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This paper investigates the relationship between trade openness and income inequality in 11 Latin American countries over the period 1989–2015. The authors use a panel dynamic approach to take into account the high persistence of income inequality. The analysis classifies trade flows, exports and imports according to trading partner’s income level. Then, the authors split trade flows according to different stages of production. The results show that overall trade flows do not statistically affect income inequality in Latin America. However, trade has divergent effects depending on the trade partners: trade with similar- and lower-income countries exacerbates inequality, while trade with higherincome countries reduces income dispersion. The results also emphasise the role of the export channel (in particular in primary commodities) in explaining income inequality in Latin American countries and imports of consumption goods seem to matter more than imports of intermediate and capital goods.

In this paper we focus our attention on the estimation of dynamic discrete systems in which the observed signal is not always related to all components in the state vector, but on the contrary, it can be considered incomplete, since we do not know if a particular observed signal contains information regarding all components or not. We develop a very simple framework to deal with these situations by introducing Bernoulli random variables in the observation equation. The algorithm derived can be considered as an extension of Nahi [8] algorithm for uncertain observations. Mathematics Subject Classification: 62M20, 60G35