What is the relationship between Fisheries dependence and poverty?

4.3.3 What is the relationship between Fisheries dependence and poverty? Despite efforts to help the poor, the total number of fisheries households living in poverty is increasing in mainland West Sumatra. Twenty five of the thirty one coastal sub-districts recorded an increase in poor fisheries households between 2005 and 2011 BPS, 2011. Only one of the eleven fishing dependent areas Tanjung Mutiara showed a decline in household poverty. According to the results of this analysis, 39 of the total fishers in the province are poor. Figure 3: Fishing dependency and poverty amongst fishers in coastal sub-districts of mainland West Sumatra. Sub-districts are ranked using normalized composite indexes. If poor households were evenly distributed throughout the population one would expect that the sub-districts that contain most fishers would also contain most poor fishers. This assumption proved to be generally true with a strong Kendall Tau correlation 0.84, P0.01. Nine of the eleven fishing dependent sub- Sei Beremas Sasak Tarusan Sutera Linggo Sari Baganti Bungus Koto Tangah Lengayang Batang Kapas Tanjung Mutiara Sungai Limau IV Jurai Lubuk Begalung Padang Selatan Bayang Pariaman Selatan Pariaman Utara Pariaman Tengah Ulakan Tapakis Kinali Batang Gasan Pancung Soal Padang Utara Padang Barat Sungai Aur Ranah Pesisir Koto Balingka Batang Anai Koto KP Dalam Nan Sabaris Lunang Silaut -2 -1,5 -1 -0,5 0,5 1 1,5 2 2,5 -1 -0,5 0,5 1 1,5 2 2,5 3 3,5 4 P o ve rty I n d e x Fishing Dependency Index districts ranked highly on the poverty index Figure 4.3 and there was a significant positive correlation between the poverty index and the fishing dependency index 0.45, P0.01. These nine sub-districts represented in the top right of the scattergraph are the poor fishing dependent group. They include Sasak Ranah Pasisie where 28 of the workforce are fishers and 58 of these are poor. This means that 16 of the entire workforce of this sub-district are poor fishers. Similarly, in Sei Beremas 15 of the total workforce across all economic sectors are poor fishers. These sub-districts clearly demonstrate exceptionally high fishing dependency coupled with high incidences of poverty amongst fishers. The second group, represented in the bottom right corner of the plot, are the two exceptions, namely those fishing dependent sub-districts that demonstrate a low poverty index. In both Tanjung Mutiara and Sutera the proportion of fishers in poverty 23 and 27 respectively is considerably lower than the provincial average. The analysis of poverty amongst all economic sectors section 3.4 coupled with the interviews provided some explanations for the low poverty index amongst fishers in Sutera. Proportions of poverty in all major economic sectors in Sutera including agriculture, construction, transportation and trade are lower than average. Poverty amongst fishers appears to be lower because poverty amongst all sectors in Sutera are lower, seemingly because of the wider availability of fertile flat land. Tanjung Mutiara is similar, it also has a strong plantation and smaller crop farming sector which contributes to lower total poverty. Site visits to both locations suggested that two mechanisms were resulting in the spillover of ‘prosperity’ to fishers. The first was occupational multiplicity meaning that some fishing households had alternative land based sources of livelihood. Secondly, through occupational multiplicity or through prosperous family members, fishers were able to access financial credit in order to invest in better fishing boats and equipment in these locations. These two factors were helped by the physical geography of both areas where sheltered mooring was available in contrast to adjacent fishing