Vol. 6(9), pp. 169-178, November 2014 DOI: 10.5897/JASD2014.0290 Article Number: E56F67848812 Journal of African Studies and ISSN 2141 -2189 Copyright © 2014 Development Author(s) retain the copyright of this article http://www.academicjournlas.org/JASD

Full Length Research Paper

Poverty and livelihood of coastal communities in Mainland and

Huruma Luhuvilo Sigalla

University of , Department of Sociology and Anthropology, P. O. Box 35043 Dar es Salaam, Tanzania.

Received 14 May, 2014; Accepted 10 October, 2014

This article discusses levels and forms of among coastal communities in Tanzania. The objective of study from which this article is grounded was to explore and describe the impacts of global market on the livelihood of coastal communities along the Indian Ocean. A combination of sociological and economic paradigms of poverty analysis was adopted. The poverty analysis focused on three poverty indices namely, head count, poverty gap and poverty severity index. The analysis focused on levels of expenditures per month by region (rural or urban district) as well as by main occupation of household head. The entry point for the analysis was the household expenditure per capita. Also determined was whether level of and sex of household head influenced income and poverty levels. The poverty analysis was further narrowed down to household level in order to address the link between the global market and livelihood at the household level. To that end, the analysis focused closely on expenditure by sex, level of education and main occupation of the head of the household. In order to make comparison between currencies, readers should use $ 1 equivalent to TZS 165,342.57 published by on 18 September 2014. The Findings suggest that poverty levels as measured by income are caused by underlying factors of non-income poverty such as lack of assets (mainly land), low level of education, accessibility to market, and poor and weak social safety network (such as offered by cooperative unions).

Key words: Poverty, livelihood, globalization, coastal communities, coastal resources.

INTRODUCTION

The concept of poverty and its dimensions Livelihoods are derived to varying degrees from smallholders’ farming-including livestock production and Poverty and livelihood are hardly analyzed separately in artisanal fishing...while smallholders rely primarily on one Africa. For instance, The International Fund for Agriculture type of activity; most see diversity in their livelihood base and Development (IFAD) (2011) points out that: as a way to reduce risk”. “The livelihoods of poor rural are diverse across regions and countries and within countries. The debate on poverty has been influenced by two major

