to dream of fish: the causes of icelandic skippers' fishing success

To Dream of Fish: The Causes of Icelandic Skippers' Fishing SuccessAuthor(s): Gísli Pálsson and Paul DurrenbergerSource: Journal of Anthropological Research, Vol. 38, No. 2 (Summer, 1982), pp. 227-242Published by: University of New MexicoStable URL: http://www.jstor.org/stable/3629599 .

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TO DREAM OF FISH: THE CAUSES OF ICELANDIC SKIPPERS' FISHING SUCCESS1

Gisli P~ilsson Department of Anthropology, University of Iceland, Reykjavik, Iceland

Paul Durrenberger Department of Anthropology, University of Iowa, Iowa City, IO 52242

Contrary to the folk image that skippers are important in determining fishing success, we conclude that the skipper effect is a myth. We reach this conclusion by three converging analyses: (1) we show that the size of fishing boats and the number of trips account for most of the variance in catch, leaving very little unexplained variance to be explained by a skipper effect; (2) we show that there is no relationship between experience and success; and (3) we suggest that skippers in fact avoid risky hunting strategies and fish where they can count on a catch, even if only a small one. We argue that skippers use the myth of skipper effect in a rhetoric of impression management to enhance their prestige and hence their access to larger boats and better crews, which are the real determinants of success. Competition for prestige is not just a game; it is of critical economic importance.

THE SKIPPER IS CENTRAL in the Icelandic view of fishing. He guides a small boat across the sea in search of elusive and invisible prey. Not only must he face an inhospitable sea, he must locate and catch the fish, which are even less pre- dictable than the weather. The success of a skipper determines his reputation, and his reputation determines his prestige. Skippers are very conscious of their prestige and reputations, and guard them jealously. Some skippers say dreams guide them to the fishing grounds, and popular accounts give weight to the importance of such

"mystical" techniques of finding fish. Fishing is competitive; one skipper's gain in

prestige is a loss for others. Some skippers even gain national status and fame. The

top skippers of each winter season are given mass media exposure, and the causes of their success are a popular topic of discussion. Crew members nearly always refer to their skipper as "The Man" (kallinn), which signifies a powerful position of respect.

A skipper is solely responsible for the fate of his boat. He demands and gets the

unquestioning obedience of his crew. He decides when and where to fish. The crew share the work and the rewards of a skipper's success or failure. Skippers are thus

appropriate material for the heroic imagination. They are strong individuals, testing their skill and luck against the elements in the archetypical literary role of the hero. Icelandic literature keeps alive this image in memoirs and stories about fishing captains (see, for instance, J. Amrnason 1956, 1963; I. Arnason 1976; and Mennirnir

i brinni). A skipper is supposed to be reserved, modest, brave, and assertive. In a culture which values individualism, the skipper provides a compelling image. The Icelandic word for his occupation, skipstjori, is a highly respected label.

Related to the romantic view of fishing is the idea of the "skipper effect," the

assumption that the skipper's qualities determine the size of the catch. Jakobsson (1964:305) reports that in asdic-guided fishing of pelagic species in Icelandic waters, "There have . . . been several cases where a change of captain, with no change in instruments or gear, has made a particular boat's fishing power rise or fall 10 to 20 times." Such reports are widespread.

227



228 JOURNAL OF ANTHROPOLOGICAL RESEARCH

Baks and Postel-Coster (1977:29) have noted that among fishermen on the east coast of Scotland, honors for successful fishing are bestowed upon skippers alone: "His skill as a fisherman . . . and an amount of good luck that is also regarded more or less as a personal quality, are held responsible for a skipper's success or failure." Similar conceptions are reported by Firth (1946), Kottak (1966), and LIfgren (1977).

Since prestige is a result of fishing success, an obvious question is what determines the success of these skippers? Why do they fare better than others at finding and catching fish? Exactly what is this "skipper effect"? Is it a better memory for

patterns, a keener perception of some natural clues as to the location of fish, some innate ability to compute the odds, a photographic memory for detail, an ability to function in a state of nonordinary awareness akin to dissociation?