communities. These conditions in the case of Tanjung Mutiara resulted in migrant workers moving to the area and living in simple houses on the beach. As was the case in Sei Beremas, it was these migrant workers as opposed to the original residents who were often the poorest section of the fishing community according to the community leaders. The third group of note are those sub-districts that exhibited low fishing dependency and yet had exceptionally high incidences of poverty amongst fishers top left of the plot. In Kinali, Bayang and Koto Balingka 58-64 of fishers are poor. Although none of these areas registered as highly fisheries dependent at the sub-district level these data suggest pockets of significant deprivation exist amongst fishers. Again, the information gleaned from the wider sectoral analysis helps to explain what is happening in Bayang, Koto Balingka and Kinali. In each of these sub-districts both agricultural dependency and total poverty across all sectors is higher than average. Hence what seems to be happening is that these sub-districts are poorer than average and that no sector, including fisheries, is immune from this effect. The poverty amongst fishers in Kinali is further magnified by geographical isolation and the use of simple traditional fishing gear Zein et al., 2007. Does fishing dependency go hand in hand with poverty? The data has demonstrated that more fishers means more poor fishers in absolute terms but is it also the case that highly fishing dependent areas have greater proportions of fishers in a state of poverty as well? Because the poverty index contained an absolute measure of poor fishers, it was necessary to test the correlation between the fishing dependency index and the percentage of poor fishers and this showed that there was no statistically significant relationship between the two 0.07. This is important because it demonstrates that highly fishing dependent areas do not necessarily need to “rhyme with poverty” and that poverty in fisheries needs to be understood in the context of poverty amongst other economic sectors. 4.3.4 How is poverty in the fisheries sector related to poverty in other economic sectors? In terms of percentage poverty, fishing ranks as the second poorest economic sector. The average incidence of poverty amongst fishers in the coastal sub-districts of West Sumatra 39 is higher than both the average incidence of poverty across all economic sectors in these sub-districts 23 and, in particular, the total agricultural sector excluding sea fishers 36. Outside of agriculture, the construction sector average 31 is the closest to the fishing industry in terms of the incidence of poverty. The only sector to have a higher incidence of poverty than the fishing sector is crop farming average incidence of poverty, 42. This sector employs more people than any other and is four times larger than the fishing sector. Of those actively employed in the 31 coastal sub-districts included in this analysis, 37 of the workforce were employed in agriculture but 59 of the total poor work in agriculture. From individual economic sectors there are significant positive correlations between total poverty and percentage employment in the crop farming p0.01, fishing p0.05 and plantation sectors p0.01 Table 4.1. This correlation coefficient is increased although not significantly when all agricultural sectors are correlated with total poverty. Outside of the agricultural sector there is an inverse relationship between total percentage poverty and employment in all the other economic sectors. All of the correlation coefficients are negative and for the finance, service, transportation, construction and trade sectors there is a statistically significant inverse correlation p0.01 between higher employment in these sectors