This study investigates household borrowing behavior in Ethiopia using data from the 2025 Global Findex Survey. The analysis employs Generalized Structural Equation Modeling (GSEM) to examine five financial inclusion outcomes: mobile money account ownership, receipt of mobile money credit, formal borrowing, borrowing from friends and family, and participation in informal savings groups (iqub/ROSCAs). The results reveal a segmented financial ecosystem in which access to financial services is shaped by education, borrowing purpose, and demographic characteristics. Mobile money account ownership is strongly associated with higher educational attainment, active saving behavior, urban residence, and female gender, while age and income exert relatively limited effects. In contrast, receipt of mobile money credit is only weakly related to socioeconomic characteristics and is primarily associated with health- and business-related borrowing needs, suggesting that digital credit functions mainly as a liquidity management tool. Formal borrowing is driven largely by business-related financing needs rather than demographic factors, indicating that formal financial institutions primarily support productive investments. Borrowing from friends and family serves as a key source of financing for health, food, and business expenditures, highlighting the continued importance of informal networks in household risk management and consumption smoothing. Participation in informal savings groups is more common among younger individuals and is positively. Therefore, expanding digital financial inclusion, strengthening access to formal credit, and leveraging informal financial institutions as complementary channels could contribute to a more inclusive and resilient financial system.
| Published in | Innovation Business (Volume 1, Issue 2) |
| DOI | 10.11648/j.ib.20260102.11 |
| Page(s) | 98-115 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2026. Published by Science Publishing Group |
Formal, Informal, Family, Friends, Digital, Mobile Money
| [1] | Abay, K. A., Kahsay, G. A., & Berhane, G. (2017). Social networks and factor markets: Panel data evidence from Ethiopia. World Development, 90, 203–215. |
| [2] | Anderson, S., & Baland, J. M. (2002). The economics of ROSCAs and intrahousehold resource allocation. The Quarterly Journal of Economics, 117 (3), 963–995. |
| [3] | Aredo, D. (1993). The Iddir: A study of an informal insurance institution in Ethiopia. Savings and Development, 17(3), 277–294. |
| [4] | Armendáriz, B., & Morduch, J. (2010). The economics of microfinance (2nd ed.). MIT Press. |
| [5] | Ayele, G. (2016). Rural credit markets in Ethiopia: A dynamic analysis of borrowing and lending behavior [Doctoral dissertation, University of Gothenburg]. Gothenburg Studies in Economics. |
| [6] | Banerjee, A. V., & Duflo, E. (2007). The economic lives of the poor. Journal of Economic Perspectives, 21(1), 141–167. |
| [7] | Banerjee, A., Breza, E., Chandrasekhar, A. G., Duflo, E., Jackson, M. O., & Kinnan, C. (2023). Changes in social network structure in response to exposure to formal credit markets. Review of Economic Studies, 90(4), 1655–1692. |
| [8] | Baraki, M. B. (2018). The role of microfinance in rural livelihood improvement: A case study of Oromia Credit and Savings Share Company (OCSSC) in Sabata Hawas Woreda, Ethiopia. Journal of Poverty, Investment and Development, 44, 1–12. |
| [9] | Beck, T., & Demirgüç-Kunt, A. (2006). Small and medium-size enterprises: Access to finance as a growth constraint. Journal of Banking & Finance, 30(11), 2931–2943. |
| [10] | Bertrand, M., Karlan, D., Mullainathan, S., Shafir, E., & Zinman, J. (2010). What’s advertising content worth? Evidence from a consumer credit marketing field experiment. The Quarterly Journal of Economics, 125(1), 263–306. |
| [11] | Besley, T., & Coate, S. (1995). Group lending, repayment incentives and social collateral. Journal of Development Economics, 46(1), 1–18. |
| [12] | Besley, T., Coate, S., & Loury, G. (1993). The economics of rotating savings and credit associations. The American Economic Review, 83(4), 792–810. |
