Query: Artificial intelligence and economic growth

Candidate papers:

abstract: This paper examines the potential impact of artificial intelligence (A.I. on economic growth. We model A.I. as the latest form of automation, a broader process dating back more than 200 years. Electricity, internal combustion engines, and semiconductors facilitated automation in the last century, but A.I. now seems poised to automate many tasks once thought to be out of reach, from driving cars to making medical recommendations and beyond. How will this affect economic growth and the division of income between labor and capital? What about the potential emergence of "singularities" and "superintelligence," concepts that animate many discussions in the machine intelligence community? How will the linkages between A.I. and growth be mediated by firm level considerations, including organization and market structure? The goal throughout is to refine a set of critical questions about A.I. and economic growth and to contribute to shaping an agenda for the field. One theme that emerges is based on Baumol's "cost disease" insight: growth may be constrained not by what we are good at but rather by what is essential and yet hard to improve.
author_names: Philippe Aghion

Benjamin F Jones

Charles I Jones
title: Artificial Intelligence and Economic Growth
year: 2017

abstract: This paper describes the use of five machine learning methods for predicting economic growth based on a countryaEUR(tm)s attributes and presents a comparison of their prediction accuracy. The methods used are four neural network (NN) methods with different activation functions, and eXtreme Gradient Boosting (XGBoost) Their performance is compared in terms of their ability to predict the economic growth rate using three measures (prediction accuracy rate, area under the curve (AUC) value, and F score) The results obtained can be summarized as follows: 1) XGBoost outperforms the NNs in terms of prediction accuracy and F score for original data; 2) data standardization enhances the reliability of NNs, improving their prediction accuracy, AUC value, and F score; 3) XGBoost has smaller standard deviation of prediction accuracy rate than that of NNs; and 4) "Political institution" "Investment and its composition" "Colonial history" and "Trade" are important factors for cross country economic growth.
author_names: Shigeyuki Hamori

Takahiro Kume
title: Artificial Intelligence And Economic Growth
year: 2018

author_names: Michael McAleer
title: Artificial Intelligence and Economic Growth*
year: 2018

abstract: Artificial Intelligence (AI) is performing an increasingly significant role in everyday life as it has the potential to increase the efficiency of productivity and boost the economy. While the technology world is advancing, the mortality rate and the fertility rate of people are declining. These demographic trends have urged many countries to pay a concern to the apparent problems associated with the aging society like the shrinking proportion of the working age population, which in turn can bring about the decline in productivity and the national economic slow down. Hence, we aim to investigate the relationship among investment in AI, Aging society, and Economic growth in three advanced countries, namely China, Japan, and the U.S. using the Copula based simultaneous equations model. The results obtained from the model can conform to our hypothesis only in the case of China and Japan. However, the result of the U.S. is deviated from the hypothesis but challenging.
author_names: Kantika Khanthawithoon

Paravee Maneejuk

Woraphon Yamaka
title: Analyzing the Relationship Among Aging Society, Investment in Artificial Intelligence and Economic Growth
year: 2020

abstract: The economic impact of Articial Intelligence (AI) is studied using a (semi) endogenous growth model with two novel features. First, the task approach from labor economics is reformulated and integrated into a growth model. Second, the standard representative household assumption is rejected, so that aggregate demand restrictions can be introduced. With these novel features it is shown that (i) AI automation can decrease the share of labor income no matter the size of the elasticity of substitution between AI and labor, and (ii) when this elasticity is high, AI will unambiguously reduce aggregate demand and slow down GDP growth, even in the face of the positive technology shock that AI entails. If the elasticity of substitution is low, then GDP, productivity and wage growth may however still slow down, because the economy will then fail to benefit from the supply side driven capacity expansion potential that AI can deliver. The model can thus explain why advanced countries tend to experience, despite much AI hype, the simultaneous existence of rather high employment with stagnating wages, productivity, and GDP.
author_names: Thomas Gries

