Classical Macrodynamics and Real Competition in Latin America, 1980-2020

This project centers on two main research questions: (1) how is it possible to explain the behavior of the aggregate rate of profit during the neoliberal era? and (2) how did these changes affect the long-run performance of the Latin American economies from 1980 until 2020? Thus, rather than adopting a mainstream approach that lacks a historical and class understanding, this research project provides an alternative systemic interpretation of the economic and political aspects of Latin America during the neoliberal era. In this regard, two principal outcomes are expected to arise: (1) an alternative approach to understand the dynamism of Latin America’s socioeconomic scenario for the past forty years through the evaluation of the behavior of the aggregate rate of profit and the economic policies that have been implemented in this region. On the other hand, (2) this project presents the first application of real competition theory to a comparison of Latin American countries.

We follow the real competition framework developed by Anwar Shaikh’s Capitalism: Competition, Conflict, Crises (2016). That perspective contemplates the competition of capitalism as a global system, instead of considering economic policies for each country in an isolated manner. For example, increasing real wage in an abstract economy could contribute to the improvement labor income. Yet, the same policy could reduce competitiveness, decrease the profit and accumulation rates and increase unemployment if we look at open economies. Furthermore, we aim to historicize the neoliberal operations applied to attract investment in open Latin American economies since the 1980s. This implies that capitalism’s dynamics must be studied from a holistic perspective.

César Castillo-García
César Castillo-García
PhD Student in Economics

My research interests include the history of neoliberalism, income inequality, and the economics of precarious work. I am also into computational economics (max-ent method and machine learning).