| [13] | Björkegren, D., & Grissen, D. (2018). The potential of digital credit to expand financial inclusion. Science, 359(6381), 1207–1208. |
| [14] | Björkegren, D., & Grissen, D. (2019). Behavior revealed in mobile phone usage predicts loan repayment. The World Bank Economic Review, 34(3), 618–634. |
| [15] | Carroll, C. D. (1997). Buffer-stock saving and the life cycle/permanent income hypothesis. The Quarterly Journal of Economics, 112(1), 1–55. |
| [16] | Castellani, F. (2014). Shocks and credit choice in Southern Ethiopia. Savings and Development, 38(3), 245–278. |
| [17] | Chen, X., Huang, B., & Ye, D. (2021). The role of cultural diversity in peer-to-peer lending: Evidence from China. Pacific-Basin Finance Journal, 67, Article 101552. |
| [18] | Coase, R. H. (1937). The nature of the firm. Economica, 4(16), 386–405. |
| [19] | Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120. |
| [20] | Collins, D., Morduch, J., Rutherford, S., & Ruthven, O. (2009). Portfolios of the poor: How the world's poor live on $2 a day. Princeton University Press. |
| [21] | Cook, T., & McKay, C. (2015). How M-Shwari works: The story so far. Consultative Group to Assist the Poor (CGAP). |
| [22] | Deaton, A. (1991). Saving and liquidity constraints. Econometrica, 59(5), 1221–1248. |
| [23] | de Mel, S., McKenzie, D., & Woodruff, C. (2008). Returns to capital in microenterprises: Evidence from a field experiment. The Quarterly Journal of Economics, 123(4), 1329–1372. |
| [24] | Dercon, S., De Weerdt, J., Bold, T., & Pankhurst, A. (2006). Group-based funeral insurance in Ethiopia and Tanzania. World Development, 34(4), 685–703. |
| [25] | Floro, M. S., & Ray, D. (1997). Vertical links between formal and informal financial institutions. Review of Development Economics, 1(1), 34–56. |
| [26] | Francis, E., Blumenstock, J., & Robinson, J. (2017). Digital credit: A snapshot of the current landscape and open research questions. Center for Effective Global Action (CEGA) Working Paper. |
| [27] | Friedman, M. (1957). A theory of the consumption function. Princeton University Press. |
| [28] | Gebremariam, H. G. (2020). Mobile phones and credit uptake: Evidence from rural Ethiopia. Journal of African Economies, 29(5), 482–506. |
| [29] | Gertler, P., Levine, D. I., & Moretti, E. (2009). Do microfinance programs help families insure consumption against illness? Health Economics, 18(3), 257–273. |
| [30] | Hussen, M. S. (2023). Financial inclusion and household welfare in Ethiopia: An endogenous switching regression approach. Cogent Economics & Finance, 11(1), Article 2175447. |
| [31] | Jack, W., & Suri, T. (2011). Mobile money: The economics of M-PESA. National Bureau of Economic Research Working Paper No. 16721. |
| [32] | Kaffenberger, M., & Totolo, E. (2018). A broader perspective on the welfare effects of digital credit. CGAP Focus Note, No. 108. |
| [33] | Karlan, D., Ratan, A. L., & Zinman, J. (2014). Savings by and for the poor: A research review and agenda. Review of Income and Wealth, 60(1), 36–78. |
| [34] | Karlan, D., & Valdivia, M. (2011). Teaching entrepreneurship: Impact of business training to microfinance clients. The Review of Economics and Statistics, 93(2), 510–527. |
| [35] | Khandker, S. R. (2005). Microfinance and poverty: Evidence using panel data from Bangladesh. The World Bank Economic Review, 19(2), 263–286. |
| [36] | Kofarmata, Y. I. (2019). Determinants of credit rationing among rural farmers in Kano State, Nigeria: A multinomial logistic regression approach. Agricultural Finance Review, 79(5), 634–649. |
| [37] | Kumar, A. (2022). Mobile money and credit access in Ghana: A treatment effects approach. Review of Development Finance, 12(1), 45–58. |
| [38] | Laibson, D. (1997). Golden eggs and hyperbolic discounting. The Quarterly Journal of Economics, 112(2), 443–478. |
| [39] | Modigliani, F., & Brumberg, R. (1954). Utility analysis and the consumption function: An interpretation of cross-section data. In K. K. Kurihara (Ed.), Post-Keynesian economics (pp. 388–436). Rutgers University Press. |
