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Motives for occupational risk management in large uk companies
Safety Science, Vol. 22, No. 1-3, pp. 229-243, 1996 Copyright 0 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0925-7535/96 $15.00 + 0.00
Pergamon
SO9257535(96)00017-3
MOTIVES FOR OCCUPATIONAL RISK MANAGEMENT IN LARGE UK COMPANIES S.G. Ashby a, S. R. Diacon b,* a Sheffield Hallam University, Sheffield, UK b University of Nottingham Insurance Centre, School of Management and Finance, University of Nottingham, Nottingham, NG7 2RD, UK
Abstract-This paper attempts to explain why large UK corporations undertake measures to reduce the risks to which their employees are exposed, namely those of occupational injury and/or redundancy. Empirical results are based on a postal questiomtaire survey of 127 corporate risk and fmance managers selected from a population of the 350 largest UK companies. ‘Ibis survey shows that the primary motives associated with occupational risk management am those of regulatory compliance and avoidance of legal liabilities. In addition, the importance attached to these incentives appears to be influenced by a number of firm-specific characteristics and is positively related to capital intensity. This result has implications for government safety policy, since effectiveness of government regulations could be increased if safety inspectors were able to identify firms that place a high priority on the management of occupational risk. Copyright 0 1996 Elsevier Science Ltd
1. Introduction Numerous theories exist that try to explain why firms would wish to reduce the risks faced by employees. The purpose of this paper is to investigate these theories and to identify reasons that large UK firms spend resources on occupational risk management in practice. The main part of this study reports results of a postal questionnaire survey distributed to the risk, insurance and finance managers of 127 large UK companies in summer-autumn of 1993. As well as summarizing respondents’ motives for the practice of occupational risk management (or their perception of such motives), we also attempt to discover whether these factors differ systematically across our sample according to firms’ financial and organizational characteristics. The paper proceeds with a brief survey of the hypothesized motives for occupational risk management expenditure. The methodology used for collecting the data is then addressed and summary statistics presented. Section 4 tests for differences between firms’ occupational risk
l
Corresponding author.
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management objectives using cross-section econometric analysis. There then follows a brief conclusion, with some comments on the implementation of risk reduction strategies.
2. Motives for managing occupational risks Employees are exposed to the risks of both physical injury and redundancy - risks they generally cannot readily remove for themselves. Employees faced with a high risk of physical injury or redundancy are likely to require some form of extra compensation in the form of higher wages. They may also lower their productivity, leave their company, or sue their employers for negligence in the event of physical injury. In addition, there are a number of health and safety regulations - for example, the Health and Safety at Work etc. Act (Great Britain, 1974) and the Management of Health and Safety at Work Regulations (Great Britain, HSC, 1992) - designed to protect employees, and failure to comply with these laws can lead to the imposition of fines and possibly even the imprisonment of senior managers. 2.1. How market forces promote safety Since Adam Smith’s observation in the 18th century that workers in unsafe or otherwise unpleasant jobs demand a compensating wage differential, numerous authors have explored this risk-wage trade-off (for example, Oi, 1973, 1974; Thaler and Rosen, 1976; Rosen, 1986). However, Viscusi (1978, 1979a, 1993) has perhaps made the biggest contribution to this literature. Viscusi predicts that employees’ wage demands will increase (or conversely that they will sacrifice an increasing amount of their income to improve safety) with both the frequency and severity of loss and the degree of risk aversion they exhibit. Many labour market studies have supported Viscusi’s hypotheses, revealing that even individuals who are less risk averse often attach quite significant values to their own lives. Estimations of an individual’s value-of-life range between $3 million and $8 million (1990 US$) for a fatal injury, and between $25,000 and $50,000 for a nonfatal one (see Viscusi, 1993, for a review). Faced with the payment of sizeable wage premiums to workers exposed to occupational risk, firms should possess an incentive to invest in safety in order to increase their profits. Safety expenditure should then be increased up to the point where the marginal cost of safety equals its marginal benefit in terms of a reduction in labour costs. However, Viscusi (1993) doubts the methodological validity of many of the studies on compensating wage differentials, so that estimated wage risk premiums may not always be accurate. Statistics on reported occupational accidents can be unreliable and often vary depending on the definition of serious injuries, whether only work-related accidents are included, and on the criteria used to demarcate specific industry sectors. In addition, published statistics rarely analyse causes of accidents in sufficient detail. While some studies have attempted to counter the measurement problems by analysing workers’ own assessments of the risks they face (see Viscusi and O’Connor, 1984, Gegax et al., 1991), such evaluations can often far exceed actual injury rates (Rundmo, 1992; Viscusi, 1993). If translated into real wage demands, employee overestimations should stimulate safety improvements. However, presentation of questionnaires used by these researchers is likely to have encouraged perceptual biases in respondents, either as a result of framing or mental availability effects. Employees facing a high level of redundancy risk may also demand risk-compensated wage
Occupational risk management
231
differentials. Employees often find it difficult to switch jobs: even if they are geographically mobile and possess transferable skills, they may not want to lose all the firm-specific benefits they have acquired, such as long service pay or extra holiday awards, promotion opportunities and status. Consequently, if risk management can reduce the risk that a particularly serious incident or major product defect may cause bankruptcy, then wage costs may be reduced (Mayers and Smith, 1982; Shapiro and Titman, 1985). In addition, firms may find that the productivity of their workforce will rise in response to increases in risk management expenditure, since firms failing to ensure adequate protection against physical injury or redundancy are likely to lose their most productive workers (Shapiro and Titman, 1985) and de-motivate those that remain (Brockner et al., 1992). 2.2. The role of governmen? regulation If workers are fully informed about the risks they face, then government regulation is unnecessary. Unfortunately, while the available evidence suggests that workers have some idea about the risks inherent in their chosen occupation, this information is rarely perfect or processed correctly (see Viscusi et al., 1992; Rundmo, 1992). The presence of imperfect information and bounded rationality constrains workers’ ability to respond to risk, thereby diminishing employers’ incentives to introduce effective occupational risk reduction measures. Even if employees are unaware initially of occupational risk, they can often acquire on-the-job experience. Consequently, employers should still invest in occupational risk management, since employees may subsequently exercise their right to leave the firm, thereby raising labour turnover and exposing the firm to additional recruitment and retraining costs (see Viscusi, 1979b, 1980). However, latent long-term hazards (for example, exposures to radiation or other carcinogens such as asbestos) pose a particular problem (see Ringleb and Wiggins, 1990, 1992; Barney et al., 1992) because the effects of such hazards can take years to materialize. Employees will not learn about the potentially fatal consequences of their exposure until long after it has occurred - so preventing them from negotiating a “market” solution in the form of risk-compensated wages. The existence of latent hazards provides a major justification for safety regulations and legal liability laws. However, very few regulations exist to protect employees against effects of legitimate redundancy or wage losses, in recognition of the fact that redundancy risks are often easily observed and much less serious. Liability laws enable employees to receive compensation after the occurrence of injury or illness. Such laws increase the incentive for employers to improve safety even if workers are unaware of the risks involved. However, the compensation provided by liability laws may not provide an effective stimulus for employee safety for a number of reasons. First, the legal uncertainties over whether the employer was actually responsible for an injury (arising from disputes over negligence or contributory negligence, for example) may reduce or even invalidate legitimate compensation claims (Kolstad et al., 1990). Second, firms may be able to avoid paying for any future legal liability claims by divesting hazardous activities into small, legally separate, process-specific companies that do not have a natural interest in safety (Germ, 1993). These small companies may have already ceased operation before the discovery of employee injury, or may have few assets to meet claims for compensation. Ringleb and Wiggins (1990, 1992) and Barney et al. (1992) provide empirical evidence in support of this proposition in the US, concluding that firms will be less vertically integrated as their potential liability increases.
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Regulatory responses are also rarely perfect. While governments generally possess a comparative advantage in information gathering and processing, they may lack the resources to monitor compliance with occupational safety regulations (Kolstad et al. 1990). A recent study in the UK by the Eagle Star Insurance Group revealed that only 30% of firms fully comply with employee health and safety regulations (Corporate Cover, 1994). Gun (1993) blames non-compliance on the generality of many regulations: governments that find it hard to dictate specific responses to safety problems frequently resort to rather vague regulatory requirements, such as demanding firms reduce risks “as far as is reasonably practicable”. Such laws are open to interpretation and can allow firms to exploit legal loopholes in order to avoid necessary safety expenditures.
3. Methodology
and descriptive
statistics
A postal questionnaire was circulated to the finance managers and risk managers of large UK companies in order to explore the reasons that their company’s risk management programme might be targeted at reducing physical and financial risk to employees. Risk management was defined in the opening paragraphs of the questionnaire as follows: “Companies spend money on risk management in an attempt to deal with the aduerse impact that fortuitous risk may have upon their operating cashflows arisingfrom: asset damage (machine breakdown, property loss through fires etc), business interruption, employee injury, damage, liability costs (pollution, product malfunction etc.).”
