Does FinTech credit scale stimulate financial institutions to increase the proportion of agricultural loans?

Mohsin, Akm et al. Article Does FinTech credit scale stimulate financial institutions to increase the proportion of agricultural loans? Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Mohsin, Akm et al. (2022) : Does FinTech credit scale stimulate financial institutions to increase the proportion of agricultural loans?, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 10, Iss. 1, pp. 1-16, https://doi.org/10.1080/23322039.2022.2114176 This Version is available at: https://hdl.handle.net/10419/303773 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Akm Mohsin, MD Rashidul Islam Sheikh, Hasanuzzaman Tushar, Mohammed Masum Iqbal, Syed Far Abid Hossain & Md. Kamruzzaman To cite this article: Akm Mohsin, MD Rashidul Islam Sheikh, Hasanuzzaman Tushar, Mohammed Masum Iqbal, Syed Far Abid Hossain & Md. Kamruzzaman (2022) Does FinTech credit scale stimulate financial institutions to increase the proportion of agricultural loans?, Cogent Economics & Finance, 10:1, 2114176, DOI: 10.1080/23322039.2022.2114176 To link to this article: https://doi.org/10.1080/23322039.2022.2114176 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 02 Sep 2022. Submit your article to this journal Article views: 1574 View related articles View Crossmark data Citing articles: 5 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Does FinTech credit scale stimulate financial institutions to increase the proportion of agricultural loans? Akm Mohsin 1 *, MD Rashidul Islam Sheikh 2 , Hasanuzzaman Tushar 3 , Mohammed Masum Iqbal 1 , Syed Far Abid Hossain 4 and Md. Kamruzzaman 1 Abstract: FinTech has raised the risk-taking level of financial institutions. This paper aims to explore FinTech Credit (FTC) scale of non-financial institutions and the risktaking level of financial institutions into Opiela’s model, constructs objective functions and constraints for representative financial institutions by conducting theoretical analysis and research hypothesis. It also explores the relationship between FTCscale and the proportion of agriculture-related loans. Based on the balanced panel data of 31 provinces and municipalities in China from 2009 to 2017, the individual fixed effects model is used to test the research hypothesis. Then, based on the balanced panel data of 31 provinces and municipalities in China from 2009 to 2017, the research hypothesis was tested using an individual fixed-effects model to explore the relationship between FTCscale and the proportion of agriculturerelated loans. The results show that the FTCscale can increase the share of agriculture-related loans in financial institutions. Still, the percentage of agriculturerelated loans and e-commerce factors increase at a marginal decreasing rate. Furthermore, the study shows that marketization and real estate development also indirectly affect the proportion of agricultural loans through the mediating part of the FTCscale. Finally, policy recommendations are proposed to develop FTC and the implementation of rural revitalization strategy. Subjects: Economics and Development; Economics; Finance; Business, Management and Accounting Keywords: FinTech credit; risk-taking level; agricultural loans; marketization level 1. Introduction Rural revitalization depends on the development of agricultural economy. The development of the agricultural economy depends on the growth of agricultural total factor productivity, and the input of agricultural loans is a necessary condition for the growth of total factor productivity (Li & Wu, 2018). The increase of agriculture-related loans is also conducive to the “three rural problems” and thus to the implementation of rural revitalization strategy. However, with the withdrawal of many rural financial networks and the intensification of the separation of institutions and funds from farmers, the coverage rate of agriculture-related loans is still low, and the supply of agriculturerelated loans is still insufficient (Yin et al., 2019). On 29 January 2019, the People’s Bank of China, the Banking and Insurance Regulatory Commission, the Securities Regulatory Commission, the Ministry of Finance, and the Ministry of Agriculture and Rural Affairs jointly issued the “Guidance on Financial Services for Rural Revitalization The guiding opinions also clearly put forward the active Mohsin et al., Cogent Economics & Finance (2022), 10: 2114176 https://doi.org/10.1080/23322039.2022.2114176 Page 1 of 16 Received: 19 July 2021 Accepted: 12 August 2022 *Corresponding