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approaches. The first which uses income to measure economic or in non-economic terms, the two perspectives poverty understands poverty in purely economic terms. are linked dialectically. For instance, Thorbecke (2007:5) The second, however, attributes poverty in multi- argues that 'limitation of income as measurement of dimensional terms (Lerer, 2007; UNDP, 2003), involving poverty includes the fact that market in many places is in addition to income, other factors like access to social not perfect. Some services and goods attributing to the services and participation in political affairs (Chamber, well-being cannot be measured by income’. Another 2007). The latter view characterizes poverty as structural problem with these dimensions is the fact that different phenomenon at international, national and community individuals, communities and countries have attained levels, implying as well that the levels are linked (Aikael, different levels of development and consequently are 2010; May and Carter, 1999). confronted with different problems. As a result, their The measurement or definition of poverty itself has not social realities and circumstances influence their priority been without contradictions (Likwalile, 2000, setting. Moreover, the use of concepts like decent life, Msambichaka, 2003). Nonetheless, a number of scholars extreme or absolute, or moderate poverty can be (among them Nissen, 1993) agree that poverty refers to problematic, considering the diversity of settings in which lack of basic needs for survival such as food, shelter and people live. clothes. Nohlen and Nuscheler (1993:32) however The poverty analysis and discussion in this article was understand poverty in development terms. They argue however alive to the range of perspectives and that people tend to refer to poverty in line with 'what they contradictions considered above. The determination of don't have as well as what they would wish to have', poverty goes beyond mere household earning or assets bypassing their situation of 'being'. To Lugalla (1995), into processes and circumstances which enable them to poverty is a material condition but which also expresses earn a certain amount of money or to live a certain type of power relationships. A constellation of these views on life. Thus poverty is understood as a complex, poverty is probably provided by Sen'si Capability and multidimensional process. It is sometimes influenced by Function framework (Thorbecke, 2007:17). factors within or beyond an individual’s capacity to The 2002 Poverty and Human Development Reportii change. Therefore, our analysis links structural (in the describes poverty as a ‘situation in which households are sense of external, if international), national and placed below a socially defined minimum level of well- community levels with individual factors. The analysis being, usually manifest in hunger, sickness, power- also examines the ability of an individual or a community lessness, illiteracy etc.’. In his book ‘The End of Poverty: to fairly participate in, access, afford and use resources How we can make it happen in our life time’, Sachs (human and physical) that are necessary for survival and (2005) distinguishes three degrees of poverty: Extreme development. In particular, we attempted to focus as (or absolute) poverty, moderate poverty and relative closely as we could on the underlying forces of poverty in poverty. Extreme poverty refers to situations in which coastal communities of Tanzania. These, among others, households cannot meet basic needs for survival. This include: inadequate access to capital, low level of type of poverty occurs mainly in developing countries education, training and information related to value of such as Tanzania. According to Sachs, moderate poverty resources and markets, poor or underdeveloped refers to conditions of life in which basic needs are just technology (such as fishing vessels, gears, storage barely met. Relative poverty refers to households with facility), poor marketing and valuing system especially for income below the societal income average. In this case, fish, and lack of cooperatives among artisanal fishermen. relative poverty can exist even if the poor can afford to Within this context, therefore, our poverty analysis in have basic needs. The World Development Report the era of globalization includes not only access and 2000/2001 acknowledges that poverty cannot be management of coastal resources but also market (local understood in economic terms only (Word Bank, 2000), and international), access, affordability and use of that as a social phenomenon, poverty must be charac- modern technology by the local people. The poverty terized in non-economic terms as well (cf. Sigalla, analysis and findings presented in this article focused 2005:5). mainly on household expenditure. Mkenda et al. (2004) Income poverty is measured by using income as a suggest that the principal indicator of , and measure of welfare, while non-income poverty uses other therefore poverty, is the household expenditure. Apart attributes in addition to income to measure poverty. Non- from understanding other dimensions of poverty such as income poverty examines access to, affordability and use education, assets, access to resources and various of social services such as , water, shelter, clothes, means of livelihoods, it is important to understand sanitation and education which are important to decent household levels of income across regions, districts and living. The income and non-income dimensions of poverty more importantly the variations between areas and explain the disparities that exist worldwide with regard to different occupations. The expenditure is based on recall capability be it within regions, countries or households. of what heads of households remembered having spent Regardless of whether poverty is understood only in over the previous month.

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Table 1. Average per capita Package for the Social Sciences) while STATA (a data analysis and expenditure per month by region. statistical software) was used to analyze poverty indices (i.e. head count index, and poverty severity index) and to Region TZS calculate the . South 4,404

Tanga 6,197 FINDINGS 4,092 Spatial dimensions of poverty in the coastal communities in Mainland Tanzania and Zanzibar

Table 2. Average per capita expenditure per month by district. It is important to take note of the fact that coastal communities are among the poorest in Tanzania, despite District TZS being endowed with diverse natural resources. Curran South Unguja 4,825 (2002:90) also suggests the same by arguing that:

Central Unguja 4,216 “Coastal ecosystems are among the most rich and Mkinga, Tanga 4,724 diverse in the world, providing important global functions Tanga urban 6,406 for marine ecosystems and atmospheric composition. Mtwara urban 4,128 Finally, coastal ecosystems have proved more difficult to 4,007 manage through privatization or market relations”

However, the levels of poverty in these communities vary not only across regions but also within regions when METHODS compared in terms of rural and urban settings. Table 1

