Conclusion. 27 A Appendix: Tables of Results. 31 A.1 Sickness absence across time A.2 Sickness absence across economic characteristics A.3 Sickness absence across demographic characteristics

3 List of Figures 1 Sickness absence by year Sickness absence by calendar quarter Sickness absence by month Mean weekly absence hours and work hours by month Sickness absence by year and gender Sickness absence by year and public/private sector split Sickness absence by real hourly earnings (2000 prices) Sickness absence by usual weekly work hours excluding overtime USUHR Sickness absence by month of tenure with employer Sickness absence by Standard Industrial Classification (SIC 1992) Sickness absence by Standard Occupational Classification 1990/ Sickness absence by number of employees at workplace Sickness absence by age and gender Sickness absence by employees gender, marital status and by age of employee s youngest dependent child in the family unit Sickness absence by region Sickness absence by main long term health problem List of Tables 1 Sickness absence by Year Sickness absence by Calendar Quarter Sickness absence by Year for Female Employees Sickness absence by Year for Male Employees Sickness absence by Year and Public/Private sector Sickness absence by real hourly earnings (2000 prices) Sickness absence by usual weekly work hours USURH (excluding overtime) Sickness absence by SIC(1992) Sickness absence by SOC(1990/2000) Sickness absence by number of employees at workplace Sickness absence by Region Sickness absence by long term health problem

4 1 Introduction. In this article, calculations for the proportion of usual work hours, excluding overtime, lost due to sickness absence are reported. These rates of sickness absence are calculated using the largest possible sample of employees from the UK Labour Force Surveys (LFS). The LFS samples approximately 0.1% of the UK population each quarter. The LFS are representative samples of the UK population and the calculated sickness absence rates are therefore representative for UK employees as a whole. The calculations show that approximately 3% of usual work hours are lost due to sickness absence. Although 3% sickness absence is low by European standards, see Barmby et al. (2004), it is still a significant proportion of all usual work hours and significant when compared to the proportion of labour lost due to unemployment. In everyday terms, a 3% absence rate translates into an average annual absence of one and a half weeks for every UK employee. The statistics and figures provided by this article are intended to be of interest to practitioners who are interested in either the health of employees or the working hours arrangements between employers and employees. The provided statistics can also be used by researchers to carry out further statistical analyses relating to labour market and health care issues. Readers interested primarily in the results may wish to first read Section 3 and then return to the technical details in Section 2. In generating these measures of sickness absence every care has been taken to address four concerns: capturing sickness absence rather than shirking, avoiding underreporting due to the stigma associated with sickness, producing measures that are consistent over the entire sample period, and overcoming interviewees need to recall absences over long periods of time. The details of how this is achieved are reported in Section 2 but the procedure can be summarised in the following way: Rather than use responses to LFS questions on the number of days sick, use responses to questions on usual and actual work hours (excluding overtime) in the week before the interview. The fact that the LFS questionnaire is conducted by the Office for National Statistics (ONS) and the Department of Enterprise, Trade and Investment for Northern Ireland (DETINI), institutions independent of the employer and away from the workplace, are also important factors in eliciting accurate responses. A final crucial factor is that although many questions in the LFS have undergone revisions over the years, questions on usual and actual work hours have remained largely unchanged since This article takes as its starting point the synthetic measure of sickness absence proposed by Barmby et al. (1999, p.406, p.415), Barmby et al. (2004, p.70), and offers some extensions to the analysis. Two trivial, yet useful, extensions include the cover- 4

5 age of the UK 1 rather than Great Britain 2 and the extension of the dataset to cover More substantive contributions of this paper include the derivation of 95% confidence bands for the statistics, the inclusion of additional statistics that employ population weights, and the addition of futher analyses by additional socio-economic characteristics. These additional analyses include comparisions of absence rates by: hourly earnings, weekly work hours, employment tenure, employees at workplace, parenthood and by long-term health problems. The ONS does not itself produce official summary measures of sickness absence, instead it relies on papers such a this one to generate these measures based on its raw data. The only nationally representative alternative to the the hours-based measure of sickness absence are measures based on sick days questions in the UK LFS. The sick days based research papers include Barham and Leonard (2002), Barham and Begum (2005) and Leaker (2008). The authors of these papers highlight that computing sickness absence using sick days may present some shortcomings. One shortcoming is that questions on sick days have only been asked as of Spring 2000 while the hours-based measures presented in the present paper go back to the Spring of Another shortcoming is that the sick days based measures may create some loss of information due to censoring. For example, the loss in work for a part-time worker working five half-days is indistinguishable from that of a full-time worker working five days. On a more technical note, absences lasting only part of a day would not be recorded using the sick days measures. 3 However, the sick days based measures do offer an approach that is far less computationally cumbersome for researchers than the approach advocated in this paper. The sickness absence rates reported in the sick days based papers are systematically lower than those reported in this working hours based paper. For example, in Table 1 of this paper we see 2.81% of working time lost due to sickness absence in 2007 while Leaker (2008) reports 2.5% of working days lost in the mid-year 2006/7. This is most probably due to the censoring caused by using days measures rather than the hours measures. Public and private bodies sometimes tender surveys of their members sickness absence rates and some of the more notable ones include CBI (2008) and CIPD (2007). These tendered surveys offer the advantage of being tailored to the customer s needs. The sampling method of these surveys differs insofar as hundreds of organizations 1 The United Kingdom includes both Great Britain and Northern Ireland. 2 Barmby et al. (1999, 2004) only covered Great Britain in order to allow the extension of the timeseries into the past using the General Household Survey data which only surveys Great Britain. 3 A substantive study to analyse the responses to LFS questions on numbers of days sick and responses on work hours would be needed to highlight the degree of consistency between the two. The daysbased absence measures are censored compared to the hours based measures. The degree of censoring and therefore bias may vary by any number of patterns according to the employment conditions. These obviously include the various working patterns per week but could also vary by other socioeconomic characteristics. 5

