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POLITICAL & ECONOMIC RESEARCH COUNCIL The Benefits of Reporting Positive Payment Data in Latin America By Michael Turner, Ph.D. Intercontinental Hotel Tegucigalpa--11 May 2006 Agenda Introduction Findings Conclusion 2 Why are We Here? Objectives: Broaden access to affordable mainstream credit Reduce delinquencies/defaults in financial services and nonfinancial services sectors. Increase growth in private sector lending and overall economy. Methods: Increase full-file reporting from financial and non-financial firms for increased predictive power of scoring models. capturing more consumers, especially lower income. Increase access to public record data for greater accuracy. better matching. 3 Why are We Here? (con’t) What is being asked of you?: Provide comprehensive financial and non-financial payment information Delinquencies and defaults, but also Regular on-times payments (and 30-day and 60-day) Not Income/salary Asset values Dependents, spouse, parents, etc. 4 Benefits of Reporting Positive Payment Data Consumers Reduced probability of over-extension Greater and fairer access Credit offers reflect credit risk and credit capacity Lenders Improved loan portfolio performance Reduced provisioning and capital adequacy requirements (Basel 2) Sustainable & affordable growth into new markets The Economy Consumers Lenders The Economy Better financial services efficiencies Affordable growth in domestic consumption 5 Credit Reporting & Its Impact This presentation demonstrates these benefits by answering four critical questions: What is the impact of reporting positive payment information on credit access & growth in credit markets? What is the impact of reporting positive payment information on loan performance? What is the impact of reporting positive payment information on economic growth? What is the impact of reporting positive payment information on the distribution of credit? 6 Types of Reporting Systems NEGATIVE ONLY Applications (not approvals) Delinquencies (90+) Defaults Bankruptcies POSITIVE PAYMENT All negative data, or delinquencies (30+ days past dues) All Positive (ontime) payment data Public record data Account balance Account type Lender Date opened Purged 5 years Inquiries FULL FILE (also includes) Debt ratios (revolving to total debt) Portion of accounts repossessed/ written off Estimated income range Assets Obsolete 7-10 years 7 Latin American Context Large differences among Latin American credit reporting systems. CREDIT REPORTING COVERAGE AND COMPREHENSIVENESS IN LATIN AMERICA Country Argentina Bolivia Brazil Chile Colombia Costa Rica Dominican Republic Ecuador El Salvador Guatemala Honduras Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela Mean (excl. absent bureaus) Max Min (excl. absent bureaus) Public registry coverage1 (% adults with files) 22.10% 10.30% 9.60% 45.70% 0.00% 34.80% 19.20% 13.60% 17.30% 0.00% 11.20% 0.00% 8.10% 0.00% 8.70% 30.20% 5.50% 16.80% 18.1% 45.7% 5.5% Private bureau coverage (% adults with files) 95.00% 24.60% 53.60% 22.10% 31.70% 73.40%3 34.60% 0.00% 78.70% 9.90% 18.70% 49.40% 0.00% 40.20% 52.20% 27.80% 80.00% 0.00% 46.13% 95.0% 46.13% Positive Information on Consumer in Files (% of total)2 25% to 49% < 5% n/a 25% to 49% 75% to 100% < 5% 75% to 100% 25% to 49% 10% to 24% 75% to 100% 75% to 100% 75% to 100% n/a n/a n/a 50% to 74% 75% to 100% n/a QUESTION Source: FOR RESEARCH: /. For 2005. World Bank,Doing Business Database. w ww.doingbusiness.org/Ex ploreTopics/GettingCredit 1 nduras, w hich is from 2005. From Arturo Galindo and Margaret Miller, “Can Credit The data is for 2001, sav e for Costa Rica, Colombia and Ho How do differences affect profitability availability of l credit? Bank, Washington, D.C. Additiona Dev elopment Department. I nter-Americanand 2001. Research Reduce Credit Constraints.” March Registries 2 information from interv iews w ith TransUnionLatin America. -143 fm?fuseaction=Publications.View &pub_id=S w ww.iadb.org/res/index .c 3 8 Latin American Context: Financial Services Sector Relatively small and modest private