To determine the strength of the skipper effect, we used data Pilsson had col- lected during the course of an in-depth study of the fishing industry in Sangerci, near Keflavik in Iceland. Table 1 presents data on tonnage, catch, gear, and number of fishing trips for boats operating out of Sandgerdi in the 1981 winter season. The data are divided into two categories of fishing techniques-that using nets and that

using lines.

Early in the winter season (from January to March) cod will take bait. Fisher- men set out weighted longlines, up to twelve miles long, to which they attach baited hooks. They draw the lines daily, as the bait is only attractive to fish for a limited period. Later in the season (from March to May), when the species that cod

prey upon are more plentiful, they are less likely to take bait. The fishermen then

begin to shift to gill nets. They string together ten or fifteen units (trossur) of net to make a single barrier. Each boat has a number of trossur, depending on the size of the crew. The gills of fish that swim into the net become entangled; if left for

any length of time, the fish die for lack of motion. After a day or two, depending on the weather, the fishermen draw the nets and untangle the fish.

Some skippers are known as good witi lines but indifferent with nets; others have the reputation of being skillful with nets but not with lines, and still others are

thought to be good at both methods. We therefore treated line and net fishing as two separate categories.

We supposed that, apart from any skipper effect, the success of a fishing boat would be determined by the number of times it went fishing, the size of the boat, and the amount of fishing gear it used, whether nets or lines. We further supposed that the size of the boats would determine the number of fishing trips made, since large boats could set out in worse weather than smaller boats, and could therefore make more trips in the course of a season. We supposed that the size of the boat would determine the amount of gear along with the number of trips it could take, and that these two variables would determine the total catch for the season. We represent these relationships in the model in Figure 1.

We represent this system as path model. Path models provide a way of describing how independent variables interact to determine a dependent variable. Hadden and DeWalt (1974:105) describe the technique as "a procedure whereby the relative importance of the effects of several independent variables may be evaluated." Path analysis requires that assumptions of dependency relations among variables be ex- plicit, and permits systematic assessment of the effects of intervening variables.



TO DREAM OF FISH 229

TABLE 1

Data on SandgercTi Fishing Fleet, Winter 1981

Boat size Line catch Line Line Net catch Net Net

Skipper (tons) (metric tons) trips gear (metric tons) trips gear

1 47 0 0 0 66.6 32 102 3 195 0 0 0 766.2 51 150 4 33 56.2 15 40 124.1 42 78 5 12 35.3 11 23 0 0 0 6 56 130.4 19 45 497.6 61 102 8 76 102.9 17 45 140.8 17 114 9 34 82.3 19 40 221.9 46 90

11 12 35.2 11 23 0 0 0 12 11 1.2 2 23 0 0 0 13 11 55.5 18 23 91.8 31 54 14 203 0 0 0 350.0 28 102 17 16 14.4 5 23 0 0 0 18 53 0 0 0 566.2 79 102 20 11 30.9 11 23 131.2 41 54 21 14 0 0 0 65.5 21 54 22 10 39.8 15 23 0 0 0 23 43 0 0 0 390.2 77 90 24 11 28.1 31 23 0 0 0 26 36 161.7 25 40 0 0 0 27 11 91.8 37 23 0 0 0 28 14 61.0 16 23 148.2 40 54 29 11 21.7 8 23 0 0 0 30 103 115.3 17 45 194.0 46 102 31 176 0 0 0 769.2 49 114 32 35 0 0 0 234.2 60 90 34 124 0 0 0 442.3 38 126 35 38 101.4 20 40 0 0 0 36 75 115.4 17 45 320.6 46 102 37 11 54.8 21 23 100.0 35 54 39 21 34.9 15 40 154.5 45 66 43 28 65.6 20 40 0 0 0 44 11 5.2 4 23 20.5 9 42 45 29 36.0 11 40 147.7 34 90 46 74 0 0 0 385.0 66 102

Mean 48.38 61.54 16.04 31.63 275.14 43.23 88.44

Standard deviation 53.31 42.34 7.96 91.72 214.95 17.47 27.39

Mean size of line-fishing boats = 28.38, S.D. = 25.27 Mean size of net-fishing boats = 63.00, S.D. = 59.32




trips

tons catch

gear

Figure 1. Path Diagram of Ship Tonnage, Gear, and Trips as Independent Variables to Account for Variation in Catch