6 are interviewed compared to the LFS with annual samples now exceeding a hundredthousand individual workers, see Table 1 on page 31. These tendered surveys may also limit themselves to surveying managers and not employees directly. The rates of absence at or above 3.5% reported in these tendered surveys are slightly higher than those found in this paper because they are designed to measure absences not only due to sickness but also those due to shirking. Finally, some studies have focused on firm-specific datasets that, though not representative of the UK workforce as a whole, do offer more detailed information in terms of the duration of firm-specific sickness absence, these include: Brown (1994) and Brown and Sessions (1996). The paper proceeds as follows. In the next Section the statistical and research procedure adopted to calculate the rates of sickness absence is described. In Section 3 the resulting sickness absence rates are illustrated. A final section concludes and the Appendix contains detailed tables for most of the results. 2 Quantifying Rates of Sickness Absence. In subsection 2.1 the statistical procedure for calculating the rates of sickness absence is described. In subsection 2.2 the research procedure for extracting information on usual and actual work hours from the LFS is described. 2.1 Statistical Procedures. In this article the absence rate is defined as the ratio of hours reported absent due to sickness (excluding overtime) to work hours (excluding overtime). More precisely, for all workers i in category j the estimated ratio R j of sickness absence to work hours for the population is defined by equation (1a), the proportion of work hours lost due to sickness absence in the employee workforce: R j = n A ij i=1 n H ij i=1 (1a) where j can index any category, or categories, of interest; such as year (e.g. Figure 1) or both year and gender (e.g. Figure 5). A i indicates the number of hours absent due to illness for worker i during the reference week. H i indicates the number of work hours for worker i during the same reference week. i indexes the individual respondent. 4 Although the absent and work hours (A ij,h ij ) are based on the survey 4 Starting in 1992, the LFS surveyed any one individual as much as five times over five consecutive quarterly surveys. Because of the short time-span, the high attrition rates and the fact that some 6

7 week, the proportion of absent hours for any given time interval, such as month, quarter or year, can still be generated and no bias is induced by the aggregation. Other ways of generating rates can induce biases, such as in equation (4). Equation (1a) can obviously be reformulated as a ratio of means: R j = n i=1 A ij n n i=1 H ij n = A j H j (1b) Though equation (1b) is a trivial reformulation, it is useful in constructing standard errors and therefore confidence bands for any estimated absence rate. Based on either Cochran (1977, section 2.11) or Kendall et al. (1994, section 10.6) equation (1b) can be used as a basis for calculating the standard error for the ratio of two means: 5 s.e.(r j ) = Var(A j) + R 2 jvar(h j ) 2R j Cov(A j,h j ) H 2 j where by the central limit theorem the variances and covariance of the means are given by Var(A j ) = Var(A j )/n, Var(H j ) = Var(H j )/n and Cov(A j,h j ) = Cov(A j,h j )/n. The 95% confidence inteval for R j is calculated by R j ± (1.96 s.e.(r j )), where R j is given by equation (1a) and the standard error s.e.(r j ) by equation (2). Although equation (2) is an approximation from a Taylor Series Expansion of the statistic and evaluating expectations, it is likely to be appropriate for large sample sizes such as those used in this paper. The UK LFS is not designed using an equal probability of selection method but the large sample sizes usually mean that statistics based on the UK LFS are representative of the UK population as a whole. The UK LFS does include population weights that can be used to ensure the sample is representative of the population. Therefore, as a consistency check, the weighted proportion of work hours lost due to sickness absence is also calculated and presented along with the un-weighted rates in the Appendix tables. This weighted rate of sickness absence W j is calculated by taking the ratio of the weighted mean of sickness absence to the weighted mean of work hours, the formula for this is in equation (3) where the second line is a valid questions are only intermittently in the LFS, the panel element of the data are too weak to be exploited in this context. 5 Note that equation (2) can also be reformulated as, s.e.(r j ) = i A2 ij +R2 j i H2 ij 2Rj i AijHij n(n 1)H 2 j (2). 7