The study design was explorative and descriptive using a combi- presents data of average per capita expenditure per nation of qualitative and quantitative data collection techniques. month for the two regions of Mainland Tanzania and Furthermore, review of relevant government documents and South Unguja in Zanzibar. scholarly materials was also used. Non-participant observation The results above show that the had a especially along the landing sites and tourist attractions com- higher per capita expenditure (TZS 6,197) per month than plemented the process of data collection. Data were collected from both South Unguja and Mtwara. The higher per capita coastal communities of mainland Tanzania (in Tanga and Mtwara regions) and from Zanzibar Island between 2010 and 2011. A total expenditure in Tanga could be attributed to its relatively of 53 in-depth interviews and 16 Focus Group Discussions were higher disposable income compared to South Unguja and conducted. These interviews and discussions involved governmental Mtwara. Furthermore, we analyzed average per capita officials at region, district and communities levels. Some expenditure per month by district as indicated in Table 2. respondents who later participated in the survey were also included. Results from Table 2 show that the Tanga Urban District Qualitative data were collected and analyzed first, followed by the had a higher per capita expenditure (TZS 6,408) per social survey. Thus, the questionnaire for the survey was prepared partly based on qualitative data. The sample size for the social month compared to the other five districts where the survey was 800 participants, but the analysis is based on 797 which study was carried out. It is also worth noting that per were considered complete. The respondents were purposively capita expenditure in Tanga urban is much higher than selected based on specific criteriaiii. For instance, the sample frame that of Mkinga Tanga which falls in the same region. included residents of the coastal area who engage in fishing, Mtwara Rural District on the other hand had the lowest tourism, agriculture or any activity directly or indirectly linked to these sectors. The sample distribution for the social survey was as per capita expenditure (TZS 4,007) per month. The follows: Tanga (354), Mtwara (205) and South Unguja / Zanzibar distribution of per capita expenditure across these (238). The sampling unit was a ‘household’. districts may be attributed to the differences in disposable income. These findings suggest that the more urbanized a place is, the less poor its residents; which perhaps Data processing and analysis reflects the fact that there are more economic oppor-

The analysis of poverty at the regional level focused on the monthly tunities through which people can earn income in urban average per capita expenditure. From the social class model of areas. analysis, respondents at rural and urban levels were grouped into two classes based on their involvement in the production activities like access to market, asserts, education as well as formal and Poverty indices informal social networks. Qualitative data were tape recorded, transcribed, translated from The spatial distribution of poverty varies markedly across Kiswahiliiv to English and content analysis as well as its interpretation followed. The tool of analysis for quantitative data the coastal areas of Tanzania. such as frequencies and cross-tabulation was SPSS (Statistical The results from Table 3 show that the Mtwara region

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Table 3. Poverty indices by regions. Table 5. Poverty indices by location area.

P0 (Head count index) Estimate Std error Estimate Std. error South Unguja 0.76 0.028 P0 (Head count index) Tanga 0.63 0.026 Urban 0.69 0.028 Mtwara 0.81 0.027 Rural 0.73 0.019

P1 (Poverty gap index) P1 (Poverty gap index) South Unguja 0.29 0.016 Urban 0.30 0.017 Tanga 0.25 0.014 Rural 0.29 0.011 Mtwara 0.37 0.019 P2 (Poverty severity index) P2 (Poverty severity index) Urban 0.17 0.012 South Unguja 0.15 0.012 Rural 0.15 0.008 Tanga 0.13 0.010 Mtwara 0.21 0.015

poverty gap index and poverty severity index compared to South Unguja and Tanga. This implies that income of Table 4. Poverty indices by districts. poor people in Mtwara is further below the poverty line compared to those in South Unguja and Tanga. Tanga Indices Estimate Std error Region on the other hand had the lowest head count P0 (Head count index) index, poverty gap index and poverty severity index South Unguja 0.75 0.05 indicating that Tanga had relatively higher per capita Central Unguja 0.76 0.03 income compared to South Unguja and Mtwara. Mkinga, 0.70 0.07 From Table 4, Mtwara Urban District had the highest Tanga urban 0.62 0.03 head count index (at 0.82) followed by Mtwara rural, Mtwara urban 0.82 0.03 while Tanga Urban District had the lowest head count Mtwara rural 0.80 0.05 index. In addition, Mtwara urban and Mtwara rural districts had the highest poverty gap index, while Tanga P1 (Poverty gap index) urban district had the lowest poverty gap index. Similarly, South Unguja 0.28 0.03 Mtwara urban and rural districts had the highest poverty Central Unguja 0.30 0.02 severity index compared to the other districts. From the foregoing, it is evident that poverty levels are higher in Mkinga, 0.32 0.04 Mtwara compared to Unguja and Tanga. Tanga urban 0.24 0.01 From Table 5, urban locations had lower head count Mtwara urban 0.37 0.02 index compared to rural areas. This suggests the Mtwara rural 0.37 0.03 possibility that there are more economic opportunities in urban areas than rural areas. It is however surprising that P2 (Poverty severity index) the poverty gap index and poverty severity index are South Unguja 0.13 0.02 higher in urban locations compared to rural locations. Our Central Unguja 0.15 0.01 observations show that most of urban locations of coastal Mkinga, 0.19 0.03 communities in Tanzania are diverse as far as sources of Tanga urban 0.13 0.01 income or means of livelihood are concerned. In the Mtwara urban 0.21 0.02 same context, such diversifications determine as well as Mtwara rural 0.21 0.03 cause differences in income. The Gini coefficient which ranges from 0 to 1 measures the extent of income inequality. When the Gini coefficient is equal to zero, there is perfect equality in the distribution had the highest head count index (of 0.81) compared to of income, in the sense that every person in the society South Unguja and Tanga. Since the head count index is receives the same amount of income (which is the extre- a measure of the proportion of population for whom the me case). Another extreme case is when the Gini welfare metric is less than the poverty line, it is evident coefficient is equal to one (Table 6). In this case, a single from the results that Mtwara has a higher proportion of individual receives all the income while the rest of the poor population compared to South Unguja and Tanga. It members of society receive nothing. The results indicate is also evident that the Mtwara Region had the highest that income inequality is fairly high (at a Gini coefficient of

