Factors Determining Fish Catch in Indonesian Fishing Villages The data is a subset from a survey of saltwater fishermen in the Minahasa region of Northern Sulawesi, Indonesia (source of data: Professor Randall Kramer, Duke University) Data includes the following variables: Average Catch (in Kg/trip) Type of Gear Used (nets, hook & line, etc) Boat ownership Type of boat used (canoe, sail, etc) Daily fishing habits Education level Family Size

Research Questions 1)Which of the factors studied has an effect on the fishermen’s catch size? 2) Can we recommend a policy based on this data which will reduce overall catch?

Data Collection Method Systematic sampling from random lists of fishermen was used in order to select individuals for the survey. A two-stage cluster sampling method was used first by sub-districts and second by villages within selected sub-districts. Raw data was obtained from government statistics on population and occupation from 1997 and from village chief records (March June 1999). 6 coastal sub-districts (3 from the East and 3 from the West) were randomly selected out of a total of 17 (6 of these were excluded due to having less than 5% fishermen). 30% (18) of the coastal villages within the selected sub-districts were randomly selected for a survey using a random number generator.

Data Collection Method A target of 600 completed interviews was set in order to allow sufficient degrees of freedom for various econometric analyses. The numbers of interviews needed for each village was determined (based on the fishermen population size), and the interviewers acquired a list of all fishermen living in the village. They randomly selected fishermen to interview until they had exhausted the quota for the village. Fishermen in those sub-districts of the Minahasa region which had over 5% of the working male population primarily engaged in saltwater fishing had the same probability of being interviewed.

Analysis of Data: Catch vs. Family Size Hypothesis Testing *: Let  1 be the median fish catch for small family size Let  2 be the median fish catch for medium family size Let  3 be the median fish catch for large family size H o :  1 =  2 =  3 H A : not all  i are equal *Statistical tests were run using log transformed data Categories : The o riginal data contained discrete ordered variables (1 through 9 members per household) and was pooled into three categorical groups because of low sample sizes prior to pooling. Test Assumptions (One-Way ANOVA) : The three groups had relatively equal variances after log transformations and were close to normally distributed. Results: There is insufficient evidence to suggest a difference in the median fish catch between the small, medium and large family groups (two-sided p-value of 0.788, one-way analysis of variance F-test). Family SizeMeanMedianStd. Dev. Small Medium Large Summary Statistics (log data)

Analysis of Data: Catch vs. Boat Ownership 4 outliers from same village Hypothesis Testing *: Let  0 be the median fish catch for non boat-owners Let  1 be the median fish catch for boat-owners H o :  0 =  1 H A :  0   1 *Statistical tests were run using log transformed data Categories : The original data contained the discrete categorical variables shown and was not changed. Test Assumptions (Welch’s 2 Sample t-test) : Due to unequal variances after log transformations, a Welch’s Modified t-test was used. The samples were close to being normally distributed after transformation. Own Boat?MeanMedianStd. Dev. No (0) Yes (1) Summary Statistics (log data) Results: There is insufficient evidence to suggest a difference in the median fish catch between the boat owners group and non boat-owners (two-sided p-value of 0.115, Welch two-sample t-test).

Analysis of Data: Catch vs. Boat Type Boat TypeMeanMedianStd. Dev. Canoe Sail Motor Summary Statistics (log data) Results: There is insufficient evidence to suggest a difference in the median fish catch between the groups with the three different boat types (two-sided p-value of 0.053, one-way analysis of variance F-test). Hypothesis Testing *: Let  1 be the median fish catch for canoe users Let  2 be the median fish catch for sail boat users Let  3 be the median fish catch for motor boat users H o :  1 =  2 =  3 H A : not all  i are equal *Statistical tests were run using log transformed data Categories : The o riginal data contained discrete categorical variables and was not changed. Test Assumptions (One-Way ANOVA) : Given that the sample size in the group with motor boats is much smaller than the other two groups, we assumed that its variance would have been larger had the sample size been larger. Normality assumptions were met.

Results: There is insufficient evidence to suggest a difference in the median fish catch between the groups with primary or no education, secondary and high school education (two-sided p-value of 0.315, one-way analysis of variance F-test) Analysis of Data: Catch vs. Education Level EducationMeanMedianStd. Dev. None/Prim Secondary High School Summary Statistics (log data) Hypothesis Testing *: Let  1 be the median fish catch for group 1 Let  2 be the median fish catch for group 2 Let  3 be the median fish catch for group 3 H o :  1 =  2 =  3 H A : not all  i are equal *Statistical tests were run using log transformed data Categories : The o riginal data contained four discrete categorical variables. The primary and no education variables were pooled due to a low sample size in the latter. Test Assumptions (One-Way ANOVA) : Given that the sample sizes in the secondary and high school education groups were much smaller than the other two groups, we assumed that their variances would have been larger had the sample size been larger. Normality assumptions were met.

