{"title":"The impact of research and development (R&D) on economic growth: new evidence from kernel-based regularized least squares","authors":"J. Minviel, Faten Ben Bouheni","doi":"10.1108/jrf-11-2021-0177","DOIUrl":null,"url":null,"abstract":"PurposeResearch and development (R&D) is increasingly considered to be a key driver of economic growth. The relationship between these variables is commonly examined using linear models and thus relies only on single-point estimates. Against this background, this paper provides new evidence on the impact of R&D on economic growth using a machine learning approach that makes it possible to go beyond single-point estimation.Design/methodology/approachThe authors use the kernel regularized least squares (KRLS) approach, a machine learning method designed for tackling econometric models without imposing arbitrary functional forms on the relationship between the outcome variable and the covariates. The KRLS approach learns the functional form from the data and thus yields consistent estimates that are robust to functional form misspecification. It also provides pointwise marginal effects and captures non-linear relationships. The empirical analyses are conducted using a sample of 101 countries over the period 2000–2020.FindingsThe estimates indicate that R&D expenditure and high-tech exports positively and significantly influence economic growth in a non-linear manner. The authors also find a positive and statistically significant relationship between economic growth and greenhouse gas emissions. In both cases, the effects are higher for upper-middle-income and high-income countries. These results suggest that a substantial effort is needed to green economic growth. Internet access is found to be an important factor in supporting economic growth, especially in high-income and middle-income countries.Practical implicationsThis paper contributes to underlining the importance of investing in R&D to support growth and shows that the disparity between countries is driven by the determinants of economic growth (human capital in R&D, high-tech exports, Internet access, economic freedom, unemployment rate and greenhouse gas emissions). Moreover, since the authors find that R&D expenditure and greenhouse gas emissions are positively associated with economic growth, technological progress with green characteristics may be an important pathway for green economic growth.Originality/valueThis paper uses an innovative machine learning method to provide new evidence that innovation supports economic growth.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":null,"pages":null},"PeriodicalIF":5.7000,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Risk Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/jrf-11-2021-0177","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 2
Abstract
PurposeResearch and development (R&D) is increasingly considered to be a key driver of economic growth. The relationship between these variables is commonly examined using linear models and thus relies only on single-point estimates. Against this background, this paper provides new evidence on the impact of R&D on economic growth using a machine learning approach that makes it possible to go beyond single-point estimation.Design/methodology/approachThe authors use the kernel regularized least squares (KRLS) approach, a machine learning method designed for tackling econometric models without imposing arbitrary functional forms on the relationship between the outcome variable and the covariates. The KRLS approach learns the functional form from the data and thus yields consistent estimates that are robust to functional form misspecification. It also provides pointwise marginal effects and captures non-linear relationships. The empirical analyses are conducted using a sample of 101 countries over the period 2000–2020.FindingsThe estimates indicate that R&D expenditure and high-tech exports positively and significantly influence economic growth in a non-linear manner. The authors also find a positive and statistically significant relationship between economic growth and greenhouse gas emissions. In both cases, the effects are higher for upper-middle-income and high-income countries. These results suggest that a substantial effort is needed to green economic growth. Internet access is found to be an important factor in supporting economic growth, especially in high-income and middle-income countries.Practical implicationsThis paper contributes to underlining the importance of investing in R&D to support growth and shows that the disparity between countries is driven by the determinants of economic growth (human capital in R&D, high-tech exports, Internet access, economic freedom, unemployment rate and greenhouse gas emissions). Moreover, since the authors find that R&D expenditure and greenhouse gas emissions are positively associated with economic growth, technological progress with green characteristics may be an important pathway for green economic growth.Originality/valueThis paper uses an innovative machine learning method to provide new evidence that innovation supports economic growth.
期刊介绍:
The Journal of Risk Finance provides a rigorous forum for the publication of high quality peer-reviewed theoretical and empirical research articles, by both academic and industry experts, related to financial risks and risk management. Articles, including review articles, empirical and conceptual, which display thoughtful, accurate research and be rigorous in all regards, are most welcome on the following topics: -Securitization; derivatives and structured financial products -Financial risk management -Regulation of risk management -Risk and corporate governance -Liability management -Systemic risk -Cryptocurrency and risk management -Credit arbitrage methods -Corporate social responsibility and risk management -Enterprise risk management -FinTech and risk -Insurtech -Regtech -Blockchain and risk -Climate change and risk