8 simplification of the standard formulation: W j = = n i=1 n i=1 w ij n i=1 w ij A ij w ij n i=1 w ij H ij n w ij A ij i=1 n (3) w ij H ij i=1 where w ij is the weight for each individual i with characteristic j. The Census weight factor is given by the LFS variable PWT03, this was developed using the 2001 Census population estimates published in the Spring of As can be seen in the Appendix Tables the resulting values for R and W are similar to the fourth decimal place reassuring us that the un-weighted sample is representative of the population. Many studies focus on reporting the mean values for the individuals in a survey therefore, for the sake of completeness, the tables in this paper also include measures of mean sickness absence rates per worker as calculated by applying the mean of ratios formulation described by equation (4), r j = n i=1 A ij H ij n. (4) The associated confidence bands are calculated by r j ± (1.96 s.e.(r j )) where the standard error is s.e.(r j ) = Var(A ij /H ij )/n. Note that R j and r j differ very slightly because the rates of absence are not equal across groups with differing weekly work hours H ij. In particular, the measure r j generates a biased measure of R j because it gives too much weight to employees who have low working hours H ij for the week. The measure r j more appropriately represents the proportion of working time lost per worker independently of the volume of working time lost. In the limit when j is used to index each single individual the two measures of sickness absence converge to the same value R 1 = r 1, this can be seen by setting n = 1 in equations (1a) and (4). 2.2 Data Extraction Procedures. In the introduction the claim was made that the LFS question structure with regard to usual and actual work hours is so indirect that it mitigates the effect of immediately admitting to illness and of having to recall information over long periods of time. In this subsection this claim is justified by exploring the LFS question structure and explaining how the data are extracted. In the Hours Worked part of the 8

9 questionnaire the interviewee is asked: USUHR Thinking of your (main) job/ business, how many hours per week do you... usually work please exclude meal breaks and overtime? The response to this question is interpreted as the usual weekly hours, excluding overtime. Further on in the questionnaire, the respondent is asked: ACTHR Thinking now about the seven days ending Sunday the [date], how many hours did you actually work in your (main) job/business - please exclude meal breaks and overtime? The response to this question is interpreted as the actual week s hours worked, excluding overtime. This requirement of recall only the hours worked over the preceding week mitigates many of the problems associated with recall error. Note that workers who state they never work overtime are asked similar questions (TOTUS1, TOTAC1) that make no reference to overtime and these alternative variables are used in their cases. In these cases we let USUHR = TOTUS1 and ACTHR = TOTAC1. After a number of further questions to check the consistency of the stated usual and actual work hours, the interviewer checks whether the actual work hours are lower than the usual work hours. If this is so, the respondent is asked: YLESS6 6 What was the main reason that you did fewer hours than usual/were away from work in the week ending Sunday the [date]? 1. number of hours worked/overtime varies 2. bank holiday 3. maternity or paternity leave 4. parental leave 5. other leave/holiday 6. sick or injured 7. attending a training course away from own workplace 8. started new job/ changed jobs 9. ended job and did not start new one that week 10. laid off/short time/work interrupted by bad weather 11. laid off/short time/work interrupted by labour dispute at own workplace 6 In the Spring 2006 LFS the variable YLESS6 replaced YLESS which had not included the option 4. parental leave. In the Spring 1992 LFS the variable YLESS replaced the combined variables WHYLESS and WHYAWAY which together covered the same options. The proportions in each response category are: Did not work fewer hours than usual 64.30%, number of hours/overtime varies 13.69%, bank holiday 5.21%, maternity or paternity leave 0.60%, parental leave 0.01%, other leave or holiday 9.51%, sick or injured 3.84%, training course away from workplace 0.41%, started new job or changed jobs 0.09%, ended job and did not start new one 0.09%, ld off,st-time,interrupn - bad weather 0.17%, ld off,st-time,interrupn - labour disp. 0.02%, ld off,st-time,int - econ/other reason 0.30%, other personal or family reasons 0.65%, other reasons 1.10%. 9