Causal relationships are indicated by arrows from the independent to the dependent variable. The strengths of relations are shown by path coefficients-the numbers between 0 and 1 on the arrows in subsequent figures. If a path coefficient from variable A to variable B has a value of x, the interpretation is that an increase of one standard deviation in A results in an increase (or a decrease, if x is negative) of x standard deviations in B (Hadden and DeWalt 1974:108). The relative causal importance of each independent variable can thus be compared by comparing the path coefficients. The effects of all variables outside the system of variables in the model are represented by residuals (R in the figures). The residual squared (R2) is the amount of variance in the dependent variable which the independent variables do not account for. Path models allow us to determine whether hypothesized relation-

ships of determination are consistent with the data. The first stage in developing a path model is to assess empirical knowledge about

the variables and map them onto a path diagram such as Figure 1 (Land 1969:8). There are two types of variables: endogenous and exogenous. Endogenous variables are dependent on exogenous ones, whose total variation is assumed to be dependent on variables outside the model. In Figure 1, the variable "tons" is the only exogenous variable.

The next step in developing the model is to determine the strengths of relation-

ship by computing the regression coefficients for the regression of the independent variable(s) on the dependent variable. The standardized regression coefficients are estimates of the strength of determination, or path coefficients. Paths which are determined not to be significant by the F test are eliminated and the regressions are

recomputed using only significant independent variables. The residual (R) represents the effects of all variables outside the system which cause variation in a dependent variable. If the residual is low, then most of the variance in the dependent variable is accounted for by the variables in the system. The curved arrows in the figures between exogenous variables represent the coefficients of correlation between them, not path coefficients.

We supposed that the strength of the residual would be a measure of the skipper effect, along with all other variables we did not include as independent variables- everything that can affect fishing success except the size of the boat, the number of trips, and the amount of fishing gear. Such factors would include the mechanical condition of the boat and its equipment, the weather, the skill of the crew, and the skipper effect.

For net fishing the path analysis shows that the size of the boat largely determines the number of nets, but the number of nets does not affect the catch. The size of the boat likewise determines the number of lines, but again the number of lines




0 trips .52 trips .46

tons 7 catch tons > catch

.81 .53 .79 0 .48 gear R gear R * .58 .61 R R

trips 50

. 67 0

tons .67 catch

.37 R

Figure 2. Path Diagrams with Path Coefficients and Residuals (upper left) Net Fishing, 1981

(upper right) Line Fishing, 1981 (lower) Net and Line Fishing Combined, 1981

trips trips 0.53

.14 catch .10 catch

tons .52 tons .42

R R

trips

.24 catch

tons .39

R

Figure 3. Revised Path Diagrams with Insignificant Relationships Removed (upper left) Line Fishing, 1981 (upper right) Net Fishing, 1981

(lower) Net and Line Fishing Combined, 1981




TABLE 2

Trips from and Catches Landed at Sandgerdi

1979 1980

Boat size Catch at Trips from Boat size Catch at Trips from Skipper tons Sandgerdi Sandgerdi tons Sandgerdi Sandgerdi

1 47 273 61 47 230 49 3 195 501 44 195 943 59 5 12 92 34 12 90 29 6 56 493 72 56 619 67 8 76 272 31 9 34 172 47 34 265 49

13 11 86 51 11 174 59 14 88 321 42 18 53 313 50 53 495 59 20 11 73 40 11 151 53 21 14 93 45 22 10 36 21 10 35 15 23 43 394 79 43 472 75 24 10 86 42 11 71 29 27 11 26 12 28 14 166 58 14 202 52 29 11 36 17 11 23 10 30 22 166 59 22 118 32 31 126 477 66 126 623 53 32 35 327 62 35 232 41 35 71 188 43 71 231 46 36 15 209 64 75 334 56 37 11 151 59 11 191 57 38 12 35 21 39 21 210 65 21 240 60 41 51 281 43 42 65 254 54 65 408 64 43 28 178 29 45 38 59 15 10 131 43 46 74 396 55 74 415 46

