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Time-series analysis reveals a stable long-run equilibrium between terms of trade and fishing GDP in Peru, indicating external price conditions dictate sectoral growth.
This study examines the long-run relationship between terms of trade (TOT) and real fishing-sector GDP in a small open economy, focusing on the Peruvian case. Despite the strategic importance of fisheries for exports, employment and foreign exchange generation, the extent to which external price conditions are associated with fishing-sector performance remains insufficiently explored in sector-level research. Building on the notion that TOT summarise opportunities and constraints arising from the international environment, the paper evaluates whether persistent external conditions are linked to the long-run trajectory of the fishing sector. The analysis employs the Johansen cointegration approach and a bivariate Vector Error Correction Model (VECM) using quarterly data for the period 2001–2025. Seasonal effects are incorporated through quarterly dummy variables, while robustness is assessed by controlling for extreme El Niño–Southern Oscillation (ENSO) episodes and the COVID-19 pandemic. The results reveal the existence of a unique long-run equilibrium relationship between TOT and fishing-sector GDP. The error-correction mechanism indicates that deviations from equilibrium are actively corrected over time, whereas the adjustment coefficient for TOT is statistically insignificant. Robustness tests further show that El Niño episodes are negatively and significantly associated with short-run sectoral performance, while no statistically significant association is detected for La Niña. The COVID-19 control does not materially alter the long-run relationship identified by the model. The findings contribute sector-level evidence for a resource-dependent economy and suggest that long-run equilibrium and sectoral adjustment dynamics are important elements for understanding the long-run behaviour of the fishing sector.
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Abraham et al. (2026) studied this question.
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