sector borrowing (Private sector borrowing as a share of GDP, 1995-2004) 120% 100% 80% 60% 40% 20% 0% North America NA/E/ANZ Europe (N=16) Aust/NZ (N=16) East Asia EA (N=5) (N=5) Middle East MENA (N=5) N. Africa (N=5) E. EEEurope (N=8) (N=8) LA (N =18) Latin America S. (N=3) Asia SA (N=3) Sub-Saharan Afr (N=12) Africa (N=12) (N=18) 1995 Source: International Financial Statistics, IMF 1996 1997 1998 1999 2000 2001 2002 2003 2004 9 Methodology: Two Ways to Show Benefits 1. Statistically compare the private sector lending in economies with different reporting systems and different participation rates 2. Simulate different reporting systems using 5.1 million complete files from “close” or similar economy (Colombia): a. b. c. Generated 4 scenarios of varying participation* Tested distributional impact of changes in participation (sociodemographic analysis). Used commercial grade scoring model. * Scenarios 75% provide positive and negative information, 25% only negative 50% provide positive and negative information, 50% only negative 25% provide positive and negative information, 75% only negative 100% provide only negative information 10 Agenda Introduction Findings Conclusion 11 Finance is Crucial to Economic Growth Established: Financial sector mobilizes savings and allocates capital for investment and consumption growth. Some estimates of impact.* If private sector lending, increased by 33% of GDP, results for economy: +1.0% annual per capita GDP growth +0.8% annual per capita capital stock growth +0.8% annual productivity growth *Derived from findings of Ross Levine, “Financial Development and Economic Growth: Views and Agenda” Journal of Economic Literature, Vol. 25(June 1997), pp. 688–726. Their findings are consistent with those of other studies, see Jose De Gregorio and Pablo Guidotti, “Financial Development and Economic Growth.” World Development, Vol. 23, No. 3, (March 1995) pp. 433-448. Their reported impacts were larger. 12 Economic Growth-Australia Evidence suggests the use of comprehensive credit data allows: One-off increase in capital productivity of 0.1%, which would translate into economic benefits to the Australian economy of up to $5.3 billion, in net present value terms, over the next decade. ACIL Tasman (2004) 13 Estimations: Private Full-File Coverage and Private Sector Borrowing VARIABLE Consta nt Log of GDP per capita (adjusted for PPP) Avg. Change in GDP (1995-2004) Legal Rights of Creditors (from 0 to 10) Credit Information 1 (from 0 to 6) Private Full-file Coverage (0 to 100, as percentage of adults) Private Negative-only Coverage (0 to 100, as percentage of adults) Public Full-fi le Coverage (0 to 100, as percentage of adults) Public Negative-only Coverage (0 to 100, as percentage of adults) R squared F-stat (p value) Residual Standard Error N Model I -142.40*** (35.31) 20.31*** (4.65) -1.20* (0.70) 4.55** (2.07) -3.87 (2.88) 0.72*** (0.20) -0.02 (0.86) -0.11 (0.41) 0.16 (0.46) 0.7075 16.93 (1.88e-012) Model IV (reduced) -130.80*** (32.20) 16.85*** (3.87) 4.80** (1.97) 0.67*** (0.16) 0.6883 44.9 (1.887e-015) 29.45 65 29.12 65 Lesson: what matters? • Wealth • Creditor Rights • Reporting o private o full-file o with widespread participation For a country, going from no adults to having all (100% of) adults with positives and negatives in a private bureau increases private sector lending by more than 60% of GDP. (Without the US and UK, which have high private sector lending, the estimated increase is still more than 45% of GDP.) * p < 0.1 ** p < 0.05 ***p < 0.01 Source: IMF International Financial Statistics; World Bank, Doing Business database 14 Estimations Consistent With Previous Studies Study by Harvard and World Bank economists of 129 countries (for years 1999-2003)* Private bureaus increase lending as a share of GDP by an estimated 20 percentage points But didn’t take into account effects of participation rate or reporting system (negative only vs. full-file) 15 *Simeon Djankov Caralee McLiesh Andrei Shleifer, “Private Credit In 129 Countries.” National Bureau Of Economic Research, Working Paper 