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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 • 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 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 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 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 310% of financial assets), so inferring from total assets risky 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) Actual and fitted access indicators 100 90 80 Fitted 70 60 50 40 30 20 10 0 0 20 40 60 Actual 80 100 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 for country i mi = # of MF accounts per adult population bi = # of bank accounts per adult population hi = household survey-based percentage access zi = average deposit size yi = per capita GDP ̂ k = estimated coefficients from regression bi 0 1 log( mi ) ui ̂ k = estimated coefficients from regression z i 0 1 yi ui . Let bˆi bi for countries where data on bi is available; bˆi ˆ 0 ˆ1 log( mi ) otherwise; Let zˆi z i for countries where data on zi is available; zˆi ˆ0 ˆ1 yi otherwise. Let ˆ k be the estimated coefficients from regression h 0 1 log bˆ 2 log zˆ ui . The synthetic access percentages are = ˆ0 ˆ1 log bˆ ˆ2 log zˆ . Actual and fitted access indicators 100 90 80 70 Fitted 60 50 40 30 20 10 0 0 20 40 60 Actual 80 100 Developing country access to finance - deciles 70 CGAP series WSBI series Synthetic series Composite series 60 50 % 40 30 20 10 0 1 2 3 4 5 6 7 8 9 Access by region 100 90 80 70 % 60 50 40 30 20 10 0 AFR EAP ECA LAC MNA SAR Access vs. financial depth • Correlated but not the same (see chart) Access and depth Private credit % of GDP 140 120 100 80 60 40 20 0 0 20 40 60 80 Access % of adult population 100 Using the data • Is higher access (as measured) associated with less poverty? • Or is mainstream financial depth more important? • How about inequality? Access and GNI per capita 100 % access composite data 90 80 70 60 50 40 30 20 10 0 0 5000 10000 15000 20000 GNI per capita PPP 25000 30000 35000 Table 2. Poverty and Financial Access This table shows regressions relating the $1 per day poverty percentage to financial access percentages across countries Equation: Constant GNI per cap (log) Access (log) R-squared / NOBS Adjusted R-squared S.E. of regression Log likelihood 2.A 2.D Coeff. t-Stat Coeff. 173.6 **11.6 65.6 -18.8 **10.4 -13.7 0.546 91 0.175 0.541 0.166 15.2 20.7 -375.7 -395.0 t-Stat **6.1 **4.3 89 2.E Coeff. t-Stat 175.5 **11.5 -19.7 **8.4 1.6 0.5 0.549 89 0.538 15.4 -368.1 Table 4. Poverty and Financial Access – additional variables [Continued] (b) Removing outliers Equation: Constant GNI per cap lower 90% (log) Share of top 10% Access (log) Private credit (log % of GDP) Inflation (log) Institutions (KKZ index) Institutions (Freedom house bank) SS Africa not ZAF dummy Which measure? Outliers omitted? R-squared / NOBS Adjusted R-squared S.E. of regression Log likelihood 4.H 4.J 4.K 4.P Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. 