Using an Artificial Neural Networks Experiment to Assess the Links among Financial Development and Growth in Agriculture

Cosimo Magazzino (), Marco Mele and Fabio Santeramo
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Marco Mele: Department of Political Sciences, University of Teramo, 64100 Teramo, Italy

Sustainability, 2021, vol. 13, issue 5, 1-15

Abstract: Financial development, productivity, and growth are interconnected, but the direction of causality remains unclear. The relevance of these linkages is likely different for developing and developed economies, yet comparative cross-country studies are scant. The paper analyses the relationship among credit access, output and productivity in the agricultural sector for a large set of countries, over the period 2000–2012, using an Artificial Neural Networks approach. Empirical findings show that these three variables influence each other reciprocally, although marked differences exist among groups of countries. The role of credit access is more prominent for the OECD countries and less important for countries with a lower level of economic de-elopement. Our analysis allows us to highlight the specific effects of credit in stimulating the development of the agricultural sector: in developing countries, credit access significantly affects production, whereas in developed countries, it also has an impact on productivity.

Keywords: credit access; TFP; economic growth; agricultural sector; Artificial Neural Networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:5:p:2828-:d:511344

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