In the previous sections, we identified some of the positive and negative socioeconomic effects and adaptations by members of the Peruvian fishing sector to ENSO events. At the artisanal level, this may include gear switching or migration to take advantage of changing marine resources. At the industrial level, many firms have adapted by diversifying, including moving into canning as well as fishmeal, but also into other industries. Some industrial fishermen and plant workers have second jobs, or small shops, often run out of their homes by family members. This helps to carry them through the leaner fishing seasons, as there are no laws that guarantee a minimum wage or labor security during poor fishing periods. Banks respond by altering their loan policy (usually in unfavourable ways) and by refinancing existing loans. Scientific institutions adapt by increasing their monitoring efforts, as well as using the event itself to lobby for more funding from the central government. Regulatory agencies may increase vigilance, change gear restrictions, or in some cases alter quotas.
Secondary impacts on the sector include the increased spoilage and health effects induced by the high air temperatures and transportation problems caused by intense rains. On a macroeconomic level, ENSO events favourably affect the growing, marketing, and sale of soy bean products in other parts of the world. Soymeal is the main competitor with fishmeal as an animal feed supplement. Buyers chose between these two meals, based on their relative prices.
Given the range of impacts and adaptations that are made in response to climate variability, combined with the substantial improvement in understanding and predicting the ENSO phenomenon, one can imagine more efficient political and private-sector decision-making possibilities. Observations from the 1997-1998 ENSO highlight some of the challenges in policy formation based on climate forecast and information. For a thorough treatment of this point using the case of fisheries and food security, see Broad et al. (2002) and Broad and Agrawala (2000). These difficulties stem from two primary sources: (1) scientific uncertainty and (2) societal constraints on the use of climate information.
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