Mean 40.44 211.44 48.96 45.43 277.54 45.00

Standard deviation 42.87 149.31 17.13 42.15 208.56 16.57




does not determine the catch. A more surprising finding is that the size of the boat has no relationship to the number of trips; these variables are independent of one another. We also computed beta coefficients for a similar model, taking all the

fishing boats as a single category. Here we could not include the variable of amount of gear because nets and lines are not comparable. For all boats taken together, as well as for the separate categories of line fishing and net fishing, there was no significant relationship between the size of the boat and the number of trips. From the

point of view of our original question, the most significant finding is that in all three cases the residuals were small. Only 14 to 28 percent of the variance in total catch was not accounted for by the variables in the system. Figure 2 shows these

models, in the same form as Figure 1. "Tons" is again the only exogenous variable, determining the value of "trips" and "gear." These two variables and "tons" together determine the value of "catch."

Because there is no relationship between "tons" and "trips," we changed the

assumptions of the model to suppose that the number of trips was exogenous to the system and that the amount of gear did not exert any effect on the catch. Figure 3 presents the results of this exercise.

From this analysis we would have to conclude that there are a number of highly skilled and competent skippers, and that we can account for their differential success in terms of the size of their boats and the number of fishing trips they take. Fortu-

nately, we have more data for the years 1979 and 1980. These data give the number of trips each boat took from Sandgerdi and the total catch of those trips. During a

single season a boat may travel from several ports and deliver its catch to different

ports. Our data show only trips from and catches landed at Sandgerci (see Table 2). The boats represented are "local boats," ones that deliver most of their catch at

Sandgerdi, and operate out of Sandgerdi, so that there is no major difference between the total season's catch and that landed at Sandgerci.

If our conclusions from the analysis of the 1981 data are supported by these other data, then we would expect very high correlations between each independent variable (trips and tons) and the dependent variable (catch). Table 3 shows these correlations.

TABLE 3

Correlations of "Tons" and "Trips" with "Catch"

Catch 1979 1980 1981 Tons .77 .87 .77 Trips .69 .66 .63

Since the catch landed at Sandgerdi does not represent the total catch for the season, a slightly more accurate estimate of the relationship between the size of the boat and the total catch would be the correlation between tons and catch per trip. Table 4 shows these correlations.

Another way of checking this is to hold one of the independent variables constant and check the correlation of the other independent variable with the dependent variable. This is equivalent to asking: If all boats were the same size what would be




the correlation between the number of trips and the catch? Alternatively, we could ask: If all boats made the same number of trips, what would be the correlation between the size of the boat and the catch? If both of these figures are high, we can be sure that they account for most of the variance in total catch. Table 5 presents these correlations.

TABLE 4

Correlations of "Tons" with "Catch per Trip"

Catch/trip 1979 1980 1981 Tons .93 .93 .91

TABLE 5

Correlations with One Independent Variable Constant

1979 1980 1981 Catch with tons controlling for trips .89 .90 .85 Catch with trips controlling for tons .85 .77 .80

Finally, we should check for the correlation between number of trips and tons to confirm the finding of 1981 that there is no relationship. For the year 1979 there is no significant correlation. For 1980 the correlation is weak (.35) but significant (s = .034).

For comparison, Figure 4 presents the path models for 1979 and 1980. Notice that the unexplained variance (R2) is only 10 or 11 percent.

trips trips

.2 catch .35 catch

.66 f332 .f tons tons

R R

Figure 4. Path Diagrams for Line and Net Fishing Combined, 1979 (left) and 1980

We expected that skippers' experience would affect their success; this would be the simplest and most straightforward form of skipper effect. Older, more experienced skippers should know the features of the sea bed in greater detail than younger skippers. They should have a long memory of successful fishing locations from the past. They should have a greater range of available techniques and "tricks," due to more years of active experimentation. Using age as a measure of experience, we found no direct correlation with catch, but it is correlated with boat size (negatively) and number of trips for net fishing, as Table 6 shows.




TABLE 6

Experience (Age) Correlated with Boat Size and Trips

Line and net Net Line

Age skippers skippers skippers

Tons -.28 -.35 -.37 (s = .052) (s = .05) (s = .04)

Trips 0 .41 0 (s = .03)

If experience, measured by age, is important, then there should be correlations with catch if we control for boat size and number of trips. If all skippers commanded boats of the same size and made the same number of trips, then age should correlate with success. When we control for tons and trips, there is again no significant correlation between age and catch. We must conclude, then, that even experience does not affect skippers' success.

