POLITICAL & ECONOMIC RESEARCH COUNCIL By Michael Turner, Ph.D. Intercontinental Hotel Tegucigalpa--11 May 2006 The Benefits of Reporting Positive Payment Data in Latin America

2 Introduction Findings Conclusion Agenda

3 Objectives:  Broaden access to affordable mainstream credit  Reduce delinquencies/defaults in financial services and non- financial 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. Why are We Here?

4 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. Why are We Here? (con’t)

5 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  Better financial services efficiencies  Affordable growth in domestic consumption The Economy Lenders Consumers

6 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? Credit Reporting & Its Impact

7 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 (on- time) 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

8 Large differences among Latin American credit reporting systems. QUESTION FOR RESEARCH: How do differences affect profitability and availability of credit? Latin American Context

9 Latin American Context: Financial Services Sector Relatively small and modest private sector borrowing (Private sector borrowing as a share of GDP, ) North America Europe Aust/NZ (N=16) East Asia (N=5) Middle East N. Africa (N=5) E. Europe (N=8) Latin America (N=18) S. Asia (N=3) Sub-Saharan Africa (N=12) Source: International Financial Statistics, IMF

10 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.Generated 4 scenarios of varying participation* b.Tested distributional impact of changes in participation (sociodemographic analysis). c.Used commercial grade scoring model. Methodology: Two Ways to Show Benefits * 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

11 Introduction Findings Conclusion Agenda

12 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 Finance is Crucial to Economic 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 Their reported impacts were larger.

13 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)

14 Estimations: Private Full-File Coverage and Private Sector Borrowing * p < 0.1 ** p < 0.05 ***p < 0.01 Source: IMF International Financial Statistics; World Bank, Doing Business database Lesson: what matters? Wealth Creditor Rights Reporting oprivate ofull-file owith 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.)

15 Estimations Consistent With Previous Studies Study by Harvard and World Bank economists of 129 countries (for years )* 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) *Simeon Djankov Caralee McLiesh Andrei Shleifer, “Private Credit In 129 Countries.” National Bureau Of Economic Research, Working Paper 11078,

16 Private Sector Lending in Honduras Source: Hong Kong Monetary Authority Growth in private sector lending12.3%15.5%18.25% Private sector lending (as share of GDP) 40.92%41.5%42.7% GDP growth (in 1978 prices)3.5%5.0%4.2%

17 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

18 Background: Existing Research World Bank study uses Latin American credit files to make a case for full-file reporting (Miller and Galindo, 2001) Source: World Bank 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?

19 Change in Acceptance Rates (Market Size) for a Performance Target For a target loss rate, consumers shrink with a loss of positive information. For a target loss rate, consumers shrink with a loss of positive information. Full sample (5.1 million files)

20 Change in Non-Financial Acceptance Rates for a Performance Target Source: Hong Kong Monetary Authority Full sample (3.1 million files)

21 Change in Default Rates for a Target Market Size Furnishers can reduce losses. Consistent with World Bank results. Furnishers can reduce losses. Consistent with World Bank results.

22 Reducing Overextensions: The Case of Hong Kong  , 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  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  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%. Source: Hong Kong Monetary Authority

23 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 Q 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

24 Acceptance-Default Trade-Offs Furnishers can reduce losses.

25 Change in Share Accepted by Gender Women are hit disproportionately; thinner files. Women are hit disproportionately; thinner files. 25%0% 100%75%50%

26 Change in Share Accepted by Age Young also hit disproportionately; thinner files. Young also hit disproportionately; thinner files. 25%0% 100%75%50%

27 Loss of Information Means More Mistakes are Made Issues of giving credit where credit is due.

28 Implications of Shifts in Error Rates If the 25% scenario had obtained: onearly 181,000 people who are bad risks would be extended credit onearly 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 socio- demographic categories.

29 Evaluating Payment History vs. Socio-Demographic Information Results of comparison are meant to be suggestive. Starting points are rather different. oCosta Rica’s per capita GDP is twice that of Colombia’s oYet, private sector lending as a share of GDP is largely equivalent averaging for the period % in Colombia and 26.7% in Costa Rica Some differences: oOverall default rates in Costa Rica are small, observed 90+ day delinquency rate of 3.78%. oColombia’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. oApproximately two-thirds of data furnishers in Costa Rica do not report negatives less than 120 days past due. oMany delinquencies, defined as 90+ days past due, therefore do no make it on the credit reports. oNon-financials

30 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. Issue 2: why the difference is starting points (40.5 vs. 54.2)?  Accuracy  For better predictions  For matching (also reduces mistakes)

31 Introduction Findings Conclusion Agenda

32 Lessons  Reporting positive payment data enables growth in lending to private sector oPERC up to 45% of GDP (moving from 0% to 100% participation in private bureaus) oHarvard/World Bank 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). oPERC 2006, World Bank 2001, Hong Kong Monetary Authority 2005  Comprehensive data results in fairer credit. (financial & non-financials) oImproves mainstream access for the under-served (developed & emerging economies). oIncreases access to affordable mainstream credit for women and young.  Positive payment data has no impact on personal security.  Benefits financial and non-financial sectors

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