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Table 6. Income inequality measure (Gini who had short training, adult education, madrasa/Islamic coefficient). school and vocational training. But it is important to note that the low per capita expenditure per month associated Area Estimate Std. error from the data with higher education was because only a Urban 0.46 0.027 few respondents had higher education. Rural 0.39 0.020 Results from Table 9 show that all the three poverty Whole sample 0.42 0.016 indices (head count index, poverty gap index and poverty severity index) are higher for those who are engaged in farming as an occupation for the household. This is consistent with a known fact that a large number of the 0.42) in urban areas, suggesting the distribution is higher poor in Tanzania are living on either subsistence farming there as well. The Lorenz curves in Figure 1 reflect the or employment in the informal sector. same picture since the curve for urban areas is farther As can be seen from Table 10, the head count index is away from the 45 degree line than the curve for the rural low among those who are engaged in fishing as an areas.v occupation for their households while it was high for those who are not engaged in fishing. This may be an indication that earnings from fishing are much higher than Socio-economic aspects of poverty in the coastal income from other occupations. It is however surprising communities in Tanzania that the poverty gap index and poverty severity index do not show the same trend since the two indices were the In addition to understanding poverty level at regional and same for fishing and non-fishing occupations. district level, we were more interested in understanding Table 11 shows that all the three poverty indices are the level of income and expenditure at an individual level higher for those who are engaged in seaweed production in general, particularly between men and women. Income as an occupation for the household. Those who did not and poverty may vary between sexes due to livelihood have seaweed farming as a main occupation for their means available in a particular community; a situation households had lower poverty indices and were poorer that seems more apparent in coastal communities where than households whose heads had other occupation. This fishing (which is mainly engaged in by males) is the suggests that seaweed farming which was introduced dominant economic activity. However, our study revealed from Asia to address the high demand for carrageen in that women are engaged in the fishing industry as well; the global market and to alleviate poverty especially they buy fish at the landing sites and sell food to among women in Tanzania may have contributed towards fisherman. In the case of Chwaka in South Unguja for addressing the global demand for carrageen but with no instance, women are also engaged in seaweed farming. evidence of poverty alleviation. It is revealed in the data discussed farther below that The three poverty indices on Table 12 are higher for households whose heads depend on seaweed farming those who are engaged in self-employment as an experience higher levels of poverty than households occupation for their households but lower for those who whose heads are engaged in other income generating are not engaged in self-employment. This suggests that activities. Thus, men and women are specialized in self-employment provides low earnings and is therefore different income generating activities that may, in one associated with high levels of poverty. way or another, influence their level of income or poverty. The results in Table 13 show that all the three poverty Table 7 shows average per capital expenditure per month indices are lower for those who are engaged in tourism by sex. as an occupation for the household. Those who did not The results from Table 7 show that male household have tourism as an occupation for their households had heads had higher per capita expenditure (TZS 5,294) per higher poverty indices (Table 14). This suggests that month compared to female household heads (4,486). tourism activities provide higher earnings (income) This situation could be explained perhaps by the fact that compared to other occupations. men are engaged in more remunerating economic The results in Table 15 show that depletion of certain activities. It could also be attributed to the traditions and fish species results in poverty among the fishing customs that discriminate against women. households as shown by higher poverty indices against From Table 8, it is evident that respondents who com- the yes responses in the table above. pleted primary school had higher per capita expenditure (TZS 5,199) compared to those who had no education at all or those who had incomplete primary school. Likewise, DISCUSSION respondents who had completed secondary school had higher per capita expenditure (TZS 7,544) compared to Our analysis of coastal poverty in the era of the global those who did not complete secondary school or those market clearly shows that there are variations of poverty