and higher poverty. In the West Sumatra context the phrase “fishery rhymes with poverty” could legitimately be replaced with “agriculture including fishery rhymes with poverty”. High dependency on agriculture correlating with poverty and an inverse relationship between the other economic sectors and poverty suggested an urban rural divide. At the district level the divisions between urban and rural areas are clear. The district Pasaman Barat has the highest employment in the agricultural sector 74, followed by Pesisir Selatan 57, Agam 56 and Padang Pariaman 40. The two cities, Padang and Pariaman have much smaller agricultural sectors 8 and 14 respectively and much larger service sectors 32. A similar trend follows regarding poverty distribution. Eighty five percent of the working poor in Pasaman Barat, are in the agricultural sector, 81 in Pesisir Selatan, 68 in Agam and 60 in Padang Pariaman. In Padang and Pariaman the poverty is more evenly distributed between the agricultural, construction, trading, transport and service sectors between 8 and 24 in each. There is a degree of similarity in the total percentage poor between the two urban districts Padang and Pariaman 17 and 18 poverty and between Pesisir Selatan, Agam and Padang Pariaman 24, 25 and 26 respectively. Pasaman Barat stands alone with a total proportion of poverty much higher than the other districts 35. The reasons for this appear to be both geographical and political in nature. Interviews with government staff and community leaders highlighted that the major road network to Pasaman Barat was only built in the 1980s and that this area only became an independent district in 2004 and prior to this was overlooked. There is also the issue of the palm oil industry in Pasaman Barat. The relationship between the plantation mostly palm oil economic sector and poverty both within and outside the fisheries sector is complicated. Large palm oil companies negotiated agreements with community leaders in the past to use vast tracts of land for palm oil. This agreement provides that a proportion of the land will be cultivated with the profits going to local communities. So in some villages all of the families are receiving regular substantial monthly payments direct into their bank account in addition to their normal source of livelihood. Culturally these payments are having a huge effect with people now able to borrow money secured on the basis of these palm oil payments. In other areas the payments are much smaller and infrequent and fishing families bemoan that their land was sold off without any benefit to them. In these contexts all that the palm oil offers is the opportunity to work as a labourer for a small daily wage. Besides the large-scale company plantations some fishing families also have their own land where they grow palm oil. Typically those that do not own land are the families that move to the fishing centres of Sei Beremas and Sasak in order to look for work in the fishing industry often as crew members. It is these migrant workers perantau that form a significant proportion of the poor fishing families in Pasaman Barat. Some of these migrant workers have an identity card registering them to a different location making them ineligible for government assistance. Percentage employment in this economic sector Percentage poverty in this economic sector Plan Fish. An. Hus. Tot. Ag. Ind. Con. Trad. Fin. Hot. Tran. Serv. Crop Perk Fish. An. Hus. Tot. Ag. Ind. Con. Trad. Hot. Fin. Tran. Serv. Tot. pov. Tot pov - fish. Crop farming 0.38 -0.08 0.30 0.58 -0.11 -0.44 -0.66 -0.60 -0.19 -0.33 -0.51 0.30 0.13 0.19 -0.07 0.27 -0.10 0.03 -0.01 -0.15 -0.16 -0.33 -0.13 0.38 0.40 Plantation 1.00 0.15 -0.08 0.73 -0.41 -0.65 0.61 -0.41 -0.53 -0.46 -0.70 0.40 0.09 0.09 0.02 0.15 -0.15 0.08 -0.08 -0.09 -0.12 -0.28 -0.17 0.34 0.33 Fisheries 0.15 1.00 0.09 0.17 -0.18 -0.12 0.03 -0.11 -0.03 -0.03 -0.15 0.26 0.30 0.14 0.09 0.25 0.35 0.08 0.11 -0.02 -0.06 0.09 -0.00 0.30 0.27 Animal Husbandry -0.08 0.09 1.00 0.00 0.27 0.14 0.07 -0.23 0.26 0.09 -0.04 0.09 0.08 -0.10 -0.16 0.18 0.21 -0.10 0.07 -0.25 -0.07 -0.19 -0.02 0.13 0.14 Total Agriculture 0.73 0.17 0.00 1.00 -0.38 -0.71 -0.79 -0.58 -0.38 -0.61 -0.80 0.47 0.16 0.20 0.00 0.26 -0.10 0.12 0.02 -0.11 -0.08 -0.22 -0.14 0.45 0.46 Industry -0.41 -0.18 0.27 -0.38 1.00 0.39 0.29 0.15 0.41 0.28 0.26 -0.28 -0.13 -0.19 -0.07 -0.12 0.36 -0.03 0.23 -0.01 -0.05 0.13 0.28 -0.18 -0.15 Construction -0.65 -0.12 0.14 -0.71 0.39 1.00 0.55 0.41 0.41 0.56 0.66 -0.37 -0.10 -0.16 -0.03 -0.20 0.20 -0.11 -0.02 0.11 0.17 0.18 0.17 -0.39 -0.40 Trade -0.61 -0.03 -0.07 -0.79 0.29 0.55 1.00 0.54 0.27 0.54 0.67 -0.39 -0.09 -0.15 0.03 -0.22 0.12 -0.07 -0.02 0.17 0.07 0.24 0.15 -0.41 -0.43 Financeinsurance -0.41 -0.11 -0.23 -0.58 0.15 0.41 0.54 1.00 0.15 0.41 0.60 -0.41 -0.19 -0.30 0.01 -0.38 -0.04 -0.08 -0.17 0.14 0.18 0.16 -0.01 -0.50 -0.49 Hotelrestaurant -0.53 -0.03 0.26 -0.38 0.41 0.41 0.27 0.15 1.00 0.18 0.31 -0.09 -0.04 0.00 -0.05 0.03 0.26 0.03 0.33 -0.23 0.09 0.19 0.28 -0.05 -0.05 Transportation -0.46 -0.03 0.09 -0.61 0.28 0.56 0.54 0.41 0.18 1.00 0.57 -0.31 -0.00 -0.16 -0.05 -0.18 0.14 -0.14 -0.11 0.06 0.02 0.01 0.09 -0.33 -0.32 Services -0.70 -0.15 -0.04 -0.80 0.26 0.66 0.67 0.60 0.31 0.57 1.00 -0.47 -0.14 -0.20 -0.03 -0.29 0.05 -0.16 -0.11 0.11 0.12 0.16 0.05 -0.51 -0.51 Crop farming 0.40 0.26 0.09 0.47 -0.28 -0.37 -0.39 -0.41 -0.09 -0.31 -0.47 1.00 0.14 0.23 0.13 0.61 0.15 0.24 0.26 -0.15 -0.07 -0.02 0.10 0.67 0.66 Perkebunan 0.09 0.30 0.08 0.16 -0.13 -0.10 -0.09 -0.19 -0.04 0.00 -0.14 0.14 1.00 0.23 0.11 0.32 0.19 0.22 0.15 -0.00 0.15 0.12 0.11 0.28 0.28 Fisheries 0.09 0.14 -0.10 0.20 -0.19 -0.16 -0.15 -0.30 0.00 -0.16 -0.20 0.23 0.23 1.00 0.20 0.48 0.14 0.32 0.33 -0.03 -0.06 0.29 0.23 0.45 0.42 Animal Husbandry 0.02 0.09 -0.16 0.00 -0.07 -0.03 0.03 0.01 -0.05 -0.05 -0.03 0.13 0.11 0.20 1.00 0.16 0.25 0.24 0.28 0.14 0.13 0.32 0.16 0.19 0.21 Total Agriculture 0.15 0.25 0.18 0.26 -0.12 -0.20 -0.22 -0.38 0.03 -0.18 -0.29 0.61 0.32 0.48 0.16 1.00 0.27 0.39 0.39 -0.05 0.00 0.15 0.28 0.73 0.69 Industry -0.15 0.35 0.21 -0.10 0.36 0.20 0.12 -0.04 0.26 0.14 0.05 0.15 0.19 0.14 0.25 0.27 1.00 0.24 0.44 0.04 0.08 0.33 0.39 0.26 0.27 Construction 0.08 0.08 -0.10 0.12 -0.03 -0.11 -0.07 -0.08 0.03 -0.14 -0.16 0.24 0.22 0.32 0.24 0.39 0.24 1.00 0.47 0.08 0.06 0.50 0.33 0.43 0.44 Trade -0.08 0.11 0.07 0.02 0.23 0.02 -0.02 -0.17 0.33 -0.11 -0.11 0.26 0.15 0.33 0.28 0.39 0.44 0.47 1.00 -0.11 -0.03 0.51 0.55 0.42 0.43 Hotelrestaurant -0.09 -0.02 -0.25 -0.11 -0.01 0.11 0.17 0.14 -0.23 0.06 0.11 -0.15 0.00 -0.03 0.14 -0.05 0.04 0.08 -0.11 1.00 0.23 0.07 0.11 -0.13 -0.13 Financeinsurance -0.12 -0.06 -0.07 -0.08 -0.05 0.17 0.07 0.18 0.09 0.02 0.12 -0.07 0.15 -0.06 0.13 0.00 0.08 0.06 -0.03 0.23 1.00 -0.01 0.02 -0.05 -0.04 Transportation -0.28 0.09 -0.19 -0.22 0.13 0.18 0.24 0.16 0.19 0.01 0.16 -0.02 0.12 0.29 0.32 0.15 0.33 0.50 0.51 0.07 -0.01 1.00 0.38 0.14 0.16 Services -0.17 0.00 -0.02 -0.14 0.28 0.17 0.15 -0.01 0.28 0.09 0.05 0.10 0.11 0.23 0.16 0.28 0.39 0.33 0.55 0.11 0.02 0.38 1.00 0.24 0.25 Total poverty 0.34 0.30 0.13 0.45 -0.18 -0.39 -0.41 -0.50 -0.05 -0.33 -0.51 0.67 0.28 0.45 0.19 0.73 0.26 0.43 0.42 -0.13 -0.05 0.14 0.24 1.00 0.95 Table 4.1: Relationships between poverty and dependency across all economic sectors. Correlation coefficients from a Kendall-Tau analysis. Names of sectors with a white background refer to percentage employment in that sector. Names of sectors with a grey background refer to the percentage of poor in that economic sector. A single asterisk with a pale grey background means significant p0.05 and a