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Factors Affecting Fishers' Readiness to Exit Declining Fishery

26 February 2012, at 12:00am

50 per cent of fishermen would not give up their livelihood in the face of drastically declining catches according to research led by the University of East Anglia (UEA). A new report, published by PLoS ONE, challenges previously held notions about poverty and adaptation by investigating why fishermen in developing countries stick with their trade.

Researchers surveyed almost 600 fishers across Kenya, Tanzania, the Seychelles, Mauritius and Madagascar about how they would respond to hypothetical catch declines.

They then investigated how social and economic conditions, such as local culture and socioeconomic development, influenced whether fishermen were willing to give up their trade.

Results

Greater proportions of fishers responded that they would exit fisheries in response to higher levels of hypothetical decline. At 50% reduction in catch just under half of the 599 fishers reported that they would stop fishing although the proportions of fishers exiting varied between countries from 19% in Seychelles to 60% in Madagascar.

Multi-scale analysis

Classification-tree analysis identified the variable ‘site’ as having the greatest power to predict readiness to exit, which was also better for predicting effort than individual- or household-level factors. The nominal variable for ‘site’ was responsible for the first three splits (dividing fishers by four different groups of sites) before other variables were incorporated lower in the tree. Financial factors, including material style of life and catch value, divided fishers in Groups 2 and 3, dominated by Kenyan and Tanzanian sites. Wealthier fishers from Group 2 were more likely to exit, while fishers with higher catch value in Group 3 were less likely to exit. Individual variables (age, education, why they started fishing and used gears) appeared lower in the tree to separate groups 3 and 4. Age, education and catch value had contradictory effects in different branches of the tree suggesting context-dependent interactions between factors. Regression tree splits using stated reason to start fishing indicated that fishers who cited tradition and free choice were less likely to opt for exit. Classification-tree analysis without the site variable used site-level biomass to split off fishers from the three Madagascar sites with high biomass and % exit, then mostly used individual and household factors suggesting that the predictive power of site was not fully reflected in any of site-level variables (e.g. infrastructure) included in this analysis.

Site-scale factors

Given the importance of site for predicting willingness to exit, we examined how site-scale factors were related to the proportion of fishers at that site who would exit the fishery. Regression tree analysis resulted in splits according to the infrastructure index which separated low infrastructure sites (dominated by Madagascar) with high proportions of fishers exiting. A second split also used infrastructure, and split off a group of high infrastructure sites, dominated by Seychelles, with low proportions of fishers exiting. This negative infrastructure-exit relationship was also reflected in a significant negative linear relationship between infrastructure and proportion of fishers exiting across the region (ß = -0.129, p = 0.003). Although there were some trends between countries, with Madagascar and Seychelles sites tending to have high and low percentage exiting respectively, the infrastructure variable was selected by the regression tree in preference to the term ‘country’.

The proportion of household surveys reporting a favourable change in occupation in the previous five years was weakly to moderately correlated with infrastructure (Spearman's Rho = 0.499), and also showed a significant negative linear relationship with proportion of fishers willing to exit (ß = -0.788, p = 0.027). These negative relationships across the region were not evident among sites within individual countries. Within individual countries, the only relationships between infrastructure and percentage exiting were in fact positive (Seychelles: ß = 1.223, p = 0.015, Mauritius, ß = 0.301, p = 0.055).

The proportions of households within a site with fishing as a primary occupation was not significantly related to the percent exiting (p = 0.150). Biomass of fish on adjacent reefs was positively related to percentage exiting (p = 0.028), but this relationship was driven by high biomass values at three Madagascar sites.

Household and individual scale analysis

When the effect of site was accounted for by a random effect, only two of the sub-site scale variables were significant in the full GLMM model. Both occupational multiplicity at the household scale, (i.e. the number of occupations additional to the fishers') (p = 0.017) and typical daily catch value (p < 0.001), were positively related to exit. This was further supported by the stepwise selection of the GLMM, which resulted in only these two fixed effect factors being maintained.

Discussion

As with the literature, a wide range of economic and non-economic variables measured at different scales were related to readiness to exit fisheries. Although responses differed between countries, with high levels of readiness to exit in Madagascar and low levels in Seychelles, site-level differences were most prominent. Beyond the strong effect of site, we also found that household and individual characteristics also influenced fishers' willingness to exit. Individual fishers whose fellow householders had more occupations and whose catches were larger were less willing to exit.

