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Cross-country Variation in Household Access to Financial Services

Cross-country Variation in Household Access to Financial Services Patrick Honohan, World Bank and Trinity College, Dublin Access to Finance Conference, World Bank, March 15-16 A new cross-country series on financial access Household rather than firm level

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Cross-country Variation in Household Access to Financial Services

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  1. Cross-country Variation in Household Access to Financial Services Patrick Honohan, World Bank and Trinity College, Dublin Access to Finance Conference, World Bank, March 15-16

  2. A new cross-country series on financial access • Household rather than firm level • Combines micro and mainstream finance • Concept is: Proportion of the adult population with an account at a formal or semi-formal financial intermediary Deposit or loan? Semi-formal: e.g. NGO-sponsored credit-only MFI, pays taxes, but is unregulated by financial regulator Susu collector operating out of a roadside kiosk 

  3. Different sources • Microfinance institutions: CGAP “big numbers” • (AFIs – with a double bottom line) 2004. • No. of accounts/members. • Augmented by WSBI (2005) for savings banks • And survey of commercial bank account nos. (Beck et al., 2005) • And household surveys

  4. Cleaning the raw sources • Double counting! • Caisse d’Epargne, CCP etc. • Strategy adopted: go through all individual MFIs with more than 100,000 a/cs for duplication • Incredible imputation methods • WSBI: total assets/(0.24 x GDP per cap) • Strategy adopted: go through all countries where WSBI imputation gives a 10 per cent figure and find independent info about the savings bank

  5. How to combine different sources • Problems • varying incidence of multiple accounts – less serious for MFIs than for banks (ICBC China – 430 million a/cs, 150 million customers) • MFIs, savings banks and commercial bank categories overlap at the boundaries China: ICBC is in WSBI data, CCB with 143 mn customers is not • dead accounts (or in one case a dead bank) • the poor hold little of the total (bottom half of wealth distribution hold 3-10% of financial assets), so inferring from total assets risky

  6. How to combine different sources (2) • But household survey-based data on access percentages is quite closely correlated with data on bank account numbers and on average bank account size (% GDP) • Regressing the former on the latter two we get an equation which can be used to project access percentages where we have the bank account data (see chart)

  7. How to combine different sources (3) • We have MFI and WSBI account nos for 160 countries; the commercial bank data for only 43 countries. • Regressing bank deposit nos. on MFI nos; and average bank deposit size on GDP, we have adequate projection equations which can be used for all 160 countries (chart) • Some issues around functional form

  8. Accessvs. financial depth • Correlated but not the same (see chart)

  9. Using the data • Is higher access (as measured) associated with less poverty? • Or is mainstream financial depth more important? • How about inequality?

  10. Table 2. Poverty and Financial Access This table shows regressions relating the $1 per day poverty percentage to financial access percentages across countries

  11. Issues /next steps • More comprehensive data on control variables • Issue of endogeneity – does it really not matter much here?

  12. Conclusion • Even if does not robustly help explain absolute poverty, financial access is negatively correlated with income inequality (Gini). • (Access does more for those somewhat higher up the ladder). • Whatever about impact of direct access, regressions confirm favorable inverse association between financial depth and poverty.

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