2,007
Views
6
CrossRef citations to date
0
Altmetric
Original Articles

Which social program supports sustainable grass-root finance? Machine-learning evidence

ORCID Icon
Pages 389-395 | Received 07 Nov 2019, Accepted 13 Dec 2019, Published online: 20 Dec 2019

References

  • Allen H, Panetta D. 2010. Savings groups: what are they? Technical Report. SEEP Network.
  • Bouman F.J. 1995. Rotating and accumulating savings and credit associations: A development perspective. World Dev. 23(3):371–384.
  • Breiman L. 2001. Random forests. Mach Learn. 45:5–32.
  • Burlando A, Canidio A. 2017. Does group inclusion hurt financial inclusion? Evidence from ultra-poor members of Ugandan savings groups. J Dev Econ. 128:24–48.
  • Can U, Alatas B. 2017. Big social network data and sustainable economic development. Sustainability. 9(11):2027.
  • Christensen G. 1993. The limits to informal financial intermediation. World Dev. 21:721–731.
  • Delany A, Storchi S. 2012. SaveAct savings and credit groups and small enterprise development. South Africa: FinMark Trust.
  • Dupas P, Karlan D, Robinson J, Ubfal D. 2018. Banking the unbanked? evidence from three countries. Am Econ J: Appl Econ. 10:257–297.
  • Feldman R, Sanger J. 2007. The text mining handbook: advanced approaches in analyzing unstructured data. New York, NY: Cambridge university press.
  • Flynn J, Sumberg J. 2017. Youth savings groups in Africa: they’re a family affair. Enterp Dev Microfinance. 28(3):147–161.
  • Gabriel CA, Kirkwood J. 2016. Business models for model businesses: lessons from renewable energy entrepreneurs in developing countries. Energy Policy. 95:336–349.
  • Genuer R, Poggi J-M, Tuleau-Malot C, Villa-Vialaneix N. 2017. Random forests for big data. Big Data Res. 9:28–46.
  • Greaney BP, Kaboski JP, Van Leemput E. 2016. Can self-help groups really be self-help? Rev Econ Stud. 83(4):1614–1644.
  • Gregorutti B, Michel B, Saint-Pierre P. 2017. Correlation and variable importance in random forests. Stat Comput. 27:659–678.
  • Guha S, Gupta G. 2005. Microcredit for income generation: the role of RoSCA. Econ Polit Wkly. 40(14):1470–1473.
  • Holloway J, Mengersen K, Helmstedt K. 2018. Spatial and machine learning methods of satellite imagery analysis for sustainable development goals. 16th Conference of the International Association of Official Statisticians (IAOS); Sep 19–21; Paris, France.
  • Hoque N, Rahman ARA, Molla RI, Noman AHM, Bhuiyan MZH. 2018. Is corporate social responsibility pursuing pristine business goals for sustainable development? Corporate Social Resp Environ Manage. 25:1130–1142.
  • Hossain S, Saleh MA, Drennan J. 2017. A critical appraisal of the social entrepreneurship paradigm in an international setting: a proposed conceptual framework. Int Entr Manage J. 13:347–368.
  • Karlan Ratan L, Zinman J. 2014. Savings by and for the poor: A research review and agenda. Rev Income Wealth. 60:36–78.
  • Kolk A. 2016. The social responsibility of international business: from ethics and the environment to CSR and sustainable development. J World Bus. 51:23–34.
  • Le Polain M, Sterck O, Nyssens M. 2018. Interest rates in savings groups: thrift or threat? World Dev. 101:162–172.
  • Ledgerwood J, Earne J, Nelson C. 2013. The new microfinance handbook: a financial market system perspective. NW Washington, DC: The World Bank.
  • Maliti E. 2017. Deviation of community savings groups from their apparent methodology: lessons from the field. Int J Soc Econ. 44:326–336.
  • Nicodemus K, Shugart Y. 2007. Impact of linkage disequilibrium and effect size on the ability of machine learning methods to detect epistasis in case-control studies. In Genetic Epidemiology. 31(6):611–611.
  • Seelos C, Mair J. 2005. Social entrepreneurship: creating new business models to serve the poor. Bus Horiz. 48:241–246.
  • Strobl C, Boulesteix A.L, Kneib T, Augustin T and Zeileis A. 2008. Conditional variable importance for random forests. BMC Bioinformatics. 9(1):307.
  • United Nations Secretary-General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development 2014. A world that counts mobilizing the data revolution for sustainable development. Technical Report, Data Revolution Group.
  • Venkatraja B. 2019. Are SHGs catalysts of inclusive sustainable rural development? Impact assessment of SKDRDP interventions in India. Int J Sustainable Dev World Ecol. 26(4):302–312.
  • Vijayarani S, Ilamathi MJ, Nithya M. 2015. Preprocessing techniques for text mining-an overview. Int J Comput Sci Commun Networks. 5:7–16.
  • Weiss SM, Indurkhya N, Zhang T. 2015. Fundamentals of predictive text mining. London: Springer.
  • Zhou Y, Tong Y, Gu R, Gall H. 2016. Combining text mining and data mining for bug report classification. J Software: Evol Process. 28:150–176.