The empirical analysis is based on responses from 127 risk and finance managers out of a population of the 350 largest UK commercial companies. Following detailed discussions with practising risk managers, the questionnaire was piloted in April 1993 and then circulated in June, from which 96 replies were received. Two copies of the questionnaire were sent to each company, addressed to both the “Finance Manager” and the “Risk Manager”: double replies were received in only five cases and these have been eliminated. A reminder despatched in early September (with a second copy of the questionnaire) produced a further 31 responses. Of the 127 replies, 74 were from people working as risk or insurance managers, 36 described themselves as finance managers, and the remaining 17 were mainly company secretaries. Three-quarters of the respondents reported directly to either the Chief Executive Officer or a main board director. Ashby (1994) provides a complete summary of the questionnaire results.
Table 1 Details of sample firm (n = 127, year = 1992)
Turnover f million Pretax profit as % turnover Gearing ratio
Mean
Standard deviation
Min
Max
Quartiles 1
2
3
28% 8.3 97.8
5244 11.1 157.4
238 - 24.5 0.43
33250 57.3 1633.8
585 . 2.2 35.9
1358 6.6 64.6
3176 11.9 111.0
FT actuaries sector: Capital goods and oil and gas Consumer groups Other groups Source: FAME, London Business School.
51 40 36
Occupational
risk management
Table 2 Details of respondents
233
(n = 127, year = 1992)
Variable name Personal insurance (from I = wholly Company insurance (from 1 = wholly Risk-taker (from I = less to Long-term view (from 1 = less to Direct superior:
Job description:
Years experience:
Mean
% important
% unimportant
2.190
68.2 (insured)
8.7 (uninsured)
2.643
42.9 (insured)
19.1 (uninsured)
2.845
21.4 (more)
28.5 (less)
3.293
33.3 (more)
10.5 (less)
insured to 5 = wholly uninsured) insured to 5 = wholly uninsured) 5 = more) 5 = more) CEO board level non-board level risk/insurance fmance other mean standard deviation minimum maximum
Profit-related pay: (including options)
yes no
Source: Questionnaire
data.
19 77 31 74 36 17 15.9 8.2 0.5 32.0 99 28
Sample firms were selected on the following criteria: firms had to be in the top 350 in the listings; they had to be listed on the FAME database; and banks, insurance underwriting, insurance broking and other financial companies were excluded. A description of the sample firms is provided in Table 1. Times Top One Thousand 1992-1993
Table 3 Description of dependent variables for multinomial-choice 4, where 0 = unimportant and 4 = important) Question:
model (all variables have observed scores y = 0, 1, 2, 3 or
Considering the impact of the risk of physical injury on employees, how important your company’s risk management programme in contributing to the following corporate objectives?
Wording: A more productive workforce, Reducing labour turnover Reducing your company’s wage costs Reducing the legal liability costs of your company Conforming to government safety regulations Question:
is
“Productivity (injury)” “Turnover (injury)” “Wages (injury)” “Liability (injury)” “Government (injury)”
The risk of corporate insolvency exposes employees to the possibility of redundancy. In this respect how important is your company’s risk management programme in contributing to the following corporate objectives?
Wording: A more productive workforce Reducing labour turnover Reducing your company’s wage costs Scores on the original questionnaire
“Productivity (redundancy)” “Turnover (redundancy)” “Wages (redundancy)” were scaled from 1 to 5.
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S.G. Ashby and S.R. Diacon 70
60
50 CA
m
Productivity
m
Turnover
m
Wages
m
Liability
0
Government
z40 4 g 5? 2 30 ep 20
10
0 I
2
Score: 1 unimportant
3
4
to 5 important
Fig. 1. Importance of risk management in reducing employee injury.
The questionnaire sought to collect data on a variety of control variables relating to personal characteristics of respondents. Managers were asked questions relating to their job description, qualifications, pay structure and level of experience. Respondents were also asked questions on their risk attitudes, including a description of the most suitable level of insurance cover for their own possessions and their company’s assets (scored from 1 = wholly insured to 5 = wholly uninsured), and whether they saw themselves as being more or less of a risk-taker than their senior management (scored from 1 = less to 5 = more). Finally respondents were asked whether, in comparison with senior management, they were more or less likely to consider the long-term impact of company investment decisions (scored from 1 = less to 5 = more). Replies to this section are summarized in Table 2. The main section of the questionnaire was designed to find out why firms spend money to reduce both the physical and redundancy risk to which their employees are exposed. Respondents were asked to express, on a scale of 1 (unimportant) to 5 (important>, how important they thought their company’s risk management programme was in contributing to a number of corporate objectives, concerned with reducing the cost associated with occupational risk. The precise wording of these questions is given in Table 3, and responses are summarized in Figs 1 and 2. As can be seen in Fig. 1 in relation to physical risk, respondents placed most emphasis on ensuring statutory compliance with government health and safety regulations (84.0% listed this as 4 = important or 5 = very important) and limiting possible legal liabilities (81.8%). This evidence appears to reject the hypothesis that current regulatory mechanisms are ineffective and confirms the observation by GeM (1993) that the motivation to achieve good health and safety standards is linked primarily with regulatory requirements. Given the relative
Occupational risk management
235
30
25
m
Productivity
m
Turnover
m
Wag=
20 L.l t G! 8 e 15 ;; &z be 10
5
0 1 Score: Fig. 2. Importance
4
2
3
1 unimportant
to 5 important
of risk management
in reducing
5
the risk of redundancy.
unimportance of reduced wages and turnover and increased productivity in motivating safety expenditure, it would appear that government regulations are necessary in order to ensure that employees are not exposed to excessive degrees of risk. In addition, employees do not appear to motivate risk management in order to reduce the risk of redundancy (see Fig. 2).
60 t
50 m z d
40-
g 30;; CL d
zo-
*:m 1
2
Score:
3 1 Discourage
Fig. 3. Does the presence of risk encourage
4
I
to 5 encourage or discourage
divestment?
5
I
236
S.G. Ashby and S.R. Diacon
Score:
1 never
to 5 always
Fig. 4. Do you contract out risky activities?
In order to provide some insight on the risk divestment behaviour noted by Ringleb and Wiggins (1990, 1992) and Barney et al. (19921, respondents were asked whether the level of long-term risk (pollution, latent injuries, etc.> present in some activities encouraged or discouraged divestment, and secondly whether companies contracted out risky activities to small, independent, specialist companies. The answers are summarized in Figs 3 and 4. It would appear that most firms in our sample prefer to control their own risky activities. However, not all firms face large latent hazards, and it is quite possible that those who do divest are exposed to the most serious risks and are seeking to avoid liability claims.
4. The relationship characteristics
between
occupational
risk
management
and
firm-specific
In this section we attempt to discover the extent to which motives for occupational risk management expenditure (as described in Figs 1 and 2) can be explained by variations in the firm’s financial performance and/or by respondents’ personal characteristics. This is achieved by estimating a linear econometric model with questionnaire scores as the dependent variable, regressed against a variety of explanatory variables that reflect the characteristics of the respondents and their employers. The explanatory variables in the regression models use a mixture of questionnaire data on the personal characteristics of the respondent (summarized in Table 2) and company data for the 1992 accounting year from the CD-ROM database FAME. Company size was measured by “turnover’ ’ in SO00 million and profitability by the ratio of profit before tax to sales (“profit ratio”). “Capital intensity” was calculated using the ratio of net tangible assets to
Occupational
237
risk management
number of employees: labour-intensive firms should possess an incentive to reduce the costs associated with both the risk of physical injury and redundancy. “Diversification” was estimated by summing the total number of SIC codes in which a firm operated. “Risk” was measured using the standard deviation of percentage returns on a firm’s shares (non-quoted companies were allocated the industry equally weighted average score) using data kindly supplied by the London Business School Risk Measurement Service. The type and level of risk inherent in an industry could influence how much firms spend on risk management to reduce occupational risk: a dummy variable was therefore included to pick up firms included in the FT Actuaries sector under capital goods or oil and gas (“capital goods dummy”). The firm’s “gearing ratio” (de fme d as the ratio of long-term liabilities and bank overdrafts to share capital and reserves) was included to measure the firm’s exposure to debt and the risk of financial distress and possible bankruptcy. In a more general study of the motives for corporate risk management, Ashby and Diacon (1994) found little evidence that these firm-specific financial variables had any appreciable impact on broad risk management objectives as stated by questionnaire respondents. A description of the various dependent variables (on the importance of the various motives for the management of occupational risk) is provided in Table 3: the questionnaire scores have been recorded to range from O-4 (rather than l-5 as in the original) to comply with requirements of the ordered probit model. The ordered but discrete nature of the questionnaire responses means that the usual ordinary least squares estimation is inappropriate and requires the use of techniques that treat the dependent variables as ranked rather than continuous. We therefore estimate an ordered multinomial probit model y * = p’x + E where y * is a latent (unobserved) variable, /3’ is a row vector of parameters, x is a column vector of explanatory variables, and E is a random disturbance with Standard Normal distribution (for further details see Greene, 1993). The ordered multinomial probit model treats the dependent variable ( y * > as unobservable. However, it is assumed that the actual questionnaire responses ( y> provide censored information on y * in the following form (where the terms pi partition the range of y* ): y=Oif
y*IO
= 1 if o
(1)
= 2 if p,
duly*.