Relationships between exit and site-level factors

In agreement with previous work this study found that the readiness to exit a fishery varied significantly between sites. Site-level variation in responses may be due to differences between individuals in different sites, for example one community may have wealthier households than another. Alternatively, site differences may be due to site-level factors, such as the economic context or local geography, which influence responses to decline independently of the variation between individuals. Without large, multi-scale studies it has been difficult to untangle the scale at which adaptive response of fishers is primarily determined. This study strengthens distinction between individual, household, or site-level factors in influencing fisher's decisions to exit fisheries and provides strong support for the importance of site-level factors. The local social and cultural context appears to play a large role in fishers' perceived willingness to exit the fishery. Place-based factors have similarly been found important in determining adaptation and resilience in a range of risk settings. In particular, sites with lower levels of infrastructure, and less evidence of favourable occupational mobility had higher proportions of fishers who would exit.

The direction of relationships between site-scale variables and fishery exit were surprising and contrary to conventional understanding of fishery bioeconomics.

The site-level variables are indicative of a broadly defined ‘economic development’. The infrastructure index indicates both material community development as well as the degree of connectedness to global and national economies. The proportion of households whose members had favourably changed jobs within the previous five years, and the proportion of households with fishing as a primary occupation are indicators of the nature of employment opportunities available in the local economy.

Smith et al. identify the importance of the non-farm economy being either “a residual sector offering only coping activities and absorbing labour displaced from traditional activities of farming and fishing etc., or a dynamic one creating new jobs, exerting upward pressure on wages”. Indicators of the vibrancy of the local economy would be expected to accompany more alternative livelihood opportunities for fishers, increased opportunity costs of labour and higher readiness to exit. In contrast, we found the proportion of households where members had favourably changed jobs within the previous five years was negatively related to the proportion of fishers willing to exit. In summary, levels of economic development, broadly defined were negatively correlated with exit when the reverse would be expected.

We suggest three plausible and possibly complementary explanations for these results:

  • Levels of economic development and diversification, indicated by the infrastructure index and occupational mobility variable may be associated with specialisation of livelihoods and professionalization of employment, including fisheries. In regions and countries with better infrastructure and more diversified economies, fisheries are more specialised and professionalised and less frequently part of multiple household livelihoods. Specialised fishers may be well vested and have lower capacity to diversify employment as an adaptation.
  • Fishers in more economically developed sites may be more committed due to the history of change in fisheries during the process of development. Fishers at developed sites have chosen fisheries in the context of other available occupations. In contrast, less economically developed sites with fewer opportunities have higher proportions of fishers, many of whom pursue a diverse livelihood strategy and may be willing to exit if alternatives were available. Thus, as local economies develop and provide alternative occupations, less committed fishers may exit and take up other occupations, leaving a smaller, but more specialized and ‘hard-core’ population of fishers, resulting in the negative relationship between readiness to exit and level of economic development observed here. Our cross-site comparative snapshot methodology does not capture the dynamics of change over time as contextual factors evolve, and studies over time would allow for a better evaluation of this hypothesis.
  • Markets, technology and government assistance facilitate and reward fishing more in economically developed sites. The value of the catch varied greatly between countries, being for example two orders of magnitude greater in Seychelles than Madagascar, despite the high biomass densities in some Madagascar sites. While, some studies in developed countries have found that abundance of fishery resources can lead to lower occupational mobility the high biomass in Madagascar sites was associated with a high readiness to exit. We suggest this is an association with development and pricing where economic development and connectivity promote higher prices for these resources. Thus, despite abundant reef resources in some sites in Madagascar, technology, specialisation, catch volumes and market access allowed higher catch revenues in more developed areas, which may reduce the willingness to exit. Similar trends have been reported for bushmeat, where lower supplies are related to higher value across rural-urban gradients in Africa.

Government support may also influence fisheries behaviours more in developed locations through subsidies and infrastructure. For example in Seychelles, fishers are supported by government funded fuel subsidies and health insurance, while in Mauritius registered fishers receive a bad weather allowance, which compensates them when fishing activity is disrupted by unfavourable weather.

A simplistic perspective in which poverty, lack of alternative occupations and overexploitation are mutually reinforcing predicts that economic development supports fishers' occupational mobility by increasing opportunity costs of labour. But if development also results in specialisation of livelihoods, increased profitability, and greater institutional support for the industry, the readiness of fishers to exit may decrease. This view is supported by the observed fidelity of fisheries labour in relatively wealthy western nations. Low mobility of fishing labour has largely been attributed to poverty and a lack of livelihood alternatives that is associated with economic underdevelopment. Yet, in our case study, the richness of rewards may also lead to high fidelity especially in contexts of market access, technology, infrastructure, and government subsidy.

Interestingly, the unexpected direction of relationships between economic development and readiness to exit was apparent only at the largest scale. The negative relationship between readiness to exit and infrastructure across the region was not apparent within individual countries. Instead, the only significant within-country relationships between exit and infrastructure, in the Seychelles and Mauritius were positive. Consequently, the direction of the relationship may differ with the scale of analysis because of different processes operating at different scales. For example, the differences in infrastructure within a small island country may improve alternative livelihood options, while the larger differences observed across the region may be associated with structural differences in national economies and fisheries policy, leading to the large-scale negative relationship.