Wim Naude
title: Artificial Intelligence, Income Distribution and Economic Growth
year: 2020

abstract: The economic impact of Artificial Intelligence (AI) is studied using a (semi) endogenous growth model with two novel features. First, the task approach from labor economics is reformulated and integrated into a growth model. Second, the standard representative household assumption is rejected, so that aggregate demand restrictions can be introduced. With these novel features it is shown that (i) AI automation can decrease the share of labor income no matter the size of the elasticity of substitution between AI and labor, and (ii) when this elasticity is high, AI will unambiguously reduce aggregate demand and slow down GDP growth, even in the face of the positive technology shock that AI entails. If the elasticity of substitution is low, then GDP, productivity and wage growth may however still slow down, because the economy will then fail to benefit from the supply side driven capacity expansion potential that AI can deliver. The model can thus explain why advanced countries tend to experience, despite much AI hype, the simultaneous existence of rather high employment with stagnating wages, productivity, and GDP.
author_names: Wim Naude
title: www.econstor.eu Artificial Intelligence, Income Distribution and Economic Growth
year: 2020

author_names: Patrick Francois
title: Comment on "Artificial Intelligence and Economic Growth"
year: 2018

abstract: Abstract Artificial intelligence as a factor of competitiveness growth is beginning to be widely used by leading companies today. The potential for introduction of artificial intelligence into the national economy system is enormous and cannot be limited to individual companies. First of all, it is the possibility of strategic planning on the scale of the entire economy, that is, the search for optimal models of sectoral balance sheets, building target indicators for large businesses, forecasting of aggregate demand and supply, optimization of the monetary crediting system, etc. Thus, introduction of strategic planning based on artificial intelligence into the national economy system, would build a model of extended reproduction, without distortions between different sectors of the economy, and thus implement a model of its sustainable, crisis free growth.
author_names: Rafael Envarovich Abdulov
title: Artificial Intelligence as an Important Factor of Sustainable and Crisis Free Economic Growth
year: 2020

abstract: The economic impact of Artificial Intelligence (AI) is studied using a (semi) endogenous growth model with two novel features. First, the task approach from labor economics is reformulated and integrated into a growth model. Second, the standard represen tative household assumption is rejected, so that aggregate demand restrictions can be introduced. With these novel features it is shown that (i) AI automation can decrease the share of labor income no matter the size of the elasticity of substitution between AI and labor, and (ii) when this elasticity is high, AI will unambiguously reduce aggre gate demand and slow down GDP growth, even in the face of the positive technology shock that AI entails. If the elasticity of substitution is low, then GDP, productivity and wage growth may however still slow down, because the economy will then fail to benefit from the supply side driven capacity expansion potential that AI can deliver. The model can thus explain why advanced countries tend to experience, despite much AI hype, the simultaneous existence of rather high employment with stagnating wages, productivity, and GDP.
author_names: Thomas Gries

Wim Naude
title: Artificial Intelligence in Economic Growth: Modelling the Dynamic Impacts of Automation on income distribution and growth
year: 2020

abstract: This study presents a new hybrid algorithm for forecasting economic growth using indicators of knowledge based economy (KBE) The algorithm consists of three steps, namely preprocessing, processing, and postprocessing. Preprocessing consists of principal component analysis and reproduction algorithm, which are used to decrease the number of variables and increase the volume of data. Economic growth is predicted during processing using multilayer perceptron (MLP) adaptive neuro fuzzy inferences system, and gene expression programming (GEP) The variables are added separately to the process. The best model is selected during the postprocessing step to forecast economic growth. GEP model is used to forecast unique indicators. The last step involves substitution of forecasted indicators in the best model. In this study, the KBE indicators of Iran from 1993 to 2013 are predicted in the processing step. The MLP model is used, which includes four indicators, namely technological foundation, structure of trained manpower, export and trademark, and employee. Indicators are also forecasted using the GEP model between 2013 and 2020. The results are used to estimate economic growth between forecasting periods. A self organizing map is used to recognize relationships between variables. The results show the efficiency of the algorithm in multivariate forecasting.
author_names: Mohsen Ahmadi

Saeid Jafarzadeh-Ghoushchi

Rahim Taghizadeh

Abbas Sharifi
title: Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches
venue: Neural Computing and Applications
year: 2019