Journal of Occupational Accidents, 1 (1976/1977) 281-294 281

o Elsevier Scientific Publishing Company, Amsterdam - Printed in The Netherlands

THE EFFECT OF PIECEWORK ON ACCIDENT RATES IN THE LOGGING INDUSTRY (INCORPORATING A DIFFERENT APPROACH TO THE EXPOSURE PROBLEM)

KEITH MASON

Workers’ Compensation Board of British Columbia, Actuarial and Research Department, 5255 Heather Street, Vancouver, B.C., V5.Z 3L8 (Canada)

(Received November 11,1976)

ABSTRACT

Mason, K., 1977. The effect of piecework on accident rates in the logging industry. Journal of Occupational Accidents, 1: 281-294.

The issue investigated is whether or not piecework has an effect on accident rates in the logging industry of the province of British Columbia. The 1.430 time loss claims for 1972

accidents to buckers and fallers are examined. Several indirect approaches are used in searching for differences in the accident experience of piecework-failers and salary-fallers. An “almost direct” procedure is developed in which the dependent variable is an individual

accident rate and the independent variables are piecework/salary, age, size of firm and geographic location. The conclusion is that piecework does not affect accident rates for buckers and failers. However, a similar analysis finds that piecework-failers do have more severe accidents than salary-fallers.

A. INTRODUCTION

The purpose of this study is to determine whether or not piecework affects accident rates for buckers and fallers in the British Columbia logging industry. A faller is an employee in the logging industry who uses a chain saw to “fell” standing trees; a bucker saws the “felled” trees into log segments. For brevity, the term “buckers and failers” is often replaced with “fallers” in the rest of this report.

There is widespread suspicion that fallers paid by piecework have higher accident rates than those on salary. This is attributed to a presumed increased willingness to cut comers in order to increase output. As an aside, it is inter- esting to note that Kerr, prior to a 1950 study he conducted with manufac- turing workers, speculated that the increased alertness of piecework employees would improve their safety record. (He found no effect in either direction.)

In investigating this question ourselves, we examined all the time loss claims paid by the Workmen’s Compensation Board of British Columbia for 1972 accidents to buckers and fallers. We determined the method of payment,

whether by piecework or salary, from the claim file and analysed the resulting data in various ways, searching for differences between piecework-faller accidents and salary-faller accidents.

B. DATA COLLECTION

We considered all time loss claims for 1972 accidents which were first paid by March 31st, 1973. Bucker and faller claims were identified by means of the occupation code (as it happens, one particular code number identifies buckers and fallers).

In determining the method of payment from the claim documents, we made primary use of the answers to the following two questions:

(1) (Employee form) “What were you earning before deductions at the time you were injured? (Indicate whether by the hour, day, week of month, If paid on some other basis please explain.) $ ____ per ____ ”

(2) (Employer form) “Workman’s gross earnings (enter one rate only) at time of injury. per day $ per week $ per month $ __ ”

Our coding rule was to put a man in the piecework category if there was any indication to that effect on either form. That is, if on either form there was a production rate quoted, or was used a phrase such as “production basis”, “piecework basis” or “contract basis”; or if the worker was a partner or an owner, then he was put in the piecework category. The rationale for this procedure was the belief that a man would not translate a time rate into an equivalent production rate in filling out a W.C.B. form, so that any evidence of piecework should be accepted. Because the other situation is not so clear, however (that is, because a piecework faller might well translate his production rate into an approximate time rate for W.C.B. purposes), we did not put a man in the salary category unless time rates were quoted consistent- ly between forms and were uniformly free of such modifiers as “about” or “approximately”. The existence of discrepancies in quoted time rates, or of such modifiers as “about”, resulted in the man being put in the uncertain category (and thereby not included in the analysis). Table 1 shows the results of this coding.

