Notes 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.
16. 17. 18. 19. 20. 21. 22.
23. 24. 25.
26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42.
Ayyagari, Demirgüç-Kunt, and Maksimovic (2011). Byrne et al. (2021). Berger et al. (2021) found that relationship clients of some US financial institutions were more negatively affected by loan contract terms than other borrowers. Haughwout et al. (2021). Shleifer and Vishny (2011). Bodovski et al. (2021). Wagner and Winkler (2013); Wehinger (2014). Apedo-Amah et al. (2020). OECD (2021d). ICC (2020). Gourinchas et al. (2021b). Koulouridi et al. (2020). Simanowitz, Hennessy-Barrett, and Izaguirre (2021). World Bank (2019b). Some definitions of big data add a fourth “v”—veracity. It is a characteristic needed so that the data are truly useful. See Lukoianova and Rubin (2014). Some add a fifth “v”—value (BBVA 2020). Berg et al. (2020). Agarwal et al. (2020). Björkegren and Grissen (2020). Jagtiani and Lemieux (2018). Gambacorta et al. (2020). A data subject is a person who can be identified directly or indirectly through personal data identifiers. World Bank (2021c). Parlour, Rajan, and Zhu (2020) examine the impacts of financial technology (fintech) competition in the payments market, concluding that, although payment innovations promote financial inclusion, they may lead to ambiguous effects on consumer welfare. Ostmann and Dorobantu (2021). Berg et al. (2020); Hurley and Adebayo (2017). Personal characteristics are regulated by legislation seeking to prevent discrimination or bias. Although these characteristics may vary by legislation, commonly regulated characteristics include gender, familial status, race, disability, religion or belief, and sexual orientation. Leo, Sharma, and Maddulety (2019); Ostmann and Dorobantu (2021). Feyen et al. (2021). IFC (2020a). McKinsey (2019). Alonso-Gispert et al. (2022). Ziegler et al. (2021). For a comprehensive review, see Teima et al. (forthcoming). Cornelli et al. (2020). World Bank and CCAF (2020). Blackmon, Mazer, and Warren (2021). World Bank and CCAF (2020). OECD (2018, 2021b); World Bank (2017, 2021a). OECD (2021c). Izaguirre, Kaffenberger, and Mazer (2018). Calvano et al. (2020) find that AI/ML techniques can encourage collusive behavior. Carpena et al. (2017). Garz et al. (2021).
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43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57.
58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70.
71. 72. 73. 74. 75. 76. 77. 78.
79. 80. 81. 82.
OECD (2021a). World Bank (2021c). Bharadwaj and Suri (2020). Gwer (2019); Melzer (2011); Morse (2011); Skiba and Tobacman (2019). Fan, Nguyên, and Qian (2020). Banco de México (2021). World Bank (2019c). World Bank (2020a). Love, Martínez Pería, and Singh (2016). Jack et al. (2016). Bari et al. (2021). GOGLA (2021); Waldron (2016). Feyen et al. (2021). Automation of payments is a behavioral strategy to overcome inattention. See Moulton et al. (2014). Buy now, pay later (BNPL) products—an e-commerce version of point-of-sale financing—experienced a surge in adoption, fueled by the growth in e-commerce and digital payments during the pandemic. See Alfonso et al. (2021). Feyen et al. (2021). Gambacorta et al. (2019). World Bank (2020c). Amosun and Unger (2020); Maylie (2020). IFC (2019). Caniato, Moretto, and Rice (2020). Dunbar and Singh (2020). For a review, see IFC (2019). SEC (2021). IFC (2017a, 2020c). Perú21 (2021). Citigroup (2020). See Meki, Quinn, and Roll (2021) for another example of how technology can enable product innovations that allow anchors to sustainably take on more risk to improve the performance of a last-mile distribution network leading to higher profits for microdistributors and a substantial increase in sales for the anchors. See Xu, Marodon, and Ru (2021) for an effort to map development banks worldwide. Calice (2020). Corredera-Catalán, di Pietro, and Trujillo-Ponce (2021). Gourinchas et al. (2021a). OECD (2021d). World Bank and FIRST Initiative (2015). Gourinchas et al. (2021a). Ecuador has one of the largest gender gaps in access to finance in Latin America. Men are twice as likely as women to borrow from a financial institution or use a credit card, and they are nearly 40 percent more likely than women to have a savings account. See BCE (2020) and World Bank, Global Findex Database 2017 (Global Financial Inclusion Database), https://globalfindex. worldbank.org/. Banco Pichincha (2021). Annex 4A can be found at http://bit.do/WDR2022 -Chapter-4-Annex. Alonso-Gispert et al. (2022); IMF and World Bank (2018); World Bank (2021c). Feyen et al. (2021).