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Lorenz Curve(s) 1 .8 .6 L(p) .4 .2 0 0 .2 .4 .6 .8 1 Percentiles (p)

line_45° Urban Rural

Figure 1. Comparison of income inequality between rural and urban areas.

Table 7. Average per capita Table 9. Poverty indices by whether farming is an expenditure per month by sex. occupation for the household.

Sex TZS Estimate Std. error Male 5,294 P0 (Head count index) Female 4,486 Yes 0.81 0.030 No 0.66 0.022

P1 (Poverty gap index) Table 8. Average per capita expenditure Yes 0.34 0.020 per month by level of education. No 0.26 0.012

Education level TZS P2 (Poverty severity index) None 4,875 Yes 0.18 0.015 Complete primary school 5,199 No 0.14 0.009 Incomplete primary school 4,539 Complete secondary school 7,544 Incomplete secondary school 4,609 Short training 2,857 and Mtwara. Similarly, Mtwara Region has the highest Adult education 2,556 head count index (0.81) compared to South Unguja (0.76) Madrasa/Islamic college 5,335 and to Tanga (0.63). This suggests respondents from Vocational training 4,548 Mtwara are poorer than those from Unguja and Tanga. Indeed, other determinants of poverty such as access to Higher education 4,667 vi market , education and social safety network (cooperative unions) are limited in Mtwara compared to Tanga or South Unguja. based on some demographic variables. Geographically, The findings also reveal that household expenditure in the data suggest that respondents from Tanga coastal the coastal communities varies across gender, in the communities were economically better off, followed by sense that men have higher per capita expenditure per respondents from coastal areas in Zanzibar and finally month than women. This could in part be attributed to the respondents from Mtwara. The data particularly suggest fact that our sample consisted mostly of men. However, that Tanga urban and rural districts have higher per similar findings were observed by Sesabo and Tol (2005) capital expenditure than other districts from South Unguja who suggested that fishing activity is male-dominated in

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Table 10. Poverty indices by whether fishing is an Table 12. Poverty indices by whether a household is self occupation for the household. employed.

Estimate Std error Estimate Std error P0 (Head count index) P0 (Head count index) Yes 0.69 0.020 Yes 0.78 0.027 No 0.74 0.035 No 0.66 0.023

P1 (Poverty gap index) P1 (Poverty gap index) Yes 0.28 0.011 Yes 0.32 0.018 No 0.28 0.021 No 0.26 0.013

P2 (Poverty severity index) P2 (Poverty severity index) Yes 0.17 0.014 Yes 0.15 0.008 No 0.14 0.009 No 0.15 0.016

Table 13. Poverty indices by whether tourism is an Table 11. Poverty indices by whether seaweed occupation for the household. production is an occupation for the household.

Estimate Std error Estimate Std error P0 (Head count index) P0 (Head count index) Yes 0.61 0.102 Yes 0.80 0.058 No 0.68 0.020 No 0.67 0.020

P1 (Poverty gap index) P1 (Poverty gap index) Yes 0.20 0.043 Yes 0.31 0.036 No 0.27 0.011 No 0.26 0.011 P2 (Poverty severity index) P2 (Poverty severity index) Yes 0.08 0.023 Yes 0.16 0.026 No 0.14 0.008 No 0.14 0.008