TABLE 1

Categorization of claims

Piecework 369

Salary 1,035

Uncertain 26

Total 1,430

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C. ANALYSIS

The direct approach would have been to compute an accident rate for piecework-fallers and compare it to the corresponding figure for salary-fallers. We did not do this because we did not have the necessary exposure base (manhours worked by each type of faller) with which to compute these rates. We therefore took the indirect approach of looking for any differences that may exist in the accident experience of the two groups, in particular differ- ences that may be caused by an accident rate differential. Actually, as well as this, we developed an approach that might be called “almost direct”.

1. Indirect methods

In this section we made use of the various accident variables that are coded from each claim by the Statistics section and then stored on tape. We took each of these variables in turn and tested for independence against the piece- work/salary variable. When definite relationships were found, the plausibility of a causative piecework/salary interpretation was assessed.

(a) Part of body injured For those claims with sufficient data, we examined a breakdown by major

groupings of the area of injury (see Table 2). The test for significance indicates that area injured is independent of

piecework/salary.

TABLE 2

Part of body injured

Salary Piecework

Claims % Claims %

Head 86 9.1 33 9.5 Arms, hands 245 25.9 95 27.5

Trunk 198 21.0 82 23.7

Legs, feet 416 44.0 136 39.3

Total 945 100.0 346 100.0

(b) Nature of injury Table 3 gives a breakdown by nature of injury. Nature of injury also turns out to be independent of method of payment.

(c) Source of injury “Source of injury” is the actual agent that contacted the body and caused

the injury. “Bodily motion” is the category for sprain injuries that have no external source (e.g. a man twists awkwardly) (Table 4).

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TABLE 3

Nature of injury

Abrasion Contusion cut

Sprain

Concussion Fracture Multiple

Total

Salary

Claims %

27 2.7 205 20.3 379 37.6 197 19.5

9 0.9 160 15.9

31 3.1

1,008 100.0

Piecework

Claims %

14 3.9 66 18.3

123 34.0 67 18.6

5 1.4 71 19.6 15 4.2

361 100.0

TABLE 4

Source of injury __~~~ _

Salary

-.- __~ Slivers Logs Trees, limbs Ground Powered tools Vehicles Bodily motion

Claims

25 2.6 5 1.4 110 11.4 21 5.8 316 32.8 162 45.2

88 9.1 28 7.8 352 36.5 115 32.0

12 1.2 6 1.7

62 6.4 22 6.1

__ -- %

Piecework

Claims %

Total 965 100.0 359 100.0

“Source of injury” does show a statistical dependence with the method of payment. Interpretation is not obvious and will be combined with that for Table 5.

(d) Type of accident

The “Type of Accident” variable also shows a dependence with method of payment. Looking at Tables 4 and 5, we see that piecework loggers are apparently incurring “struck by falling trees or limbs” accidents as a higher proportion of their accidents than is the case for salary loggers. This is a major type of accident in the logging industry and, one might speculate, a type that would increase with comer cutting work procedures.

This is a risky conclusion, however, since virtually all accident types would lend themselves to such a generalization. As an alternative explanation of

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TABLE 5

Type of accident - _..._._.. -.- --_-..-.-__~.~- --.-_-_--~~-~-_--..-~

Salary Piecework

Claims % Claims %

Struck by stationary objects 13 1.4 Struck by falling objects 218 23.6 Struck by handled material 310 33.5 Struck by N.E.C. 140 15.1 Fall from trees 51 5.5 Fall on same level 72 7.8 Caught in 44 4.8 Foreign matter in eyes 15 1.6 Involuntary motion 48 5.2 Voluntary motion 14 1.5

-37 13 20 15

5 16

6

1.5 34.7 30.2 11.1

3.9 6.0 4.5 1.5 4.8 1.8

Total 925 100.0 332 100.0

Tables 4 and 5, we wondered whether there were proportionately more fallers (than buckers) in our piecework sample. To test this possibility we examined a subsample of claims, and found that failers constituted some 90% of the piecework group but only 80% of the salary group. This explained a part of the dependence in Tables 4 and 5 but still left a significant difference. If, for example, we assume that “struck by falling object” accidents occur only to failers (not to buckers), then we have:

SdarY Piecework

“Struck by falling object” claims 218 115

Number of faller claims ~0.8)(925)=740 tO.9)(332)=299 % of faller claims that are

“struck by falling object” 29.5% 38.5%

Unfortunately, the most realistic assessment is probably that the type of data in Tables 2-5 is not capable of shedding much light on the issue in question.

