|
on Economic Growth |
| By: | Jones, Charles I. (Stanford U) |
| Abstract: | Advances in artificial intelligence (A.I.) are a double-edged sword. On the one hand, they may increase economic growth as A.I. augments our ability to inno- vate. On the other hand, many experts worry that these advances entail existen- tial risk: creating a superintelligence misaligned with human values could lead to catastrophic outcomes, even possibly human extinction. This paper considers the optimal use of A.I. technology in the presence of these opportunities and risks. Under what conditions should we continue the rapid progress of A.I. and under what conditions should we stop? |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:ecl:stabus:4179 |
| By: | Matthew Higgins |
| Abstract: | Rapid GDP growth, due in part to high rates of investment and capital accumulation, has raised China out of poverty and into middle-income status. But progress in raising living standards has lagged, as a side-effect of policies favoring investment over consumption. At present, consumption per capita stands some 40 percent below what might be expected given China’s income level. We quantify China’s consumption prospects via the lens of the neoclassical growth model. We find that shifting the country’s production mix toward consumption would raise both current and future living standards, with the latter result owing to diminishing returns to capital accumulation. Chinese policy, however, appears to be moving in the opposite direction, to reemphasize investment-led growth. |
| Keywords: | China; consumption; investment |
| JEL: | E13 E20 E27 I31 O40 |
| Date: | 2024–11–14 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:99098 |
| By: | Ho, Sin Yu; Beri, Parfait Bihkongnyuy |
| Abstract: | Although small and medium-scale enterprises (SMEs) finance and technical support have become critical economic development strategies for many countries in Africa and numerous micro-level studies have examined their effects on firm performance, evidence of how SMEs impact economic growth and the causal pathways remains mixed and largely debatable. Based on different strands of the literature, this study hypothesises a nonlinear relationship between SMEs and economic growth. Regressing growth on SME data as measured by the number of newly registered businesses in 40 African countries from 2006 to 2022, we find support for a nonlinear relation of an inverted U-shape. The results suggest that African countries may pursue policies aimed at boosting SME support as a tool for macro-level development. However, the transient effects of SMEs also suggest the need to consider strategies to ensure that its effects remain positive and sustainable over the long run. While policymakers could consider country-specific studies to understand and design innovative strategies to support the SME sector, more research is required on the types of SMEs and the conditions under which they may influence growth in Africa. |
| Keywords: | Small and medium-size enterprises, SMEs, entrepreneurship, economic growth |
| JEL: | M21 O3 O4 O47 P5 P52 |
| Date: | 2024–10–17 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122552 |
| By: | Jesús Fernández-Villaverde (UNIVERSITY OF PENNSYLVANIA, NBER, CEPR); Galo Nuño (BANCO DE ESPAÑA, CEPR, CEMFI); Jesse Perla (UNIVERSITY OF BRITISH COLUMBIA) |
| Abstract: | We argue that deep learning provides a promising approach to addressing the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges involved in solving dynamic equilibrium models, particularly the feedback loop between individual agents’ decisions and the aggregate consistency conditions required to achieve equilibrium. We then introduce deep neural networks and demonstrate their application by solving the stochastic neoclassical growth model. Next, we compare deep neural networks with traditional solution methods in quantitative economics. We conclude with a review of the applications of neural networks in quantitative economics and provide arguments for cautious optimism. |
| Keywords: | deep learning, quantitative economics |
| JEL: | C61 C63 E27 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2444 |