author: AKM Mohsin, AKM Mohsin, Faculty of Business and Entrepreneurship, Daffodil International University, Dhaka, Ashulia, Bangladesh E-mail: [email protected] Reviewing editor: David McMillan, Stirling Management School University of Stirling, Stirling, UK Additional information is available at the end of the article © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. implementation of the project of Internet financial services for “three rural areas” and regulate the development of Internet finance (IF) in rural areas. IFapproximates financial technology (FinTech) in China (Chen, 2016; He & Ong, 2014; Zhang et al., 2020; Zhang & Zhuang, 2020). FinTech innovations include financial institutions such as banks, securities, and insurance, and non-financial institutions such as Internet companies. Both the International Monetary Fund (IMF) and the U.S. Treasury Department consider that the FTC can practice financial inclusion and have a positive attitude toward it. In 2016 and 2017, the Party Central Committee and the State Council attempted to serve the “three rural areas” and achieve rural revitalization through FinTech innovation by financial and non-financial institutions. The “Guiding Opinions on Financial Services for Rural Revitalization” issued by the People’s Bank of China and four other ministries and commissions in 2019 also continued this policy theme. The Guidance on Financial Services for Rural Revitalization issued by the People’s Bank of China and four other ministries in 2019 also continues this policy keynote. Therefore, FinTech innovation of financial institutions and FTC of non-financial institutions are expected to become the two carriages for serving the “three rural areas” and practicing rural revitalization. Apart from directly serving the “three rural areas” and increasing agricultural-related loans, is there a spillover effect of FTC on the proportion of agricultural-related loans of financial institutions? If so, what kind of spillover effect is there? Is there any trust factor (Roh et al., 2022) affect FTC? 1.1. Review of literature Fintech and agriculture has a close bonding in terms of sustainable development. Due to the digital marketplace, fintech aid agricultural sustainability (Anshari et al., 2019). Recent research revealed that Fintech firms’ portfolios have a advanced financial threat (Najaf et al., 2020). In addition, due to block chain technology, fintech has significant impact on the environment (Schinckus, 2021). The existing literature revolves around risk, cost, impact on financial institutions’ operating performance (Schinckus, 2020), and influencing factors regarding agriculture-related loans and e-commerce factors (Z. Hu et al., 2019). Regarding risks and costs, a large body of literature argues that agriculture-related loans are riskier and more costly than non-agricultural loans and e-commerce factors (Avkiran & Morita, 2010; Dong et al., 2012; Huang et al., 2006; Lin et al., 2019; Zhao et al., 2014). Regarding the impact on financial institutions’ operating performance, some literature has argued that agriculture-related loans can enhance the operating performance of financial institutions (Guo & Jia, 2009). Regarding the factors influencing agriculture-related loans and e-commerce factors the main ones are government subsidies, urban-rural differences, financial decentralization, GDP growth rate, agricultural output and population size, interest rates and credit construction and bank concentration (Holly Wang, 2008; Jin & Turvey, 2002; Y. P. Chen et al., 2010; Ran et al., 2020). In general, the existing literature provides a large number of useful studies on agriculture-related lending. However, there is a lack of research on whether the FTC scale has spillover effects on the proportion of financial institutions’ agricultural loans and what spillover effects exist. Therefore, this study proposes research hypotheses based on theoretical analysis and tests the research hypotheses based on balanced panel data of 31 provinces and municipalities from 2009 to 2017 to study “whether the scale of FTC stimulates financial institutions to increase agricultural loans and increase investment.” This article’s innovations and contributions are mainly reflected in the following three aspects: First, based on the effect of FTC on the risk-taking level of financial institutions, this article is the first to study the spillover effect of FTCscale on the proportion of agricultural loans. Second, this paper is the first to empirically examine the path through which the marketization level and real