(e) Fu tulities One might suspect that piecework accidents would result in proportionate-

ly more fatalities (i.e. that any comer cutting would occur more frequently in high risk work decisions, since they would tend to be more related to in- creased output).

As we can see in Table 6, however, there is no evidence of this in our data.

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TABLE 6

Fatalities --. -_-.--.

Salary Piecework --.- Claims % Claims %

~__

Fatality 11 1.1 3 0.8 Non fatality 1,024 98.9 366 99.2

Total 1,035 100.0 369 100.0 ---

ff) Time of day One might hypothesize that if piecework-failers have higher accident rates

it would be because of a faster work pace resulting in fatigue by day’s end. Table 7 shows accidents distributed by the time elapsed since the start of the shift. It is possible to construct this table because time of accident and time of shift start are both coded by the Statistics section.

TABLE 7

Elapsed time between start of shift and accident ---. ..--_____-.-_-._--.-----..-.-__ _

Salary Piecework ___.--~ I_-. __ -__ -......-. -.- ~ _-.- . - Claims % Claims %

I_-~-~.--.__---- ____-.___.__...-_-_ __ __

First hour 47 4.9 19 5.7 Second hour 118 12.3 43 12.8 Third hour 168 17.6 60 17.8 Fourth hour 151 15.7 53 15.8 Fifth hour 59 6.1 33 9.8 Sixth hour 90 9.4 20 6.0 Seventh hour 129 13.4 29 8.6 Eight hour 107 11.1 54 16.0 Ninth hour 66 6.9 18 5.4 Tenth hour 25 2.6 7 2.1

Total 960 100.0 336 100.0 ~ _. _..~_____ --_-_--..- ---- .-- --

This table does indicate that elapsed time and payment method are not independent, To assist us in investigating the dependence, we have Fig. 1, a picture of Table 7. The most prominent feature of this graph is the mid-day decline in accident occurrence for both payment groups. This is obviously happening because of meal breaks at that time reducing the exposure to injury. It is insisting to note that the minimum for piecework-fees occurs in the sixth hour and for salary-failers in the fifth hour. Piecework-fahers evidently work a little longer before eating lunch. Both groups reach a peak two hours after their lunch minimum (presumably as fatigue increases) and then decline thereafter (presumably as exposure decreases).

20 I

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5 c

I 1 / I / _-^L

First Second Third Fourth Fifth Sixth Seventh Elgth Nnth Tenth

Hour

Fig. 1. % Distribution of accidents for B.C. buckers and fallers, 1972, by hour of working day in which accident occurred.

It would therefore seem that the difference in patterns between the graphs can be explained by reasons other than accident rate differentials between salary and piecework-fallers.

(g) Day in working week Since the Statistics section also codes the days worked in the week, we are

able to produce Table 8.

TABLE 8

Number of days between last day off work and accident

Salary Piecework

Claims % Claims %

First day back 210 22.2 68 20.3 Second day back 197 20.8 77 23.0 Third day back 182 19.2 65 19.5 Fourth day back 182 19.2 62 18.6

Fifth day back 147 15.5 54 16.2 Sixth day back 29 3.1 8 2.4

Total 947 100.0 334 100.0

This variable turns out to be statistically independent of method of pay- ment. The motivation for examining it in the first place was the thought that with a presumably harder piecework work pace there would be some

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cumulative fatigue by week’s end and perhaps more accidents. Instead we see in Fig. 2 a remarkable similarity. Both groups exhibit a higher percentage of accidents on the first day back to work and a decline thereafter. (This is a pattern we have observed in other industrial studies.)

