Technological development and occupational accidents as a conditional relationship: A study of over eighty years in the Swedish Mining industry

Journal ofSafety Rcscad, Vol. 27, No. 3, pp. 137-146.1996 Copyright 0 1996 National Safety council and Elswicr Science Ltd Printed in the USA. AU rights reserved 002243751% StS.00 + .OO

Pergamon

PII 50022-4375(%)00014-X

Technological Development and Occupational Accidents as a Conditional Relationship: A Study of Over Eighty Years in the Swedish Mining Industry Vera L. G. Blank, Finn Diderichsen, and Ragnar Anderson

The paper analyzes the relationship between technological development and occupational accidents in the Swedish mining industry from 1911 to 1990. Technological development is divided into three technological stages: handicraft, mechanization, and automation. Some contextual factors related to industrial relations and legislation are also investigated regarding the direction and magnitude of this relationship. It was observed that two types of variables can explain variations in accident rates: (a) variables that affect the likelihood of accidents occurring, such as mechanization, reduction in working hours spent underground, and unemployment; and (b) variables that affect or are affected by the propensity to declare accidents, as determined, for instance, by Sweden’s Work Insurance Act of 1955 or the Work Insurance Act of 1929. This study shows that the relationship between technological development and occupational accidents can be regarded as conditional, since changes in technology are not sufficient in themselves fully to explain variations in accident frequencies.

Vera L. G. Blank, RN, MPH, Assistant Professor at the Department of Public Health, Health Science Center, Federal UnivexsityofSanta-inBrazil;andGuest-and PhDstt&ntatthe~of-HealthandSocial Medick&unitof!3ocialMedic&Kamli&aInsriMeinsweden. Finn Diderichsen, MD, PhD, Associate Professor and Deputy Ch&nan of the Department of International Health and Social Medicine, Unit of Social Medicine at the Kamlinska Institute; and Head of the Social Epidemiology Unit at the Stockho~ County Council. Ragnar Andersson, M.Eng., PhD, Injury Ptngram Director and Head of the Safety Promotion Unit at the Stockholm County Council and the Department of Intemational Health and Social Medicine, Unit of Social Medicine at the Karolinska ItlStiNk

Fall 19%Nolume 27mumber 3

INTRODUCTION This study analyzes the relationship between technological development and occupational accidents in the Swedish mining industry from 1911 to 1990, taking into account three main technological stages: handicraft, mechanization, and automation. It also investigates the role played by certain contextual factors (industrial relations and legislation) regarding the direction and magnitude of the relationship.

137

Technology as a Determinant of Occupational Accidents Knowledge accumulated thus far on the relationship between technological development and occupational accidents is mainly based on studies that compare different technological altematives as they evolve over time. A search of the literature revealed only two longitudinal studies in this arena (Saari, 1982a; 1982b), both of which showed a decrease in injury risk (in terms of number of accidents and severity of injury) as technology developed. The author pointed out, however, that technological change could explain only part of this decrease, since other changes (e.g., economic, organizational, or of some other kind ) can clearly influence accident occurrence and injury severity. Studies comparing different technological alternatives show conflicting results (for a review, see Laflamme, 1993). Some studies demonstrated a positive effect of technological change (i.e., a decmase in accident rates; Asogwa, 1988;Laflamme & Cloutier, 1988); some others have pointed to a negative one (Novek, Yassi, & Spiegel, 1990; Kersten & Uhich, 1976); while still others have shown either an increase or a decrease, largely according to occupation or tasks (Karwowski, Rahimi, & Mihaly, 1988; Laflamme & Vinet, 1988). Although the latter (comparative) type of studies help to illuminate conceivable hazards created by various technological alternatives, it remains difficult to draw conclusions on the effects of any given technology on occupational accidents. This is for at least two reasons, which complement each other. First, there is the complexity of the accident phenomenon, which has a multifactorial genesis (involving technical, human, and organizational factors; Laflamme, 1990, 1993; Andersson, 1991). Second, accident occurrence has both social and economic determinants as well as technological ones (Novek et al., 1990; Saari, 1982a, 1982b); changes in technology are likely to be accompanied by other relevant changes, such as to work organization, labor relations (Novek et al.), or work-safety conditions (Asogwa, 1988). Stages in Technological Development In the few studies that have considered technological development and occupational acci138