If the random disturbance E has Standard Normal distribution with pdf 4 and cdf @, then Prob[ y = 0] = @( -@‘x) Prob[ y = l] = @( p, - p’x) - @( -p’~) Prob[ y = 2]= @( pz - p’x) - @( p, - /3’~) Prob[ y = 3]= @( pL3- p’x) - @( p* - p’x) and Prob[y=4]=1-@(p3-_‘x).
(2)
238
S.G. Ashby and S.R. Diacon
Table 4 Results of multinomial
probit estimation
Constant Turnover (fOOOm) Turnover
* capital dummy
Profit ratio (o/o) Diversification Capital intensity Gearing Risk Capital goods dummy
Profit-related
pay dummy
Risk manager dummy Years Personal insurance Company
insurance
Turnover
Wages
Liability
(injury)
(injury)
(injury)
(injury>
3.067 f3.841 0.168 [1.65] - 0.265
1.671 [2.36] 0.848E-01 il.731 - 0.8598-01
[2.34] - 0.639E-02 [OS 11 -0.174 t2.861 - 0.3 17E-03 [0.61] 0.310E-02
2.602 [3.06] - 0.664E-01 LO.751 -O.l59E-01 [0.16]
11.901 - 0.257E-01 [1.78] 1.259 13.301
[I.491 0.212E-02 [O. 171 - 0.7 19E-0 1 il.541 - 0.884E-03 L1.341 -O.lOlE-02 11.161 0.907E-02 [0.63] 0.468 il.631
2.659 13.591 O.l34E-01 [0.33] - 0.846E-02 [0.171 - 0.970E-02 [0.67] -0.81OE-01 [ 1.641 - 0.624E-03 LO.441 -O.l67E-02 [1.34] - 0.3 16E-02 IO.251 0.203 LO.741
0.251 [0.84] 0.200 [0.79] -O.l27E-01 [1.20] -0.312 L2.101 0.5868-01 LO.351
0.638E-01 [0.24] 0.7338-01 [0.30] - 0.377E-02 [0.36] -0.819E-01 IO.591 - 0.298 f1.931
0.854E-01 LO.331 0.457 t1.931 - 0.269E-02 [0.25] -0.172 il.311 - 0.377 [2.35]
0.163 LO.541 0.321 t1.131 - 0.247E-01 [ 1.761 - 0.996E-01 LO.571 -0.22IE-01 LO.131
0.933 [3.20] 1.521 [4.67] 2.741 L7.971
0.63 1 L5.091 I .622 L9.331 2.136 (10.61
0.689 f5.191 1.435 f8.281 2.095 [IO.31
0.770 [ 1.671 1.452 [2.87] 2.381 [4.64]
18.7 0.134 9.9 0.272
27.2 0.012 9.4 0.310
20.1 0.093 14.8 0.063
37.9 0.0003 30.9 O.CQOl
x2(13) Significance x2(8) Significance
Constant Turnover (fOOOm) Turnover
(n = 114, t value, df = 100)
Productivity
* capital dummy
O.l41E-01 [0.84] O.l89E-01 LO.301 0.245E-02 Il.321 O.l42E-02 LO.771 - 0.746E-02 [0.56] 0.202 LO.551
Government (injury)
Productivity (redundancy)
Turnover (redundancy)
Wages (redundancy)
2.586 [2.95] 0.865E-01 LO.731 -0.155 [I .22]
1.517 [2.28] 0.9788-02 [O. 121 - 0.406E-0 LO.431
1.633 [2.64] 0.328E-0 1 fO.441 - 0.324E-0 1 (0.381
1.906 [2.85] 0.398E-01 [OS71 - 0.485E-01 LO.581
I
-continued
opposire
Occupational
risk management
239
Table 4 -continued Government (injury)
Capital intensity
O.l82E-01 [1.14] 0.442E-01 [0.76] 0.582E-02
Gearing
L1.901 - 0.696E-03
Risk
[0.401 - 0.885E-02
Profit ratio (%) Diversification
Capital goods dummy
Profit-related
pay dummy
Risk manager dummy Years Personal insurance Company
insurance
[OS71 0.201 LO.521 -0.158 LO.431 0.139 LO.431 - 0.703E-02 [OX] - 0.857E-01 (0.521 - 0.250 f1.351 0.333
[ 1.281 0.838 [2.62] 1.674 14.751
IL3
x2(13) Significance x2(8) Significance
28.1 0.009 22.6 0.004
Productivity (redundancy)
Turnover (redundancy)
Wages (redundancy)
- 0.220E-01 [ 1.801 - 0.307E-01 [0.61] - 0.258E-03 [0.16] O.l04E-02
- 0.880E-02 IO.691 -O.l26E-01 [0.25] - 0.227E-03 fO.171 -O.l21E-02
-O.l92E-01 [I.451 - 0.436E-01 [0.81] - 0.282E-03 IO.171 - O.l52E-02
11.OO] -O.l98E-01 L1.451 0.147 LO.451
[0.83] - 0.783E-02 IO.591 0.224 IO.691
io.911 - O.l24E-0 1 [0.84] 0.306 LO.941
aProb[ y = O]/ax
0.619 i5.491
0.475 [4.62]
[ 1.041
[Oh01 - 0.292 L2.001 - 0.7OOE-01 LO.491 0.456 i4.551 1.069 [7.88] 1.721 [8.65]
1.369
[8.(31
20.6 0.08 1 7.2 0.516
The marginal effects of a change in a regressor obtained by partial differentiation of (2) to get:
0.439 [1.67] -O.lOlE-01 il.011 -0.190 Il.211 - 0.276 Il.811
0.262 IO.951 0.541 [2.23] -O.l09E-01 IO.981 -0.179 f1.151 - 0.283 il.981
0.286
0.529 l1.911 0.586 L2.311 - 0.628E-02
1.058
2.174 [8.65]
L7.391 1.627 [8.98]
17.6 0.175 4.5 0.813
22.0 0.056 8.6 0.378
on the probabilities
= - C#J(- p’x) p
Prob[ y = jl
are (3)
8Prob[y=l]/dx=[~(--P’x)--~(~,-/3’x)]/3 aProb[ y = 21/8x
= [ 4( pl - p’x)
- 4( CLZ- P’x)]
P
aProb[ y = 3l/ax
= [ 4( CL~- TX)
- 4( p3 - TX)]
P
aProb[ y = 41/8x
= +( p3 - p’x) p.