This is rather surprising. We can assume a close relationship between age and

years of skipperhood. One would also expect the amount of ecological and social information to increase with experience, and that as a result the skipper's age should be related to success. There is in fact clear ethnographic evidence for this, other

things being equal. Some evidence points, however, to the argument that the greater the skipper's age, the less successful he is. Approximately three years ago a new

navigational aid, LORAN, was introduced to the fishermen. This device has far

greater implications for fishing than radar, which was introduced shortly after the Second World War. LORAN receives radio waves from transmitters in the North Atlantic, and it enables the skipper to locate himself very precisely in a coordinate

system (with an error of only several meters). Most skippers in Sandgerdi used LORAN in the 1981 season, but older skippers may use it less because experience has taught them to rely on landmarks and visual triangulation. Young skippers have less experience using the old system, and are probably more eager to use the potential of LORAN.

The introduction of electronic aids to fishing and navigation, such as LORAN, radar, sonar, sounders, and radios, might have reduced the skipper effect over the

years. To test this hypothesis, we constructed a set of path models for the Sand-

gerci local boats in past years. If there were major changes due to electronic aids, there would be steadily decreasing residuals. For 1943 the residual is .28; for 1948, .35; for 1952, .47; for 1957, .40; for 1962, .42; and for 1971, .51. This series does not indicate the progressive decline in residual values we would expect if modern aids were replacing the skipper effect. We could locate no earlier data on Sand-

gerdcTi, but the same model for cod fishing with motorboats in 1919 in Nordfj6cTur had a residual of .36. This suggests a consistent pattern through time in spite of the introduction of more sophisticated equipment.

Thus, the statistics argue that the skipper effect is actually quite weak, not at all as strong as we would expect from folk accounts of fishing or from the popular image of the skipper.




ALTERNATIVES TO THE SKIPPER EFFECT

It is not reasonable to suppose that the size of the boat itself affects fishing success. The size variable must, however, represent some process which directly affects

fishing success. If it actually represents the effects of amount of gear, then the correlations between catch and tons should be zero when we control for gear. That

is, if all boats have the same amount of gear, then there should be no relationship between catch and boat size. The correlation for nets is .37 (s = .05), and for lines it is .30 (s = .08). Technically the hypothesis is born out for nets alone. But the

relationship between tons and catch still exists, though it is weak and not very

significant. We suggest that this represents the effect of crew size, and that if we controlled for both gear and crew size, the relationship between tons and catch would be close to zero. In other words, the partial correlations allow us to make the reasonable assertion that boat size itself is not the important feature, but that it

represents crew size and amount of gear. There is another argument to be made, although it is much weaker than those

we have presented so far. It is in fact so weak that we hesitate to present it until we have analyzed more extensive data; but it may be seen as suggestive. After a

period of inactivity, the location of the fish is unknown, and must be established

by trial and error. We plotted the catches and reported locations of the eighteen boats that went out on 19 February 1981, after a period of inactivity due to bad weather. We then compared the locations of boats on the next day to see whether

any pattern of success or failure on the 19th could be related to locations of fishing boats on the 20th. In two of the four areas in which more than one boat fished, the mean catches and coefficients of variation were low. In one area, the mean was

high and the coefficient of variation was low. On the next day one boat went from a place with a high coefficient of variation to one with a low coefficient of variation. Two other boats fished and two stayed in on the 20th. The two new boats went to locations with low coefficients of variation. One of the boats that stayed in was from a location with a high coefficient of variation, and one from an area with a low coefficient of variation. The other boat that changed locations explored a location that no one had fished on the 19th. No real conclusions are possible, but there is a pattern of boats seeking locations with low variation in catch. This suggests that the skippers avoid seeking high catches with low probabilities and seek high probabilities of some catch, even if a small one. Such a conservative fishing strategy would in itself be evidence of a small skipper effect.

