Assessing the Effect of FinTech Adoption on Country's Productivity - Metwally, Mai (AUC Knowledge Fountain, 2023)
The relationship between total factor productivity of countries, for both low-income and high-income countries, and Fintech adoption will be examined in this paper. Also, a background on Fintech history will be discussed and explored briefly along with Fintech future risks and opportunities. Starting off with the importance of TFP, it is also known to be "Solow residual" (named after American Economist "Robert Solow"). TFP shows and examines the performance along with efficiency of the entity or country. It shows how well and efficient the firm or country in transforming its inputs to the desired outputs. Moreover, it is the ratio between GDP (gross domestic product) of firms along with weighted sum of labor and capital. As per earlier studies, TFP is considered the engine of economic growth noting that Solow's study (over years 1900 to 1949) showed that it has a significant impact on U.S economy. Higher productivity has many benefits to both firms and countries. It means that the resources were effectively utilized, better quality, less overhead costs and less time-to-market. Consequently, this will lead to higher profits for stakeholders and per capita income generated. Finally, there will be overall prosperity and growth. In a nutshell, progression and growing of country's TFP is crucial, where policymakers, governments and businessmen should pay more attention to it. Previously, there were many papers addressing and identifying positive correlation between Fintech adoption and either economic growth of a country or one of the factors affecting countries’ TFP (financial market developments, firms’ efficiency, etc.). In this paper, it will add and highlight not only the relationship between TFP and Fintech proxies but also, it will show significant correlation especially between low-income countries and Fintech proxies. Results show that there is a significant correlation between said proxy and TFP. Moreover, results show that Fintech proxies affect low-income countries even more than high-income countries. Worth mentioning that, data were extracted from “Penn World Tables” for all variables except for Fintech Variables are exported from “Global Fintech Index”.