and
Greene (1993) notes that care should be taken in interpreting the signs of the coefficients p in ordered multinomial models because the impact dProb[ y = jl/ax depends on j. If p > 0
240
S.G. Ashby and S.R. Diacon
then, from (3) JProb[ y = O]/ax < 0 (so that the probability that the questionnaire respondent chooses the lowest score is reduced) and aProb[ y = 41/8x > 0 (i.e., the probability of the highest score is increased), but the signs of the remaining marginal impacts are indeterminate. The estimation of this ordered multinomial probit model with explanatory variables that represent the characteristics of both the firm and the respondent allows us to investigate a number of premises about motives underlying management of occupational risk. We are interested to test the following hypotheses: (1) that the motivation for occupational risk management is not randomly distributed among firms but depends on the firm’s financial performance and/or the preferences of its management. Germ (1993) suggests that large companies will be more concerned about the health and safety of employees than their smaller competitors will be, while Moses and Savage (1994) found that the motor accident rates of US trucking companies were strongly inversely related to company size; (2) that occupational risk management varies according to specific measures of financial performance. Shapiro and Titman (1985) argue that the incentive for corporate risk management will increase as the firm’s financial position deteriorates (profits fall, gearing rises), since any injury or accident is more likely to involve financial distress for the firm’s stakeholders. On the other hand, capital and liquidity constraints may prevent the firm from investing in risk reduction devices in practice; (3) that concern about occupational risk management should increase the more capitalintensive are production methods (and in firms involved in traditionally riskier industrial sectors); and (4) that respondents’ concern about occupational risk management should be stimulated by their personal level of risk aversion, and by their expertise and experience in risk management. The estimation was undertaken on a restricted sample of 114 companies for which a complete data set was available, and the results are summarized in Table 4. The x*(13) statistic is the result of a likelihood ratio test of the null hypothesis that all non-constant parameters are zero, while the x2(8) figure tests the restriction that the parameters of all firm-specific variables are zero (i.e., no firm-specific effects). The null hypothesis that the motivation for the management of occupational risk is randomly distributed is tested by the x2 (13) statistics in Table 4. This is rejected at the 5% significance level in the case of the dependent variables “productivity (injury)“, “wages (injury)” and “government (injury)“, and at the 10% level for the variables “liability (injury)“, “productivity (redundancy)“, and “wages (redundancy)“. However, there is no evidence of any systematic application of risk management among respondents directed at reducing either of the two labour turnover variables. The x2 (8) figure provides a likelihood ratio test of the impact of the firm-specific explanatory variables (turnover, profit ratio, gearing, etc.) in motivating occupational risk management. The only models to show any statistically significant evidence of differences according to corporate financial performance were ones concerned with physical risk to employees [“productivity (injury)“, “liability (injury)” and “government (injury)“]. These are also the reasons illustrated in Fig. 1 as being the most important rationale for the management of occupational risk. There is little evidence that a firm’s financial performance influences the incentive to manage redundancy risk. The insight that a firm’s financial performance can influence its motivation for occupational risk management - in the case of the dependent variables “productivity (injury)“, “liability (injury)” and “government (injury)” - can be investigated further by examining
Occuputional
risk management
241
the estimated coefficients and their significance in Table 4. Occupational risk management that reduces physical injury in order to encourage labour productivity [“productivity (injury)“] is significantly affected by gearing, the capital goods dummy, turnover, the interaction variable between turnover and capital goods, diversification and risk. Respondents were more likely to regard this motive as important (i.e., choose the highest value of the dependent variable) in non-diversified firms in the capital goods sector with high gearing ratios and low variability of return on equity. Occupational risk management in order to reduce liability costs arising from physical injury [“liability (injury)“] and conform to safety regulations [“government (injury>“] appear most responsive to changes in the firm’s capital intensity: in both cases the estimated coefficient is positive (with t = 1.32 and 1.90, respectively) indicating that the marginal impact is negative for j = 0 (i.e., “unimportant”) and positive for j = 4 (“important”). Thus, an increase in the capital intensity of production should increase the probability that occupational risk management (which reduces liability costs and/or increases regulatory compliance) is regarded by respondents as important. The insignificant coefficient for turnover, combined with the negative coefficient for the interaction between turnover and the capital goods dummy, shows that concern for employee safety is not influenced by the mere size of the firm (as suggested by Genn, 1993) but rather by the size of the firms’ capital assets. The fourth hypothesis was concerned with the preferences, experience and status of individual respondents. An examination of the coefficients of the respondent-specific variables for the various models of Table 4 shows a remarkable degree of conformity in their sign (if not significance). In general, the profit-related pay dummy and the risk manager dummy variables have positive coefficients across all dependent variables: thus, respondents who describe themselves as risk managers or who have some kind of profit-related pay are more likely to regard risk management as important on all fronts. On the other hand, the length of job experience in the respondent’s existing field (“years”) has a consistently negative coefficient. Finally, two variables were used to proxy respondents’ risk aversion: “personal insurance” denotes the response to a question about the most suitable level of insurance for their personal possessions (where 1 = wholly insured and 5 = wholly uninsured), and “company insurance” reports the response to a similar question about the companies’ assets. The negative coefficients support the contention that respondents who are risk averse are more likely to favour occupational risk management.
5. Conclusion
In this paper we seek to identify the reasons that large UK companies spend money on occupational risk management. A survey of 127 risk managers and finance managers conducted in late 1993 indicated that respondents placed most emphasis on ensuring statutory compliance with health and safety regulations and avoidance of legal liability suits. In contrast, respondents do not believe that employees are able to encourage corporate risk management expenditure via the various “market” mechanisms. Whether this is due to information gathering problems or simply a lack of bargaining power, it would appear that labour market forces are largely ineffective in motivating occupational risk reduction by companies, so that government regulations are necessary in order to protect employees against excessive levels of workplace risk. Subsequent estimations using a multinomial probit model revealed that the various motives
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for occupational risk management are not randomly distributed across firms but depend on the firm’s financial characteristics and the preferences of its management (as represented by respondents). However, the firm’s financial performance seemed to influence motives for reducing the risk of physical injury in only three cases: in order to increase labour productivity, reduce legal liability costs and conform to safety regulations. The conclusion that risk management objectives might depend on the firm’s financial characteristics supports the arguments of those (such as Shapiro and Titman, 1985) who regard risk management as a valid financial instrument. In some instances, a firm’s financial performance can change the motivation for occupational risk management. For example, although an increase in capital intensity increases the importance attributed to risk management directed at reducing liability awards or complying with safety regulations, it has the opposite effect on motivation to improve labour productivity. Similar (but weaker) behaviour is observed in response to an increase in profitability. While capital intensity does not influence the importance of wages, productivity or labour turnover in motivating risk management, it does seem to have a significantly positive effect on the importance attached to both regulatory compliance and liability cost reduction. This suggests that health and safety regulation is given greater emphasis in capital intensive firms and less so in labour-intensive ones. Although there are a number of possible explanations for this result, the research of Barney et al. (1992) suggests that capital-intensive firms are more concerned at the prospect of financial penalties (as imposed by the courts and/or regulators) because they possess the assets with which to pay (that is, they are not “judgment proof”). The result that the motivation for occupational risk management is more prevalent amongst those whose job specification covers risk management (that is, when the dummy variable “risk manager dummy” = 1) is not very surprising. It is reassuring nonetheless to discover that the appointment of a risk manager is associated with an increase in the importance attached to the risk management function. Finally, the results of this paper provide some clear indicators for the direction of further research into the management of workplace risk. First, there seems to be some value in distinguishing between the various motivations for managing risk to employees, since our analysis highlights particular differences between risk management in order to increase labour productivity on the one hand, and reducing legal and/or regulatory costs on the other. Second, we find little evidence that firms respond actively to labour market pressures in managing workplace risk (as implied by Viscusi, 1979a,b, 1993), which suggests that the recent focus on regulatory compliance (of authors such as Genn, 1993 and Gun, 1993) is likely to be more fruitful. Third, our results point to systematic variations among firms in the importance attributed to managing workplace risk - with capital intensity, rather than firm size, playing an important role: future research will therefore need to be more precise in allowing for scale effects.
Acknowledgement
Simon Ashby gratefully acknowledges financial support from the Association of British Insurers.