10 12. laid off/short time/work interrupted by economic and other causes 13. other personal/family reasons 14. other reasons For any interviewee that answers 6. sick or injured a sickness indicator variable equal to one s i = 1 is defined and for all others this is set to zero s i = 0 where i indexes the individual respondent. The implication is that this indirect question structure elicits genuine responses in revealing absences. In practice it is impossible to ever confirm this, it may be that additional absences due to shirking may be attributed to other categories. It may also be that some employees take sick leave out of their holiday entitlement. Finally, different employees may have a different absence propensities for any given level of illness. 7 Using these three items of information the two variables used in subsection 2.1 are generated by these two equations: A i = (USUHR i ACTHR i )s i (5) H i = ACTHR i (1 s i ) + USUHR i s i (6) Therefore, A i is a measure of any reduction in work hours that is mainly due to illness (s i = 1). Any other reductions in hours (s i = 0) imply the sickness absence hours equal zero A i = 0. H i is a measure of the working hours for the week. If sickness is recorded (s i = 1) the working hours equal the usual working hours H i = USUHR. If no sickness is recorded (s i = 0) the working hours equal the actual hours worked H i = ACTHR. This adjustment is made to H i because any other reduction is assumed to be due to a valid variation in the working contract (e.g. due to a bank holiday or holiday leave). 3 Estimated Rates of Sickness Absence. The calculated sickness absence rates are illustrated in the Figures within this section and the corresponding values are listed in the Appendix Tables. The chosen characteristics are based on those that may be of interest to policy-makers/researchers and constrained by the data availability. All proportions are reported as percentages. 7 This raises the potential problem of presenteeism, first cited in O Kelly (1994), where sick employees attend work when they should really be resting. The exact wording of the question What was the main reason you did fewer hours than usual... also presents potential problems. It may be that some short sickness spells are missed if another more important reason for the reduction in hours occurred. Conversely, it may be that too much absence is attributed to sickness if other minor reasons for reductions in hours also occurred. We expect these effects to be small and that, if they do occur, the overestimates and the underestimates tend to cancel each other out in the aggregate. 10

11 4 Percentage of working hours lost (R%) Sickness Absence 0 Year Figure 1: Sickness absence by year. In subsection 3.1 Figures for rates of sickness absence computed across time are illustrated. In subsection 3.2 Figures for rates of sickness absence computed across what are loosely termed economic characteristics are shown. Finally, in subsection 3.3 Figures for rates of sickness absence computed across demographic characteristics are illustrated. The notable feature of these sickness absence rates is the very low and constant rate across the years but the very high seasonal variation. Also notable is the substantial variation in rates of sickness absence across economic and demographic characteristics. 3.1 Sickness absence across time. The statistics across time are of interest because they permit the observation of long run trends and the seasonal variation in sickness absence. Using equation (1a) and letting j index the year, the percentage sickness absence rate (R%) is computed and illustrated in Figure 1 along with the 95% confidence bands shaded in grey. The corresponding values are listed in Table 1 in the Appendix. Notable is the comparatively steady and low level of sickness absence across these years. The standard error for the sickness absence rate s.e.(r j ) is computed using equation (2) and listed in Table 1 of the Appendix. This value is used to compute the 95% confidence bands illustrated 11

12 4 Percentage of working hours lost (R%) Sickness Absence 0 Quarter 1984q1 1985q1 1986q1 1987q1 1988q1 1989q1 1990q1 1991q1 1992q1 1993q1 1994q1 1995q1 1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 Figure 2: Sickness absence by calendar quarter in Figure 1. The confidence bands are quite narrow due to the large sample sizes, as reported by column n in Table 1. Note also from Figure 1 how the confidence bands are comparatively wider in the period 1984 to 1991 due to the smaller sample size n. The sample size during is around fifty-thousand when the LFS were conducted only in the Spring. The sample size from 1992 onward is closer to two-hundred-thousand when the LFS are conducted through the year. Table 1 also reports the values of the percentage rate of sickness absence calculated by weighted means (W j %) as described by equation 3. As one can see, the values for the weighted means are very similar to those for the un-weighted ratio of means (R j %). This re-enforces the confidence in the un-weighted measure. Finally, Table 1 reports the mean of ratios for the percentage averages sickness absence rate (r j %) as derived by equation 4 along with the standard errors. Using equation (1a) and letting j index the quarter, the percentage sickness absence rate is computed, the results are illustrated in Figure 2 and listed in Table 2. 8 The notable feature of the quarterly sickness absence rate is its high seasonal 8 Note that early LFSs were based on seasonal quarters and that as of 1996 they have been based on calendar quarters. All the data are presented on the basis of calendar quarters. This is achieved by aggregating all the data into a single, large dataset and using the reference date for the interview to determine which calendar quarter the interviewee responses refer to. 12

