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Peruvian Fishmeal Industry Resilience And Adaptions To El Niño Southern Oscillation (Enso) Events

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Introduction

Amidst a change in food demand patterns in emerging economies such as China and India leading to an increase in the consumption of fish (Gandhi and Zhou 2014) hand in hand with growing concerns on feeding World population and climate change, not only supply and distribution of food becomes more important, but also the availability of the inputs involved in aquaculture. Under this idea, one of the main inputs in the diets involved in the production of farmed fish is fishmeal. Its relevance rotates on the fact that fishmeal increases feed intake because of its attributes of high palatability and high amounts of amino acids helping in the rapid adjustment of fish production (Jackson 2006). In 2013, world fishmeal production reached the 4,940,000 metric tons (MT) being Peru the major producer and exporter with 1,115,000 MT produced and 849,000 MT exported only followed by China with 560,000 MT produced and by Chile with 236,000 MT exported (SEAFISH 2016). Only behind gold, copper, and oil, Peruvian fishmeal represents their fourth largest export generating more than 13,000 jobs in the processing stage and foreign currency income (Nolte 2017; Christensen et al. 2014).

Since November 2014 to April 2016, the Peruvian fishmeal industry was affected by an El Niño Southern Oscillation (ENSO) event dropping its production by 24% and its exports by 17% (SEAFISH 2016). From 2009, the Peruvian government has established measures like Individual Vessel Quotas (IVQs) and fishing seasons depending on the availability of biomass in order to reduce the impacts of ENSO on its fisheries and a subsequent overfishing (Tveteras, Paredes, and Peña-Torres 2011). Despite the government efforts, some companies have to leave the market when an ENSO event is present, and as a consequence, people lose their jobs.

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With concentration indicators such as the Four-Firm Concentration Ratio (CR4) and the Herfindhal-Hirschman Index (HHI) demonstrating that the Peruvian fishmeal exports are becoming more concentrated over time, producers and other stakeholders are forced to analyze the shocks that make some firms leave the industry in order to preserve its disintegration. Furthermore, patterns show that during the adversities brought by El Niño Southern Oscillation (ENSO) events, the industry becomes more concentrated, occurring the opposite when ENSO is not present. Due to a reduction in the anchovy landings caused by ENSO, some firms are not able to survive hence making the market more concentrated, and leaving some market share available for those who were resilient enough to survive the shock. Moreover, ENSO has affected Peruvian fisheries for 9 different times since 1980 thus changing the market concentration significantly during those periods. This important weather event leads us to investigate the resilience of some firms to the shocks that ENSO brings, and the factors that make them more resilient.

Resilience is a term that is being applied in different industries to refer to the ability of firms and the industry as a whole to recover from events that cause some disturbances to the system as well as the risk management strategies applied in order to avoid or reduce losses (Rose 2007; Rose and Liao 2005). In fact, it is well known that industries and firms in them suffer from inevitable shocks that alter their performance, and those who have adapted through different measures are considered to be resilient establishments surviving to the shock or taken less time to recover (Jüttner and Maklan 2011; Lindbloom, Shanoyan, and Griffin 2017). The shocks that ENSO causes are not new to the fishmeal industry and to this day they continue to change to counteract them. These adaptations include opening more processing facilities, increasing fishing fleet size, and obtaining biological assets. Existing studies have focused on the effects of ENSO on fishmeal prices and other commodities as well as the changes in price ratios between fishmeal and substitutes such as soybean meal (Ubilava 2014, 2017) without. Although the resilience framework has been widely used in different scopes such as automotive industry, urban infrastructure, and farm resilience (Barroso et al. 2015; Carvalho et al. 2011; Güller et al. 2015; Pant, Barker, and Zobel 2014; Sheffi 2005; Tierney and Bruneau 2007; Zobel 2010; Lindbloom, Shanoyan, and Griffin 2017), literature on the resilience framework in fisheries is still underdeveloped. The purpose of this paper is to fill the gap in the literature by identifying risk management strategies of Peruvian fishmeal exporters that makes them more resilient to ENSO.

Methods and Data

Being used in different industries, the conceptual framework is the resilience triangle where shocks are plotted in a coordinate plane (Sheffi 2005; Tierney and Bruneau 2007; Barroso et al. 2015; Lindbloom, Shanoyan, and Griffin 2017). The vertical axis represents the performance measure to be used and the horizontal axis is the time during the shocks. Under this premise, Lindbloom et. al. (2017) indicated it consists of three point: (A) The level of performance before being affected by a shock, (B) the level of performance after being affect by a shock, and (C) level of performance after recovering from the shock.