Analysis of Data: Catch vs. Daily Fishing Habits Fish Daily?MeanMedianStd. Dev. No (0) Yes (1) Hypothesis Testing *: Let  0 be the median fish catch for non-daily fishermen Let  1 be the median fish catch for daily fishermen H o :  0 =  1 H A :  0   1 *Statistical tests were run using log transformed data Categories : The original data contained the discrete categorical variables shown and was not changed. Test Assumptions (2 Sample t-test) : The three groups had relatively equal variances and were close to being normally distributed after log transformations. Results: There is insufficient evidence to suggest a difference in the median fish catch between the group that fishes on a daily basis and the one that does not (two- sided p-value of 0.59, two-sample t-test). 3 outliers from same village (Borgo) 4 outliers from same village (Borgo)

Analysis of Data: Catch vs. Gear Type Outliers from village Borgo One outlier from Village Sapa Hypothesis Testing (K-W Rank Sum Test) *: Let  0 be the median fish catch for non daily fishers Let  1 be the median fish catch for daily fishers H o :  1 =  2 =  3 H A : not all  i are equal *Statistical tests were run using log transformed data Categories : The original data contained six different types of fishing gear. The four net types were combined into a single group due to low sample size in some of the net subgroups (see next slide). Test Assumptions: Due to unequal variance in the net group, the Kruskal- Wallis Rank Sum Test was used. The Tukey-Kramer pairwise comparison test was run to determine the difference. Results: There is sufficient evidence to suggest a difference in the median fish catch between at least two of the three different gear type groups (two-sided p- value of , Kruskal-Wallis Rank Sum Test). The group with the net gear type had a median fish catch which was 1.23 to 7.21 kg/trip higher than the spear- fishing group (95% confidence interval,Tukey-Kramer multiple comparison).

Outlier from village Borgo Analysis of Data: Catch vs. Gear Type “Net” group consisted of 4 different sub-groups shown above. Small sample sizes suggested the need for pooling.

Further Analysis of Gear Data Above: Data in “Net” group of fishing gear was comprised of 4 different types of fishing nets. The data was combined into a single group due to small sample size in some groups, as seen in the table to the left. (Note the relative high abundance of counts for “Fly Fishing Net” catch in the kg/trip & the kg/trip categories.) Left: Count Table for Gear Type and Average Catch. Nets seem to account for the majority of catches in the higher average catch categories, while Hook and Line fishing accounts for the majority of counts in the low (0-30) average catch group.

Further Analysis of Gear Data Using count tables to compare all groups to gear type, the following was determined: The majority of fishermen using nets also –Have a medium-sized family (3-5) –Low level of education (Primary only) –Use a canoe –Fish daily –Own their own boat NOTE: Above statements are casual observations (not statistical conclusions) from count tables. No inferences about the general population can be made as a statistical test was not possible for these comparisons because of low observations (<5) in some groups.

Unique Features of Data Types of variables: The data consisted of one continuous variable (the response variable: average fish catch) and six categorical variables. Some categorical variables were created while others were original. Outliers: All outliers were identified throughout the study and had the following characteristics: all were from the village Borgo and did not own boats while one of them (average catch = 340 Kg/trip) was from the village Sapa and owned a boat. It has been suggested that fishermen from Borgo are a part of larger commercial operations, which may partake in multi-day fishing trips.

Shortcomings of the Experimental Design Is This Sample Representative of the Larger Population? There are doubts as to whether the survey sample is representative of the population at large as only 6 out of 17 coastal sub-districts were selected and from these only 30% of the coastal villages were sampled. Small sample sizes also accounted for difficulty in making inferences about the larger population of fishermen. Accuracy of Preliminary Data? The method used by interviewers to acquire the lists of fishermen currently living in coastal villages could be subject to inaccuracies. Government statistics proved to be outdated when compared with actual observations. The local team members, therefore, resorted to gathering this information by asking village chiefs or their staff the names of the fishermen currently living in each village. There is no guarantee that these figures were accurate or that the chiefs were honest when sharing this information. Accuracy of Survey Methods? Fishermen who completed surveys were not guaranteed anonymity and this may have influenced the answers they provided. Interviewers contacted the fishermen they selected from their lists (through systematic random sampling) and then asked them to complete the survey on the spot. There is no indication as to how many fishermen refused to fill out the survey or how many were unavailable to do so, although the target of 600 completed surveys was reached.

Conclusions Statistical conclusions: The analysis failed to provide evidence of a significant effect on fish catch by all of the variables studied with the exception of gear type. The only statistical difference found was that fishermen using nets showed a significantly higher catch than spear-fishermen. The effect of the other variables on gear type could not be determined due to small sample sizes and the limited scope of statistical tools available for this analysis. Implications: There is not enough evidence based on this study to make any policy recommendations, although obtaining larger sample sizes may provide valuable insight into fishery management strategies. Future studies should focus on determining the effects of the different gear types on average fish catch.