13 5 Percentage of working hours lost (R%) Sickness Absence 0 Month 1992m1 1993m1 1994m1 1995m1 1996m1 1997m1 1998m1 1999m1 2000m1 2001m1 2002m1 2003m1 2004m1 2005m1 2006m1 2007m1 2008m1 Figure 3: Sickness absence by month. variation, with peaks occurring during either the first or fourth quarter of each year. The high degree of seasonal variation is reassuring in light of the low variation of the annual series. One may have suspected that the low variation in the annual rate of sickness absence is merely an artefact of the data. However, in view of the very high seasonal variation shown in Figure 2 one can be more confident that the calculated values for the annual data are valid. Although the LFS are designed to be representative surveys of the UK population every quarter, one can use equation (1a) and let j index the month in order to generate tentative measures of sickness absence by month as illustrated in Figure 3. Again, the high seasonal variation in sickness absence rates is confirmed. These results must be interpreted with caution because the LFS is only designed to be a representative survey of 0.1% of the UK population in each quarter and not in each month. Tables for these results are not presented due to the very large number of cases. An interesting analysis of this seasonal variation can be attained by separately considering the numerator and the denominator in equation (1b). From this equation it becomes apparent that the seasonal variation may arise due to variation in the number of mean work hours n i=1 H ij n as much as the variation in absent hours n i=1 A ij n. Thus, letting j denote the months within each year and plotting these two gives rise to Figure 4. This illustrates that much of the variation in the sickness absence rate 13

14 Mean working hours per week (H) Month: Mean absent hours per week (A). Figure 4: Mean weekly absence hours and work hours by month. is due to the variation in the mean number of work hours mean number of hours absent n i=1 A ij n n i=1 H ij n as well as the. In particular, much of the upward variation in the sickness absence rate R j in December of each year is actually due to falls in the number of mean working hours. Given the gender decomposition is simply a binary one, one can reasonably let j in equation (1a) denote both year and gender in order to observe gender differences in sickness absence and how these evolve across time. The resulting values are illustrated in Figure 5 along with their associated 95% confidence bands and are listed in Table 3 for females and Table 4 for males. From this one can see that the female sickness absence rate is systematically higher than the male one. We can see that in all years the difference in sickness absence between males and females is significant. Whilst the female absence rate nearly always exceeds 3.5% the male one rarely exceeds 3% and from the confidence bands we can see that these differences exceed the 95% confidence bands. In the early years these differences are at their narrowest but they tend to increase during the sample period. If we interpret the responses attributing reductions in hours as being genuinely due to differences in the health of the respondent we must then look for systematic differences in health between females and males. Out-of-work commitments of the two gender groups may on 14

15 5 Percentage of working hours lost (R%) Female employees Male employees 0 Year Figure 5: Sickness absence by year and gender Public sector employees Percentage of working hours lost (R%) Private sector employees 0 Year Figure 6: Sickness absence by year and public/private sector split. 15

16 average expose females to higher risks to health. For example, taking care of children predominantly by females may expose them to common illnesses picked up by children more than it exposes males. The concern here is that given these are more measures of sickness absence rather than shirking, these differences in absence rates are therefore picking up health differences between the genders. The final time-series proposed in this subsection regards differences in sickness absence rates between employees in the private and public sector. The LFS questions on public sector employment were only introduced in 1993, therefore the period covered is shorter than for the other series. The resulting values for the sickness absence rate are illustrated in Figure 6 and listed in Table 5. The notable features here are the higher sickness absence rates in the public sector and the fall over time for both sectors. The differences in sickness absence rates between these two sectors is substantial and the reasons for this may include differing contractual arrangements between the two groups of workers or demographic characteristics. 3.2 Sickness absence across economic characteristics. In this subsection percentage sickness absence rates across various economic characteristics are presented. These are of interest in the context of labour market conditions and workplace characteristics. The procedure is the same as described above, equation (1a) is applied, letting j denote the economic characteristic of interest. The resulting values are plotted against the characteristic j. One sickness absence rate relation that should yield an unambiguous effect is the ratio of sickness absence with respect to hourly earnings. In general, one would expect the sickness absence rate to fall with rises in hourly earnings. The one, largely theoretical exception, may be the case where hourly earnings and the preference for leisure are both extremely high. For the seminal discussion on this topic see Allen (1981). Calculating the sickness absence rate by hourly earnings, deflated to year 2000 prices, rounded to the nearest integer yields Figure 7. The values are also listed in Table 6 along with the corresponding standard errors. These values are only available from December 1992 when the first questions on earnings were asked in the LFS. One can see that as hourly earnings increase, the sickness absence rate falls. The 95% confidence bands also become wider as real hourly earnings increase, in large part this is due to the very large number of individuals earning the lower hourly earnings rates. Presented next is the percentage sickness absence rate calculated by the number of usual working hours excluding overtime USUHR, again, rounded to the nearest integer. Usual contracted hours are defined by H u in equations (5) and (6). With regards to the ratio of sickness absence to working hours one may expect either a 16

