Rastreando la trayectoria de los precios de la quinua en Bolivia : quiebres estructurales y persistencia de choques

Quinoa has undergone a remarkable transformation in recent decades, consolidating its position as a fundamental pillar for Andean farming communities and emerging as a prominent player in the global superfood market. However, the prices of this grain have shown complex dynamics, with large fluctuations that have had a direct impact on the income of small producers. This research aims to analyze the price dynamics of quinoa in Bolivia by identifying the main events and factors that have generated structural breaks in its trend, as well as identifying the persistence of shocks over time. An approach was used that combines, on the one hand, the analysis of structural breaks by means of the Bai and Perron contrast, and also the estimation of the long memory through the 2ELW estimator. Additionally, the influence of exogenous variables that affect prices was evaluated. For this purpose, the world commodity activity index, the El Niño ocean index and world quinoa production were considered. The findings reveal multiple structural breaks in the quinoa price series that are related to certain key events. For example, there are the changes in research and development, the production and marketing boom, and the momentum of government initiatives and international cooperation. These breaks are also associated with varying degrees of persistence of shocks in each identified regime. Although the exogenous variables do not show significant effects in the short term, it is recognized that they could have a relevant influence in different periods. This study demonstrates the complexity of quinoa price dynamics in Bolivia, which is characterized by multiple structural breaks. To take advantage of the opportunities in this market, producers and policy makers must implement flexible strategies and constantly monitor the evolution of the sector, taking into account the key factors that have driven changes in price trends over time.