| [40] | Nayaka, N. M., [additional authors not provided]. (2024). Machine learning for risk assessment in peer-to-peer lending: Portfolio management applications. [Journal name missing], [volume missing] (issue missing), page range missing. |
| [41] | Ntwiga, D. B., Ogutu, C., & Kirumbu, M. (2018). Modeling micro-credit use in Kenya: A hidden Markov model approach. Journal of Finance and Economics, 6(3), 91–98. |
| [42] | O’Donoghue, T., & Rabin, M. (1999). Doing it now or later. American Economic Review, 89(1), 103–124. |
| [43] | Pham, T. T. T., & Lensink, R. (2007). Lending policies of formal and informal credit sectors in Vietnam. Journal of Development Studies, 43(8), 1408–1427. |
| [44] | Putnam, R. D. (1993). Making democracy work: Civic traditions in modern Italy. Princeton University Press. |
| [45] | Safaricom Ethiopia. (2025). Quarterly financial and operational update: December 2025. Safaricom Ethiopia Investor Relations. |
| [46] | Siamwalla, A., Pinthong, C., Poapongsakorn, N., Satsanguan, P., Nettayarak, P., Mingmaneenakin, W., & Tubpun, Y. (1990). The Thai rural credit system: Public subsidies, private information, and segmented markets. The World Bank Economic Review, 4(3), 271–295. |
| [47] | Sinha, S., & Matin, I. (1998). Informal credit transactions of micro-credit borrowers in rural Bangladesh. Institute of Development Studies, University of Sussex. |
| [48] | Stiglitz, J. E., & Weiss, A. (1981). Credit rationing in markets with imperfect information. American Economic Review, 71(3), 393–410. |
| [49] | Sulle Joseph, E., Chegere, M. J., & Mdadila, K. (2025). Mobile money adoption and credit access in Sub-Saharan Africa: Evidence from the 2021 Global Findex. Journal of African Business, 26(1), 88–106. |
| [50] | Tang, Y. (2024). Peer-to-peer lending, mobile payments, and inclusive finance: A conceptual framework. Financial Innovation, 10(1), Article 22. |
| [51] | Tarozzi, A., Desai, J., & Johnson, K. (2015). The impacts of microcredit: Evidence from Ethiopia. American Economic Journal: Applied Economics, 7(1), 54–89. |
| [52] | Thaler, R. H. (1999). Mental accounting matters. Journal of Behavioral Decision Making, 12(3), 183–206. |
| [53] | Tressel, T. (2003). Dual financial systems and inequalities in access to credit: A theoretical model. Journal of Development Economics, 72(2), 471–494. |
| [54] | Wang, N. (2003). Caballero meets Bewley: The permanent-income hypothesis in general equilibrium. American Economic Review, 93(3), 927–936. |
| [55] | Williamson, O. E. (1975). Markets and hierarchies: Analysis and antitrust implications. Free Press. |
| [56] | Williamson, O. E. (1985). The economic institutions of capitalism. Free Press. |
| [57] | World Bank. (2022). The Global Findex Database 2021: Financial inclusion, digital payments, and resilience in the age of COVID-19. World Bank Group. |
| [58] | World Bank. (2025). Global Findex Database 2025: Financial inclusion, digital payments, and resilience. World Bank Group. [Forthcoming; cross-country analysis cited as in-press or personal communication]. |
| [59] | Zeldes, S. P. (1989). Consumption and liquidity constraints: An empirical investigation. Journal of Political Economy, 97(2), 305–346. |
APA Style
Milkano, T. T. (2026). An Analysis of Household Borrowing Behavior: Evidence on Institutional Choice, Mobile Money, and Credit Demand Drivers in Ethiopia. Innovation Business, 1(2), 98-115. https://doi.org/10.11648/j.ib.20260102.11
ACS Style
Milkano, T. T. An Analysis of Household Borrowing Behavior: Evidence on Institutional Choice, Mobile Money, and Credit Demand Drivers in Ethiopia. Innov. Bus. 2026, 1(2), 98-115. doi: 10.11648/j.ib.20260102.11
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Download@article{10.11648/j.ib.20260102.11,
author = {Tesema Tadesse Milkano},
title = {An Analysis of Household Borrowing Behavior: Evidence on Institutional Choice, Mobile Money, and Credit Demand Drivers in Ethiopia},
journal = {Innovation Business},
volume = {1},
number = {2},
pages = {98-115},
doi = {10.11648/j.ib.20260102.11},
url = {https://doi.org/10.11648/j.ib.20260102.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ib.20260102.11},
