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INFORMATION POLICY INSTITUTE Summary of Research Findings: Full File Credit Reporting in Latin America By Michael Turner, Ph.D. January 16, 2006 What is Known and Unknown About Credit Reporting I Without payment information, lenders face three problems: “asymmetric information”: lenders are relatively ignorant of risk profiles and make bad decisions “adverse selection”, lend excessively to high-risk consumers “moral hazard”, have a hard time dissuading defaults because of difficulty of punishing defaulters Benefits of a well-structured system of information sharing: consumers: lower prices for credit, wider access lenders: increased profits, larger markets economy: a more stable financial sector. 2 What is Known and Unknown About Credit Reporting II Research has established that credit reporting is better for private sector lending and loan performance than no reporting but also: reporting full-file (positive and negative information) is better than reporting only negatives, and in a full-file setting, private bureau information leads to better performance of consumer and small-business loan portfolios than public bureau information. Unknown: the degree to which differences in participation in a fullfile reporting affect the financial sector. But how much participation is needed? In other words, how much more accurate are assessments of risk when lenders have more information? Measure effect of participation to convince furnishers to provide information 3 Why These Objectives? No legal hurdles to full-file reporting in Latin America, but substantial differences in participation rates Research can resonate in Latin America because: Economic development depends generating savings, allocating capital, and transforming risk, especially with end of state-led models of development. Can assist in improving the efficiency of the financial sector, which has been relatively inefficient Can expand private sector lending, which has been relatively stagnant Can help to reduce the chances of financial crises, which has been chronic 4 Methodology: Two Ways to Show Benefits 1. Statistically compare the private lending sector in economies with different reporting systems (private full-file, private negative-only, public full-file, public negative-only) and different participation rates 2. Simulations using 5+ million complete files from “close” or similar economy (Colombia) to: Generate 4 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 Using generic commercial scoring model (ACIERTA) and reoptimzed research-grade model, test impact of changes in participation on market size, loan performance, and the distribution of credit among genders and age groups Compare positive payment information vs. rich socio-demographic information (as in Costa Rica) 5 Estimations (Tests of the Impact of Participation) No direct data on participation rates. Coverage (%) of adults in public and private credit registries (World Bank data) as proxy. Controls for estimations: GDP per capita at purchasing power parity Economic growth rates Index for legal rights of borrowers and lenders* Measures how much collateral and bankruptcy laws facilitate lending Based on studies of collateral and insolvency laws Includes 3 aspects related to legal rights in bankruptcy and 7 aspects found in collateral law. Index measuring depth of credit reporting† derived based on scope, accessibility and quality of credit information, based on 6 factor * (i) Secured creditors are able to seize their collateral when a debtor enter reorganization (ii) Secured creditors, rather than other parties such as government or workers, are paid first out of the proceeds from liquidating a bankrupt firm. (iii) An administrator, not old management, is responsible during reorganization. (iv) General, rather than specific, description of assets is permitted in collateral agreements. (v)General, rather than specific, description of debt is permitted in collateral agreements. (vi) Any legal or natural person may grant or take security in the property. (vii) A unified registry that includes charges over movable property operates.(viii) Secured creditors have priority outside of bankruptcy. (ix)Parties may agree on enforcement procedures by contract. (x) Creditors may both seize and sell collateral out of court. † (i) Full-file; (ii) information on individuals and firms, (iii) contains information from retailers, trade creditors and financial institutions, (iv) has more than 2 years of data, (v) loans above 1% of per capita income are reported, and (vi) borrowers have right to access data. 6 Estimations: Private Full-File Coverage and Private Sector Borrowing VARIABLE Constant Log of GDP per capita (adjusted for PPP) Avg. Change in GDP (1995-2004) Legal Rights of Creditors (from 0 to 10) Credit Information (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-file 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 I II III IV -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) -139.48*** (35.49) 18.37*** (4.45) -0.82 (0.64) 4.99** (2.06) -133.97*** (35.41) 