Household and individual-scale influences on exit

The study also found that household–scale factors affected readiness to exit as reported in Kenya, where household occupational multiplicity was positively related to readiness to exit. This study extends this result in two key ways. Firstly, we find that the positive relationship between household occupational multiplicity and exit exists over a range of countries. Secondly, we identified that this relationship operates at the household- rather than either individual or community scales. Specifically, the multiplicity of additional household occupations rather than the fishers' own occupational multiplicity was the significant variable, and this persisted after accounting for between-site variability. Fishers may benefit from the support of their fellow household members if they have to stop fishing due to fishery declines. Conversely, where fishers are the only source of livelihood, they may need to continue fishing to support their household regardless of declines.

This suggests that adaptation of fishers to disturbances or fisheries declines, and reduction of fishing effort, may be facilitated by alternative livelihoods, even if they are not suitable to fishers themselves. This contrasts with the assertion concerning seaweed farming, often conducted by women, that extra household occupations may subsidise rather than reduce fishing activity. Complex household decisions require further empirical study at the household scale but it seems clear that the overall household livelihood portfolio is relevant to fisher decisions and willingness to exit. Household member occupations may subsidise continued fishing or exiting from the fisheries depending on other contextual variables such as job choices and cultural affinity to fishing. The lack of relationship with the individual's occupational multiplicity is also surprising. This may be due to part-time fishers, who on one hand have other options and could stop fishing but, on the other, do not rely on fisheries, and so can absorb declines without exiting.

Household wealth was found to positively affect the willingness to exit in Kenya. This was broadly supported here by the classification tree analysis in which absolute material style of life appeared within one branch of the tree for the group 2 sites, dominated by Kenya and Tanzania, where fishers from wealthier households were more likely to exit. It was not a significant factor, however, when examined relative to other households within the community and accounting for community-scale effects. Thus wealth may be an important factor at higher scales rather than affecting exit decisions of fishers within their community. Alternatively, household wealth was collinear to catch value, a stronger indicator in this study that may have masked the household wealth effect.

Normal daily catch value was the strongest determining factor at the individual scale, and was negatively related to readiness to exit. This makes practical sense, as we asked about a catch decline proportional to stated normal catch. Fishers with higher value catches may have more scope to absorb declines. Different results may have been found by comparing responses to absolute as well as relative declines, although a full standardization of the effect of absolute declines across the wide range of gears, species and contexts in this region would be challenging. The result also agrees with observations from decommissioning and microeconomics, that less successful fishers have greater incentives to exit due to the lower value of their labour within the fishery. This has implications for the dynamics of the fishery system and recovery of overexploited fisheries. If the least successful fishers preferentially exit, the decline in fishing mortality will be less than predicted by the numbers of fishers exiting.

Several of the characteristics we looked at were poor predictors of readiness to exit. Individual-scale characteristics identified in other studies as influencing occupational mobility were not significant. Age and education were identified on lower branches of the classification tree but responded differently in two branches. The stated reason for starting to fish, an indicator of cultural links or the personal appeal of fisheries as an occupation, appeared in two lower branches of the classification tree, suggesting limited local relevance. Further, the ownership of capital-intensive gears had no detectible effect in this study. One possible explanation is that patron-client arrangements with middlemen, common in the region, may reduce readiness to exit in non-gear owners due to credit arrangements that may encourage fishing in times of poor catches.

Relevance to adaptive capacity

Climate change, globalisation and environmental degradation are leading to unprecedented levels of change and disturbance to social-ecological systems such as fisheries, and their associated livelihoods. Our study provided further evidence that adaptation is influenced by multiple scales that go beyond the assets and characteristics of individuals and include the social and economic environment enabling adaptation. Our study, using hypothetical questions within the context of a livelihood activity provides a means to empirically test determinants of an element of adaptive capacity over a large scale and range of contexts, in response to incremental environmental change rather than extreme events. The results do not support the widespread belief and policy theme that the poor are less able to adapt than the wealthy. Rather these findings add to growing literature which identifies multiple interlocking and dynamic factors which make up adaptive capacity, and specifically emerging insights into existing or shifting livelihood as an adaptive response.

Conclusions

Fishers face an increasing variety of changing conditions related to overexploitation, climate change, globalization, and conservation of marine biodiversity. Understanding how fishers will respond to these ecosystem and institutional changes is critical to better managing fisheries and improving the livelihoods of those dependent on fisheries. Some conventional fisheries economic thinking was supported by this large-scale study, such as the greater occupational mobility of less-successful fishers, while some was refuted. In particular, the often-assumed positive relationship between economic development, and mobility of fishing labour was contradicted by our results. Our results highlight the strong context dependency in adaptive responses, requiring different recommendations and interventions in different contextual conditions.

February 2012

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