*4.3. Robustness Checks*

For robustness, we first replace fintech credit with total alternative credit as measured by a sum of fintech credit and big tech credit (*LNALTERCAP*), as shown in Table 3. In contrast to prior studies such as Cornelli et al. (2020) and Claessens et al. (2018), we do not report the results of big tech credit as a dependent variable in SEM with the GMM estimator because of the smaller number of countries and years in which big tech credit is present. The number of observations for big tech credit only accounts for 14.84% of the total observations.

**Table 3.** Results of second-stage SEM analysis using an alternative measure of fintech credit.


Notes: *LNALTERCAP*, the natural logarithm of the volume of total alternative credit per capita; *EF*, efficiency score of the individual banking system as derived from VRS DEA estimation; *GDPCAP*, the GDP per capita; *GDPCAP2*, the squared term of *GDPCAP*; *REGFIN*, a dummy variable that takes a value of 1 for a country where an explicit fintech credit regulation is in place, and O otherwise; *MOBILE*, the mobile phone subscriptions per 100 persons given the mobile-based nature of most fintech credit platforms; *BRANCH*, the number of bank branches per 100,000 adult population; *LERNER*, the Lerner index of the banking sector mark-ups in an economy; *CONCEN*, the ratio of three largest banks' assets to all commercial banks' assets; *RS*, a regulatory stringency index for the banking sector of an economy; *GDPGR*, the economic growth rate; and *INF*, the inflation rate. The table contains results estimated using a simultaneous equations model (SEM) with the GMM estimator and the Newey–West method. *EF* and *LNALTERCAP* represent the two endogenous variables in SEM. \*, \*\* and \*\*\* denote the two-tail significance at the 10%, 5%, and 1% levels, respectively.

Part 2 of Table 3 confirms the positive impact of total alternative credit on banking efficiency, while the development of total alternative credit is not affected by the banking efficiency, as indicated in Part 1 of Table 3. This insignificant impact can be explained by the fact that big tech firms have operated in different countries, and thus may go beyond the capacity of domestic controls to capture the global nature of big tech business models.

Big tech firms often have a wide range of business lines, in which lending accounts for only one (often small) part. However, the volume of big tech credit is usually large (i.e., this was at least twice as large as fintech credit in 2019) (Cornelli et al. 2020). The advantage of using large volumes of information allows big tech firms to effectively measure the loan quality of potential borrowers based on a large existing and cross-border user base, given the application of advanced technology in lending segments. Additionally, big tech firms may focus on serving potential borrowers who have already been existing customers in their ecosystem.

Following the classification by Cornelli et al. (2020), we also divide our sample into two groups, including developed and non-developed economies. Although the results cannot be reported here but are available upon request, the findings show that a two-way relationship between fintech credit and banking efficiency still holds for the case of nonadvanced economies. Again, this confirms our above findings. When observing advanced economies, there appears only a one-way negative impact of fintech credit on banking efficiency, suggesting that fintech credit tends to substitute for the banking system. This is because more developed economies will have a higher demand for credit from firms and households, and thus, these potential borrowers tend to switch to new intermediaries. The advantages of fintech credit and big tech credit are comprehensively discussed by Cornelli et al. (2020) and Claessens et al. (2018). However, these findings need to be cautiously interpreted because of the small sample size used in SEM with the GMM estimator (i.e., there are only 92 observations in the case of developed economies).

Furthermore, we use the natural logarithm of the volume of fintech credit as an alternative measure of fintech credit. A positive impact of fintech credit on banking efficiency still holds, while the relationship in the other direction is insignificant. Additionally, we replace *REGFIN* with other variables that reflect countries' institutional characteristics (i.e., barriers to entry, as expressed by the ease of doing business variables, and investor disclosure and efficiency of the judicial system). For the ease of doing business, we used score starting a business (overall), score-time (days), score-paid-in minimum capital (% of income per capita), and score-cost (% of income per capita). For the investor protection and judicial system, we used the extent of disclosure index, trial, and judgment (days), enforcement of judgment (days), and enforcement fees (% of claim). All indicators were collected from the World Bank Ease of Doing Business database. We ran each indicator individually to avoid multicollinearity issues. The findings indicate a two-way relationship between fintech credit and banking efficiency, although the reports are not presented but are available upon request. Nonetheless, our above findings are confirmed.