162.1 **14.2 187.5 **10.2 187.1 **10.0 136.1 -16.5 **7.6 -20.3 **8.0 -20.2 **7.8 -15.5 0.609 **2.9 0.355 *2.4 0.358 *2.4 0.339 3.67 1.0 2.31 0.7 2.98 0.8 3.48 -6.67 -3.2 -7.46 **3.6 -6.78 **3.0 -5.14 -1.86 -1.3 -1.26 0.4 -1.02 0.7 -1.38 6.96 *2.1 7.07 *2.1 -1.75 0.9 8.96 Comp Comp Comp Comp 62,129,139, 184,192,204 0.791 0.774 9.9 -238.0 t-Stat **7.2 **5.8 *2.3 0.3 *2.4 1.0 *2.0 ETH,MNG, ETH,MNG, ETH,MNG, ETH,MNG, 62,129,139, 62,129,139, 62,129,139, NIC,TZA, NIC,TZA, NIC,TZA, NIC,TZA, 184,192,204 184,192,204 184,192,204 UGA,YEM UGA,YEM UGA,YEM UGA,YEM 65 0.806 0.786 9.6 -235.7 65 0.793 0.767 9.7 -231.8 64 0.805 0.785 9.6 -235.8 65 Table 4. Poverty and Financial Access – additional variables [Continued] (c) Interaction term: credit depth x access Equation: Constant GNI per cap lower 90% (log) Share of top 10% Access (log) Private credit (log % of GDP) Access x private credit (log) Inflation (log) Institutions (KKZ index) SS Africa not ZAF dummy Which measure? Outliers omitted? R-squared / NOBS Adjusted R-squared S.E. of regression Log likelihood 4.Q Coeff. 265.3 -18.2 0.430 -27.1 -41.1 9.94 -1.19 t-Stat **6.8 **8.0 *3.1 *2.3 **3.3 **2.8 0.9 Comp 4.R Coeff. 271.1 -19.8 0.376 -24.3 -37.3 8.69 -0.83 5.16 t-Stat **7.0 **8.1 *2.7 *2.1 **3.0 *2.4 0.6 1.6 Comp ETH,MNG, ETH,MNG, 62,129,139, 62,129,139, NIC,TZA, NIC,TZA, 184,192,204 184,192,204 UGA,YEM UGA,YEM 0.817 0.798 9.3 -233.8 65 0.824 0.803 9.2 232.4 65 Table 6. Poverty and Financial Access This table shows regressions relating the Gini coefficient to financial access percentages across countries Equation: 6.A 6.B Coeff. t-Stat Coeff. 51.7 **11.0 68.9 -3.49 -3.15 *2.4 0.42 Constant GNI per cap (log) Access (log) Private credit (log % of GDP) Inflation (log) Institutions (KKZ index) Institutions (Freedom house bank) Population (log) SS Africa dummy Which measure? Comp Comp Outliers omitted? None R-squared / NOBS 0.049 112 0.104 Adjusted R-squared 0.040 0.087 S.E. of regression 10.6 10.3 Log likelihood -422.4 -419.1 ** and * indicate significance at the 1% and 5% levels, respectively t-Stat **8.5 **2.6 0.2 6.C Coeff. 50.7 -1.28 -0.25 7.76 Comp None 112 0.162 0.138 10.0 -415.4 t-Stat **4.9 0.8 0.1 **2.7 None 112 Equation: 6.D Coeff. 19.4 4.20 -6.21 2.48 0.04 t-Stat 1.4 1.9 *2.2 1.4 0.0 6.E 6.F Coeff. t-Stat Coeff. 49.3 **4.4 17.7 1.84 0.9 3.54 -6.17 *2.1 -6.21 3.36 0.70 0.08 t-Stat 0.9 1.4 *2.2 1.8 0.5 0.0 6.G Coeff. 20.4 3.34 -5.96 3.62 0.74 -0.21 t-Stat 0.8 1.3 *2.0 1.8 0.5 0.1 Constant GNI per cap (log) Access (log) Private credit (log % of GDP) Inflation (log) Institutions (KKZ index) Institutions (Freedom house 0.78 0.4 0.65 0.3 bank) Population (log) -0.14 0.1 SS Africa dummy 10.25 **3.3 10.25 **2.9 9.56 **2.8 Which measure? Comp Comp Comp Comp See note Outliers omitted? None None None R-squared / NOBS 0.223 74 0.073 74 0.221 72 0.220 72 Adjusted R-squared 0.166 0.047 0.136 0.119 S.E. of regression 9.5 10.1 9.5 9.6 Log likelihood -268.2 -274.7 -260.0 -256.6 NB: The sample for equation 6.E was the set of countries for which all the data for regression 6D was available. There were no large outliers in 6D-G. Issues /next steps • More comprehensive data on control variables • Issue of endogeneity – does it really not matter much here? 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.