dents, productivity and consumption of electrical energy are the two parameters that have generally been employed to measure technological development (Saari, 1982a, 1982b). But these measures are indirect, and do not address the question of the manner in which technology is actually evolving. Despite this, different ways of describing or classifying stages of technological development in qualitative terms have been presented. They vary in level of specificity according to the objective of the investigation at hand or to its area of application, such as a specific work site, a company, or an industrial sector (for reviews, see Seming, Bjiirkstedt, & Westlund, 1987; Helgeson & Johansson, 1984; Lennerltif, 1984; Blauner, 1964). Roughly speaking, as suggested by Blauner as early as in 1964, there are three main stages in technological development: (a) Hundicraf, where most work is done by hand rather than by machines, and where there is a lack of product standardization; (b) Mechanization, where the production process becomes more standardized, where the pace and rhythm of work are largely determined and controlled by machines, and where the level of standardization of products is higher; and (c) Automation, where the production process is accomplished automatically by machines, most commonly operated by remote control or by computer. (In relation to automation, it should be noted that the term is commonly employed simply to designate a “high level of mechanization;” for examples, see Seming et al., 1987; Lennerldf, 1984.) Why Investigate Technological Development Specifically in the Mining Industry? The mining industry has historically been one of the core industries in the Swedish economy. By the mid-18th century, Sweden had become Europe’s largest iron exporter (Nisser & Morger, 1992). Given the importance of mining to the country’s economy and also to Swedish society, it is not surprising that the history of the sector is extremely well documented, not only with regard to technological development, but also concerning occupational accidents. Further, having a high-hazard occupational environment, the mining industry has traditionally had one of the highest accident rates among industrial sectors in Sweden (National Board of Occupational Safety and Health [NBOSH], Journal of Safety Research

1992). Despite this, the accident rate of 170-180 per million working hours in the 1950s had dropped to approximately 60 by the mid- 1980s (Brand, 1990). Material and Methods This ecological time-series-study covers data compiled for the entire Swedish mining industry for the period 1911 to 1990. Information on occupational accidents (excluding accidents traveling to and from work), on number of workers employed, on industrial activities, and on labor laws have been gathered from the publications of the Official Statistics of Sweden (Statistics of Sweden, 1911-1977, 1978-1990, 1911-1954, 1955-1977,1977-1984,1985-1990). Information on the evolution of the Swedish mining industry, with regard to technology, unionization, and legislation on working hours has been obtained from specialized reports and publications (Hult & Nystriim, 1992; Holmgren & Seming, 1991; Eriksson, 1991; Serning et al., 1987). For the 80 years for which the relationship between technology and occupational accidents is examined, there is a focus on the following study variables: Dependent Variables

1. Annual accident rate: number of occupational accidents per 1,OOBworking hours. 2. Annual mortality rate: number of fatal accidents per 1,000 working hours. Independent

Variables

The independent variables are divided into three main groups: technological development, production intensity, and labor history and legislation.

1. Technological development variables (dummy variables): the technological evolution of the Swedish mining industry described by dividing its development into three phases. The time periods are based on the analyses of Serning et al. (1987) and Eriksson (1991), and consist of: (a) Handicraft (1911-1939); (b) Mechanization (1940-1959); (c) Automation (1960- 1990). 2. Production intensity variables (continuous variables): (a) Productivity: total volume of pmducFall 1996Nolume 27LVumber 3

tion (in tons) per working hour. Productivity is an indicator commonly employed to measure production intensity; an increase in productivity may be an expression of more efficient production technology, rationalization of the production process, change in work organization, or change in working methods (Helgeson, 1986). (b) Consumption of electrical energy: total volume of energy in kilowatt hours (kWh). Although only one of the kinds of energy involved in running the #oduction process is considered, an increase in consumption of electrical energy is commonly regarded as an indicator of a rise in production intensity (Saari, 1982a, 1982b). (c) Unemployment rate: percentage of unemployed workers in the labor force per year. Unemployment is a labor-market factor that influences production intensity (Starrin, Lundberg, Angelow, & Wall, 1989). Data on unemployment rates specific to the mining industry were not available for the entire period (only for 1951-1981); the national unemployment rate was used for the other years covered by the study. 3. Labor history and legislation variables (dummy variables): (a) The general strike of 1969 (December 1969 to February 1970). This strike has been regarded as one of the most important events in the history of the Swedish labor movement, since the bargaining issues it involved concerned drastic changes in conditions of work, not simply levels of remuneration (Navarm, 1983). (b) The Work Insurance Acts of 1917, 1929, 1955 and 1977. This legislation determined the definitions of concepts and criteria for the reporting of occupational accidents. (c) The Working Hours Regulations of 1919, 1936 and 1948. These regulations stipulated maximum numbers of working hours per week in the mining industry, in particular for underground workers. As the study covers a period of 80 years, during which several modifications were made to both legislation and national statistical procedures, two adjustments have been made to render data as comparable as possible throughout the time period. Conversions have been made between “number of workers” and “number of hours worked,” and between “horse power” and “kilowatt hour.” All dummy variables were constructed using the values of 1 or 0; 0 indicating the years of 139

