Accidents and fatal accidents—some paradoxes

Accidents and fatal accidentsÐsome paradoxes

A. Saloniemi*, H. OksanenUniversity of Tampere, School of Public Health, PO 607, SF 33101 Tampere, Finland

Abstract

This study discusses the relationship between fatal accidents in the workplace and certain

macro-structural features of production life in Finland during the period from 1977 to 1991.The fatality rate was studied in relation to variables describing economic activity and thefrequency of non-fatal accidents. Construction and manufacturing were studied separately.

According to the results the relationship between fatal and non-fatal accidents was reverse, inthe case of construction, in a statistically signi®cant way. In the construction industry the fatal-ity rate increased with declining number of cubic metres under construction. The results did not

lend support to pro-cyclic approximations of the relation between business cycles and fatalaccidents, but supported the hypothesis of the di�erent causation of di�erent accident types.On the other hand, in the case of non-fatal accidents, the social construction of accidentstatistics must also be taken into account. # 1998 Elsevier Science Ltd. All rights reserved.

1. Introduction

Throughout the western world the number of fatal accidents at work has been onthe decline since the 1940s (Robinson and Shor, 1989). Fatal accidents tend to con-centrate in branches with more-or-less temporary work organisations and labourmarkets fraught with various kinds of instabilities (Kisner and Fosbroke, 1991;Schnitzer et al., 1993). The branches with the highest level of fatalities are thus likelyto include such industries as mining, o�shore activities and construction (Mikkola,1987; Stout-Wiegand, 1988; Fatal Accidents at Work, 1993).The more detailed structural aspect of deaths at work also appears in other

spheres. Fatal accidents at work happen most often to male workers at the lowestlevels of hierarchies. Race seems to be an important explanatory factor, and it is notpurely by chance that in many cases migrant workers are over-represented in fatalaccident statistics (Niskanen, 1989; Cone et al., 1991; Dwyer, 1991; Sorock et al.,1993). Without stretching the point too much it can be argued that fatal accidentsare the only oneÐand ultimateÐindicator of general deprivation in working life.

SAFETY SCIENCE

Safety Science 29 (1998) 59±66

0925-7535/98/$19.00+0.00 # 1998 Elsevier Science Ltd. All rights reserved.

PII: S0925-7535(98)00016-2

* Corresponding author.

Though these results are important and valid, the main concerns of this study lieelsewhere, i.e. in searching for other ways to analyse existing data on fatal accidents.In this brief study the analysis of fatal occupational accidents is enlarged in twomutually dependent directions.Firstly, one classic way of analysing the `social' or `structural' nature of occupa-

tional accidents is to relate them to business cycles (Kossoris, 1938). The results tendsupport to the `pro-cyclical' relation or theory, i.e. the number of accidents tends toincrease during economic upswings and vice versa (Robinson, 1988; Robinson andShor, 1989; Nichols, 1991; Fabiano et al., 1995).This study follows up this discussion by focusing on fatal accidents at work: how

are business cycles related to fatal accidents?Secondly, the relation between fatal and òther' accidents points in another

interesting direction. As early as the 1930s Heinrich et al. (1980) introduced an in¯u-ential iceberg model of occupational accidents. According to this model materialhazards, near misses, major and minor accidents and deaths at work follow eachother with the same, albeit reducing, logic. Thus, one death at work is a `sign' of alarge number of safety and production problems at the workplace, and the safetyproblems can be tackled to prevent the one death.The empirical testing of Heinrich's ideas can be formulated as a problem of

causation: are the causal chains behind fatal and non-fatal accidents identical ordi�erent? A reasoning based on Heinrichian ideas requires this isomorphism. On theother hand, the validity of this logic has also been challenged. For example, in theirempirical comparison of causation processes between fatal and non-fatal accidents,Salminen et al. (1992) found conclusive support for the hypothesis of di�erentcausation. This is the angle which provides the justi®cation for the analysis of therelationship between fatal and non-fatal accidents. The situation where the two arenot increasing and declining simultaneously can be interpreted as evidence againstidentical causation and vice versa.

2. Materials and methods

The empirical case-analysis focuses on the Finnish manufacturing and constructionindustries during the period from 1977 to 1991 (TyoÈ tapaturmat, 1976±1990, 1993a;Talonrakennustilasto, 1988; Rakentaminen ja asuminen, vuosikirja, 1990, 1991,1992; TyoÈ voimatilasto, 1993). During this period there have been no importantchanges in compensation practices or in the way that accident data were collected.Also the classi®cations of occupations and accidents have remained unchanged. Thedata only cover accidents occurring in the working place: occupational diseases andaccidents happening on the way to or from work have been excluded.Accidents are operationalised using the conventional indicators of accident

frequency (accidents per million working hours) and fatality rate (fatal accidentsper 100 000 persons employed). Markers of economic activityÐnumber of employees,number of working hours, unemployment rate and cubic metres under con-structionÐare used as explanatory variables. All the data used here are based on