compared to rural locations. One of the possible many coastal communities in Tanzania, and that fishing is explanations for this variation could be the fact that very a major source of income, accounting for 52% of total often global market activities are urban based. Another income for all households. reason could be that urban locations have diverse Furthermore, levels of education seem to influence sources of income or means of livelihood compared to expenditure. Household heads with relatively higher levels rural locations. Similarly, our findings show that house- of education (such as “completed secondary education”) holds whose heads are engaged in the sector of tourism had higher per capita expenditure in comparison to those had higher earnings than head of households with other with lower levels of education such as primary school, occupation such as agriculture, fishing or self-employ- madrasa or those who are illiterate. Going by the poverty ment. gap and severity indices, Tanga has relatively higher per In this study, the mean per capita expenditure was used capita income than South Unguja and Mtwara regions. as the poverty line. This enabled us to compare poverty As to whether there are poverty variations between incidence across sub populations. The poverty line in this urban and rural areas along the coastal communities, the case was determined at TZS 5125 per month. The head data suggest that respondents from urban coastal areas count index for the regions indicates that the poverty have lower head count index of 0.69 compared to those incidence is worse in Mtwara where about 81 percent of from rural areas (scored at 0.73). This may imply that the population is poor. At Tanga, the incidence of poverty urban respondents have higher income than their rural was relatively low (about 63 percent of the population are counterparts. However, it was surprising that the poverty poor). Nonetheless, the poverty gap and severity indices gap and severity indices were higher in urban locations from the data also indicate that poverty is a problem in

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Table14. Poverty indices by whether there is a alleviation. They also see these factors as the key household participant in tourism activities. challenges faced by efforts towards attainment of the Millennium Development Goalsvii. Findings from the social Estimate Std error survey in this study show that from a sample size of 797, P0 (Head count index) a whole 46.8 % (or 372) have only primary school Yes 0.67 0.069 education while 14.6% (or 116) did not even complete No 0.76 0.026 primary school. Moreover, 11.7% (or 93) confirmed they were illiterate. It can therefore be concluded that a P1 (Poverty gap index) staggering 73% of the sample have no secondary Yes 0.24 0.035 education. No 0.32 0.016 The other undermining factor to improvement of liveli- hood among coastal peoples is their loss of land P2 (Poverty severity index) especially in areas where tourism is advanced like South Yes 0.11 0.023 Unguja (where locals have sold their land to investors). A No 0.17 0.012 case in point is illustrated from the qualitative data in this study in which an unemployed 21 year old male respon-

dent with secondary education from Kizimkazi-Mkunguni Table 15. Poverty indices by whether there is a South Unguja describes the effects of free market depletion of certain fish species. on land tenure for his Sheihia (village) by saying: Estimate Std error P0 (Head count index) “..Aspects associated with globalization here are more Yes 0.72 0.017 linked to ocean and land tenure. There have been an increasing number of Tourist hotels along our beaches No 0.71 0.058 and dolphin tourism is very popular here. Scarcity of Dont-know 0.72 0.083 resources has been increasing since these investors P1 (Poverty gap index) started to come here, for instance, land for agriculture is Yes 0.30 0.010 difficult, people have no freedom with their land. When No 0.28 0.030 the Tourist hotels are constructed, sometimes people are Don't-know 0.21 0.035 not even able to pass through these areas, and a large area of land is occupied. For instance, there is one hotel P2 (Poverty severity index) which has constructed a bridge and extended it to the Yes 0.16 0.008 ocean. When you have a vessel, you cannot pass No 0.13 0.017 through. We are not employed in these hotels, they only Don’t-know 0.08 0.017 hire us during the construction as causal labourers, we carry bricks, sand, but when they start operating their business, there is no employment for us...”

Mtwara. The discussion with district officials revealed that the The analysis has also revealed a number of factors government is yet to guide local people on how they undermining improvement in people’s livelihoods along could profitably use land resources for their own the coastal communities in Tanzania. First among these development by engaging in joint ventures with investors is low level of education among coastal people (which instead of selling the land. From our FGD with district limits these communities from diversifying into other officials from South district in Unguja, one participant said means of livelihood such as tourism and agriculture). the following: Some respondents actually observed that majority of primary school leavers (mainly boys) turn to fishing as “When people see money, they sell their land and by so their sole means of survival. With time, unsustainable doing jeopardize an important resource for their livelihood. pressure on the fishing industry is foreseeable, coupled In the near future, on the one hand our family size with uncontrolled competition, illegal fishing, and conflicts increases, while our island is not increasing. It will reach between stakeholders. a point when the negative impacts will surface. Instead of The incidence of low education as a challenge to po- these people selling land, it would have been better to verty alleviation among coastal communities has received have a joint venture between owners of land and attention from the authorities as well. In Zanzibar, leaders investors, in the sense that, there must be some sort of have singled out the population growth rate (which is at contract, this would have been helpful for the next 3.1 per cent), poor education opportunities, and youth generation instead of selling it leize-fairly as is a practice unemployment (17.1 per cent) as the obstacles to poverty now”