Occupationul
risk munugement
243
References Ashby, S.G., 1994. The practice of risk management in large UK companies: Some empirical evidence. Discussion Paper. School of Management and Finance, University of Nottingham, Nottingham. Ashby, S.G. and Diacon, S.R., 1994. The value of corporate risk management: Empirical evidence from large UK companies. Paper presented at the 4th Conference of the European Association of Operational Research Societies (EURO), Litge, Belgium. Barney, J., Edwards, F. and Ringleb, A., 1992. Organisational responses to legal liability: Employee exposure to hazardous materials, vertical integration, and small firm production. Academy of Management Journal, 35: 328-349. Brockner, J., Grover, S., Reed, T. and Dewitt R., 1992. Lay-offs, job insecurity and survivors’ work effort: Evidence of an inverted-U relationship. Academy of Management Journal, 35: 413-425. Corporate Cover, 1994. UK ignores health and safety laws, Corporate Cover, 4 September. Gegax, D., Gerking, S. and Schulze, W., 1991. Perceived risk and the marginal value of safety. Review of Economics and Statistics, 73: 589-596. Genn, H., 1993. Business responses to the regulation of health and safety in England. Law and Policy, 15: 219-233. Great Britain, 1974. Health and Safety at Work etc. Act 1974. HMSO, London. Great Britain: Health and Safety Commission (HSC), 1992. Management of Health and Safety at Work: Approved Code of Practice - Management of Health and Safely at Work Regulations 1992. HMSO, London. Greene, W.H., 1993. Econometric Analysis, 2nd ed. Macmillan Publishing, New York. Gun, R., 1993. The Role of Regulations in the Prevention of Occupational Injury. Safety Science, 16: 47-66. Kolstad, C., Ulen, T. and Johnson, G., 1990. Ex-post liability for harm vs ex-ante safety regulation: Substitutes or complements. The American Economic Review, 80: 888-901. Mayers, D. and Smith, C., 1982. On the corporate demand for insurance. Journal of Business, 55: 281-296. Moses, L.N. and Savage, I., 1994. The effect of firm characteristics on truck accidents. Accident Analysis and Prevention, 26: 173- 179. Oi, W., 1973. Au essay on workmen’s compensation and industrial safety, in: Supplemental Studies for the National Commission on State Workmen’s Compensation Laws. US Government Printing Oftice, Washington. Oi, W., 1974. On the economics of industrial safety. Law and Contemporary Problems, 38: 538-555. Ringleb, A. and Wiggins, S., 1990. Liability and large-scale, long-term hazards. Journal of Political Economy, 98: 574-595. Ringleb, A. and Wiggins, S., 1992. Adverse selection and long-term hazards: The choice between contract and mandatory liability rules. Journal of Legal Studies, 11: 189-215. Rosen, S., 1986. The theory of equalising differences, in: 0. Ashenfelter and R. Layard (Ed& Handbook of Labour Economics. North-Holland, Amsterdam. Rundmo, T., 1992. Risk perceptions and safety on offshore petroleum platforms. Part II: Perceived risk, job stress and accidents. Safety Science, 15: 53-68. Shapiro, A. and Titman, S., 1985. An integrated approach to corporate risk management. Midland Corporate Finance Journal, 3: 41-56. whaler, R. and Rosen, S., 1976. The value of saving a life: Evidence from the labour market, in: N. Terleckyz (Ed), Household Production and Consumption. Columbia University Press, New York. Viscusi, W., 1978. Wealth effects and earnings premiums for job hazards. Review of Economics and Statistics, 68: 408-416. Viscusi, W., 1979a. Employment Hazards: An Investigation of Market Performance. Harvard University Press, Cambridge. Viscusi, W., 1979b. Job hazards and worker quit rates: An analysis of Adaptive worker behavior. International Economic Review, 20: 29-58. Viscusi, W., 1980. Imperfect job risk information and optimal workmen’s compensation benefits. Journal of Public Economics, 13: 319-337. Viscusi, W., 1993. The value of risks to life and health. Journal of Economic Literature, 31: 1912-1946. Viscusi, W. and O’Connor, C., 1984. Adaptive responses to chemical labelling: Are workers bayesian decision makers? American Economic Review, 74: 942-956. Viscusi, W., Vernon, J. and Hanington, J., 1992. Economics of Regulation and Antitrust. DC Heath, Lexington, MA.
Pergamon
SO9257535(96)00017-3
MOTIVES FOR OCCUPATIONAL RISK MANAGEMENT IN LARGE UK COMPANIES S.G. Ashby a, S. R. Diacon b,* a Sheffield Hallam University, Sheffield, UK b University of Nottingham Insurance Centre, School of Management and Finance, University of Nottingham, Nottingham, NG7 2RD, UK
Abstract-This paper attempts to explain why large UK corporations undertake measures to reduce the risks to which their employees are exposed, namely those of occupational injury and/or redundancy. Empirical results are based on a postal questiomtaire survey of 127 corporate risk and fmance managers selected from a population of the 350 largest UK companies. ‘Ibis survey shows that the primary motives associated with occupational risk management am those of regulatory compliance and avoidance of legal liabilities. In addition, the importance attached to these incentives appears to be influenced by a number of firm-specific characteristics and is positively related to capital intensity. This result has implications for government safety policy, since effectiveness of government regulations could be increased if safety inspectors were able to identify firms that place a high priority on the management of occupational risk. Copyright 0 1996 Elsevier Science Ltd
1. Introduction Numerous theories exist that try to explain why firms would wish to reduce the risks faced by employees. The purpose of this paper is to investigate these theories and to identify reasons that large UK firms spend resources on occupational risk management in practice. The main part of this study reports results of a postal questionnaire survey distributed to the risk, insurance and finance managers of 127 large UK companies in summer-autumn of 1993. As well as summarizing respondents’ motives for the practice of occupational risk management (or their perception of such motives), we also attempt to discover whether these factors differ systematically across our sample according to firms’ financial and organizational characteristics. The paper proceeds with a brief survey of the hypothesized motives for occupational risk management expenditure. The methodology used for collecting the data is then addressed and summary statistics presented. Section 4 tests for differences between firms’ occupational risk
l
Corresponding author.
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S.G. Ashby and S.R. Diacon
management objectives using cross-section econometric analysis. There then follows a brief conclusion, with some comments on the implementation of risk reduction strategies.
2. Motives for managing occupational risks Employees are exposed to the risks of both physical injury and redundancy - risks they generally cannot readily remove for themselves. Employees faced with a high risk of physical injury or redundancy are likely to require some form of extra compensation in the form of higher wages. They may also lower their productivity, leave their company, or sue their employers for negligence in the event of physical injury. In addition, there are a number of health and safety regulations - for example, the Health and Safety at Work etc. Act (Great Britain, 1974) and the Management of Health and Safety at Work Regulations (Great Britain, HSC, 1992) - designed to protect employees, and failure to comply with these laws can lead to the imposition of fines and possibly even the imprisonment of senior managers. 2.1. How market forces promote safety Since Adam Smith’s observation in the 18th century that workers in unsafe or otherwise unpleasant jobs demand a compensating wage differential, numerous authors have explored this risk-wage trade-off (for example, Oi, 1973, 1974; Thaler and Rosen, 1976; Rosen, 1986). However, Viscusi (1978, 1979a, 1993) has perhaps made the biggest contribution to this literature. Viscusi predicts that employees’ wage demands will increase (or conversely that they will sacrifice an increasing amount of their income to improve safety) with both the frequency and severity of loss and the degree of risk aversion they exhibit. Many labour market studies have supported Viscusi’s hypotheses, revealing that even individuals who are less risk averse often attach quite significant values to their own lives. Estimations of an individual’s value-of-life range between $3 million and $8 million (1990 US$) for a fatal injury, and between $25,000 and $50,000 for a nonfatal one (see Viscusi, 1993, for a review). Faced with the payment of sizeable wage premiums to workers exposed to occupational risk, firms should possess an incentive to invest in safety in order to increase their profits. Safety expenditure should then be increased up to the point where the marginal cost of safety equals its marginal benefit in terms of a reduction in labour costs. However, Viscusi (1993) doubts the methodological validity of many of the studies on compensating wage differentials, so that estimated wage risk premiums may not always be accurate. Statistics on reported occupational accidents can be unreliable and often vary depending on the definition of serious injuries, whether only work-related accidents are included, and on the criteria used to demarcate specific industry sectors. In addition, published statistics rarely analyse causes of accidents in sufficient detail. While some studies have attempted to counter the measurement problems by analysing workers’ own assessments of the risks they face (see Viscusi and O’Connor, 1984, Gegax et al., 1991), such evaluations can often far exceed actual injury rates (Rundmo, 1992; Viscusi, 1993). If translated into real wage demands, employee overestimations should stimulate safety improvements. However, presentation of questionnaires used by these researchers is likely to have encouraged perceptual biases in respondents, either as a result of framing or mental availability effects. Employees facing a high level of redundancy risk may also demand risk-compensated wage
Occupational risk management
231
differentials. Employees often find it difficult to switch jobs: even if they are geographically mobile and possess transferable skills, they may not want to lose all the firm-specific benefits they have acquired, such as long service pay or extra holiday awards, promotion opportunities and status. Consequently, if risk management can reduce the risk that a particularly serious incident or major product defect may cause bankruptcy, then wage costs may be reduced (Mayers and Smith, 1982; Shapiro and Titman, 1985). In addition, firms may find that the productivity of their workforce will rise in response to increases in risk management expenditure, since firms failing to ensure adequate protection against physical injury or redundancy are likely to lose their most productive workers (Shapiro and Titman, 1985) and de-motivate those that remain (Brockner et al., 1992). 2.2. The role of governmen? regulation If workers are fully informed about the risks they face, then government regulation is unnecessary. Unfortunately, while the available evidence suggests that workers have some idea about the risks inherent in their chosen occupation, this information is rarely perfect or processed correctly (see Viscusi et al., 1992; Rundmo, 1992). The presence of imperfect information and bounded rationality constrains workers’ ability to respond to risk, thereby diminishing employers’ incentives to introduce effective occupational risk reduction measures. Even if employees are unaware initially of occupational risk, they can often acquire on-the-job experience. Consequently, employers should still invest in occupational risk management, since employees may subsequently exercise their right to leave the firm, thereby raising labour turnover and exposing the firm to additional recruitment and retraining costs (see Viscusi, 1979b, 1980). However, latent long-term hazards (for example, exposures to radiation or other carcinogens such as asbestos) pose a particular problem (see Ringleb and Wiggins, 1990, 1992; Barney et al., 1992) because the effects of such hazards can take years to materialize. Employees will not learn about the potentially fatal consequences of their exposure until long after it has occurred - so preventing them from negotiating a “market” solution in the form of risk-compensated wages. The existence of latent hazards provides a major justification for safety regulations and legal liability laws. However, very few regulations exist to protect employees against effects of legitimate redundancy or wage losses, in recognition of the fact that redundancy risks are often easily observed and much less serious. Liability laws enable employees to receive compensation after the occurrence of injury or illness. Such laws increase the incentive for employers to improve safety even if workers are unaware of the risks involved. However, the compensation provided by liability laws may not provide an effective stimulus for employee safety for a number of reasons. First, the legal uncertainties over whether the employer was actually responsible for an injury (arising from disputes over negligence or contributory negligence, for example) may reduce or even invalidate legitimate compensation claims (Kolstad et al., 1990). Second, firms may be able to avoid paying for any future legal liability claims by divesting hazardous activities into small, legally separate, process-specific companies that do not have a natural interest in safety (Germ, 1993). These small companies may have already ceased operation before the discovery of employee injury, or may have few assets to meet claims for compensation. Ringleb and Wiggins (1990, 1992) and Barney et al. (1992) provide empirical evidence in support of this proposition in the US, concluding that firms will be less vertically integrated as their potential liability increases.