absence of an attribute, and 1 indicating the years of presence of that attribute (Table 1). Regression analysis using the Ordinal Least Square (OLS) method was performed to study the relationship between technological develop ment and occupational accidents. Two models for each dependent variable (annual accident rate and annual mortality rate) were constructed. The first model included only the independent variables that represent technological development. Then, in order to investigate the extent to which other factors influenced annual accident or mortality rates, a second model was constructed including technological development variables and the other independent variables on production intensity, and labor history and legislation. The explanatory variables were entered into the regression models according to their strength in explaining portions of the variance in the dependent variables. The t-distribution was used to test a set of null hypotheses that had no relationship between annual accident or mortality rates and the explanatory variables; an F value equal or less than 0.05 was regarded as significant.

RESULTS

Annual Accident Rates A regression of mechanization and automation on annual accident rate is shown in Table 2. Mechanization was followed by a significant increase in the annual accident rate, and automa-

tion by a significant decrease. The two technological-development variables explain 46% of the variation in accident rates. Table 3 shows the full regression model (with all variables that contributed to the final model) on annual accident rate. The variables are displayed in the order they were entered into the model, and they jointly explain 65% of the variation in accident rates. After including other explanatory variables in the regression, the increase in annual accident rate following mechanization remains significant. By contrast, the decrease following automation, while still evident, is no longer statistically significant. The Work Insurance Act of 1955 is the explanatory variable that has the strongest negative relationship with annual accident rate. This Act, which came into force on January 1, 1955, stipulated that occupational-injury insurance was to be coordinated within the framework of general health insurance; at the same time a compulsory sickness-insurance scheme was introduced. Unemployment rate and the Working Hours Regulation of 1936 are the two other variables that are significantly related to an increase in the annual accident rate. The Working Hours Regulation of 1936 reduced maximum working hours from 48 to 45 per week for workers at underground level. Figure 1 shows the curves for “observed” and “predicted” values of the annual accident rate for the full model. The fit of the model is rather good, although some discrepancies are

TABLE 1 DUMMY VARIABLES Variable

Years with

Handicraft

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Mechanization Automation General Work

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..,,.......,..................,..............................

strike of 1969

Insurance

140

...

.

1940-l

990

1960-l

990

1970-l

990

Acts of . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1917-1990

-1929

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

197.9-l

-1955

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1955-1990 1977-t 990

Hours

1

1911-1990

-1917

-1977 Working

.....

Value

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regulations

990

of

-1919

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1919-1990

-1936

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1936-l

990

-1949

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1949-l

990

Journal of Safety Research

TABLE 2 REGRESSION ANALYSIS OF MECHANIZATION AND AUTOMATION ON ANNUAL ACCIDENT RATE

Regression Variable

Parameter

Intercept

....................

Adjusted

Standard

Error

T

I

f

59.2000

................

Mechanization Automation

Estimate

Parameters

19.2450

..................

-28.9095

R’=0.46

observable, particularly for the period 1942-1954 when the observed annual accident rates are higher than predicted.

occurring, and those that influence or am infhrenced by the propensity to declare accidents. Variables affecting the likelihood of accidents occurring. Thrm variables proved to affect the

Annual Mortality Rates The regression analysis of mechanization and

automation on annual mortality rate shows that the rate is significantly related to stage of technological development (Table 4). Following both mechanization and automation, there were significant declines in mortality rates, the variables jointly explaining 40% of the variation. Figure 2 shows the curves for “observed” and “predicted” values for this regression model. None of the other explanatory variables, however, significantly contribute to the “goodness of fit” of the model. DISCUSSION Variations in Annual Accident Rates The variables found to explain variations in accident rates can be divided into two categories: those that affect the likelihood of accidents

likelihood of accidents occurring: mechanization, reduction in working hours for underground miners, and unemployment. Both regression equations reveal that the mechanization of the mining-production process significantly increased the overall risk for accidents, a finding that is consistent with observations made in Saari’s (1982a) study of the construction industry and of the manufacturing of fabricated metal products. The start of the mechanization stage in the 1940s coincides with, and is in part a consequence of, the increase in world demand for Swedish iron ore that took place during this period, particularly following the Second World War. This created the basis for the production of new mining technology and the expansion of the Swedish mining-machine industry (Serning et al., 1987). Previous studies have revealed that the mechanization of a production process might go handin-hand with work intensification; an increase in the pace of work can then be followed by a

TABLE 3 FULL REGRESSION MODEL Regression Variable

Parameter

Intercept Work

Insurance

Act-l

.