estate development affect the share of agriculture-related loans through the partial intermediary effect of the FTC scale. Third, this paper’s findings provide further theoretical support for the policy of “actively implementing the Internet financial services ‘three rural’ projects”. The rest of the paper is organized as follows: the second part constructs a theoretical model for theoretical analysis and proposes research hypotheses; the third part conducts research design, the fourth part conducts empirical analysis and robustness tests based on 31 provincial and urban Mohsin et al., Cogent Economics & Finance (2022), 10: 2114176 https://doi.org/10.1080/23322039.2022.2114176 Page 2 of 16 areas’ balanced panel data from 2009 to 2017, the fifth part conducts mediating effects analysis, and finally concludes the full paper and proposes policy recommendations. 2. Model construction and research hypothesis 2.1. Basic assumptions and model construction The FTC will enhances the risk-taking level of banks and other financial institutions (Cui, 2020; Li et al., 2021; Turuev, 2020; Zver’kova, 2019). Referring to the extension of Kishan and OPIELA (2012), this paper first introduces the scale of FTC and risk-taking level into the model to construct the theoretical analysis framework. The basic assumptions of the model are as follows. Hypothesis 1: Representative financial institutions (the financial institutions in this article refer to commercial banks, postal savings banks, rural credit cooperatives, etc., however, commercial banks and postal savings do not have the same power or willingness to invest in FinTech services) that obtain funds and issue loans by absorbing various deposits) aim at maximizing profits and their asset-liability equation is: RþBþNþA¼DþK. Where, R is deposit reserve, B is government bond, N is non-agricultural loan, A is agricultural loan, D is deposit, and K is capital. Hypothesis 2: Representative financial institutions only hold statutory reserves and do not hold excess reserves. The statutory reserve ratio is. Therefore, R¼ωD且0<ω<1. Learning from the practice of Guo and Shen (2016), the rate of return of the statutory reserve is set to zero. To respond to temporary liquidity needs and maximize profits, representative financial institutions also hold government bonds B with strong liquidity but low yields, with a yield rate of rb. Hypothesis 3: The non-agricultural loan N of a representative financial institution increases with the increase of its risk-taking level, but the growth rate decreases at the margin. Let N¼Nθð Þ ¼ N0þ2ηnffiffiffiθ p, ηn>0. Where, θ is the risk-taking level of representative financial institutions, and ηn is the nonagricultural loan tendency of representative financial institutions. The interest rate of non-agricultural loans is rn, because loan interest rates have been market-oriented. Therefore, representative financial institutions are price takers in the non-agricultural loan market and rn>rb. Hypothesis 4: The agricultural loan A of a representative financial institution also increases with the increase of its risk-taking level, and the growth rate is also decreasing marginally. Let A¼Aθð Þ ¼ A0þ2ηaffiffiffiθ p, ηa>0. Where, θ is the risk-taking level of the representative financial institution, and ηa is the agricultural loan tendency of the representative financial institution. The interest rate of agriculture-related loans is ra. Similarly, representative financial institutions are price takers in the agriculture-related loan market, and ra>rb. Agricultural loans are riskier and more costly than non-agricultural loans (Liu et al., 2019; Weber & Musshoff, 2012). Hence, representative financial institutions are more willing to invest in non-agricultural loans. Therefore, the propensity for non-agricultural loans is greater than the tendency for agricultural loans, ηn>ηa. Hypothesis 5: The level of risk-taking of a representative financial institution increases with the size of FinTech credit, i.e., θ¼θFð Þ ¼ θ0þθcF;θc>0. where F is the FTC size θ0 is the initial risk-taking level of the representative financial institution, that is, the risk-taking level when the FTC size is zero; θc is the risktaking propensity, that is, the marginal change in the risk-taking level of the representative financial institution as the FTC size increases. c is the risk-taking propensity, i.e., the marginal change in the representative financial institution’s risk-taking level as the FTC size increases. Mohsin et al., Cogent Economics & Finance (2022), 10: 2114176 https://doi.org/10.1080/23322039.2022.2114176 