A study on the determinants of growth and political stability in the neoclassical model framework


1 Introduction

In alignment with contributions like Robert J. Barro’s Economic Growth in a cross section of countries (Barro, 1991) the main scope of this project is to study whether or not convergence, as predicted in neoclassical growth models, is a reasonable result. Therefore, this empirical study aims to further contribute to the validation (or not) of the results and assumptions of neoclassical growth theory.

Moreover, further analysis of the determinants of long-run growth and the factors that condition political stability is also performed. Specifically, convergence may be tested in the context of Solow’s (Solow, 1956) model, referred to in the appendix for further understanding of its mechanisms and implications. Yet, it is worth stating one of its main conclusions, convergence. In the Solow model (also called this way), growth in output per capita can be expressed by the following equations:

This implies that, for countries with similar parameters, the more distant the output level of a country is from its steady state (or Balanced Growth Path) the larger its growth rate of output per capita. Notice that other values are assumed non-negative.

As a consequence, when considering similar steady-state levels of output per capita for countries, the growth rate of output per capita should be inversely related to the level itself. Again, this premise is tested in the obtained cross-section of countries, which is explained in the following section.

1.2 Data Discussion: Dataset and Included Variables

Cross-sectional data containing observations from 93 countries was obtained by integrating diverse data sources. Mainly, the obtained variables come from files provided by The World Bank, although others were also integrated from Our World in Data, Penn World Tables or The Heritage Foundation. For specification of the source of each variable please direct to Table 1 in the appendix.

Variables considered for causal relation assessment and the study of convergence respond, also, to the arguments provided by scientific literature (Barro, 1991). To measure the output per capita level, real GDP per capita in terms of 2017 US dollars in 1990 was used. The growth rate of output per capita was computed using the same variable for the years 1990 and 2019. Notice that considering output per capita instead of the level itself is a way, per se of controlling for population growth.

In the same sense, the fact that GDP90 and GDP19 are real variables accounts for different price developments within countries. Notice also that the time span considered for computing the growth rate is non-trivial, as the Solow model mainly explains long-run growth. Thus, considering a rather large time period for growth rate computation also helps to neutralize short-run variations, the study of which is outside the scope of this paper.

Furthermore, ES90 acts as a proxy accounting for human capital stock. In various endogenous economic growth models, human capital is regarded as one of the main determinants of the quality of the research sector, which in turn conditions technological progress. Therefore, the addition of an educational enrollment score responds to the need to control for human capital stock and technological progress.

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