17 5 Percentage of working hours lost (R%) Sickness Absence 0 Real hourly earnings (2000 prices) Figure 7: Sickness absence by real hourly earnings (2000 prices). flat function because the absence rate is defined as a ratio or one may expect an increasing function if workers with lower hours have some degree of flexibility in making up for lost time. The results are illustrated in Figure 8 and they reject both expectations. The ratio of sickness absence shows a high degree of variation across usual working hours and this ratio rises with a peak at 30 before falling again. Some hours groups (e.g. 40) seem to have particularly high absence rates and this may be due to particularly rigid contractual arrangements being associated with these numbers of hours. The values used to generate Figure 8 are also listed in Table 7. Figure 9 illustrates the percentage rates of sickness absence for different tenures in terms of the number of months the employee has been continuously with the current employer. Note that the absence rate for employees in their first month of tenure is very low, below 1%. As the tenure increases so does the sickness absence rate. When in the 36th month of tenure, mean sickness absence is at around the national average 3%. Although tenure is correlated with age, the increase illustrated in Figure 9 is too rapid to be explained by age. One obvious explanation is that employees may be on probation in the early months of their employment and keen to impress their employers by attending work even if they feel unwell. An additional factor may also be the reduced sick pay entitlements of employees who are in their early months of tenure. Once employees have a tenure of 12 months or more their probationary period 17

18 5 Percentage of working hours lost (R%) Sickness Absence 0 Usual weekly contracted hours (excl. overtime) USUHR Figure 8: Sickness absence by usual weekly work hours excluding overtime USUHR. is likely to have ended. Figures 10 and 11 present percentage sickness absence rates by Standard Industrial Classification 1992 (SIC) and Standard Occupational Classification 1990/2000 (SOC). Both classifications have been ranked by their sickness absence rates in the Figures. The values used to plot these figures are listed in Tables 8 and 9. The SIC coding is available from December 1993 onward when the LFS switched from the SIC(1980) to the SIC(1992) classifications. From Figure 10 one can see that the industries with the lower absence rates tend to be in the private service sector. Industries with the higher absence rates tend to be in the public sector. This confirms the findings illustrated in Figure 6. Industries in the potentially healthy but dangerous outdoor activities demonstrate both the lowest absence rate (SIC A: agriculture, hunting & forestry) and the third highest absence rate (SIC B: fishing) although the sample size for the latter is very small as shown in Table 8. The SOC(1990) codes are available in the LFS from 1992 onwards and a switch to SOC(2000) codes occurs in SOC(1990) and SOC(2000) codes are explicitly only comparable at the one-digit level. The resulting absence rates by SOC are illustrated in Figure 11. The SOC codes are in general intended as broad measures of status and as such are highly correlated with earnings. It has been already seen from Figure 7 that the absence decreases with earnings and it can be seen in Figure 11 that with 18

19 4 Sickness Absence Percentage of working hours lost (R%) Tenure in months Figure 9: Sickness absence by month of tenure with employer. few exceptions the absence rate seems to decrease with occupational status. One exception is SOC 7: sales and customer service where the absence rate is lower than the status would suggest. These are private sector service occupations which, as has been shown in Figure 10, tend to have low absence rates. The highest absence rate is in SOC 8: process, plant & machine operatives; these occupations are more likely to include more hazardous manual occupations. Figure 12 illustrates percentage rates of sickness absence for different numbers of employees at the workplace. The corresponding values are listed in Table 10 along with confidence bands that highlight that these differences in sickness absence rates are significant. Although more categories are available in the LFS these have been merged to create a consistent series from 1992 onward. What is evident is that sickness absence increases with the number of employees at the workplace. Several possible explanations arise. Larger numbers of colleagues may expose workers to a higher probability of catching illnesses. In larger workplaces it may be more difficult to closely monitor absence and bring social pressure to bear on absenteeism. In larger workplaces if one worker is absent there may be greater scope for colleagues to cover for this absenteeism. In very large establishments there may even be teams of workers whose main role is to cover for absent ones. 19

20 Standard Industrial Classification SIC A: agriculture, hunting & forestry SIC K: real estate, renting & business activ SIC H: hotels & restaurants SIC F: construction SIC G: wholesale, retail & motor trade SIC O: other community, social & personal SIC J: financial intermediation SIC C: mining, quarrying SIC D: manufacturing SIC M: education SIC E: electricity gas & water supply SIC I: transport, storage & communication SIC B: fishing SIC L: public administration & defence SIC N: health & social work R% Figure 10: Sickness absence by Standard Industrial Classification (SIC 1992). SOC 1: managers & senior officials Standard Occupational Classification 1990 & SOC 2: professional occupations SOC 7: sales and customer service SOC 3: associate professional & technical SOC 5: skilled trades occupations SOC 4: administrative & secretarial SOC 9: elementary/other occupations SOC 8: process, plant & machine operatives SOC 6: personal service occupations R% Figure 11: Sickness absence by Standard Occupational Classification 1990/