To summarize Section 1, we have detected very few differences between piecework-faller accidents and salary-faller accidents. For those we did find (Sections c and d) the interpretation regarding a relationship to method of payment is not clear. Overall, then, the evidence so far offers no support for the hypothesis that piecework-fallers have higher accident rates than salary- fallers.

20 i

1 i 1 I I I

First Second Third Fourth Fifth Sixth 7day week Day

Fig. 2. % Distribution of accidents for B.C. buckers and failers, 1972, by day of working

week in which accident occurred.

2. ‘Almost direct” method

The obstacle, as previously described, to computing accident rates for the two groups of fallers is the lack of data about the relative total exposures. Using W.C.B. data, we have access to an extensive pool of information con- cerning men who have accidents, but we know nothing about the men who don’t. Therefore, instead of an overall accident rate, which would require exposure data for men without accidents as well as for men with accidents, we use, in our “almost direct” method, an individual accident rate for each man who has had an accident. We examine his history with his current firm (amount of time worked and number of accidents incurred) and compute a ratio. This individual accident rate variable can then be related to other variables for the individual, such as whether payment is by piecework or SalZUy.

The inferences derived on this basis are valid if it is the case that those

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men who have accidents are essentially a random sample from the group of all men. (The alternative theory of accident proneness has been largely dis- credited by studies showing that the number of multiple accidents that occur is just about what would be expected by chance. This is not to deny that there will be some accident “repeaters”.)

Actually, a much weaker assumption is sufficient for our purposes. If accident proneness did exist to a substantial extent, we would still only require that any method of payment effect that existed was the same for prones as for non prones, which would seem to be a reasonable zssumption.

As a possible criticism of our procedure, one might note that extreme differences in turnover rates between two firms could result in different “individual” accident rates despite similar overall rates. For example, if two firms had the same (high) accident rate but one experienced higher turnover than the other, then the accidents for the high turnover firm would tend to be for individuals with little exposure and would result in a series of large “individual” accident rates; whereas the lower turnover firm would show in- stances of smaller individual accident rates. To take the extreme example, if every employee worked only one day, then every accident that occurs produces an individual rate of one accident per one day worked.

We compensate for this possible distortion in our analysis in two ways. In the first place, we have a size-of-firm variable in the analysis. Since we would expect similar size firms to have similar turnover rates, then any effect of high turnover will be compounded with whatever size-of-firm effect may be present and will be accounted for in that fashion. As a matter of fact, the analysis would have included a size-of-firm variable in any event, as well as an age variable and a geography variable, since these factors are felt to influence accident rates among buckers and fallers, and we would therefore want to allow for their effect before attributing any effect to method of payment.

Secondly, we do the analysis in parallel, the second time considering only those instances in which the faller had a previous accident with that firm. This effectively eliminates any turnover bias, but does reduce our sample size considerably.

As a matter of convenience, the statistic we actually use is the average- length-of-time between accidents for each man, rather than his accident rate. (For first accident fallers this is the time from start of employment to date of accident.) This statistic is inversely proportional to the accident rate (the lower the accident rate the greater the average-length-of-time between accidents and vice versa) so that any factor related to the accident rate will also be related to the time statistic.

The actual analysis consists of fitting a multiple classification model. That is, we note a faller’s time between accidents and classify him according to his age, to the size of his firm, to its geographic location and to his method of payment. We then fit this model by using a statistical procedure that produces those constants, one for each breakdown of each classification, that best account for the variance in our time-between-accidents statistic. The constants

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are tested to see if they are accounting for a significant fraction of the variance, or merely an amount due to chance.

Our model is thus

Time-between-accidents = C +

where, for a particular faller, the appropriate constant from each set of brackets is selected: A 1 = less than 25 years of age, A2 = 25-34 years of age, A3 = 35-49 years of age, A4 = more than 49 years of age; S, = employer’s annual logging payroll less than $50,000, SZ = $50,000-$99,999, S3 = $100,000-$500,000, S4 = employer’s annual logging payroll more than $500,000; G1 = B.C. coast, G2 = B.C. interior;P, = salary, P, = piecework; C = overall scale constant.