Page 3 of 16 Hypothesis 6: Taking a reference from Kopecky and VanHoose (2004), the cost of managing assetliability items for a representative financial institution is set as a quadratic cost function, which is C¼C0þCbþnN2=2þaA2=2þdD2=2þkK2=2 Where n;a;dandk are constants representing the unit marginal administrative costs of nonagricultural loans, agricultural loans, deposits, and capital, respectively. C0 is a constant representing the initial fixed cost of the representative financial institution, i.e., the cost that the representative financial institution would have to incur even if it did not do any business. The marginal management cost per unit for non-agricultural and agricultural loans is the postloan management cost per unit. There may be differences regarding the post-loan management of non-agricultural and agro-related loans: lenders of non-agricultural loans are usually located in cities with relatively convenient transportation, while lenders of agricultural loans are usually located in cities with relatively convenient locations and may have inconvenient transportation. However, the amount of agricultural loans is small, and representative financial institutions can use remote and spot checks for post-lending management; even if representative financial institutions do not use remote and spot checks for post-lending management of agricultural loans (Chen & Ge, 2015; Ge et al., 2017; Hsu, 2012; Ma et al., 2019). It will also include the increased costs due to traffic inconvenience in loans’ interest rate before issuing. In this way, the unit post-loan management costs of agricultural loans and non-agricultural loans are not much different, and unit marginal management costs are almost the same. For this reason, this article assumes that the unit marginal management costs of non-agricultural loans and agricultural loans are similar, which is defined as n�a. Government bonds do not require post-loan management, so their management costs can be set as a constant Cb. In addition, the deposit market and capital market have also been market-oriented. Representative financial institutions are also price takers in the deposit market and capital market. The deposit interest rate is denoted as rd and the cost of capital is denoted as rk. Based on the above assumptions, the objective functions and constraints of the representative financial institutions are as follows: maxπ¼rbBþrnNþraArdDrkKC(1) s:t: RþBþNþA¼DþK N¼Nθð Þ ¼ N0þ2ηnffiffiffiθ pηn>0 A¼Aθð Þ ¼ A0þ2ηaffiffiffiθ pηa>0 θ¼θFð Þ ¼ θ0þθcFθc>0 C¼C0þCbþn 2N2þa 2A2þd 2D2þk 2K2 8 > > > > < > > > > : (2) rn>rb;ra>rb;ηn>ηa;n�a 2.2. Model solution and research hypothesis Combining equation (2), the partial derivative of F on both sides of equation (1) can be obtained: @π @F¼rb @B @Fþrn @N @Fþra @A @FaA @A @FnN @N @F(3) Mohsin et al., Cogent Economics & Finance (2022), 10: 2114176 https://doi.org/10.1080/23322039.2022.2114176 Page 4 of 16 Bringing (2) into (3) so that @π==@F¼0 can be obtained: rnrb ð Þηnþrarb ð ÞηaaηaA¼nηnN(4) Divide both sides of equation (4) by nηnA to obtain: N A¼rnrb ð Þηnþrarb ð ÞηaaηaA nηnA(5) Press “(numerator + denominator)/denominator = (numerator + denominator)/denominator” on both sides of equation (5), and then take the reciprocal to get: A NþA¼nηnA rnrb ð Þηnþrarb ð ÞηaaηaAþnηnA(6) The right side of equation (6) is the proportion of agricultural loans, and the partial derivatives of F on both sides of equation (6) can be obtained: @A NþA � �=@F¼nηnrnrb ð Þηnþrarb ð Þηa ½ �ηaθc rnrb ð Þηnþrarb ð ÞηaaηaAþnηnA½ �2θ0þθcFð Þ1 2(7) Let, α¼nηnrnrb ð Þηnþrarb ð Þηa ½ �ηaθc; β¼rnrb ð Þηnþrarb ð Þηa; γ¼nηnaηa. Then, equation (7) can be expressed as: @A NþA � �=@F¼α ðβþγAÞ2�θ0þθcFð Þ1 2(8) Since rn>rb;ra>rb, and α>0, β>0, therefore @A NþA � �=@F>0 (9) From equation (9), as the scale of FTCincreases, the proportion of agricultural loans increases. From equation (8), the partial derivative of F on both sides of the equation can be obtained: @2A NþA � �=@F2¼  2ηaθcαγ ðβþγAÞ3�θ0þθcFð Þ1αθc 2ðβþγAÞ2θ0þθcFð Þ3 2(10) Since ηn>ηa;n�a, and γ¼nηnaηa>0. therefore @2A NþA � �=@F2<0 (11) From equation (11), the growth rate of the proportion of agricultural loans is diminishing marginally. Equation (10) take the limit to F to get: Mohsin et al., Cogent Economics & Finance (2022), 10: 2114176 https://doi.org/10.1080/23322039.2022.2114176 Page 5 of 16 lim F!þ1@2A NþA � �=@F2¼0 (12) According to (12), the growth rate of the proportion of agricultural loans will eventually converge to zero. Therefore, by combining (9), (11) and (12), the following research hypotheses can be obtained: Research Hypothesis H: FTC scale may increase the ratio of agricultural loans, but the growth rate will slow down slightly. 