double asterisk with a dark grey background means significant p0.01 . Tot. Pov – fish means the total percentage poverty of all sectors minus the fisheries component of poverty. At the sub-district level these same urban-rural trends are demonstrated by urban pockets situated in predominantly rural districts. An example of this is the sub-district of IV Jurai in Pesisir Selatan which contains the district capital of Painan. Despite having a large fishing fleet IV Jurai had lower than average employment in the whole agricultural sector because of a large service sector comprising government employees who are based in this sub-district 32. Across all sub-districts there is a significant inverse correlation between employment in the service sector and total poverty p0.01 and IV Jurai demonstrates this with a low total poverty of 15 of the population compared to the average of 23. This low poverty effect seemingly spills over into the agriculture sector so that incidences of poverty amongst farmers and fishers in this sub-district are lower than average. The logic can be summarized as follows. In urban areas where the service, trade and financial sectors are strong and agricultural dependence is low then total poverty is low as demonstrated by significant inverse correlations between these sectors and total poverty. Where total poverty is low even those agriculturalists that are present will have lower percentage poverty. This is demonstrated by a significant positive correlation between total percentage poor and total percentage fishing poor and between percentage crop farming poor with total poor both p0.01. Where there are pockets of fishing dependency in urban areas such as Koto Tangah or Lubuk Begalung in Padang, both the total proportion of poverty across all sectors and the proportion of poverty in the fishing sector is lower than in rural areas. Fishing ranks as the second poorest economic sector as defined by the percentage of that economic sector that are poor, however the degree of poverty in fishing communities varies widely and it is the wider geographic and economic context that those fishers find themselves in which will determine the level of poverty. A fisher born into a pocket of fishing in an urban context such as Padang Barat, is less likely to find themselves in poverty than a fisher in a rural community in Pasaman Barat. However, it would also be true to recognise that someone born into a fishing family in Padang Barat where the rate of poverty is 37 is much more likely to be poor than if you are born into any other non- agriculture sector in Padang Barat. The limited and localised ability of strong service and financial sectors in the urban centres to reduce rural poverty concurs with the thrust of the wider literature which maintains that growth in non- agricultural sectors is less important for poverty reduction than growth in the agricultural sector Montavlo and Ravallion 2009; Ravallion and Datt, 2002. Specifically, Christiaensen and Demery 2007 demonstrate that the effect of growth in reducing poverty in Africa is 1.6 to 3 times larger in the agriculture sector compared to growth in other sectors. Significantly, a study examining pathways out of rural poverty in Indonesia concluded that while employment in the non-farm sector was an important pathway out of poverty, most of the rural agricultural poor escape poverty while remaining in rural areas, employed in agriculture, rather than through rural to urban migration McCulloch, Weisbrod and Timmer, 2007. While diverse economic sectors in urban areas in West Sumatra have the potential to boost poverty alleviation efforts locally, these wider studies confirm that to coherently tackle poverty in the rural areas growth must come from within the agricultural sector.

4.4 Conclusion