Sickness Absence in the UK:

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1 Sickness Absence in the UK: Tim Barmby (Universy of Drham) Marco Ecolani (Universy of Birmingham) John Treble (Universy of Wales Swansea) Paper prepared for presentation at The Economic Concil of Sweden s Conference Sickness Absence: Diagnoses and Cres September 2003 Abstract This paper ses the UK Labor Force Srvey data over the period to analyse the determinants of the level of worker absenteeism in the UK. We find that absenteeism is particlarly sensive to measred aspects of the workers contractal arrangements. We arge that this is important for policy makers who might be concerned wh reglatory measres involving sick pay and working time. Keywords: Absenteeism, Sickness JEL Codes: J28, J32, M12 1. Introdction The paper seeks to give a pictre of sickness absence amongst UK workers over the period It extends work already reported for the UK in Barmby, Ercolani and Treble (1999). In addion Barmby, Ercolani and Treble (2002) contains comparisons across some Eropean contries as well as other contries. In this paper, as well as, reporting graphical trends (or otherwise), we seek to analyse what factors might determine an individal s observed absence rate. 1

2 2. Economic Approaches to Absenteeism Economists in general se two main approaches to gide their thinking on absenteeism. The first of these involves thinking abot how a market for labor might work when workers have different preferences for the amont of absence they might want to take, and firms have different technologies which determine how costly worker absenteeism is for them. In this model workers wh differing preferences sort themselves between firms wh different costs, so firms for whom absence is relatively more costly will tend to offer higher wages for lower levels of absence. Those workers who can spply reliable attendance will sort themselves into these jobs. This approach was first explored in this context by Allen (1981), the main empirical implication of this approach is a negative relationship between absence and wages driven by this interaction of firms and workers. Coles and Treble (1993,6) se a similar framework to that described above and draw ot frther implications for the choice of technology and contracts for firms. The second approach to the analysis of workplace absence looks more closely at the way individal workers respond to variations in the terms of their contracts, the standard labor-leisre choice frame work gives s a framework whin which to think abot how variations in contracted hors and overtime working might affect absence. In these models eqilibrim is characterised by workers eqating their marginal rate of sbsttion of goods for leisre, (which is the vale the worker places on the marginal non-work hor measred in terms of consmption) to the wage rate. This is the modelling framework sed by Dnn and Yongblood (1986). They constrct a direct measre of the marginal rate of sbsttion and enter this directly into their absence eqation. In thinking abot these effects we have to recognise that the contract which we observe the worker in will have been chosen ot of those available in the market. For these chosen contracts, desired hors of work (that is the hors level at which the marginal rate of sbsttion eqals the wage rate) might exceed contracted hors in which case there is no ongoing incentive for absence, if however desired hors are less than contracted then the worker will have an ongoing incentive to deviate from his or her contract. Wh this framework in mind we can think abot the possible effects of changes in wages, overtime premia and contracted hors. For changes in wages or overtime premia, as long as any conteracting income effects are relatively small higher wages or overtime premia will be associated wh lower 2

3 absence, as this will tend to increase desired hors of work spplied. On the other hand higher contracted hors shold be associated wh higher absence as at higher hors the marginal non-work hors has higher vale. 3. Empirical Method Or sorce of data for this paper are the Labor Force Srveys, which have been condcted on an annal basis dring and on a qarterly basis since 1992 in the UK. The method of compting an absence rate for an individal worker is essentially the same as the papers Barmby et al (1999, 2002) referred to in the introdction. We smmarise this method here. The measre of sickness absence we se is the ratio of the nmber of hors absent de to sickness to the nmber of hors contracted to work. We aim to compte estimates of this for fll-time employees and part-time employees. Self-employed workers are exclded. The Labor Force Srveys elic information from respondents abot work dring a reference week, which is the last fll week before the interview date. Specifically they are asked if they did any paid work, or if they were away from a job or bsiness that they wold normally attend. Or respondents are asked what their sal hors and actal hors are, and the reason for any difference. Or estimated absence rates are constrcted by treating sal hors as contractal hors, and any difference between sal hors and actal hors as absence. In this paper, we consider only those absences that are regarded by LFS as de to sickness. Specifically, following or procedre in Barmby et al. (1999), we define the absence rate R t as the ratio of the hors reported absent de to illness in the reference week (A ) to contracted hors (C ), To constrct R n i = 1 t = n i= 1 A C A, or measre of absence hors de to illness, we take the difference between sal horsc and actal horsc and mltiply by an indicator of absence w de to illness in the reference week, s. If the absence is de to illness, we set s = 1. If the absence is not de to illness, we set s = 0. Then A is defined 1 as (1) 1 We note two problems wh this procedre: 3