3. Research design 3.1. Sample selection and data sources This article tests the research hypothesis using the amount of loans issued by FinTech credit (also known as Peer-to-Peer (P2P) online lending). Given that the earliest available FTC loan amounts are from 2009, and that China implemented considerable administrative interventions on the FTC in 2018, it is more reasonable to exclude them from the sample. Therefore, this paper selects 31 provinces and cities from 2009 to 2017 to constitute a balanced panel of FTCloan amounts for empirical analysis. Among them, the amount of P2P online loans is obtained from Zero2IPO Finance-Zero1 Intelligence; the data related to the construction of 31 provincial and urban Internet financial associations and research institutions are obtained from the China Social Organization Public Service Platform and manually collated; other data are obtained from the National Bureau of Statistics, the People’s Bank of China, and Wind database. In addition, this article interpolates the 2017 marketization process index by Winsorizing the continuous variables with an upper and lower 1% Winsorize tail. 3.2. Variable description 3.2.1. Explained variable The explained variable in this article is the proportion of agricultural loans. For this reason, the explanatory variable is designed to take the value of “agricultural loan balance/loan balance*100” for each year in 31 provinces and municipalities. The reasons for choosing the loan balance are as follows: First, the newly issued agricultural loans each year can reflect the loan structure, but this data is not available. Second, the loan balance can also reflect the financial market. Cheng (2009) used loan balance as an explained variable when studying the transmission of China’s monetary policy. Chen et al. (2018) calculated the structural indicator of the banking industry’s concentration based on the loan balance, and this article uses the loan balance as the basis to calculate the structural indicator of the proportion of agricultural loans in the banking industry. 3.3. Key variable The key variable in this article is the size of FinTech credit. For this reason, the key variable mFinTechit is designed, and the value is the amount of loans issued by P2P platforms each year in 31 provinces and municipalities. Considering the research hypothesis that FTC size has a marginal decreasing effect on the share of agricultural loans, the square root of it mFinTechit is taken as ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi mFinTech it p. Mohsin et al., Cogent Economics & Finance (2022), 10: 2114176 https://doi.org/10.1080/23322039.2022.2114176 Page 6 of 16 3.3.1. Control variable Based on the existing literature, this paper selects urbanization rate, GDP growth rate, agricultural output, total provincial population, and agricultural non-performing loans as control variables. (1) Urbanization rate (urbrateit): the value is “urban population/(urban population + rural population)” of each province, municipality. (2) GDP growth rate (ggdpit): the value is the GDP growth rate of each province, municipality, and district. (3) Agricultural output value (lagdpit): referring to Yin et al. (2020), the value is the natural logarithm of the agricultural output value of each province, municipality, and municipality, that is lagdpit ¼ln 1þagdpit ð Þ, where agdpit is the agricultural output value of each province. (4) Provincial total population (peopleit): the value is the total population of each province, urban area. (5) Agriculture-related non-performing loan ratio aloanbit ð Þ Due to the lack of provincial and municipal data, the value is taken as the “non-performing loan ratio of agriculture, forestry, animal husbandry fishery” for each year. This article will test it for robustness. In addition to the above control variables, this article also considers the following control variables: (6) Real estate sales rssalesit ð Þ and its quadratic term rssales2it; (7) Marketization level mktproit ð Þ and its quadratic term mktpro2 it; (8) Special rectification treadjustt ð Þ taken from 2011 to 2015 0, take 1 in 2016 and 2017, and take its interaction term with time treadjustt¼ t�readjust t ; (9) Government encouragement tencrgt ð Þ, take 1 from 2014 to 2015, take 0 for the rest of the year, and take its interaction term with time tencrgt¼t�encrg t for the same reason. (10) Value-added of the financial industry fgdpit ð Þ. (11) The