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Regulatory responses are also rarely perfect. While governments generally possess a comparative advantage in information gathering and processing, they may lack the resources to monitor compliance with occupational safety regulations (Kolstad et al. 1990). A recent study in the UK by the Eagle Star Insurance Group revealed that only 30% of firms fully comply with employee health and safety regulations (Corporate Cover, 1994). Gun (1993) blames non-compliance on the generality of many regulations: governments that find it hard to dictate specific responses to safety problems frequently resort to rather vague regulatory requirements, such as demanding firms reduce risks “as far as is reasonably practicable”. Such laws are open to interpretation and can allow firms to exploit legal loopholes in order to avoid necessary safety expenditures.
3. Methodology
and descriptive
statistics
A postal questionnaire was circulated to the finance managers and risk managers of large UK companies in order to explore the reasons that their company’s risk management programme might be targeted at reducing physical and financial risk to employees. Risk management was defined in the opening paragraphs of the questionnaire as follows: “Companies spend money on risk management in an attempt to deal with the aduerse impact that fortuitous risk may have upon their operating cashflows arisingfrom: asset damage (machine breakdown, property loss through fires etc), business interruption, employee injury, damage, liability costs (pollution, product malfunction etc.).”
The empirical analysis is based on responses from 127 risk and finance managers out of a population of the 350 largest UK commercial companies. Following detailed discussions with practising risk managers, the questionnaire was piloted in April 1993 and then circulated in June, from which 96 replies were received. Two copies of the questionnaire were sent to each company, addressed to both the “Finance Manager” and the “Risk Manager”: double replies were received in only five cases and these have been eliminated. A reminder despatched in early September (with a second copy of the questionnaire) produced a further 31 responses. Of the 127 replies, 74 were from people working as risk or insurance managers, 36 described themselves as finance managers, and the remaining 17 were mainly company secretaries. Three-quarters of the respondents reported directly to either the Chief Executive Officer or a main board director. Ashby (1994) provides a complete summary of the questionnaire results.
Table 1 Details of sample firm (n = 127, year = 1992)
Turnover f million Pretax profit as % turnover Gearing ratio
Mean
Standard deviation
Min
Max
Quartiles 1
2
3
28% 8.3 97.8
5244 11.1 157.4
238 - 24.5 0.43
33250 57.3 1633.8
585 . 2.2 35.9
1358 6.6 64.6
3176 11.9 111.0
FT actuaries sector: Capital goods and oil and gas Consumer groups Other groups Source: FAME, London Business School.
51 40 36
Occupational
risk management
Table 2 Details of respondents
233
(n = 127, year = 1992)
Variable name Personal insurance (from I = wholly Company insurance (from 1 = wholly Risk-taker (from I = less to Long-term view (from 1 = less to Direct superior:
Job description:
Years experience:
Mean
% important
% unimportant
2.190
68.2 (insured)
8.7 (uninsured)
2.643
42.9 (insured)
19.1 (uninsured)
2.845
21.4 (more)
28.5 (less)
3.293
33.3 (more)
10.5 (less)
insured to 5 = wholly uninsured) insured to 5 = wholly uninsured) 5 = more) 5 = more) CEO board level non-board level risk/insurance fmance other mean standard deviation minimum maximum
Profit-related pay: (including options)
yes no
Source: Questionnaire
data.
19 77 31 74 36 17 15.9 8.2 0.5 32.0 99 28
Sample firms were selected on the following criteria: firms had to be in the top 350 in the listings; they had to be listed on the FAME database; and banks, insurance underwriting, insurance broking and other financial companies were excluded. A description of the sample firms is provided in Table 1. Times Top One Thousand 1992-1993
Table 3 Description of dependent variables for multinomial-choice 4, where 0 = unimportant and 4 = important) Question:
model (all variables have observed scores y = 0, 1, 2, 3 or
Considering the impact of the risk of physical injury on employees, how important your company’s risk management programme in contributing to the following corporate objectives?
Wording: A more productive workforce, Reducing labour turnover Reducing your company’s wage costs Reducing the legal liability costs of your company Conforming to government safety regulations Question:
is
“Productivity (injury)” “Turnover (injury)” “Wages (injury)” “Liability (injury)” “Government (injury)”
The risk of corporate insolvency exposes employees to the possibility of redundancy. In this respect how important is your company’s risk management programme in contributing to the following corporate objectives?
Wording: A more productive workforce Reducing labour turnover Reducing your company’s wage costs Scores on the original questionnaire
“Productivity (redundancy)” “Turnover (redundancy)” “Wages (redundancy)” were scaled from 1 to 5.
234
S.G. Ashby and S.R. Diacon 70
60
50 CA
m
Productivity
m
Turnover
m
Wages
m
Liability
0
Government
z40 4 g 5? 2 30 ep 20
10
0 I
2
Score: 1 unimportant
3
4
to 5 important
Fig. 1. Importance of risk management in reducing employee injury.
The questionnaire sought to collect data on a variety of control variables relating to personal characteristics of respondents. Managers were asked questions relating to their job description, qualifications, pay structure and level of experience. Respondents were also asked questions on their risk attitudes, including a description of the most suitable level of insurance cover for their own possessions and their company’s assets (scored from 1 = wholly insured to 5 = wholly uninsured), and whether they saw themselves as being more or less of a risk-taker than their senior management (scored from 1 = less to 5 = more). Finally respondents were asked whether, in comparison with senior management, they were more or less likely to consider the long-term impact of company investment decisions (scored from 1 = less to 5 = more). Replies to this section are summarized in Table 2. The main section of the questionnaire was designed to find out why firms spend money to reduce both the physical and redundancy risk to which their employees are exposed. Respondents were asked to express, on a scale of 1 (unimportant) to 5 (important>, how important they thought their company’s risk management programme was in contributing to a number of corporate objectives, concerned with reducing the cost associated with occupational risk. The precise wording of these questions is given in Table 3, and responses are summarized in Figs 1 and 2. As can be seen in Fig. 1 in relation to physical risk, respondents placed most emphasis on ensuring statutory compliance with government health and safety regulations (84.0% listed this as 4 = important or 5 = very important) and limiting possible legal liabilities (81.8%). This evidence appears to reject the hypothesis that current regulatory mechanisms are ineffective and confirms the observation by GeM (1993) that the motivation to achieve good health and safety standards is linked primarily with regulatory requirements. Given the relative
Occupational risk management
235
30
25
m
Productivity
m
Turnover
m
Wag=
20 L.l t G! 8 e 15 ;; &z be 10
5
0 1 Score: Fig. 2. Importance
4
2
3
1 unimportant
to 5 important
of risk management
in reducing
5
the risk of redundancy.
unimportance of reduced wages and turnover and increased productivity in motivating safety expenditure, it would appear that government regulations are necessary in order to ensure that employees are not exposed to excessive degrees of risk. In addition, employees do not appear to motivate risk management in order to reduce the risk of redundancy (see Fig. 2).
60 t
50 m z d
40-
g 30;; CL d
zo-
*:m 1
2
Score:
3 1 Discourage
Fig. 3. Does the presence of risk encourage
4
I
to 5 encourage or discourage
divestment?
5
I
236
S.G. Ashby and S.R. Diacon
Score:
1 never
to 5 always
Fig. 4. Do you contract out risky activities?
In order to provide some insight on the risk divestment behaviour noted by Ringleb and Wiggins (1990, 1992) and Barney et al. (19921, respondents were asked whether the level of long-term risk (pollution, latent injuries, etc.> present in some activities encouraged or discouraged divestment, and secondly whether companies contracted out risky activities to small, independent, specialist companies. The answers are summarized in Figs 3 and 4. It would appear that most firms in our sample prefer to control their own risky activities. However, not all firms face large latent hazards, and it is quite possible that those who do divest are exposed to the most serious risks and are seeking to avoid liability claims.