Automation Unamploymant Working

rate

hours regulation-l

Mechanization Adjusted

955

936

. .

Estimate

Standard

Error

Parameters T

f

50.3999

3.7218

13.542

0 0001

-25.8458

5.1598

-5.009

0 0001

-8.7763

4.7590

-1.644

0 0692

0.5498

0.2734

2.011

0.0480

18.1248

5.3130

3.411

0.0011

14.4942

5.6447

2.460

0.0154

R’ = 0.65

Fall 19%Nohme

27hWunber 3

141

FIGURE 1 PLOT DIAGRAM OF OBSERVED AND PREDICTED ANNUAL

ACCIDENT RATE FOR THE FULL MODEL

110 0

0

0

100

00

0 so

-0 oooo

60

0

70

0

0

W~o,aa

‘we

0

-0

0 60 6.

ooa~&~~o 0

0000 40

0

Q

30

0

0

OOcOdI.e~#&@wfl0 “O”,

,‘“““‘~1’~‘~“~“~~~‘~““~I~‘~~“~~‘I~~~”~~’~~’~”~’~~~l~‘~~~‘~~~~ 1960 1920 1940 1950 1930

1910

o

0

Z&p0

@.@a@*~~

1960

1970

1990

Observed

0 Predicted

deterioration in work conditions (Haralarnbos h Holbom, 1991; Doray, 1988; Braverman, 1974). In turn, such deteriorating conditions may lead to an increase in the number of accidents (Novek et al., 1990). But other factors more directly related to work safety, such as poor machine protection and inadequate adaptation to new production machines and instruments (Asogwa, 1988). may also explain the increase in the annual accident rate. A further finding of the study is that there is an increase in the annual accident rate as unemployment rises as also observed by Robinson (1988) and Mason (1970). According to Robinson, this association may be due to a decrease in the number of people at work and labor union bargaining power. Nevertheless, a study conducted by Stan-in et al. (1989) found a relationship between unemployment, amount of overtime worked, and work

intensity in manufacturing industry. The fundamental hypothesis underlying the relationship between these factors is that an increase in overtime-working and work intensity will occur sirnultaneously with a rise in unemployment; work intensity can rise as unemployment and overtimeworking increase as a result of an increase in the pace of work. It seems, moreover, that them may be a disciplinary effect, ‘The threat of unemployment has a disciplinary effect on those in work. The fear of unemployment causes them to work harder and to demand less as regards the work environment” (Starrin et al., 1989, p. 273). Effects of this kind may be a possible explanation for the increase in accident risks detected, but further research in this arena is clearly needed. The reduction of hours worked underground prompted by the Working Hours Regulation of 1936 was also followed by an increase in the annual accident rate. But it seems that this vari-

TABLE 4 REGRESSION ANALYSIS OF MECHANIZATION AND AUTOMATION Regression Parameter

Variable

,

Intercept Mechanization Automation Adjusted

142

,,

. ...... . .. . . .....

.. ..

Estimate

Standard

Error

ON ANNUAL

MORTALITY

RATE

Parameters T

I

f

0.04032

17.359

0.0001

-0.1300

0.08312

-2.059

0.0429

-0.2732

0.08229

-4.397

0.0001

0.7000

R’ = 0.40

Journal of Safety Research

FIGURE 2 PLOT DIAGRAM OF OBSERVED AND PREDICI’ED ANNUAL MORTALITY RATE

,940

1szo

,930

ISL(D

0 Observed l

Predicted

able really reflects the effects of unemployment in the mining industry during the depression of the 1930s (nationwide and not mining-sector-specific unemployment rates having been used for this period). Although there was a major decline in the total volume of ore production during the 193Os,productivity was generally relatively well sustained. About 30% of the mining workforce was made redundant, and for workers who remained employed there were reductions in days worked per week and in wage payments (Serning et al., 1987). This may indicate that the observed increase in the annual accident rate was an effect of work intensification resulting from unemployment (as discussed above). Such an effect was not detectable from the nationwide unemployment rates employed for the regression model; nor was it explained by the two other variables used as indicators of production intensity (productivity and consumption of electrical energy). Variables that affect or are affected by the propensity to declare accidents. The effects of the

two legislative changes that established criteria for the reporting of occupational accidents in Sweden are discussed below: the Work Insurance Act of 1955, and the Work Insurance Act of 1929. This study shows that legislative changes for the reporting of occupational accidents have a direct impact on reported accident rates. This applies in the case of the negative relationship found between the implementation of the Work Insurance Act of Fall 19%Nolume 27LVumber 3