In the first of our parallel analyses, which we call the basic analysis, we include fallers having first accidents with their current firms. Our results are that three variables - age, size, geography - account for significant propor- tions of the variance in the time-between-accidents (and hence the accident rate) but that the fourth, method of payment, does not. From this basic analysis we thus have strong support for the hypothesis that method of payment does not affect accident rates among buckers and fallers.

The actual analysis of variance tables are presented in the Appendix (Tables Al and A2). The values of the constants that result are as given in Table 9 (the scale is in months):

TABLE 9

Fitted basic model --

Time-between-accidents = 8.0 +I ‘ii; 1 +I 1;:; I+{ _::: >

C A S G

There are some interesting patterns in the constants for the significant variables. We can see that the fallers with the best safety records are in the over 50 age group, work in the largest firms and work in the coast region; whereas those failers under 25 working in small interior operations have the worst safety records. (The fitted constants are interpreted by noting that the more positive a constant, the greater the time between accidents and hence the lower the accident rate.)

It is interesting to note that the (nonsignificant) values that resulted for P1 and PZ were P, = -0.6 (salary), P2 = 0.6 (piecework). If they had been of a significant size, then, t.hey would have suggested that piecework-fallers work more safely than salary-fallers!

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Still from Table 9, we see that the age variable and the size of firm variable influence the accident rate with approximately equal force, and that the geo- graphy variable has a lesser effect. As previously mentioned, some of the firm size effect may be due to differences in turnover rates. This issue will be examined in the supplementary analysis (Tables A3 and A4 in the Appendix) which excludes fallers with no previous accidents (and hence eliminates the effect high turnover rates might be having on accident rates).

In examining these tables we find that our conclusions from the basic analysis are unchanged; age, size of firm and geography affect the accident rate but method of payment does not. There are several interesting differences, however. In this case the fitted model is as given in Table 10.

TABLE 10

Fitted supplementary model

Time-between-accidents

The same remarks are applicable for the constants within each of the causative factors. The smallest size group still has the highest accident rate and the largest size group the lowest. The youngest age group still has the worst record, although now it is the 35-49 group rather than the over 49 that has the best record. The major difference, however, lies in the relative importance of the factors. The age factor is now the one with the major effect; the size and geography factors now showing up at a lesser, and approx- imately equal level. It would thus seem that the size variable as estimated in the basic analysis was indeed inflated by a turnover effect. This is further corroborated by the increase in the scale factor from 8 to 11 months between the basic and supplementary models. This apparently is the extent to which the “turnover” effect boosted our individual accident rates.

We were evidently correct, though, in our supposition that the fitting of the size variable would absorb this turnover effect and permit a valid analysis of the remaining variables, because the conclusions of the basic analysis were virtually unchanged when the “turnover” effect was directly eliminated in the supplementary analysis.

To make the most efficient use possible of our data in forming conclusions, we use the supplementary analysis to estimate the relative importance of the factors and the basic analysis in examining each factor individually. In this way we use the bias eliminated analysis (supplementary) in that area where bias is important and the reduced variance analysis (basic) to do that estima- tion unaffected by the bias. (The basic analysis has much less variance because

it has a sample size of 1200, whereas the supplementary has just 200.) For completeness, we also compared the accident experience of piecework

fallers to that of sag-filers on the basis of accident severity (as me~ured by days lost from work). That is, we attempted to determine if piecework accidents were more severe even if they weren’t more frequent. The same model-fitting analysis was used, except that the dependent variable was “days lost from work”, not “individual accident rates”.

The finding was that inju&s to piecework fallers are indeed more severe, lasting about six days longer than those to salary fallers. ~u~he~ore, ~thou~ finding no severity differences by size or location of firm, we did find a difference by age, the oldest group losing about twelve more days per accident than by the youngest group. That difference is likely due, at least in part, to differences in healing rates by age.

