Abstract:

We examine the macroeconomic effects of the energy transition, focusing on the impact of oil prices on GDP, productivity and inflation. We find that energy dependence on fossil fuels increases vulnerability to oil price shocks, negatively affecting Total Factor Productivity (TFP). Using the Solow decomposition and including energy as part of the capital stock, we find two key effects: The Price and Scale Effect, in which higher energy prices increase production costs and reduce TFP; and the Recomposition Effect, in which greater use of domestic renewables boosts TFP by reducing reliance on non-renewable imports. Our findings for Chile between 2001 and 2019, the TFP adjustment for energy factors provides a complementary and enriched view of productivity, especially in periods or contexts with high volatility in energy consumption or prices. Finally, using a New-Keynesian DSGE model calibrated for Chile, we examine the macroeconomic consequences of the energy transition. A counterfactual scenario shows that, without diversification of the energy matrix, the economic impact of higher oil prices would have been more severe, with larger GDP declines, higher inflation, tighter monetary policy, and a steeper fall in TFP, highlighting the benefits of Chile's shift to a more renewable energy matrix.

Presented at the Society of Economists of Chile 2024, Session 35: Central Bank of Chile - Macroeconomic Implications of Climate Change. 

Best Research Presentation Award (Master level), Universidad Adolfo Ibáñez, 2024.

Abstract: 

We quantify the potential effects of generative artificial intelligence (AI) on productivity and labor markets in Latin America and the Caribbean using a task-based framework. We combine task-level measures of AI exposure with harmonized employment and wage data to construct occupation- and country-level exposure profiles. To reflect uncertainty about diffusion, we distinguish technical feasibility from economically profitable adoption and calibrate conservative and optimistic scenarios based on recent empirical evidence. We then translate task-level productivity gains into aggregate TFP and GDP effects using an AI-adjusted GDP-share approach. We report results under two scenarios that differ in assumptions about adoption and micro-level productivity gains, with gains that are modest in the conservative scenario and larger in the optimistic scenario, and with variation across countries reflecting differences in occupational structure. Finally, we estimate general equilibrium wage effects by education, age, and gender, emphasizing a central policy question: whether labor reallocation can keep pace with AI adoption. We also analyze how AI exposure differs across the informal and formal sectors and across export- and import-oriented sectors.