1955 and the annual accident rate. As shown by Andersson (1990). the major decmase in the total number of mported accidents following the implementation of the Act can be explained both by means of an under-reporting effect (around 50%) and by earlier registration of minor accidents. Further, the decrease might reflect the impact of a campaign, generally regarded as successful, to reduce accidents in mines, which was coot&rated by the Swedish Mining Association during the 1950s (Serning et al., 1987). On the other hand, changes in propensity to declare accidents may distort the measured effects of other variables. This can be demonstrated by reference to the possible influence of the Work Insurance Act of 1929 on two study variables: mechanization and the Working Hours Regulation of 1936. Pursuant to the Act of 1929 (which remained in force until 1954) all accidents were reported from the first day following their occurrence. Naturally, this led to a substantial increase in accident frequencies reported for this period (Andersson, 1990). The influence of the Work Insurance Act of 1929 may also offer an explanation for the increase in observed accident rates shown in our plot diagram for the period 1942-1954. Variations in Annual Mortality Rates

The study shows that mechanization and automation had a favorable effect on the annual 143

mortality rate. Previous studies have demonstrated that advances in technology, mainly related to accident-avoidance and enhanced machine-protection (Springfeldt, 1993; Trinca et al., 1988). alongside improvements in medical care (Trinca et al.; Saari, 1982b), structural changes, safety rules, and safety measures (Springfeldt; Andersson, 1990). have a considerable impact in reducing the number of fatal accidents. Unfortunately, information on such aspects of occupational safety was not available for this study. Contributions

and Limitationsof the Study

Although an ecological design was the only one appropriate for a study of this kind, it is important to point to a number of methodological difficulties inherent in studies where an aggregated population comprises the unit of analysis. First, it is impossible to draw conclusions on an individual level. Second, as all information is based on averages, measurement errors and misclassifications can arise. In this study, for example, there is clear uncertainty with regard to accident frequencies. As discussed above, given that the legislation that defined the concept of and the criteria for reporting an occupational accident varied over the years, there are some periods in which the actual annual accident rates is overestimated, others in which it is underestimated. Despite the efforts made in this case to include as many factors as possible that might have had an influence on the annual accident rate, the difficulties involved in controlling for confounding variables constitute a problem in this study, as they do in any ecological investigation of this kind. Further, when analyzing the relationship between technological development and occupational accidents, a methodological question that immediately arises is that of how to find an unambiguous measure of technological development (Serning et al., 1987). The most commonly employed, and traditional, indirect indicators of technological advance (productivity and consumption of electrical energy) did not prove to affect accident or mortality rates in this study. Nevertheless, it seems that the division of technological development into three stages (handicraft, mechanization, and automation) adopted here was both applicable and useful. Although 144

the classification is to some extent arbitrary, given that new production techniques are not implemented at the same time in all mines or even in the entire production process within one and the same mine, it still provides an evolutionary scale for depicting the impact of technological development on occupational accidents. Such a scale has the advantage of enabling development to be followed over time, while also taking account of the evolution of the industrial sector itself. CONCLUSION

This study shows that there is a relationship between technological development and occupational accidents. But it also shows that the nature of this relationship is conditional on other factors. If technological-development variables are analyzed in isolation, they can clearly be regarded as at least “potential” determinants of the number and severity of occupational accidents. But when other factors and changes are taken into account, it becomes clear that changes in technology are not sufficient in themselves fully to explain variations in accident frequencies. In this recently developed subject area in the arena of accident research, it remains necessary to develop investigations that help to clarify the influence of technological development on occupational accidents. These, in turn, should help to improve practical safety work. A recommendation for future research is that studies of the relationship between technology and accidents should be conducted at organization/company level or on a specific work site, in which case the classification of stages of technological develop ment could be far more precise. Further, this would facilitate the incorporation into an analysis of other key factors that might influence accident occurrence, such as changes to work organization, forms of remuneration, safety systems, or safety regulations. Possession of such information on work safety should also further our capacity to assess, more specifically, the impact of technological development on fatal accidents. ACKNOWLEDGMENTS

The authors would like to express their gratitude to Dr. Magnus Stenbeck for statistical Journal of Safety Research

advice and to Dr. Lucie Laflamme for helpful comments and suggestions. At the time of this study, Vera L. G. Blank was financially supported by grants from CAPES (Post-Graduate Education Federal Agency/Brazil).

Journal of Gccupational Accidents, 12,155-l 65. L.aflamme, L. (1993). Technological improvement of the

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