The fitted model was:

Days lost from work = 32.2 +

D. CONCLUSIONS

-3.2

i I 3.2

Piecework/Sal~

Our conclusions from this project are as follows: 1. The method of payment, by piecework or by salary, does not affect the

accident rate for buckers and fallers in the B.C. logging industry. 2. The accident rate for buckers and fallers in the B.C. logging industry is

affected by these variables: age of man, size of firm and geography. The most important of these factors is age, with size and geography having lesser, approximately equal effects.

3. Regarding the age variable, the “less than 25” group has the highest accident rate, the 25-34 group a somewhat lower rate, the 35-49 group a lower rate still and the “over 49” group the lowest rate of all.

4. For the size of firm variable {as measured by logging payroll), we find that the “less than $50,000 firms” have the highest rates and that the “more than $500,000 firms” have the lowest rates. The two intermediate sizes seem to be comparable.

5. We find that coastal logging operations in B.C. have lower accident rates (for buckers and fallers) than do interior logging operations.

6. We conclude that our “individual accident rate” procedure, if used in conjunction with a size of firm variable to eliminate turn-over bias, is an effective tool in attempting to estimate the effect that any other observable variables might be having on accident rates.

7. We find that there is a relationship between the method of payment and the severity of accidents for buckers and fallers. Piecework-failers are away

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from work an average of six more days per accident than salary-fallers. 8. Days lost from work is also related to the age variable, increasing as age

increases, but is unrelated to the geography variable and to the size of firm variable.

E. ACKNOWLEDGMENT

The additional coding required on the 1,430 claims was done in very care- ful fashion by Mrs. Sandi Bellamy and Mrs. Bernice Smallenberg. Mrs. Susan Jergens compiled the tape file that was used in the project.

APPENDIX

In this appendix we present the analyses of variance used to test our models for significance. We find that in adding the piecework/salary variable to the size-age-geography model, we do not account for appreciably more of the variance in the time-between-accidents variable.

A. Analysis including failers with no previous accidents

TABLE Al

Short model

Degrees of Sum of freedom squares

Mean square

Fitting size, age, geography 8 18,165 2,281 Remainder 1,238 281,753 232

Total 1,246 305,918

TABLE A2

Expanded model and differential __--___

Fitting size, age, geography, payment Remainder

Total

Fitting size, age, geography Difference

Degrees of Sum of freedom squares

9 18,537 1,237 287,381

1,246 305,918

a 18,165 1 372

Mean square

232

372

Fitting size, age, geography, payment 9 18,537

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In Table A2, under the hypothesis that there is no effect due to method of payment, both the mean squares shown are estimates of the same population variance. Their F ratio, 372/232 = 1.6, is not significantly different from 1 when compared to the appropriate reference distribution (95th percentile = 3.8) to make us reject this hypothesis. We therefore accept the alternative hypothesis that there is no effect due to method of payment.

In contrast, the F ratio in Table Al, 2281/232 = 9.8, which relates to the combined significance of the size, age and geography variables, is well past the 99th percentile (= 2.5) of the appropriate F distribution for those degrees of freedom. The significance of these three variables is therefore clearly established.

B. Analysis excluding failers with no previous accidents

TABLE A3

Short model

Degrees of freedom

___.

Fitting size, age, geography 8 Remainder 203

Total 211

F ratio (size, age, geography) = 805/253 = 3.2. Significant. _____

TABLE A4

Expanded model and differential

Degrees of freedom

Fitting size, age, geography, payment 9 Remainder 202

Total 211

Fitting size, age, geography 8 Difference 1

Fitting size, age, geography, payment 9

Sum of squares

6,442 51,455

57,897

Sum of Mean squares square

6,587 51,309

57,896

254

6,442 145 145

6,587

F ratio (method of payment) = 145/254 = 0.6. Not significant.

The conclusions from the B section are the same as those in the A section.

_ ___ Mean square

805 253

REFERENCES

Keenan, V., Kerr, W.A. and Sherman, W., 1951. Psychological climate and accidents in an automotive plant. Journal of Applied Psychology, 35 (2): 108-111.