#### **5. Conclusions**

This study investigates the causal relationship between fintech credit and banking efficiency in 80 countries from 2013 to 2017 using a two-stage framework. In the first stage, DEA with the use of financial ratios was employed to estimate the efficiency of the banking systems around the world. In the second stage, the GMM estimation in SEM was used to examine the above interrelationship. The findings of the first stage show that the average efficiency scores of these banking systems are relatively low, suggesting that there is still room for them to improve.

Importantly, the findings of the second stage indicate that there is a negative relationship between banking efficiency and fintech credit, while greater fintech credit can promote banking efficiency. Additionally, a negative relationship between the density of bank branch networks and fintech credit suggests that fintech credit serves underbanked regions. Our findings further emphasize that fintech credit is more developed in economies where explicit fintech regulation is present. Therefore, the implementation of a legal framework regarding fintech credit is very important for the development of fintech credit. Additionally, our findings reemphasize the significance of monitoring and anticipating the movement of the inflation rate is very important to enhancing the efficiency of the banking system. All in all, promoting fintech credit would bring about mutual benefits, including

(1) addressing the unbanked or low-credit segments that banking systems do not serve, and (2) enhancing the efficiency of the banking systems.

This study has some limitations. We could not extend the choice of variables in our DEA model nor incorporating the country-fixed effect variables in our SEM analysis due to data limitations. Future research may extend the data so that a balanced panel could be obtained to examine the efficiency and productivity changes over time of the banking systems. Additionally, future studies are encouraged to use different DEA models under the different assumptions in the first stage. For the second stage SEM analysis, the impact of big tech credit on banking efficiency should be considered in future studies when the relevant data are more widely available.

**Author Contributions:** Conceptualization, T.D.Q.L. and T.N.; methodology and software, D.T.N.; validation, T.D.Q.L.; formal analysis, T.H.H.; investigation, D.T.N.; resources, T.H.H.; data curation, T.H.H.; writing—original draft preparation, T.H.H. and D.T.N.; writing—review and editing, T.D.Q.L. and T.N.; visualization and supervision, T.D.Q.L.; project administration, T.D.Q.L.; funding acquisition, T.D.Q.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the University of Economics and Law, Vietnam National University, Ho Chi Minh City, Vietnam.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Appendix A**

**Table A1.** The list of countries included in our sample.




**Table A3.** Descriptive statistics of variables used in the second-stage analysis.


Notes: The dependent variable was winsorized at the 1% and 99% levels. <sup>1</sup> *REGFIN* is obtained from Rau (2020). <sup>2</sup> *LERNER* was collected from the World Bank data. However, the data over the period 2015–2017 were obtained based on the estimates of Igan et al. (forthcoming). <sup>3</sup> *RS* is constructed by Navaretti et al. (2017) from the World Bank database.


**Table A4.** Correlation matrix between variables used in this study.

Notes: *t*-statistics are shown in parentheses, \*, \*\* and \*\*\* denote the significance at the 10%, 5%, and 1% levels, respectively.

#### **Notes**


#### **References**


Wooldridge, Jeffrey M. 2001. *Econometric Analysis of Cross Section and Panel Data*. Cambridge: The MIT Press.

World Bank. 2017. *World Development Indicators (WDI)*. Washington: The World Bank.

Yeo, Eunjung, and Jooyong Jun. 2020. Peer-to-Peer Lending and Bank Risks: A Closer Look. *Sustainability* 12: 6107. [CrossRef]