The importance of catches and prestige should result in a concentration of boats where the probility of some catch is high. Because of the humiliation experienced by those who catch "nothing" in comparison with others, one can expect that most skippers would avoid seeking high catches with low probabilities. Some years ago a skipper who was said to have fished nothing for most of the winter season "happend to" land a record catch on one trip, greater than that of any other local skippers on that day. But when he landed his catch another fisherman commented sarcastically, "I suppose the others must be getting quite something now." Even though years have passed since this happened, the skipper is said to admit that these words still make him ashamed.




Line boats tend to concentrate on a few locations. Indeed, during the thirties,

regulations concerning "rowing time" were enacted to prevent overcrowding and destruction of fishing gear. Only the latecomers on boats with the smallest engines could "afford" to ignore the group. They were forced to seek high catches with low

probabilities because they could not get to the most preferred locations as quickly as others with larger engines.

On the other hand, the fishermen would agree that some good skippers do take

"big chances." Those who do are said to be prepared to leave a "fishing area of so and so many tons" and go someplace else to catch twice as much the next day. But such independence is only reported for those who tend to be successful, and who have established strong relationships of trust with their crews. Furthermore, this

strategy may be exceptional even for the successful skippers. Even the fishermen's idiom of "leaving the group" or "fishing with the group" suggests that they normally fish the same areas.

Three arguments, of three different types, therefore converge on the conclusion that the skipper effect is weak or does not exist. If there is a weak skipper effect, why is there a folk image among fishermen and the Icelandic public at large of a

strong skipper effect? Our initial problem-to describe the nature of the skipper effect-has disappeared in the statistical analysis of the data. But people think there is a skipper effect, so we must now attempt to explain that folk conception.

SKIPPER EFFECT AS FOLK CONCEPT

Our statistical analysis gives a starting point. We are safe in concluding that the

major variables that determine a skipper's success are the size of his boat and the number of fishing trips he makes, and that these two variables are not related. We must also suppose that any astute skipper is aware of these facts. Astute skippers should therefore attempt to captain the largest boats possible and to make as many fishing trips as they safely can during a season.

Fishing boats are substantial capital investments. Their owners, whether they are co-investors, corporations, or individuals, seek to get the largest possible return from their boats. Therefore, they seek to recruit the most successful skippers-those with the best reputations and highest prestige. From the skipper's point of view, high prestige maximizes his chances for commanding the largest boat possible. The prestige of skippers is not simply a matter of personal satisfaction, then, or of winning in a competitive game. It is, rather, of central economic importance in determining their chances of future success.

A skipper's reputation also affects the number of fishing trips he can take in a season. The better a skipper's reputation and the higher his prestige, the more stable his crew. Prestigeful skippers can pick and choose among potential crewmen, and when they have a good team they can be sure of the crew's continued, unquestioning support. Thus, a highly prestigeful skipper can set out in more unfavorable conditions and make more fishing trips than a skipper with a less experienced, less stable crew. A crew that operates as a well-coordinated team can bring back a catch in conditions that would be life-threatening to the less integrated or less experienced crew of a skipper with lower prestige (see Wadel 1972).




Therefore, anything that enhances a skipper's prestige is of critical economic

importance to him. The concept of a skipper effect works to his advantage by pro- viding him with a rhetoric in terms of which to enhance his prestige. The skipper must be an expert manager of others' impressions, since it is these that determine the skipper's prestige, rather than his own words.

This impression management fosters the heroic image of the skipper. He is care- ful not to overestimate his catch because others would too easily notice; not to ask others for advice because that would show his dependence; and not to be pushed around by other skippers in the competition for space because that would signify a low position in the prestige hierarchy.

Impression management is not only practiced by limiting information. The

strategy of creating credible stories, and of eliciting the proper information, is just as important. Stories of dangers at sea are a frequent topic of discussion. Their style is objective and there are few references to the emotional states of those involved. One skipper whose ship had been caught twice by breaking seas during one fishing trip commented that everything on deck which could be broken had been smashed. "You could take it like snuff," he added laconically.

