Nearly all countries have committed to reducing carbon emissions and, more specifically, the production of non-renewable energies (NRE). This decision has significant impacts on growth, macroeconomics, and even the dynamics of social variables.
In this study, we analyze, using a panel dataset covering 121 countries with annual data for the period 1992-2020, how a decision to reduce the production value of NRE affects the external sector of economies, with a focus on the current account balance.
We conduct a two-stage procedure.
Stage 1: Automatic model selection technique for panel data. This is a common technique in machine learning and artificial intelligence, which involves using algorithms to identify the most appropriate model (based on information criteria) for a particular dataset. Thus, given a set of covariates, the choice of a subset of them is delegated by the researcher to this selection algorithm. In this work, we use the GSREG algorithm (Glüzmann and Panigo, 2015), which bases the choice of the best model on an exhaustive search, allowing residual behavior tests for each alternative model and providing a complete dataset with descriptive statistics of results.
Stage 2: Dynamic models. An additional methodological aspect relates to the static or dynamic specification of the current account equation. In this work, in line with the existing literature, we will use the generalized method of moments (Arellano and Bond, 1991; extended by Blundell and Bond, 1998, for the case of dynamic models with persistent series (System GMM), considering that the dependent variable exhibits a high level of persistence and because it is robust to the use of potentially endogenous regressors.
We find that the sign of the relationship between the current account and the value of NRE production (both as a proportion of GDP) depends on a) the level of development of the country; b) the productive and foreign trade structure; c) the four interactions between the previous characterizations.
For both Fixed Effects and SGMM estimation, we find that the coefficient of NRE production is statistically significant at 1% and has a positive sign. Specifically, a decrease in NER production will have a stronger effect on reducing the current account balance for countries that are net exporters of NRE compared to those that are net importers of NRE. Likewise, a negative shock in production will have a greater negative effect for developing economies than for advanced ones. Upon identifying the four clusters, developing economies and particularly those that are net exporters of NRE are the most affected.
Using these estimates, we compute impulse-response functions, considering a negative policy shock that is the reduction in foss given by a sample standard deviation. We find that, there is a greater deterioration in the current account balance in response to this shock for emerging and developing economies, as is also the case when analyzing NREs net exporters’ economies. Additionally, we provide a ranking of the predicted impact for each country.
Based on that results a set of specific policy recommendations are proposed.
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