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It is worth noting that the above explanation is about the presented by using quantitative data which shows the sustainability of coastal communities in relation to coastal levels, magnitude as well as comparison by spatial, resources in general, particularly land tenure. district, region, sex and occupation. But quantitative data The other challenge to improving livelihood is the alone neither show the processes nor nature by which a realization that most of the artisanal fishermen lack basic person or household becomes vulnerable to poverty. infrastructure for fishing. Without modern fishing vessels, Qualitative information has the capacity of showing other gears, and modern storage facility (such as cold rooms), underlying dimensions of poverty and its processes; in not much can be made of the fishing enterprise. Also, other words, qualitative information offers an explanation they lack proper market strategy for their catch. Due to of how globalization supports or undermines local these setbacks, the fisher folks are forced to auction their people’s endeavors to sustain their livelihood. Fishing catch at landing sites; they cannot wait for good prices activities are the major means of livelihood for the due to fear that their catch will rot. One of our FGDs with majority of coastal communities, followed by subsistence district officials in South District Unguja revealed the agriculture, tourism (especially for Zanzibar) and following information: seaweed farming which was also more apparent in Zanzibar than in other regions. Fishing activities were “In harvesting resources such as fish, local artisanal predominantly male activities; however, women were also fishermen use inferior/traditional technology. Fisheries engaged in selling food at the landing site as well as resources continue to decrease especially in our district seaweed farming. Apart from marine resources on which but the problem is poor technology therefore big fishing the majority depends, land is a vital resource for coastal companies are able to reach the deep sea, in other communities. As a result of globalization through local words, resources in deep sea are for big companies and foreign investment especially in the tourist sector, which some of them have licenses which enable them to land is becoming an increasingly scarce resource since profit more than us, should we had ability to reach and more investors are buying and occupying it for access deep sea, we would have profited more from that construction of tourist hotels especially at the beach front. resource” This is more apparent in South Unguja than Tanga and Mtwara. There has been an increasing demand for Poor technology used by local artisanal fishers is one of marine resources especially fish, and the competition for the factors contributing to their vulnerability to poverty. coastal resources is also high partly as a result of With the operation of the global markets, big fishing population increase. The data shows that there has been companies that use modern technology are in a better an increase of people in coastal areas mainly due to position to access the deep sea thereby increasing their fishing activities. Quantitative data suggest that Tanga catch compared to the local artisanal fishermen (see also (led by Tanga urban district) has a higher per capita Africa Progress Panel, 2014). expenditure than other regions. Industrial fishing is also Our observation in all the regions also revealed that more advanced in Tanga than in other regions. Poverty none of the landing sites had a toilet; a sign that hygiene indices by regions show that Mtwara has the highest has been ignored. But worse is the indeterminate way in head count index compared to Tanga and South Unguja which the fish is valued once the catch is brought to the which implies that Mtwara has a higher proportion of poor shore. Rather than use the now widespread criterion of population compared to South Unguja and Tanga. In weight, the fishing communities still rely on the addition, men were found to have higher per capita auctioneer’s estimation. One fisherman in our discussion expenditure compared to women. The analysis of poverty was of the opinion that ‘I don’t think, if there is a place in level based on occupation shows that all three poverty the world today where fish are not weighed, that place is indices (head count index, poverty gap index and poverty only in Tanzania’. This indicates that the fishermen severity index) are higher for those who are engaged in themselves also find this system unfair. farming as an occupation for the household. Finally, there Finally, artisanal fishermen lack cooperatives, which are several factors which determine the endeavors of would not only have united them together and increased coastal people to escape or slide into the vicious circle of their bargaining power at the market, but also improved poverty. These factors include but are not limited to poor accessibility to credit facilities so that they would be able technology, low levels of technology (especially fishing to purchase modern fishing vessels and equipment. vessels, gears and storage facility), poor mechanism of

valuing coastal resources especially fish and land and the

Conclusion management system (policies, regulations and laws) of coastal resources.

The findings and discussion in this article show the link between globalization, coastal resources, the livelihood Conflict of Interests and poverty of coastal communities in Tanzania Mainland and Zanzibar. Most of the income-poverty information is The author has not declared any conflict of interests.

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