60 A. Saloniemi, H. Oksanen/Safety Science 29 (1998) 59±66

statistical material collected and published by Statistics Finland. During this periodthere have been no important changes in compensation practices or in the way thataccident data were collected.Whenever occupational accident statistics are used it is necessary to address the

question of how àctual accidents' and accident statistics are related to each other(e.g. Nichols, 1992). These statistics are compiled on the basis of sick leave andcompensations granted and, therefore, practical and more-or-less rational decisionmaking plays an important role in addition to the àctual' hazard.Statistical data describing deaths at work avoid at least some of these problems:

the de®nition and outcome of a fatal accident is generally unambiguous. However,this is not to say there are no problems of interpretation. For example, the statisticalde®nitions of industrial branches vary over time and between di�erent countries. AsNichols (1992) points out, the more substantial problem for analysisÐalthough notfor real lifeÐis the decreasing tendency of fatal accidents which complicates the useof time-span analyses. In this analysis every year forms its own statistical unit; theperiod from 1977 to 1991 is still too short for time-span analysis.In this study linear regression was used to investigate the e�ect of the explanatory

variables on the fatality rate. Standard normality assumptions were employedfor statistical testing. This led to F-tests for the importance of terms for theexplanation. The p-values for correlation co-e�ciencies are against zero-hypothesis,i.e. correlation is 0.

3. Results

In Finland, where o�shore activities and mining are peripheral industrial sectors,the branch with the highest frequency of fatal accidents is the construction industry.At the end of the 1980s about 8% of all employees worked in building and con-struction, but the industry accounted for 23% of fatal accidents (TyoÈ tapaturmat,1993b).Table 1 describes the general trends in fatal accidents, fatality rate and explana-

tory variables for the construction and manufacturing industries during the periodfrom 1977 to 1991.The correlations between the fatality rate and the possible explanatory variables

remain weak throughout. In the manufacturing industry none of the correlationsreached statistical signi®cance. In building and construction the variables describingaverage labour market situationsÐworking hours, number of workers and unem-ploymentÐshow only a minimal or no correlation with the fatality rate.The ®ndings for the two variables were somewhat unexpected. In building and

construction there was a ®rm and clear connection between the fatality rate and bothcubic metres under construction ( p=0.01) and accident frequency ( p<0.001). Boththese relations are reverse. As can be seen in Fig. 1, the fatality rate in the con-struction industry increases when the accident frequency declines. In manufacturingthe correlation is positive but not statistically signi®cant. In this case the di�erencesbetween the two branches are statistically highly signi®cant ( p<0.001).

A. Saloniemi, H. Oksanen/Safety Science 29 (1998) 59±66 61

Table 1

Annual data from the study period and correlations between both fatality rate and accident frequency and

explanatory variables

Fatality rate

and number

of fatal

accidents

Accident

frequency

Number of

wage earners

(1000 persons)

Working

hours

(millions)

Unemploy-

ment rate

(%)

Cubic metres

under

construction

(millions)

Construction

1977 11.9 (20) 62.4 168 334 12.2 48.12

1978 15.2 (25) 55.6 164 325 14.4 42.78

1979 14.7 (24) 57.7 163 322 11.4 47.62

1980 14.4 (23) 65.6 160 316 8.3 49.83

1981 14.2 (24) 62.6 169 322 6.6 47.17

1982 10.8 (18) 61.9 166 326 8.0 50.32

1983 7.4 (12) 70.0 163 308 9.4 51.44

1984 10.4 (17) 71.3 163 310 9.6 52.13

1985 10.1 (16) 71.3 159 298 10.0 49.64

1986 9.8 (16) 67.9 164 295 10.9 52.27

1987 10.6 (16) 71.3 159 300 10.7 52.64

1988 7.5 (12) 72.7 160 304 9.2 59.95

1989 7.1 (12) 70.7 169 319 6.1 72.79

1990 10.5 (18) 70.1 171 318 7.0 65.52

1991 12.5 (19) 64.5 149 271 17.3 57.99

Correlation with:

fatality rate r=ÿ0.82 r=ÿ0.08 r=0.23 r=0.38 r=ÿ0.63p<0.001 p=0.77 p=0.41 p=0.17 p=0.01

accident frequency r=ÿ0.09 r=ÿ0.42 r=ÿ0.41 r=0.59

p=0.73 p=0.11 p=0.13 p=0.02

Manufacturing

1977 5.9 (34) 53.4 579 1024 4.5

1978 6.2 (35) 50.0 565 1019 5.7

1979 6.0 (35) 54.4 584 1053 3.7

1980 4.4 (27) 56.3 608 1078 2.8

1981 4.1 (25) 53.0 616 1092 3.1

1982 3.2 (18) 51.7 559 1044 4.3

1983 3.8 (22) 50.4 582 1010 5.5

1984 2.1 (12) 49.1 574 998 5.5

1985 4.1 (23) 47.6 564 1000 5.2

1986 3.9 (23) 46.9 594 975 5.9

1987 3.7 (21) 48.5 572 949 4.4

1988 2.2 (12) 48.9 544 923 4.0

1989 3.3 (18) 48.3 544 925 2.9

1990 3.0 (13) 49.0 549 855 2.8

1991 3.5 (19) 41.8 539 796 7.9

Correlation with:

