The drivers of financial development: Global evidence from internet and mobile usage
We study impact of internet and mobile usage on nine different indicators of financial development (FD), including depth, access, and efficiency of both, financial markets, and financial institutions, as well as overall financial development. We apply Granger causality and cointegration tests, PMG A...
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| Main Authors: | , , |
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| Format: | text |
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Archīum Ateneo
2020
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| Online Access: | https://archium.ateneo.edu/asog-pubs/195 https://www.sciencedirect.com/science/article/abs/pii/S0167624520301360?via%3Dihub |
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| Institution: | Ateneo De Manila University |
| Summary: | We study impact of internet and mobile usage on nine different indicators of financial development (FD), including depth, access, and efficiency of both, financial markets, and financial institutions, as well as overall financial development. We apply Granger causality and cointegration tests, PMG ARDL and PDOLS, and a two-step system GMM to a sample of 109 economies and two sub-samples (62 low- and middle-income economies (LMEs), 47 high-income economies (HIEs)) over the period of 1998–2017. The Granger causality tests show long-run bi-directional causality between internet/mobile usage and financial development. We find that internet usage has a significant negative impact on overall financial development, which could be attributed to a negative impact on financial institutions with all their three dimensions, depth, access, and efficiency. At the same time, internet has significant positive impact on financial markets with its three dimensions. Contrary to the opposing effects internet usage, mobile usage has a significant positive impact on all nine indices of financial development. The PMG ARDL and PDOLS estimations clarify that the positive impact of the internet is a short run effect, while the negative effect is a long-run one. The mobile usage impact is a long-run phenomenon. The estimations for two sub-samples show consistently positive impact of mobile phones in HIEs, whereas the results for LMEs are less robust. |
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