Any credible story that will enhance the skipper's prestige or his reputation as a fish finder and commander of a ship works to the skipper's economic advantage. Less successful skippers, lower in the prestige hierarchy, can hope to better their economic chances by bettering their prestige. Skippers' prestige ratings change with their success at fishing. The concept of the skipper effect is one aspect of the

skipper's vocabulary of impression management. To be successful at impressing others, the skipper has to be impressed with

himself. The skipper's self-evaluation is a matter of economic importance. One informant commented on a "lazy" skipper: "If anyone is staying in port, it is him. He seems to believe that going out is hopeless." There are also several proverbs such as "The sitting crow goes hungry but the flying one gets food," and "The ones who row catch fish."

We do not argue that skippers create the image of a skipper effect so they can use it to their advantage, but that others, the public at large, create the image and the skippers "play to the image." Our hypothesis is that all competent skippers are undifferentiated in their ability to locate fish, but that they interact with others in terms of a myth of the skipper effect in order to enhance their chances to gain access to larger boats and more stable and experienced crews, which enhance their chances for economic success.

The residuals, measures of the skipper effect along with all other variables except boat size and number of trips, for cod fishing in SandgercTi in 1979, 1980, and 1981, are between .31 and .39. Clearly the residuals, whatever their source, exercise less influence on catch than boat size and number of trips. High residuals might be acceptable in models with very ill-defined variables which cannot be precisely measured, e.g., "deferred gratification orientation" (see Poggie 1979). Using such variables, Poggie accepts residuals of .82 (1979:9) and .89 (Poggie et al. 1976:261). In a model with very precisely measured variables such as boat size, number of fishing trips, and tons of fish, high residuals indicate inability to predict the value of the dependent variables. They would indicate that variables outside the system, such as skipper effect, exercise stronger influences than variables in the model.




If our model is correct, and boat size and number of trips determine fishing suc-

cess, then skippers fishing the bottom-dwelling species should be consistent in their catches from year to year. They are in fact highly consistent. The correlation of catches of the twelve skippers who fished Sandgerdi in both 1978 and 1979 is .78; for the twenty-three in 1979 and 1980, .91; for the twenty-seven in 1980 and 1981, .92. This includes approximately 15 percent of the skippers who changed boats each year. If we exclude them and consider only those who fished with the same

boats, the correlations are: 1978-79, .81 (ten skippers); 1979-80, .91 (twenty skippers); and 1980-81, .94 (twenty-two skippers). This is what our model would predict.

According to the explanation we have developed for the skipper effect, a skipper's reputation affects the quality and stability of his crew, hence his success (see Wadel 1972). Skippers' reputations are based on their success. Therefore, last year's catch is a very precise measure of success or prestige. Catch statistics are widely published. It is impossible to develop a reliable path model to assign strengths to these relationships because the independent variables are obviously highly correlated. If boat size and number of trips determine success last year and this, then boat size and number of trips must be correlated with last year's catch. So the path coefficients are not meaningful if we introduce "last year's catch" as an independent variable (see Neter and Wasserman 1974:250-54, 339-47). We can, however, assess the increased explanatory power of the model. For the Sandgerdi skippers of 1979, 1980, and 1981, this model, with last year's catch as an independent variable to measure prestige, explains 93 to 95 percent of the variance in catch. There is thus a residual of only .22 to .26 to represent the combined effects of all factors outside the model. If we consider only those skippers who did not change boats, the residuals are even lower, from .10 to .25. This statistical interpretation of our explanation of

prestige, introduced into our previous models, explains nearly all the variance in catch and therefore confirms our hypotheses.

If the skipper effect is largely mythical, why does it persist? One reason is that, like other folk concepts whose functions are not immediately apparent, it is not

rigorously tested. Indeed, it would be quite impossible to test in terms of the evidence often adduced to support the concept. One argument is that if a single boat

changes skippers, its fortunes increase or decrease, and that therefore the skipper must make the difference. The relevant comparison however, is not between a single boat at two different times, but with several boats of similar characteristics at the same time. The point is that the folk comparison does not hold constant the state of the resource, the fish. Two captains can never be compared with the same boat, conditions, and crew. The terms of the folk test of the concept are therefore not conclusive, but rather misleading.