21 1 10 Number of employees at workplace (100) R% Figure 12: Sickness absence by number of employees at workplace. 3.3 Sickness absence across demographic characteristics. Finally, in this subsection Figures are presented for percentage rates of sickness absence across various demographic characteristics. These are of interest in the context of the workers own characteristics and environment. The procedure for obtaining these is the same as described in the previous sections. The first demographic characteristics presented in this sub-section are the sickness absence rates by age and gender as illustrated in Figure 13. Four interesting features are evident in Figure 13, the first being that across almost all ages the female absence rate is higher than the male one. Ichino and Moretti (2006) suggest that up to the mean age of 45 some of this male-female differential can be explained by absences that have a cycle of 28-days and that could be associated with women s menstrual cycle. The second feature is that for both genders the sickness absence rate increases with age. In part this may be due to the positive correlation between ill health and age. The third feature is the growth in mean absence as the statutory retirement age is approached. This is supported by the the similar patterns for females and males with respect to the statutory retirement age and the very steep growth in the mean absence just before the statutory retirement ages. The fourth feature is the low sickness absence for the small sub-sample of employees who work beyond 21

22 7 6 Percentage of working hours lost (R%) Female employees Male employees Age Figure 13: Sickness absence by age and gender retirement age. This may be explained by the existence of workers who have a low propensity for illness and a high propensity for attendance. These individuals are more likely to work beyond the statutory retirement age and their low propensity for sickness absence only becomes apparent once those with a high propensity for sickness absence achieve statutory retirement age. In order to attain an understanding of the systematic gender differences in sickness absence rates already illustrated in Figures 5 and 13 a decomposition by gender, marital status and age of youngest dependent child is illustrated in Figure 14. When analysing Figure 14 it should be borne in mind that reductions in hours due to maternity (or paternity) leave are already accommodated in this measure of sickness absence. This is because reductions in the number of working hours due to maternity or paternity leave are taken to be contractually agreed reductions. This feature of maternity and paternity leave is incorporated within equation (5) whereby, for maternity and paternity leave, the indicator for sickness equals zero s i = 0 and therefore any reduction in hours is not taken as sickness absence A i = 0. Equation (6) is used to generate the measure of working hours for the week and when s i = 0 working hours for the week are equal to the actual hours worked: H i = ACTHR. These data are only available from 1992 onward. In Figure 14 taking no child as the base case one can see what the impact of 22

23 Female, not single Female, single Male, not single Male, single No child No child No child No child R% Figure 14: Sickness absence by employees gender, marital status and by age of employee s youngest dependent child in the family unit. dependent children in the family unit has on sickness absence. Comparing females with no child who are married or partnered (not single) with females with no child who are single, one sees that their rates of sickness absence are virtually the same at around 4.1%. The same is true for males who have no child, regardless of marital status their rate of sickness absence is around 3%. This suggests that in the absence of young dependent children, marital status does not have an effect on sickness absence though gender difference in sickness absence rates persist. For employees who are female and not single the presence of a young dependent child aged 0-2 is associated with only a small increase in sickness absence, the adjustments for maternity leave described above must be borne in mind when considering this result. For female, not-single, employees the presence of a young dependent child aged 3-15 has the effect of lowering rates of sickness absence to around 3.6%. These rates of sickness absence are below the rates for females with no child and possible explanations for this may include the increased pressure to earn for the household or to compensate for career interruptions. For female, single employees the presence of young dependent children has the rather different effect of increasing rates of sickness absence to around 4.8%. This is a notable increase considering that any maternity leave has already been factored out of the measure. One possible explanation is that 23

24 Region of usual residence. Inner London South East, excluding London East Anglia South West Outer London Rest of West Midlands East Midlands Rest of Yorkshire & Humberside Scotland, excluding Strathclyde Rest of North West Northern Ireland West Yorkshire West Midlands (Metropolitan) Tyne & Wear Greater Manchester Rest of North Wales Strathclyde Merseyside South Yorkshire R% Figure 15: Sickness absence by region. the increased burden of being a single working mother has detrimental health effects, as will be seen, the same is true of single working fathers. For male employees who are not single, the advent of young dependent children aged 0-2 and 3-4 is associated with a substantial reduction in the rate of sickness absence to around 2.3%. One possible explanation is that the arrival of children increases male employees focus on their careers. In two-parent households the males may be more likely to specialise on their career path while females may either reduce or cease their labour market activities and specialise in home production. As the youngest dependent child becomes older the rates of sickness absence for males rise slightly up to 2.5% but they never reach the levels of sickness absence for males without children. The most intriguing set of statistics presented within Figure 14 relates to the rate of sickness absence for male employees who are single, though the small sample size for the cases of single fathers means that these results must be interpreted with some degree of caution. The onset of young dependent children sees a substantial rise in the rate of sickness absence for single fathers to around 4.7%, though the sample size for this category is the smallest at around 500 observations while most other categories run into the tens-of-thousands or even hundreds-of-thousands. As the age of the youngest child increases the rate of sickness absence falls very rapidly and by the time the youngest child is aged the rate of sickness absence for single 24