4. The relationship characteristics
between
occupational
risk
management
and
firm-specific
In this section we attempt to discover the extent to which motives for occupational risk management expenditure (as described in Figs 1 and 2) can be explained by variations in the firm’s financial performance and/or by respondents’ personal characteristics. This is achieved by estimating a linear econometric model with questionnaire scores as the dependent variable, regressed against a variety of explanatory variables that reflect the characteristics of the respondents and their employers. The explanatory variables in the regression models use a mixture of questionnaire data on the personal characteristics of the respondent (summarized in Table 2) and company data for the 1992 accounting year from the CD-ROM database FAME. Company size was measured by “turnover’ ’ in SO00 million and profitability by the ratio of profit before tax to sales (“profit ratio”). “Capital intensity” was calculated using the ratio of net tangible assets to
Occupational
237
risk management
number of employees: labour-intensive firms should possess an incentive to reduce the costs associated with both the risk of physical injury and redundancy. “Diversification” was estimated by summing the total number of SIC codes in which a firm operated. “Risk” was measured using the standard deviation of percentage returns on a firm’s shares (non-quoted companies were allocated the industry equally weighted average score) using data kindly supplied by the London Business School Risk Measurement Service. The type and level of risk inherent in an industry could influence how much firms spend on risk management to reduce occupational risk: a dummy variable was therefore included to pick up firms included in the FT Actuaries sector under capital goods or oil and gas (“capital goods dummy”). The firm’s “gearing ratio” (de fme d as the ratio of long-term liabilities and bank overdrafts to share capital and reserves) was included to measure the firm’s exposure to debt and the risk of financial distress and possible bankruptcy. In a more general study of the motives for corporate risk management, Ashby and Diacon (1994) found little evidence that these firm-specific financial variables had any appreciable impact on broad risk management objectives as stated by questionnaire respondents. A description of the various dependent variables (on the importance of the various motives for the management of occupational risk) is provided in Table 3: the questionnaire scores have been recorded to range from O-4 (rather than l-5 as in the original) to comply with requirements of the ordered probit model. The ordered but discrete nature of the questionnaire responses means that the usual ordinary least squares estimation is inappropriate and requires the use of techniques that treat the dependent variables as ranked rather than continuous. We therefore estimate an ordered multinomial probit model y * = p’x + E where y * is a latent (unobserved) variable, /3’ is a row vector of parameters, x is a column vector of explanatory variables, and E is a random disturbance with Standard Normal distribution (for further details see Greene, 1993). The ordered multinomial probit model treats the dependent variable ( y * > as unobservable. However, it is assumed that the actual questionnaire responses ( y> provide censored information on y * in the following form (where the terms pi partition the range of y* ): y=Oif
y*IO
= 1 if o
(1)
= 2 if p,
duly*.
If the random disturbance E has Standard Normal distribution with pdf 4 and cdf @, then Prob[ y = 0] = @( -@‘x) Prob[ y = l] = @( p, - p’x) - @( -p’~) Prob[ y = 2]= @( pz - p’x) - @( p, - /3’~) Prob[ y = 3]= @( pL3- p’x) - @( p* - p’x) and Prob[y=4]=1-@(p3-_‘x).
(2)
238
S.G. Ashby and S.R. Diacon
Table 4 Results of multinomial
probit estimation
Constant Turnover (fOOOm) Turnover
* capital dummy
Profit ratio (o/o) Diversification Capital intensity Gearing Risk Capital goods dummy
Profit-related
pay dummy
Risk manager dummy Years Personal insurance Company
insurance
Turnover
Wages
Liability
(injury)
(injury)
(injury)
(injury>
3.067 f3.841 0.168 [1.65] - 0.265
1.671 [2.36] 0.848E-01 il.731 - 0.8598-01
[2.34] - 0.639E-02 [OS 11 -0.174 t2.861 - 0.3 17E-03 [0.61] 0.310E-02
2.602 [3.06] - 0.664E-01 LO.751 -O.l59E-01 [0.16]
11.901 - 0.257E-01 [1.78] 1.259 13.301
[I.491 0.212E-02 [O. 171 - 0.7 19E-0 1 il.541 - 0.884E-03 L1.341 -O.lOlE-02 11.161 0.907E-02 [0.63] 0.468 il.631
2.659 13.591 O.l34E-01 [0.33] - 0.846E-02 [0.171 - 0.970E-02 [0.67] -0.81OE-01 [ 1.641 - 0.624E-03 LO.441 -O.l67E-02 [1.34] - 0.3 16E-02 IO.251 0.203 LO.741
0.251 [0.84] 0.200 [0.79] -O.l27E-01 [1.20] -0.312 L2.101 0.5868-01 LO.351
0.638E-01 [0.24] 0.7338-01 [0.30] - 0.377E-02 [0.36] -0.819E-01 IO.591 - 0.298 f1.931
0.854E-01 LO.331 0.457 t1.931 - 0.269E-02 [0.25] -0.172 il.311 - 0.377 [2.35]
0.163 LO.541 0.321 t1.131 - 0.247E-01 [ 1.761 - 0.996E-01 LO.571 -0.22IE-01 LO.131
0.933 [3.20] 1.521 [4.67] 2.741 L7.971
0.63 1 L5.091 I .622 L9.331 2.136 (10.61
0.689 f5.191 1.435 f8.281 2.095 [IO.31
0.770 [ 1.671 1.452 [2.87] 2.381 [4.64]
18.7 0.134 9.9 0.272
27.2 0.012 9.4 0.310
20.1 0.093 14.8 0.063
37.9 0.0003 30.9 O.CQOl
x2(13) Significance x2(8) Significance
Constant Turnover (fOOOm) Turnover
(n = 114, t value, df = 100)
Productivity
* capital dummy
O.l41E-01 [0.84] O.l89E-01 LO.301 0.245E-02 Il.321 O.l42E-02 LO.771 - 0.746E-02 [0.56] 0.202 LO.551
Government (injury)
Productivity (redundancy)
Turnover (redundancy)
Wages (redundancy)
2.586 [2.95] 0.865E-01 LO.731 -0.155 [I .22]
1.517 [2.28] 0.9788-02 [O. 121 - 0.406E-0 LO.431
1.633 [2.64] 0.328E-0 1 fO.441 - 0.324E-0 1 (0.381
1.906 [2.85] 0.398E-01 [OS71 - 0.485E-01 LO.581
I
-continued
opposire
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Table 4 -continued Government (injury)
Capital intensity
O.l82E-01 [1.14] 0.442E-01 [0.76] 0.582E-02
Gearing
L1.901 - 0.696E-03
Risk
[0.401 - 0.885E-02
Profit ratio (%) Diversification
Capital goods dummy
Profit-related
pay dummy
Risk manager dummy Years Personal insurance Company
insurance
[OS71 0.201 LO.521 -0.158 LO.431 0.139 LO.431 - 0.703E-02 [OX] - 0.857E-01 (0.521 - 0.250 f1.351 0.333
[ 1.281 0.838 [2.62] 1.674 14.751
IL3
x2(13) Significance x2(8) Significance
28.1 0.009 22.6 0.004
Productivity (redundancy)
Turnover (redundancy)
Wages (redundancy)
- 0.220E-01 [ 1.801 - 0.307E-01 [0.61] - 0.258E-03 [0.16] O.l04E-02
- 0.880E-02 IO.691 -O.l26E-01 [0.25] - 0.227E-03 fO.171 -O.l21E-02
-O.l92E-01 [I.451 - 0.436E-01 [0.81] - 0.282E-03 IO.171 - O.l52E-02
11.OO] -O.l98E-01 L1.451 0.147 LO.451
[0.83] - 0.783E-02 IO.591 0.224 IO.691
io.911 - O.l24E-0 1 [0.84] 0.306 LO.941
aProb[ y = O]/ax
0.619 i5.491
0.475 [4.62]
[ 1.041
[Oh01 - 0.292 L2.001 - 0.7OOE-01 LO.491 0.456 i4.551 1.069 [7.88] 1.721 [8.65]
1.369
[8.(31
20.6 0.08 1 7.2 0.516
The marginal effects of a change in a regressor obtained by partial differentiation of (2) to get:
0.439 [1.67] -O.lOlE-01 il.011 -0.190 Il.211 - 0.276 Il.811
0.262 IO.951 0.541 [2.23] -O.l09E-01 IO.981 -0.179 f1.151 - 0.283 il.981
0.286
0.529 l1.911 0.586 L2.311 - 0.628E-02
1.058
2.174 [8.65]
L7.391 1.627 [8.98]
17.6 0.175 4.5 0.813
22.0 0.056 8.6 0.378
on the probabilities
= - C#J(- p’x) p
Prob[ y = jl
are (3)
8Prob[y=l]/dx=[~(--P’x)--~(~,-/3’x)]/3 aProb[ y = 21/8x
= [ 4( pl - p’x)
- 4( CLZ- P’x)]
P
aProb[ y = 3l/ax
= [ 4( CL~- TX)
- 4( p3 - TX)]
P
aProb[ y = 41/8x
= +( p3 - p’x) p.