If we attempt to fit the model for the demersal fishing to herring fishing, the path coefficients between boat size and catch are .09 (1959), .22 (1960), and .41

(1961), while those between catch and days spent fishing are .62 (1959), .53 (1960), and .49 (1961). Even the lowest of these is significant by the F test at the .05 level. The residuals are quite high, between .74 and .84, too high to be acceptable in a model with measurable variables; the variables in the model account for only 29 to 45 percent of the variance in herring catch. Barth's (1966) and Heath's (1976) analyses of Norwegian herring fishing would lead us to expect high correlations of




skipper's success from one year to the next. For the period they discuss, there are no such correlations for Icelandic herring fishermen. For 177 boats that fished

herring in 1960 and 1961 the correlation is .51; for the 196 boats that fished in 1959 and 1960 it is .55. Even if we consider only the most successful skippers, those who caught more than the mean catch in 1959, the correlation is .43. For those 24 who caught greater than the mean plus one standard deviation, the correlation is not even significant (r = .12, s = .29). Very few herring skippers changed boats

during this period. As to increases in fishing power (fish caught per day) of boats as evidence for a

skipper effect, it had a mean value of 505 percent in 1961 over 1960. This illustrates the extreme flexibility of "fishing power" within the total fleet, and the insignifi- cance of the folk test based on increases in fishing power from one season to the next. This extreme variability of herring success contributes to the maintenance of the myth of skipper effect.

Another reason the idea of the skipper effect persists is that it "resonates" well with other Icelandic cultural concepts of luck, daring, individualism, and the hero. In this paper we cannot attempt to explain why these are salient concepts in con-

temporary Iceland; that would require another type of analysis. We do observe that these are cultural themes, and that they are compatible with the idea of skipper effect; in fact, the skipper is one of the few models available today for the realization of these concepts. Whether fishing captains want it or not, they are destined to play the role of hero to the contemporary Icelandic public.

One account some skippers give of their differential success is that they dream of the proper timing and location of fishing, or that close friends provide them with information from dreams. This is exemplified by an interview with a well-known

skipper, published in a regional magazine (Faxi 1976:5). Numerous other accounts could also be cited, such as several articles, written by sons and daughters of successful skippers, published in a book called "My Father, The Skipper" (I. Arnason 1976). This kind of account is consistent with widely held ideas in Iceland about the efficacy of dreams in predicting the future. Erlendur Haraldsson's research (1978:149) showed that a total of 91 percent of his sample believed it possible that dreams reveal the future; 25 percent believed it certain (for a summary in English see Tomasson 1980:182). We suggest that if the accounts of finding fish by dreaming were not generally credible to Icelanders, skippers would not use them.

CONCLUSION

The idea that individual differences between skippers explain differential success is related to the competitive nature of fishing. Such an idea places a heavy burden of responsibility on skippers. To minimize personal responsibility for success or failure an additional mechanism may be required. The skipper's decisions are said to be the result of "hunches" over which he has little or no control. Thus the skipper is sometimes presented as a powerless agent controlled by unconscious thoughts, "supernatural" powers, or psychological states. Thus, even though the qualities of individual skippers are said to be crucial to their success, the skipper does not really determine his own fate. Skippers, however, never account for failures in these terms, perhaps because they never discuss failure if they can possibly avoid it.




We suggest the general hypothesis that wherever fishing is organized in a similar way, skippers' accounts of their own success or failure are probably not reliable but

are, rather, culturally plausible rhetorics which should vary from one cultural con- text to another. The real reasons for success or failure among skilled skippers are to be found in the differential material conditions of the hunt-the size of their boats and the frequency of their trips.

NOTE

1. Paul Durrenberger is professor of an-

thropology at the University of Iowa. His

participation in this research was supported by a fellowship from the University of Iowa and a

grant from Sigma Xi. Gisli Palsson is lecturer in

anthropology at the University of Iceland. His research has been supported by the British Council, Visindasj6dur and FiskimailasjdcTur. This paper is collaborative; the order of our

names has no significance. We presented an earlier version of this paper at the XI Congress of the Scandinavian Sociological Association in Reykjavik in June 1981. We thank Thor6lfur Thorlindsson, professor of sociology at the

University of Iceland, for his thoughtful, detailed, and provocative comments on the earlier version of this paper, and for his coop- eration throughout our research.

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to dream of fish: the causes of icelandic skippers' fishing success

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