fatality rate r=0.43 r=0.39 r=0.49 r=0.05

p=0.11 p=0.15 p=0.07 p=0.85

accident frequency r=0.65 r=0.83 r=ÿ0.68p=0.01 p<0.01 p<0.01


Cubic metres under construction show a negative correlation, implying contra-cyclic behaviour of fatal accidents with fatality rate (Fig. 2, p=0.11). However, if welook at accident frequency, the ®gures for cubic metres under construction lend clearsupport to a pro-cyclic interpretation (Fig. 3, p=0.02). If an increase in cubic metresmeans more accidents, it also means fewer fatal accidents.

4. Discussion and conclusions

The analysis showed that there is no meaningful connection between fatal acci-dents and business cycles. None of the labour market indicators (number of workersand hours, unemployment rate) in either the manufacturing or construction indus-tries were associated with deaths at work. In this sense the results di�er clearly frommost earlier ®ndings on non-fatal accidents. In terms of fatal accidents the presentdata tend not to support the hypothesis of a pro-cyclic relation between accidentsand economic activities.In the manufacturing industry this lack of correlation with cycles applied across

the board, but in building and construction there was one notable exception. Here,declining production (measured in cubic metres under construction) was followed byan increase in the fatality rate. The accident frequency declines under the sameconditions, which leads to the question of causation.In the case of the construction industry, the present material does not corroborate

the hypothesis of the iceberg-like constitution of safety problems. The results areconsistent with earlier ®ndings which emphasise the speci®c nature of fatalaccidents, their own distinctive logic and their own causes (Robinson and Shor,1989; Salminen et al., 1992).


Statistical data from the construction industry provide yet another angle on thisquestion of the causes of minor and major accidents. If accidents are studied byoccupational group, di�erences in concentrations of minor and major accidentscan be found. In general, accidents seem to concentrate in the lower positions: therisk of accidents among non-skilled workers is twice as high as among carpenters(Rakennustoiminnan tyoÈ tapaturmat, 1986±1987; Niskanen, 1989). As pointed out

Fig. 3. Accident frequency ( y-axis) and cubic metres under construction (x-axis) in the construction

industry.


earlier, the trend was the same with fatal accidents: non-skilled workers on con-struction sites are over-represented in fatal accident statistics.The pattern of serious accidents is di�erent. Independent of business cycles, some

8±9% of accidents are classi®ed as serious, which in Finnish occupational accidentstatistics is de®ned as an accident causing a sick leave in excess of 1 month. In contrastto total accidents, carpenters have, in both relative and absolute terms, distinctlymore accidents of this type than non-skilled workers (TyoÈ tapaturmat, 1976±1990).In brief, on construction sites total accidents and fatal accidents occur more often

in groups characterised by a low occupational status, wage level and job security. Onthe other hand, serious accidents necessitating longer sick leave are, however, morewidespread among workers with better labour market resources. It must be borne inmind that this all happens in fairly similar work environments. So, neither the rela-tionship between accidents and fatal accidents nor analysis of accidents in di�erenttypes of work lends support to the iceberg model. It is not possible to reconstruct aclear and natural chain from minor accidents to fatalities.We can dwell further on these paradoxes by looking at the social constitution of

illness behaviour and accident statistics. As Virtanen (1994) has stressed, sick leaveis the outcome of both the worker's physical state and evaluation of his/her labourmarket situation, which means that there is no direct causation between healthsituation and sick leave. There is no reason to doubt the validity of this result, evenin cases where sick leave is caused by accidents. The contradictions revealed in theoccurrence of accidents, serious accidents and fatal accidents between di�erentoccupational groups point clearly in this direction. It is perhaps somewhat mislead-ing to assume that accidents happening to carpenters are in general more seriousthan those happening to non-skilled workers. More probable is that they are thegroup with su�cient resources to be able take longer sick leave.Under-reporting of accidents might be one explanation for the reverse relationship

between accident frequency and fatality rate (Nichols, 1992). In the building andconstruction industries, where job security is weaker than in manufacturing, thesituation is even more understandable.However, it has to be kept in mind that the statistical material used in this study is

limited and could hardly have provided any conclusive solution either to the problemof the causation of di�erent kinds of accidents or to the paradoxes regarding busi-ness cycles. In any event the outcome does give rise to some questions that warrantfurther consideration in other environments. The paradoxes described above are byno means unknown in the ®eld of public health (Kasl, 1979). It is not di�cult to ®ndand to show how the structural dimensions of society and, for example, health andmortality are connected with each other. It is far more di�cult to shed light on theconcrete processes through which these spheres are mediated to each other.

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Accidents and fatal accidents—some paradoxes