and
Greene (1993) notes that care should be taken in interpreting the signs of the coefficients p in ordered multinomial models because the impact dProb[ y = jl/ax depends on j. If p > 0
240
S.G. Ashby and S.R. Diacon
then, from (3) JProb[ y = O]/ax < 0 (so that the probability that the questionnaire respondent chooses the lowest score is reduced) and aProb[ y = 41/8x > 0 (i.e., the probability of the highest score is increased), but the signs of the remaining marginal impacts are indeterminate. The estimation of this ordered multinomial probit model with explanatory variables that represent the characteristics of both the firm and the respondent allows us to investigate a number of premises about motives underlying management of occupational risk. We are interested to test the following hypotheses: (1) that the motivation for occupational risk management is not randomly distributed among firms but depends on the firm’s financial performance and/or the preferences of its management. Germ (1993) suggests that large companies will be more concerned about the health and safety of employees than their smaller competitors will be, while Moses and Savage (1994) found that the motor accident rates of US trucking companies were strongly inversely related to company size; (2) that occupational risk management varies according to specific measures of financial performance. Shapiro and Titman (1985) argue that the incentive for corporate risk management will increase as the firm’s financial position deteriorates (profits fall, gearing rises), since any injury or accident is more likely to involve financial distress for the firm’s stakeholders. On the other hand, capital and liquidity constraints may prevent the firm from investing in risk reduction devices in practice; (3) that concern about occupational risk management should increase the more capitalintensive are production methods (and in firms involved in traditionally riskier industrial sectors); and (4) that respondents’ concern about occupational risk management should be stimulated by their personal level of risk aversion, and by their expertise and experience in risk management. The estimation was undertaken on a restricted sample of 114 companies for which a complete data set was available, and the results are summarized in Table 4. The x*(13) statistic is the result of a likelihood ratio test of the null hypothesis that all non-constant parameters are zero, while the x2(8) figure tests the restriction that the parameters of all firm-specific variables are zero (i.e., no firm-specific effects). The null hypothesis that the motivation for the management of occupational risk is randomly distributed is tested by the x2 (13) statistics in Table 4. This is rejected at the 5% significance level in the case of the dependent variables “productivity (injury)“, “wages (injury)” and “government (injury)“, and at the 10% level for the variables “liability (injury)“, “productivity (redundancy)“, and “wages (redundancy)“. However, there is no evidence of any systematic application of risk management among respondents directed at reducing either of the two labour turnover variables. The x2 (8) figure provides a likelihood ratio test of the impact of the firm-specific explanatory variables (turnover, profit ratio, gearing, etc.) in motivating occupational risk management. The only models to show any statistically significant evidence of differences according to corporate financial performance were ones concerned with physical risk to employees [“productivity (injury)“, “liability (injury)” and “government (injury)“]. These are also the reasons illustrated in Fig. 1 as being the most important rationale for the management of occupational risk. There is little evidence that a firm’s financial performance influences the incentive to manage redundancy risk. The insight that a firm’s financial performance can influence its motivation for occupational risk management - in the case of the dependent variables “productivity (injury)“, “liability (injury)” and “government (injury)” - can be investigated further by examining
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241
the estimated coefficients and their significance in Table 4. Occupational risk management that reduces physical injury in order to encourage labour productivity [“productivity (injury)“] is significantly affected by gearing, the capital goods dummy, turnover, the interaction variable between turnover and capital goods, diversification and risk. Respondents were more likely to regard this motive as important (i.e., choose the highest value of the dependent variable) in non-diversified firms in the capital goods sector with high gearing ratios and low variability of return on equity. Occupational risk management in order to reduce liability costs arising from physical injury [“liability (injury)“] and conform to safety regulations [“government (injury>“] appear most responsive to changes in the firm’s capital intensity: in both cases the estimated coefficient is positive (with t = 1.32 and 1.90, respectively) indicating that the marginal impact is negative for j = 0 (i.e., “unimportant”) and positive for j = 4 (“important”). Thus, an increase in the capital intensity of production should increase the probability that occupational risk management (which reduces liability costs and/or increases regulatory compliance) is regarded by respondents as important. The insignificant coefficient for turnover, combined with the negative coefficient for the interaction between turnover and the capital goods dummy, shows that concern for employee safety is not influenced by the mere size of the firm (as suggested by Genn, 1993) but rather by the size of the firms’ capital assets. The fourth hypothesis was concerned with the preferences, experience and status of individual respondents. An examination of the coefficients of the respondent-specific variables for the various models of Table 4 shows a remarkable degree of conformity in their sign (if not significance). In general, the profit-related pay dummy and the risk manager dummy variables have positive coefficients across all dependent variables: thus, respondents who describe themselves as risk managers or who have some kind of profit-related pay are more likely to regard risk management as important on all fronts. On the other hand, the length of job experience in the respondent’s existing field (“years”) has a consistently negative coefficient. Finally, two variables were used to proxy respondents’ risk aversion: “personal insurance” denotes the response to a question about the most suitable level of insurance for their personal possessions (where 1 = wholly insured and 5 = wholly uninsured), and “company insurance” reports the response to a similar question about the companies’ assets. The negative coefficients support the contention that respondents who are risk averse are more likely to favour occupational risk management.
5. Conclusion
In this paper we seek to identify the reasons that large UK companies spend money on occupational risk management. A survey of 127 risk managers and finance managers conducted in late 1993 indicated that respondents placed most emphasis on ensuring statutory compliance with health and safety regulations and avoidance of legal liability suits. In contrast, respondents do not believe that employees are able to encourage corporate risk management expenditure via the various “market” mechanisms. Whether this is due to information gathering problems or simply a lack of bargaining power, it would appear that labour market forces are largely ineffective in motivating occupational risk reduction by companies, so that government regulations are necessary in order to protect employees against excessive levels of workplace risk. Subsequent estimations using a multinomial probit model revealed that the various motives
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for occupational risk management are not randomly distributed across firms but depend on the firm’s financial characteristics and the preferences of its management (as represented by respondents). However, the firm’s financial performance seemed to influence motives for reducing the risk of physical injury in only three cases: in order to increase labour productivity, reduce legal liability costs and conform to safety regulations. The conclusion that risk management objectives might depend on the firm’s financial characteristics supports the arguments of those (such as Shapiro and Titman, 1985) who regard risk management as a valid financial instrument. In some instances, a firm’s financial performance can change the motivation for occupational risk management. For example, although an increase in capital intensity increases the importance attributed to risk management directed at reducing liability awards or complying with safety regulations, it has the opposite effect on motivation to improve labour productivity. Similar (but weaker) behaviour is observed in response to an increase in profitability. While capital intensity does not influence the importance of wages, productivity or labour turnover in motivating risk management, it does seem to have a significantly positive effect on the importance attached to both regulatory compliance and liability cost reduction. This suggests that health and safety regulation is given greater emphasis in capital intensive firms and less so in labour-intensive ones. Although there are a number of possible explanations for this result, the research of Barney et al. (1992) suggests that capital-intensive firms are more concerned at the prospect of financial penalties (as imposed by the courts and/or regulators) because they possess the assets with which to pay (that is, they are not “judgment proof”). The result that the motivation for occupational risk management is more prevalent amongst those whose job specification covers risk management (that is, when the dummy variable “risk manager dummy” = 1) is not very surprising. It is reassuring nonetheless to discover that the appointment of a risk manager is associated with an increase in the importance attached to the risk management function. Finally, the results of this paper provide some clear indicators for the direction of further research into the management of workplace risk. First, there seems to be some value in distinguishing between the various motivations for managing risk to employees, since our analysis highlights particular differences between risk management in order to increase labour productivity on the one hand, and reducing legal and/or regulatory costs on the other. Second, we find little evidence that firms respond actively to labour market pressures in managing workplace risk (as implied by Viscusi, 1979a,b, 1993), which suggests that the recent focus on regulatory compliance (of authors such as Genn, 1993 and Gun, 1993) is likely to be more fruitful. Third, our results point to systematic variations among firms in the importance attributed to managing workplace risk - with capital intensity, rather than firm size, playing an important role: future research will therefore need to be more precise in allowing for scale effects.
Acknowledgement
Simon Ashby gratefully acknowledges financial support from the Association of British Insurers.
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