11078, http://papers.nber.org/papers/w11078.pdf Private Sector Lending in Honduras 2003 2004 2005 Growth in private sector lending 12.3% 15.5% 18.25% Private sector lending (as share of GDP) 40.92% 41.5% 42.7% 3.5% 5.0% 4.2% GDP growth (in 1978 prices) 16 Source: Hong Kong Monetary Authority Rationale Behind Simulations Simulations based on the files of one country allows Measure of access Performance metrics Distribution of credit across groups In this instance, we use Colombian files: Institutionally, economically close to the rest of Latin America (cluster analysis) Robust--participation from financials and non-financials Standardized files with reliable, accurate information Consistent reporting of positives for 25 years 17 Background: Existing Research World Bank study uses Latin American credit files to make a case for full-file reporting (Miller and Galindo, 2001) Research uses Public Credit Registry data, restricted to larger, most likely collateralized loans. Focus on reported data. The open question: What is the impact of participation in private full-file system? Source: World Bank 18 Change in Acceptance Rates (Market Size) for a Performance Target Full sample (5.1 million files) ACCEPTANCE RATES BY TARGET DEFAULTS, UNDER DIFFERING LEVELS OF PARTICIPATION Share of furnishers providing full-file information (remainder provides negatives only) Target Default rate 3% 5% 7% 10% 12% 100% 10.00% 41.35% 58.82% 73.06% 77.80% 75% 6.64% 28.96% 45.59% 68.09% 77.21% 50% 4.73% 19.28% 36.42% 68.08% 76.49% 25% 4.80% 9.69% 25.71% 68.09% 75.06% 0% 2.56% 5.15% 13.60% 54.97% 72.26% For a target loss rate, consumers shrink with a loss of positive information. 19 Change in Non-Financial Acceptance Rates for a Performance Target Full sample (3.1 million files) Non-Financial Acceptance Rates, by Scenario (Colombia) Share of tradelines consisting of both positive and negative information Target De fault rate 5% 7% 10% 12% 100% 5.50% 37.30% 61.03% 69.75% 75% 4.00% 29.95% 49.36% 63.27% 50% 2.95% 17.96% 43.14% 57.70% 25% 1.96% 10.07% 36.01% 50.43% 20 Source: Hong Kong Monetary Authority Change in Default Rates for a Target Market Size DEFAULT RATES BY TARGET ACCEPTANCE, UNDER DIFFERING LEVELS OF PARTICIPATION Share of furnishers providing full-file information (remainder provides negatives only) Target Acceptance Rate 20% 30% 40% 50% 60% 100% 3.52% 4.12% 4.89% 5.86% 7.20% 75% 3.72% 4.62% 5.66% 6.70% 7.73% 50% 4.66% 5.74% 6.67% 7.49% 8.49% 25% 5.91% 6.78% 7.52% 8.22% 9.25% 0% 8.46% 9.06% 13.85% 14.40% 15.30% Furnishers can reduce losses. Consistent with World Bank results. 21 Reducing Overextensions: The Case of Hong Kong 1998-2002, Hong Kong experienced growth in personal bankruptcy of 1,900%. Around 12% of all personal bankruptcy was caused by credit card debt. Credit card write-offs stood at 13.6% by the end of 2002. Higher than comparable Asian nations, Singapore and Korea, 5.5% and 6.1% respectively. Defaulting customers in Hong Kong had acquired debts up to 55 times monthly income in 2000 and 42 times monthly income in 2002. Following the shift to more comprehensive reporting, between December 2002 and December 2004:* Credit card write-off ratios declined from 13.6% to 3.76%; and Credit card delinquency ratios declined from 1.25% to 0.44%. 22 Source: Hong Kong Monetary Authority Reducing Delinquencies: The Case of US Utilities Verizon (US) Reported 4 million landline trades in March 2005 (Virginia) to 1 bureau Raised number of trades reported to 10 million within 2 quarters By Q1 2006 reporting over 20 million landline trades nationally Delinquencies reduced substantially (poke factor) Not uncommon response Nicor Gas (Illinois) Reported full-file customer data to TransUnion (US) despite objections of state regulator Engaged in active customer communications campaign 1 year later, defaults (90+ days past due) reduced by 20% Reductions in delinquencies continue to grow WE Energies (Wisconsin) Reported full-file data to TransUnion Engage in active customer communications campaign Delinquencies and defaults reduced substantially WHY: Moral hazard--carrots and sticks 23 Acceptance-Default Trade-Offs 15% Default Rates 12% 9% 6% 3% 0% 0% 15% 30% 45% 60% 75% 90% Acceptance Rates 100% Reporting Full File 75% Reporting Full File 25% Reporting Full File 0% Reporting Full File 50% Reporting Full File Furnishers can reduce losses. 