In order to calculate resilience of fishmeal exporting industry, two ENSO events were chosen: i) July 09 – April 2010, and ii) November 2014 – April 2016. Two datasets were used to make the analysis. The first data set was obtained from ADUANET which is the Peruvian Customs Agency, Superintendencia Nacional de Aduanas y de Administración Tributaria (SUNAT), official database that reports up to date monthly exports of fishmeal from 165 firms. In addition, data from the National Economic Survey was gathered from the Peruvian Statistics Institute, Instituto Nacional de Estadística e Informática (INEI), on fleet size, financial declarations, days worked during the year, and locations from 194 firms. A resilience index was calculated on the industry and from firms i, during period t, in a similar way as Barroso 2015 and Lindbloom 2017:

  • Where is the resilience index value for Exporter, is the volume exports at time of Exporter, is the time period before the shock, and is the time when recovered from shock.
  • After obtaining each individual’s resilience, an econometric model is defined to examine the effect that different factors have on fishmeal exporter’s resilience.

Expected Results and Implications

The resilience triangle together with the compilation of data will make not only a great contribution to risk management strategies in fisheries, but also help in the developing of more sustainable policies for marine assets. Preliminary descriptive statistics show a significant reduction in fishmeal exports per firm during both shocks. In addition, only the firms with more locations around the country were able to stay in business during the two periods. Finally, this paper will contribute on giving the resilience triangle a new approach and make smaller fisheries learn from other resilient measures taken by larger firms in order to survive to the effects of ENSO and maintain competitiveness. In light of recent changes in food demand patterns and the growing concerns for climate change, this paper has the potential to generate interesting discussion among the WAEA audience.

References

  1. Barroso, A. P., V. H. Machado, H. Carvalho, and V. Cruz Machado. 2015. “Quantifying the Supply Chain Resilience.” https://doi.org/10.5772/59580.
  2. Carvalho, Helena, Ana Barroso, Virgínia Helena Machado, Susana Azevedo, and V. Cruz-Machado. 2011. Supply Chain Resilience: A Simulation Study. ASME Press. http://ebooks.asmedigitalcollection.asme.org/content.aspx?bookid=405§ionid=38787346.
  3. Christensen, Villy, Santiago de la Puente, Juan Carlos Sueiro, Jeroen Steenbeek, and Patricia Majluf. 2014. “Valuing Seafood: The Peruvian Fisheries Sector.” Marine Policy 44 (Supplement C): 302–11. https://doi.org/10.1016/j.marpol.2013.09.022.
  4. Gandhi, Vasant P., and Zhangyue Zhou. 2014. “Food Demand and the Food Security Challenge with Rapid Economic Growth in the Emerging Economies of India and China.” Food Research International, XVI IUFoST World Congress, 63 (September): 108–24. https://doi.org/10.1016/j.foodres.2014.03.015.
  5. Güller, Mustafa, Emre Koc, Michael Henke, Bernd Noche, and Lennart Hingst. 2015. “A Simulation-Based Analysis of Supply Chain Resilience.” 2015.
  6. Jackson, A.J. 2006. “The Importance of Fishmeal and Fish Oil in Aquaculture Diets.” International Aquafeed 9 (January): 18–21.
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  10. Nolte, Gaspar E. 2017. “Peru Oilseeds and Products Annual.” USDA Foreign Agricultural Service. https://gain.fas.usda.gov/Recent%20GAIN%20Publications/Oilseeds%20and%20Products%20Annual_Lima_Peru_2-24-2017.pdf.
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  13. Rose, Adam, and Shu-Yi Liao. 2005. “Modeling Regional Economic Resilience to Disasters: A Computable General Equilibrium Analysis of Water Service Disruptions*.” Journal of Regional Science 45 (1): 75–112. https://doi.org/10.1111/j.0022-4146.2005.00365.x.
  14. SEAFISH. 2016. “Fishmeal and Fish Oil Facts and Figures.” http://www.seafish.org/media/publications/SeafishFishmealandFishOilFactsandFigures_201612.pdf.
  15. Tierney, Kathleen, and M Bruneau. 2007. “Conceptualizing and Measuring Resilience: A Key to Disaster Loss Reduction.” TR News 250 (May): 14–17.
  16. Tveteras, Sigbjorn, Carlos E. Paredes, and Julio Peña-Torres. 2011. “Individual Vessel Quotas in Peru: Stopping the Race for Anchovies.” Marine Resource Economics 26 (3): 225–32. https://doi.org/10.5950/0738-1360-26.3.225.
  17. Ubilava, David. 2014. “El Niño Southern Oscillation and the Fishmeal–soya Bean Meal Price Ratio: Regime-Dependent Dynamics Revisited.” European Review of Agricultural Economics 41 (4): 583–604. https://doi.org/10.1093/erae/jbt033.
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