abstract = {This study investigates household borrowing behavior in Ethiopia using data from the 2025 Global Findex Survey. The analysis employs Generalized Structural Equation Modeling (GSEM) to examine five financial inclusion outcomes: mobile money account ownership, receipt of mobile money credit, formal borrowing, borrowing from friends and family, and participation in informal savings groups (iqub/ROSCAs). The results reveal a segmented financial ecosystem in which access to financial services is shaped by education, borrowing purpose, and demographic characteristics. Mobile money account ownership is strongly associated with higher educational attainment, active saving behavior, urban residence, and female gender, while age and income exert relatively limited effects. In contrast, receipt of mobile money credit is only weakly related to socioeconomic characteristics and is primarily associated with health- and business-related borrowing needs, suggesting that digital credit functions mainly as a liquidity management tool. Formal borrowing is driven largely by business-related financing needs rather than demographic factors, indicating that formal financial institutions primarily support productive investments. Borrowing from friends and family serves as a key source of financing for health, food, and business expenditures, highlighting the continued importance of informal networks in household risk management and consumption smoothing. Participation in informal savings groups is more common among younger individuals and is positively. Therefore, expanding digital financial inclusion, strengthening access to formal credit, and leveraging informal financial institutions as complementary channels could contribute to a more inclusive and resilient financial system.},
year = {2026}
}
TY - JOUR T1 - An Analysis of Household Borrowing Behavior: Evidence on Institutional Choice, Mobile Money, and Credit Demand Drivers in Ethiopia AU - Tesema Tadesse Milkano Y1 - 2026/07/08 PY - 2026 N1 - https://doi.org/10.11648/j.ib.20260102.11 DO - 10.11648/j.ib.20260102.11 T2 - Innovation Business JF - Innovation Business JO - Innovation Business SP - 98 EP - 115 PB - Science Publishing Group SN - 3142-8681 UR - https://doi.org/10.11648/j.ib.20260102.11 AB - This study investigates household borrowing behavior in Ethiopia using data from the 2025 Global Findex Survey. The analysis employs Generalized Structural Equation Modeling (GSEM) to examine five financial inclusion outcomes: mobile money account ownership, receipt of mobile money credit, formal borrowing, borrowing from friends and family, and participation in informal savings groups (iqub/ROSCAs). The results reveal a segmented financial ecosystem in which access to financial services is shaped by education, borrowing purpose, and demographic characteristics. Mobile money account ownership is strongly associated with higher educational attainment, active saving behavior, urban residence, and female gender, while age and income exert relatively limited effects. In contrast, receipt of mobile money credit is only weakly related to socioeconomic characteristics and is primarily associated with health- and business-related borrowing needs, suggesting that digital credit functions mainly as a liquidity management tool. Formal borrowing is driven largely by business-related financing needs rather than demographic factors, indicating that formal financial institutions primarily support productive investments. Borrowing from friends and family serves as a key source of financing for health, food, and business expenditures, highlighting the continued importance of informal networks in household risk management and consumption smoothing. Participation in informal savings groups is more common among younger individuals and is positively. Therefore, expanding digital financial inclusion, strengthening access to formal credit, and leveraging informal financial institutions as complementary channels could contribute to a more inclusive and resilient financial system. VL - 1 IS - 2 ER -
Independent Researcher, Addis Ababa, Ethiopia
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