credit gap dlrateit ð Þ. (12) Mobile phone penetration rate rmbphnit ð Þ and its quadratic term rmbphn2it. (13) The number of Internet users iusersit ð Þ and its quadratic term iusers2it . (14) Education level edulevit ð Þ. (15) The construction of self-regulatory organizations dassit ð Þ. (16) Fiscal policy gpit ð Þ. (17) Monetary policy mpt ð Þ and its interaction terms with fiscal policy gmpit ¼mp� tgpit. (18) Time tð Þ, the value is “year-2008.” (Limited to space, reasons for choosing control variables are available on request) 3.4. Model design To test the research hypothesis H, the following individual fixed effects model was designed. rargloanit ¼α0þβ1�ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi mFinTech it pþηi�Xit þαiþεit (a) Where, rargloanit denotes the share of agriculture-related loans in year t in the i-th province and city, α0 is the intercept term, αi is the individual effect in the i-th province and city, and Eit is the random error term. ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi mFinTech it pis the key variable, and Xit is the control variable listed in the previous article. In order to prevent endogeneity, ggdpit, lagdpit and fgdpit are lagging by one period, respectively, ggdpit1, lagdpit1 and fgdpit1. Mohsin et al., Cogent Economics & Finance (2022), 10: 2114176 https://doi.org/10.1080/23322039.2022.2114176 Page 7 of 16 This paper extends the model of Opiela by introducing FTC scale and risk-taking level into the model of Opiela. Constructing objective functions and constraints for representative financial institutions, conducting theoretical analysis, and proposing research hypotheses. Then, based on the amount of FTC loans in 31 provinces and municipalities in China from 2009 to 2017, an individual fixed-effects model was used to study the relationship between FTCscale and the share of agriculture-related loans. The results of the study show that the scale of FTC can increase the proportion of agriculture-related loans, and the larger the amount of FTC loans, the higher the proportion of agriculture-related loans; however, as the amount of FTC loans grows, the rate of increasing the proportion of agriculture-related loans tends to diminish. The findings of the study is useful for the financial policy-makers in the credit management and other relevant financial departments. 7. Recommendation for policymakers and future research direction First, given that the scale of FTC is conducive to increasing the proportion of agriculture-related loans, it is recommended that relevant authorities continue to encourage the development of new FinTech business models, such as FTC, to stimulate banks and other financial institutions to increase the proportion of agriculture-related loans. Second, in view of the diminishing marginal effect of the scale of FTCto increase the share of agriculture-related loans and the higher cost of agriculture-related loans, it is recommended that the relevant departments consolidate the foundation of FinTech innovation (e.g., increase the construction of rural big data platforms) so that financial institutions can reduce the pre-lending and in-lending costs of agriculture-related loans using technology. Third, given that marketization and real estate development also indirectly affect the share of agriculture-related loans through FTC-scale transmission. Therefore, in addition to continuing to encourage the development of FinTech credit, the proportion of agriculturerelated loans can also be adjusted indirectly through the construction of marketization and real estate regulation. Hence, it is suggested that the relevant departments should consider it when formulating policies. However, if the FTC scale rises, it may also affect deposit and loan interest rates, thus changing banks and other financial institutions’ credit allocation behavior. Therefore, it is a future research direction to consider the impact of FTCscale on deposit and loan interest rates and then change the proportion of loans related to agriculture. Funding The author(s) reported there is no funding associated with the work featured in this article. Author details Akm Mohsin 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0002-8730-6189 MD Rashidul Islam Sheikh 2 Hasanuzzaman Tushar 3 Mohammed Masum Iqbal 1 Syed Far Abid Hossain 4 Md. Kamruzzaman 1 1 Faculty of Business and Entrepreneurship, Daffodil International University, Dhaka, Bangladesh. 2 Department of Public Administration, Comilla University, Comilla, Bangladesh. 3 College of Business Administration, IUBAT— International University of Business Agriculture and Technology, Dhaka, Bangladesh. 