4 A = ( C C ) s (2) w The variables C, work hors C as w C and s are also sed to constrct the measre of contracted C = C ( 1 s ) + C s (3) w Therefore contracted hors are measred by actal hors worked if there was no absence de to sickness in the reference week and by sal hors if there was some absence de to sickness Overall Trends Figres 1 to 3 give an idea of the overall tend in absence for fll time and part time workers for the period we stdy in this paper. i) It is possible for A constrcted in this way to be negative in some cases. This occrred only in 0.02% of cases in Barmby, Ercolani and Treble (1999). We conclde that the error from this is very small. ii) This measre may misrepresent absence in cases where overtime is worked. We can assess the extent of the bias indced by the omission by comparing the 1989 UK rates reported here and those in Barmby et al. (1999). When overtime is exclded (we define overtime here as the worker reporting posive hors of actal overtime) the overall rate is 3.52%, which decomposes into 3.16% for men and 3.90% for women. Comparable rates when overtime is inclded 3.02% overall, 2.61% for men and 3.52% for women. These facts are consistent wh the idea that absence on overtime hors shold be lower. 2 Bliksvær and Helliesen (1997) sed a measre of absence based only on observing whether an individal was absent from work for the whole of the reference week. This ignores short absences, which can constte a large proportion of total absence (Barmby, Orme and Treble(1991)) and almost certainly nderestimates the overall rate. 4

5 Figre 1: Yearly Absence Rate Absence Rate Year Figre 2 Qarterly Absence Rate 1992q1-2003q q1 1993q1 1994q1 1995q1 1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q12003q1 Qarter 5

6 Figre 3: Monthly Absence Rate, 1992m1-2003m m11993m11994m1 1995m11996m11997m11998m11999m1 2000m12001m12002m12003m1 Month Viewed as a yearly percentage absence rate as depicted in Figre 1 the UK absence rate has hovered jst above 3% since start of or data period, there is perhaps some visal evidence of some slight decline since Of corse this yearly average masks seasonal variation whin the year and variation between individal workers whin the sample. Figres 2 and 3 give some indication of variation whin the year. We condion for seasonal variation in the regressions we report by inclding monthly dmmies 5. Trends by Gender and Age Figre 4 and 5 give a breakdown of absence rates by gender and age over or period of stdy. 6

7 Figre 4: Absence Rates by Gender.05 Male Employees Female Employees Year Figre 5: Absence Rates by Age and Gender Male Employees Female Employees age As might be expected absence rates increase wh age for both genders. There is a stark fall in absence rates at both men and women s conventional retirement age. We 7

8 interpret this is the following way; those workers who contine to work beyond retirement age are a self selected grop wh a relatively lower propensy for sickness absence. 6. Effects of Contracted Hors and Wage. If the view is taken that observed absenteeism is a labor spply response then the terms of the labor contract which the worker faces is going to be centrally important. We are able to observe two aspects of this contract in the LFS data. Firstly hors of work, and secondly an estimate of the average horly wage rate, this estimate is constrcted from gross (weekly) wage which is observed from 1993, by dividing this by sal hors pls 1.5 time sal hors of paid overtime. We concentrate on hors of work inially as this potentially has something to say abot working time reglation. Figre 6 provides a plot of the absence rate for varios levels of sal work hors. These are sal work hors that exclde overtime and are therefore taken to represent weekly contracted work hors. Figre 6: Absence Rates by weekly contracted work hors (excldes all overtime) Weekly contracted hors (no OT) 8

9 The rate of absence appears, on the evidence of this aggregate table, to increase wh sal hors of work and then fall again. This reslt is mirrored in the regression reslts where the effect of contracted hors increases inially and then falls. Figre 7 illstrates the sickness absence rate for varios levels of the natral log of the horly wage. This wage has been deflated to year 2000 prices sing the retail price index (RPI) that excldes mortgage repayments. The RPI has been sed as we assme workers are interested in the prchasing power of their earnings in their income/leisre tradeoff. Figre 7: Absence Rates by natral log of horly wage rate ln(horly Wage), at 2000 prices 7. The Data. Table 1 gives smmary statistics for the data we se in or analysis. Sample period March 1984 to Febrary 2003 for entire dataset, March 1992 to Febrary 2003 for wage variable. The wage variable is indexed to 2000 prices and covers only 19% of the sample. Note that UK LFS respondents are only asked qestions regarding their earnings in their final and (as of 1997q1) first interviews. 9

10 Variable Table 1. Smmary statistics on variables. Observations Mean Stand. Dev. Min Max Dependent Variable R = 100 A / C i i i 2,709, Contractal Characteristics w C i [Worked contracted hors, 2,709, exclding overtime] C i [Usal contracted hors, 2,709, exclding overtime] ( C ) 2 2,709, i s 2,709, A i [see eqation 2] 2,709, C i [see eqation 3] 2,709, Worked Paid Overtime 2,709, Worked Unpaid Overtime 2,709, ln(wage) 521, Demographic Characteristics Age [eqals 64 if Male Age>64, or 59 if Female Age >59] 2,709, Age 2 2,709, Retirement Age [eqals 1 if Male 2,709, Age>64 or Female Age>59] Female 2,709, Female Age 2,709, Female Age 2 2,709, Married or Cohabing 2,709, Female Married or Cohabing 2,709,