25 fathers is only 3.5%. It should be noted, however, that 3.2% absence is still higher than the absence rate of about 3% for all males without young children. The obvious explanation for the higher rates of sickness absence is that the burden of being a single working parent, whether female or male, has detrimental health effects. The next demographic characteristic analysed in this section is the percentage sickness absence rate by region of usual residence. The resulting values are presented in Figure 15 and listed in Table 11. The regions have been ranked according to their percentage sickness absence rates. The differences in between the regions may be due to several reasons, including differences in the age distributions of their populations and differences in their industrial structures. These data are available as of 1986 when there was a slight change in the region coding within the LFS. An in-depth analysis of sickness absence rates by region of residence should be possible using Blinder-Oaxaca style decompositions. However, the heavy censoring of the dependent variable, individual sickness absence, at 0% and 100% may require a non-standard application of this decomposition analysis. The final characteristic under consideration is the relationship between sickness absence and long term illnesses. Although it may seem obvious that employees with long term illnesses have a higher propensity for sickness absence, it is still of interest to find out which of these long term illnesses are associated with particularly high rates of sickness absence. Though the LFS introduced questions on long term illnesses in 1996 these changed substantially in 1997, along with further minor changes thereafter. As of Spring 1997 the LFS asks the following questions of those of working age in the questionnaire section entitled Health : LNGLIM Do you have any health problems or disabilities that you expect will last for more than a year? 1. yes 2. no If the answer to the above is yes then, further on in the questionnaire, the respondent is asked: HEALTH Which of these is your main health problem/disability? 1. problems or disabilities (including arthritis or rheumatism) connected with your arms or hands 2....legs or feet 3....back or neck 4. do you have difficulty in seeing (while wearing spectacles or contact lenses) 5. difficulty in hearing 6. a speech impediment 7. severe disfigurement, skin conditions, allergies 8. chest or breathing problems, asthma, bronchitis 9. heart, blood pressure or blood circulation problems 25

26 10. stomach, liver, kidney or digestive problems 11. diabetes 12. depression, bad nerves or anxiety 13. epilepsy 14. severe or specific learning difficulties (mental handicap) 15. mental illness or suffer from phobias, panics or other nervous disorders 16. progressive illness not included elsewhere (e.g. cancer not included elsewhere, multiple sclerosis, symptomatic HIV, Parkinson s disease, muscular dystrophy) 17. other health problems or disabilities The sampling of respondents for this questions has also undergone several minor changes, the most substantive ones of which are described here. Starting in Spring 1997 only workers of non-pensionable age were asked the Health section questions, females aged and males aged Starting in Spring 2000 two additional sets of respondent are routed to the HEALTH question, those aged 74 and below if this was their first interview and those responding yes to LIMITA Does this health problem affect the amount of paid work that you might do?. Starting in Spring 2003 those aged 75 and above who were not too distressed to continue were also asked the HEALTH question. An additional undocumented feature is a very large proportion of missing cases for the variables LNGLIM and HEALTH in the Spring 1997 dataset when these questions were first asked in this format. The results reported in this part of the analysis are therefore limited to data starting with the Summer 1997 questionnaire and omitting those who were were not asked the questions between Summer 1997 and Winter 2002/3 because of their pensioner age. Using these data to calculate rates of sickness absence generates the plot illustrated in Figure 16 and the values listed in Table 12. Some obvious patterns emerge, for those with no long term health problems the rate of sickness absence is very low while for those with a progressive illness the rate of sickness absence is very high. Though long term illnesses obviously are associated with higher rates of sickness absence it is illnesses of the mind that register as the second and third highest for rates of sickness absence. What seems remarkable is the step-change in sickness absence rates between physical long term illnesses and long term illnesses involving the mind. Progressive illnesses demonstrate the highest rates of sickness absence among the recorded long term illnesses. These results concur with the findings on stress in the Whitehall studies, in particular Marmot (1994) but also Marmot et al. (1978, 1987, 1991, 1997) and Marmot and Shipley (1996). One intriguing result is for those who are coded as Missing answer who have by far the highest rates of sickness absence. A word of caution here is that the sample size for this group is very small, though not the smallest. Every care has been taken to ensure that this set of missing answers is not due to the age profile dictated by the 26