4 Assistant Professor, BRAC Business School, BRAC University, Dhaka, Bangladesh. Disclosure statement No potential conflict of interest was reported by the author(s). Correction This article has been republished with minor changes. These changes do not impact the academic content of the article. Citation information Cite this article as: Does FinTech credit scale stimulate financial institutions to increase the proportion of agricultural loans?, Akm Mohsin, MD Rashidul Islam Sheikh, Hasanuzzaman Tushar, Mohammed Masum Iqbal, Syed Far Abid Hossain & Md. Kamruzzaman, Cogent Economics & Finance (2022), 10: 2114176. References Anshari, M., Almunawar, M. N., Masri, M., & Hamdan, M. (2019). Digital marketplace and FinTech to support agriculture sustainability. Energy Procedia, 156(9), 234–238. https://doi.org/10.1016/j.egypro.2018.11.134 Avkiran, N. K., & Morita, H. (2010). Benchmarking firm performance from a multiple-stakeholder perspective with an application to Chinese banking. Omega, 38(6), 501–508. https://doi.org/10.1016/j.omega. 2009.12.007 Chen, L. (2016). From fintech to finlife: The case of fintech development in China. China Economic Journal, 9(3), 225–239. https://doi.org/10.1080/17538963.2016. 1215057 Chen, F.-W., Feng, Y., & Wang, W. (2018). Impacts of financial inclusion on non-performing loans of commercial banks: Evidence from China. Sustainability, 10 (9), 3084. https://doi.org/10.3390/su10093084 Chen, Y., & Ge, Y. (2015). Spatial point pattern analysis on the Villages in China’s poverty-stricken Areas. Procedia Environmental Sciences, 271, 98–105. https://doi.org/10.1016/j.proenv.2015.07.098 Mohsin et al., Cogent Economics & Finance (2022), 10: 2114176 https://doi.org/10.1080/23322039.2022.2114176 Page 14 of 16 Chen, Y. P., Liu, M., & Zhang, Q. (2010). Development of financial intermediation and the dynamics of urban– rural disparity in China, 1978–1998. Regional Studies, 44(9), 1171–1187. https://doi.org/10.1080/ 00343400903365052 Cheng, Y. (2009). Reforms of the agricultural Bank of China. The Chinese Economy, 42(5), 79–97. https:// doi.org/10.2753/ces1097-1475420505 Cui, W. (2020). Research on the Green Finance Development Model of Commercial Banks Based on FinTech. Finance and Market, 5(4), 260. https://doi. org/10.18686/fm.v5i4.2608 Dong, F., Lu, J., & Featherstone, A. M. (2012). Effects of credit constraints on household productivity in rural China. Agricultural Finance Review, 72(3), 402–415. https://doi.org/10.1108/00021461211277259 Ge, Y., Yuan, Y., Hu, S., Ren, Z., & Wu, Y. (2017). Space– time variability analysis of poverty alleviation performance in China’s poverty-stricken areas. Spatial Statistics, 21(1), 460–474. https://doi.org/10.1016/j. spasta.2017.02.010 Guo, P., & Jia, X. (2009). The structure and reform of rural finance in China. China Agricultural Economic Review, 1(2), 212–226. https://doi.org/10.1108/17561370910927444 Guo, P., & Shen, Y. (2016). The impact of Internet finance on commercial banks’ risk taking: Evidence from China. China Finance and Economic Review, 4(1), 1- 18. https://doi.org/10.1186/s40589-016-0039-6 He, G., & Ong, L. H. (2014). Chinese rural cooperative finance in the era of post-commercialized rural credit cooperatives. The Chinese Economy, 47(4), 81–98. https://doi.org/10.2753/ces1097-1475470404 Holly Wang, H. (2008). Economic value of tradable farmland uses rights and mortgage loans in China. Agricultural Finance Review, 68(2), 289–300. https:// doi.org/10.1108/00214660880001231 Hsu, S. (2012). Role of informal finance in China’s continuing economic development. The Chinese Economy, 45(1), 28–45. https://doi.org/10.2753/ces1097-1475450102 Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry, 11(3), 340. https://doi. org/10.3390/sym11030340 Hu, J., Kim, J.-B., & Zhang, W. (2013). Insider Trading and stock price crash risk: International evidence from a natural experiment. SSRN Electronic Journal , 21, 1 - 69. https://doi.org/10.2139/ssrn.2359069 Huang, Y.-T., & Pan, W.