11 8. Regression Reslts Table 2 reports a linear regression of the individal absence rates for the whole 18 year period. Individal characteristics are inclded wh appropriate interactions together wh year and month dmmies. The regression reslts confirm many of the broad implications given by the aggregate plots. Women have higher absence rates that men as seen in Figre 4, however as was noted in Barmby, Ercolani and Treble (2002) this effect comes throgh the interaction of gender wh age and maral stats, as these interaction effects are significant while the gender dmmy self is insignificant. The effect of age self is interesting as can be seen in Figre 5. On attaining retirement age those workers who choose to contine to work have markedly lower absence rates, the plot appears to indicate that this is arond 2.5% points, however the regression reslts indicate that after controlling for other things this gap is closer to 1.9% points. Table 3 reports a linear regression for the 11 year sb-period for which we have earnings data, allowing s as far as we are able to analyse the effects of contract strctre. In terms of the significance of the coefficients in the regression another notable aspect is that the regressors measring aspects of the contract (Usal Hors, Usal Hors sqared, Wage and actal hors of paid overtime) appear to be relatively more significant than individal worker characteristics. This implies that the rate of absence is ssceptible to inflence from policies which affect these contractal arrangements, for example working time reglation and taxation on earnings. 11

12 Table 2 Regression of Absence Rate R = 100 A / C i i i Explanatory Variables Coefficient t-statistic Constant Demographic Characteristics Age Age Retirement Age Female Female Age Female Age Married or Cohabing Female Married or Cohabing Contractal Characteristics C i ( C i ) Worked Paid Overtime Worked Unpaid Overtime [exclded] ln(wage) Indstry Dmmies Agricltre, Forestry & Fishing Energy & Water Spply Metal Goods, Engineering, Vehicles Minerals, Ores, Metals & Chemicals Other Manfactring Indstries Constrction Distribtion, Hotels & Catering, Repair Transport & Commnication Banking & Financial Services F-test Significance Year Dmmies [joint significance test] F = Month dmmies [joint significance test] F = Significance of regression F = Observations R Omted indstry is Other 12

13 Table 3 Regression of Absence Rate R = 100 A / C i i i Explanatory Variables Coefficient t-statistic Constant Demographic Characteristics Age Age Retirement Age Female Female Age Female Age Married or Cohabing Female Married or Cohabing Contractal Characteristics C i ( C i ) Worked Paid Overtime Worked Unpaid Overtime ln(wage) Indstry Dmmies Agricltre, Forestry & Fishing Energy & Water Spply Metal Goods, Engineering, Vehicles Minerals, Ores, Metals & Chemicals Other Manfactring Indstries Constrction Distribtion, Hotels & Catering, Repair Transport & Commnication Banking & Financial Services F-test Significance Year Dmmies [joint significance test] F = Month dmmies [joint significance test] F = Significance of regression F = Observations R

14 9. Conclding Remarks We have analysed the determinants of absenteeism sing 18 years of Labor Force Srvey data sing p to 2.7 million individal observations. Althogh on aggregate the absence rate appears to change only marginally over this period, individal level regressions show that the absence rate varies between sb-grops of the data and is particlarly sensive to variables which measre aspects of the workers contractal arrangements. This is important both for individal employers who in designing labor contracts who recognise that workers will respond to these contracts in optimal ways and that these responses might well involve increased costly absence. It is also important for policy makers who might be concerned wh reglatory measres involving sick pay benefs and working time arrangements. 14

15 References Allen, S. G. (1981). An empirical model of work attendance. The Review of Economics and Statistics, 71: Barmby T, Ercolani M and Treble J (1999) Sickness Absence in Brain: New Qarterly and Annal Series from the GHS and the LFS Labor Market Trends Agst pp Barmby T, Ercolani M and Treble J (2002) Sickness Absence: An International Comparison The Economic Jornal Vol 112. pp F315-F331. Bliksvær, T. and Helliesen, A. (1997): Sickness Absence: A Stdy of 11 LES Contries Mimeo NOVA (Norwegian Social Research). Coles M G and Treble J (1993) The Price of Worker Reliabily Economics Letters Vol 41 pp Coles M G and Treble J (1996) Calclating the Cost of Absenteeism Labor Economics Vol 3 pp Dnn and Yongblood (1986) Absenteeism as a Mechanism for Approaching an Optimal Labor Market Eqilibrim Review of Economics and Statistics