17.38*** (4.41) -130.80*** (32.20) 16.85*** (3.87) 4.68** (2.06) 4.80** (1.97) 0.60** (0.18) -0.13 (0.46) -0.26 (0.40) -0.01 (0.86) 0.66*** (0.17) -0.06 (0.46) -0.17 (0.39) -0.09 (0.86) 0.67*** (0.16) 0.7075 16.93 (1.88e-012) 29.45 65 0.698 18.82 (9.65e-013) 29.65 65 0.6895 21.46 (4.251e-013) 29.81 65 0.6883 44.9 (1.887e-015) 29.12 65 100% full-file, private bureau coverage can increase lending by more than 60 percentage points of GDP over 0% coverage * p < 0.1 ** p < 0.05 ***p < 0.01 7 Simulations: Change in Acceptance Rates (Market Size) Target Default rate 3% 5% 7% 10% 12% ACCEPTANCE RATE* Share of furnishers providing positive and negative information 100% 75% 50% 25% 10.00% 6.64% 4.73% 4.80% 41.35% 28.96% 19.28% 9.69% 58.82% 45.59% 36.42% 25.71% 73.06% 68.09% 68.08% 68.09% 77.80% 77.21% 76.49% 75.06% 0% 2.56% 5.15% 13.60% 54.97% 72.26% At 5% target default rate--roughly non-performing loans rate in Colombia-- the acceptance rate (market size) drops: o by 53.4% when 50% of all data furnishers are providing only negative information (from 41.35% to 19.28%) o by 30% when even only 25% of data furnishers are providing only negative information For a healthy target default rate, market size drastically shrinks with a loss of positive information. 8 *Full sample (5.1 million files) Simulations: Change in Default Rates (Profitability) DEFAULT RATES Share of furnishers providing positive and negative information 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% At 40% target acceptance rate, the default rate (90+ days past due) increases: o from 4.89% to 6.67% (an increase of nearly 2 percentage points) that is, by 36.4%, when only 50% of furnishers provide positive information o from 4.89% to 5.66% (an increase of nearly 1 percentage point) that is, by 15.7%, when even 75% of furnishers provide positive information Loan performance worsens significantly with less positive information 9 Simulations: Participation Rates & 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 10 Simulations: Change in Acceptance Rates by Demographic Segment Acceptance rates fall but unevenly across sociodemographic groups For a 7% default rate Scenario Male Female Age categories 0-32 32-42 42-50 50-57 57+ 100% ACCPETANCE RATE Share of furnishers providing positive and negative informa tion 100% 75% 50% 25% 0% 64.92% 51.40% 44.31% 33.68% 10.99% 53.13% 42.24% 33.43% 22.30% 5.10% 16.48% 49.72% 58.31% 62.76% 77.13% 75% 15.47% 44.75% 45.20% 52.02% 72.98% 14.20% 28.42% 30.52% 39.61% 69.54% 50% 8.61% 13.71% 19.14% 19.13% 66.49% 25% 0.90% 7.67% 12.84% 13.00% 20.01% 0% Women as a share of borrowers declines as positive information is lost Those between 32-50 as a share of borrowers declines as positive information is lost 11 Worsening Model Performance SCALED K -S, Predictiveness Share o f fur nishers providing positive and negat ive inf ormation (with the remaind er providing sole ly negat ive informatio n) Scenario 100% 100.00 75% 92.42 50% 90.27 25% 87.67 0% 86.78 CHANGES IN ERROR RATES Change (+/-) in share of fur nishers providing positive and negative information over the full-fi le scenario (as percent of all credit-eligible adults) 75% 50% 25% Type I (false positives) Type II (fals e negatives) +1.00% +3.81% +2.22% +5.32% +3.31% +7.53% Important to note: Ability to tell good risks from bad ones worsens--why performance and market size deteriorate 12 Evaluating Payment History vs. Socio-Demographic Information Objective: to test and compare the Costa Rican model, which relies on extensive socio-demographic information and only negatives, to the Colombian one of extensive positive information. For both countries, we created a hypothetical files made of common variables: o a “Costa Rican restricted” purged of socio-demographic information not present in the Columbian files, and o a Colombian “negative only” Research-grade scoring models were developed for these two sets. Another model was developed to score the complete Costa Rican files. The results are then compared: o the “Costa Rican restricted” were compared to the Costa Rican complete files; o the Colombian negative-only compared to the Colombia full-file, ACIERTA instance; and o the differences in K-S score differences in the two sets 13 Positive Payment Info Provides More Lift than Socio-Demographic Info K-S Scores: Negative Only Simulations, Costa Rica and Colombia Costa Rica Restricted 40.5 Costa Rica Complete 49.3 Colombia Negative Only Colombia Full-File (ACIERTA) 54.2 67.3 Measure the relative merit of approaches with K-S, the ability to discern goods from bads (or true positives from false positives). (CAUTION: Measure differences are simply suggestive, not an overall indication of the magnitude of differences.) K-S increases considerably in moving from the Colombian negative only to the Colombian full-file scenario. (+13.1) By contrast, socio-demographic information improves the ability to distinguish goods from bads in Costa Rica files by much less of a degree. (+8.8) 14 INFORMATION POLICY INSTITUTE 306 Fifth Ave, Penthouse New York, NY 10001 www.infopolicy.org Phone: (212) 629 -4557