-C. (2015). Hypothesis test of mediation effect in causal mediation model with high-dimensional continuous mediators. Biometrics, 72(2), 402–413. https://doi.org/10.1111/biom.12421 Huang, J., Rozelle, S., & Wang, H. (2006). Fostering or stripping rural China: Modernizing agriculture and rural to urban capital flows. The Developing Economies, 44(1), 1–26. https://doi.org/10.1111/j. 1746-1049.2006.00001.x Jin, Y., & Turvey, C. G. (2002). Hedging financial and business risks in agriculture with commoditylinked loans. Agricultural Finance Review, 62(1), 41–57. https://doi.org/10.1108/ 00214870280001128 Kishan, R. P., & OPIELA, T. P. (2012). Monetary policy, bank lending, and the risk-pricing Channel. Journal of Money, Credit and Banking, 44(4), 573–602. https:// doi.org/10.1111/j.1538-4616.2012.00502.x Kopecky, K. J., & Van Hoose, D. (2004). A model of the monetary sector with and without binding capital requirements. Journal of Banking & Finance, 28(3), 633–646. https://doi.org/10.1016/S0378-4266(03) 00038-4 Li, Y., Stasinakis, C., & Yeo, W. M. (2021). Fintech and banking efficiency: Evidence from Chinese Commercial Banks. SSRN Electronic Journal, 2(15), 1- 43. https://doi.org/10.2139/ssrn.3782616 Li, W., & Wu, P. (2018). Rural financial development, agricultural fiscal investment and rural residents income. In DEStech transactions on economics, business and management. icems. https://doi.org/10. 12783/dtem/icems2018/25595 Lin, L., Wang, W., Gan, C., & Nguyen, Q. T. T. (2019). Credit constraints on farm household welfare in Rural China: Evidence from Fujian Province. Sustainability, 11(11), 3221. https://doi.org/10.3390/su11113221 Liu, T., He, G., & Turvey, C. G. (2019). Inclusive finance, farm households entrepreneurship, and inclusive rural transformation in rural poverty-stricken Areas in China. Emerging Markets Finance and Trade, 55(9), 1–30. https://doi.org/10.1080/1540496x.2019.1694506 Ma, X., Wang, J., Zhao, L., & Han, J. (2019). The effects of social capital on farmers’ wellbeing in China’s undeveloped poverty-stricken areas. China Agricultural Economic Review, 12(1), 108–121. https://doi.org/10. 1108/caer-06-2016-0087 Najaf, K., Schinckus, C., & Yoong, L. C. (2020). VaR and market value of fintech companies: An analysis and evidence from global data. Managerial Finance. Ran, M., Chen, L., & Li, W. (2020). Financial deepening, spatial spillover, and Urban–Rural income disparity: Evidence from China. Sustainability, 12(4), 1450. https://doi.org/10.3390/su12041450 Roh, T., Yang, Y. S., Xiao, S., & Park, B. I. (2022). What makes consumers trust and adopt fintech? An empirical investigation in China. Electronic Commerce Research, 10, 1–33. https://link.springer.com/article/ 10.1007/s10660-022-09578-0 Schinckus, C. (2020). The good, the bad and the ugly: An overview of the sustainability of blockchain technology. Energy Research & Social Science, 69, 101614. https://doi.org/10.1016/j.erss.2020.101614 Schinckus, C. (2021). Proof-of-work based blockchain technology and Anthropocene: An undermined situation? Renewable and Sustainable Energy Reviews, 152(6), 111682. https://doi.org/10.1016/j.rser.2021.111682 Turuev, I. B. (2020). FinTech as a new dimension for financial institutions: Problems and solutions. Finance and Credit, 26(9), 2049–2076. https://doi.org/ 10.24891/fc.26.9.2049 Weber, R., & Musshoff, O. (2012). Is agricultural microcredit really more risky? Evidence from Tanzania. Agricultural Finance Review, 72(3), 416–435. https:// doi.org/10.1108/00021461211277268 Yin, Z., Meng, L., & Sha, Y. (2020). Determinants of agriculture-related loan default: Evidence from China. BuletinEkonomiMoneter Dan Perbankan, 23, 129–150. https://doi.org/10.21098/bemp.v23i0.1160 Yin, Z., Qiu, M., & Gan, L. (2019). Information contents of collateral under heterogeneous borrower qualities on the bank loans market in China. China Economic Review, 57 (5), 101326. https://doi.org/10.1016/j.chieco.2019. 101326 Zhang, A., Wang, S., Liu, B., & Liu, P. (2020). How fintech impacts pre- and post-loan risk in Chinese commercial banks. International Journal of Finance & Economics, 25(4), 497-683. https://doi.org/10.1002/ ijfe.2284 Zhang, T., & Zhuang, Y. (2020). Research on the impact of fintech event on Chinese commercial banks’ stock price. International Journal of Wireless and Mobile Computing, 18(3), 289. https://doi.org/10.1504/ijwmc.2020.106781 Zhao, J., Zhang, J., & Barry, P. J. (2014). Do formal credit constraints affect the rural household consumption Mohsin et al., Cogent Economics & Finance (2022), 10: 2114176 https://doi.org/10.1080/23322039.2022.2114176 Page 15 of 16 in China? Agricultural Economics (ZemědělskáEkonomika), 60(No. 10), 458–468. https://doi.org/10.17221/161/2013-agricecon Zver’kova, T. N. (2019). FinTech and banks: A revolution that never happened. Finance and Credit, 25(7), 1501–1513. https://doi.org/10.24891/fc.25.7.1501 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. You are free to: Share — copy and redistribute the material in any medium or format. Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. 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