24 Change in Share Accepted by Gender 100% 75% 25% 50% 0% Women are hit disproportionately; thinner files. 25 Change in Share Accepted by Age 100% 75% 25% 50% 0% Young also hit disproportionately; thinner files. 26 Loss of Information Means More Mistakes are Made CHANG ES IN ERROR RATES (MEASURED AS A PERCENT OF ALL CREDIT-ELIGIBLE ADUL TS) Share of tradelines consisting of both positive and negative information 75% 50% Type I (false positives, or mistaking a high risk borrower for a low risk one) +1.00% +2.22% Type II (fals e negatives, or mistakin g a low risk borrower for a high risk one) +3.81% +5.32% 25% +3.31% +7.53% Issues of giving credit where credit is due. 27 Implications of Shifts in Error Rates If the 25% scenario had obtained: o nearly 181,000 people who are bad risks would be extended credit o nearly 411,000 who are good risks would be denied access. The latter is another point of fairness, in addition to distribution of loss of access across sociodemographic categories. 28 Evaluating Payment History vs. Socio-Demographic Information Results of comparison are meant to be suggestive. Starting points are rather different. o Costa Rica’s per capita GDP is twice that of Colombia’s o Yet, private sector lending as a share of GDP is largely equivalent averaging for the period 1999-2003 • 26.6% in Colombia and • 26.7% in Costa Rica Some differences: o Overall default rates in Costa Rica are small, observed 90+ day delinquency rate of 3.78%. o Colombia’s observed delinquency rate of 27.49% in files (but 3.6% for loans--Bankscope). However, this difference may very well be an artifact of the system of reporting rather than of consumer behavior. o Approximately two-thirds of data furnishers in Costa Rica do not report negatives less than 120 days past due. o Many delinquencies, defined as 90+ days past due, therefore do no make it on the credit reports. o Non-financials 29 Evaluating Payment History vs. Socio-Demographic Information Question of how to measure the relative merit of approaches. K-S. The ability to discern goods from bads (or true positives from false positives) increases considerably in moving from the Colombian negative only to the Colombian full-file scenario. By contrast, socio-demographic information improves the ability to distinguish goods from bads in Costa Rica files by much less of a degree. K-S SCORES OF ADDING SOCIO-DEMOGRAPHICS, COMPARING COSTA RICA AND COLOMBIA Costa Rica Restricted Costa Rica Complete 40.5 49.3 Colombia Negative Only Colombia Full-File (ACIERTA) 54.2 67.3 Issue 2: why the difference is starting points (40.5 vs. 54.2)? Accuracy For better predictions For matching (also reduces mistakes) 30 Agenda Introduction Findings Conclusion 31 Lessons Reporting positive payment data enables growth in lending to private sector o o PERC 2006--up to 45% of GDP (moving from 0% to 100% participation in private bureaus) Harvard/World Bank 2005--up to 21% of GDP (private v. public) Reporting positive payment data improves economic growth o (e.g. 25% of Colombia’s 3.9% GDP growth in 2005 result of increased private sector lending--40% as ratio of GDP--enabled by private full-file credit reporting system). Reporting positive payment data results in smarter lending – lower default rates with better access (developed & emerging economies). o PERC 2006, World Bank 2001, Hong Kong Monetary Authority 2005 Comprehensive data results in fairer credit. (financial & non-financials) o o Improves mainstream access for the under-served (developed & emerging economies). Increases access to affordable mainstream credit for women and young. Positive payment data has no impact on personal security. Benefits financial and non-financial sectors 32 POLITICAL & ECONOMIC RESEARCH COUNCIL 100 Europa Drive, Suite 431 Chapel Hill, NC 27517 www.infopolicy.org Phone: (212) 629 -4557