What Determines Enterprise Borrowing from Self Help Groups? An Interpretable Supervised Machine Learning Approach
Journal of Financial Services Research - Trang 1-23 - 2023
Tóm tắt
Despite several advantages associated with borrowing from micro-finance institutions, such as self-help groups (SHGs), many enterprises in developing countries continue to rely on informal lenders. Using machine learning techniques on a novel village-enterprise matched dataset from India, we predict an enterprise’s choice of credit source as a function of three key mechanisms: supply-side factors, infrastructural facilities and socio-demographic characteristics. Proximity to markets and social norms of the village, proxied by high literacy rates and sex ratios, play important roles in credit uptake from SHGs. However, the absence of financial access points, such as commercial or cooperative bank branches, is not prohibitive.
Tài liệu tham khảo
Agarwal S (2018) Do rural roads create pathways out of poverty? Evidence from India. J Dev Econ 133(February 2017):375–395
Agarwal S, Hauswald R (2010) Distance and private information in lending. Rev Financ Stud 23(7):2758–2788
Agarwal S, Alok S, Ghosh P, Ghosh S, Piskorski T, Seru A (2017) Banking the unbanked: what do 255 million new bank accounts reveal about financial access? Columbia Business School Research Paper, (17–12)
Albanesi S, Vamossy DF (2019) Predicting consumer default: A deep learning approach. No. w26165. National Bureau of Economic Research
Altmann A, Toloi L, Sander O, Lengauer T (2010) Permutation importance: a corrected feature importance measure. Bioinformatics 26(10):1340–1347
Asher S, Lunt T, Matsuura R, Novosad P (2019) The Socioeconomic High- resolution Rural-Urban Geographic Dataset on India (SHRUG)
Asher S, Novosad P (2020) Rural roads and local economic development. Am Econ Rev 110(3):797–823
Banerjee A, Duflo E, Glennerster R, Kinnan C (2015) The miracle of micro finance? Evidence from a randomized evaluation. Am Econ J: Appl Econ 7(1):22–53
Banerjee A, et al (2013) The diffusion of microfinance. Science 341.6144: 1236498
Barnes D (2019) Electric power for rural growth: how electricity affects rural life in developing countries. Routledge
Basu P (2006) Improving access to finance for India’s rural poor. World Bank Publications
Bell C (1990) Interactions between institutional and informal credit agencies in rural India. The World Bank Economic Review, pages 186–213
Besley T, Coate S (1995) Group lending, repayment incentives and social collateral. J Dev Econ 46(1):1–18
Bruhn M, Love I (2011) Gender differences in the impact of banking services: evidence from Mexico. Small Bus Econ 37(4):493–512
Bjorkegren D, Grissen D (2020) Behavior revealed in mobile phone usage predicts loan repayment. World Bank Econ Rev 34.3(2020):618–634
Bruhn M, Love I (2014) The real impact of improved access to finance: evidence from Mexico. J Financ 69(3):1347–1376
Burgess BR, Pande R (2005) Do rural banks matter? Evidence from the Indian social banking experiment. Am Econ Rev 95(3):780–795
Chaudhuri K, Sasidharan S, Raj RSN (2020) Gender, small firm ownership, and credit access: some insights from India. Small Bus Econ 54(4):1165–1181
Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17-Augu:785 794
Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I (2018) Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India (No. w24678). National Bureau of Economic Research
Cull R, Demirgüç-Kunt A, Morduch J (2014) Banks and microbanks. J Financ Serv Res 46:1–53
Davis J, Heller S (2017) Using causal forests to predict treatment heterogeneity: an application to summer jobs. Am Econ Rev 107(5):546–550
Davis J, Heller S (2020) Rethinking the benefits of youth employment programs: the heterogeneous effects of summer jobs. Rev Econ Stat 102(4):664–677
de Aghion BA, Morduch J (2006) The economics of microfinance. J Int Econ 70(1):328–333
Demiguc-Kunt A, Klapper L, Singer D, Ansar S, Hess J (2017) The Global Findex
Du M, Ninghao L, Hu X (2019) Techniques for interpretable machine learning. Commun ACM 63(1):68–77
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874
Fletschner D (2009) Rural women’s access to credit: market imperfections and intra- household dynamics. World Dev 37(3):618–631
Freyberger J, Neuhierl A, Weber M (2020) Dissecting characteristics nonparametrically. Rev Financ Stud 33(5):2326–2377
Friedman J (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232
Ghatak M (2000) Screening by the company you keep: joint liability lending and the peer selection effect. Econ J 110(465):601–631
Ghatak M, Guinnane T (1999) The economics of lending with joint liability: theory and practice. J Dev Econ 60(May):195–228
Ghosh S, Vinod D (2017) What constrains financial inclusion for women? Evidence from Indian micro data. World Dev 92(60):81
Hastie T, Tibshirani R, Friedman JH, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, New York, vol. 2, pp. 1–758
Hoerl AE, Kennard RW (1970) Ridge regression: Biased estimation for non-orthogonal problems. Technometrics 42(1):80-86.
Hoff K (2016) Caste system. World Bank policy research working paper, (7929)
Kaboski JP, Townsend RM (2012) The impact of credit on village economies. Am Econ J Appl Econ 4(2):98–133
Karlan DS (2007) Social connections and group banking. Econ J 117(52):84
Kirubi C, Jacobson A, Kammen DM, Mills A (2009) Community-based electric micro-grids can contribute to rural development: evidence from Kenya. World Dev 37.7(2009):1208–1221
Kleinberg J, Ludwig J, Mullainathan S, Obermeyer Z (2015) Prediction policy problems. Am Econ Rev 105(5):491–495
Kochar A (1997) Does lack of access to formal credit constrain agricultural production? Evidence from the land tenancy market in rural India. Am J Agric Econ 79(3):754–763
Krishna A, Bajpai D (2011) Lineal spread and radial dissipation: experiencing growth in rural India, 1993–2005. Econ Pol Wkly 46(38):1993–2005
Menon N, van der Meulen RY (2011) How access to credit affects self-employment: differences by gender during India’s rural banking reform. J Dev Stud 47(1):48–69
Mothilal R, Sharma A, Tan C (2020) "Explaining machine learning classifiers through diverse counterfactual explanations." Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
Mullainathan S, Spiess J (2017) Machine learning: an applied econometric Ap- proach. J Econ Perspect 31(2):87–106
Petersen M, Rajan R (1994) The benefits of lending relationships: evidence from small business data. J Financ 49(1):3–37
Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1(5):206–215
Schapire R (2013) Explaining adaboost. Empirical Inference, 37–52
Swain R (2002) Credit rationing in rural India. J Econ Dev 27(2):1–20
Swain R, Wallentin FY (2009) Does micro finance empower women? Evidence from self-help groups in India. Int Rev Appl Econ 23(5):541–556
Tian S, Yu Y, Guo H (2015) Variable selection and corporate bankruptcy forecasts. J Bank Financ 52(1):89–100
Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B (Methodological) 58(1):267–288
Townsend R (1997) Discrimination in Financial Services: How Should We Proceed? In: Benston GJ, Hunter WC, Kaufman GG (eds) Discrimination in Financial Services. Springer, Boston
Wachter S, Mittelstadt B, Russell C (2017) Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv J Law Technol 31(841):1–52
Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B: Stat Methodol 67(5):768