POLITICAL & ECONOMIC RESEARCH COUNCIL

By Michael Turner, Ph.D.

Intercontinental HotelTegucigalpa--11 May 2006

The Benefits of Reporting Positive Payment Data in Latin America

2

Introduction

Findings

Conclusion

Agenda

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Objectives:Broaden access to affordable mainstream creditReduce 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?

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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)

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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

LendersConsumers

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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

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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

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Large differences among Latin American credit reporting systems.

QUESTION FOR RESEARCH:

How do differences affect profitability and availability of credit?

Latin American Context

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Latin American Context: Financial Services Sector

Relatively small and modest private sector borrowing(Private sector borrowing as a share of GDP, 1995-2004)

0%

20%

40%

60%

80%

100%

120%

NA/E/ANZ(N=16)

EA (N=5) MENA (N=5) EE (N=8) LA (N =18) SA (N=3) Afr (N=12)

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

North AmericaEurope

Aust/NZ (N=16)

East Asia(N=5)

Middle EastN. Africa

(N=5)

E. Europe(N=8) Latin

America(N=18)

S. Asia(N=3)

Sub-SaharanAfrica(N=12)

Source: International Financial Statistics, IMF

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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

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Introduction

Findings

Conclusion

Agenda

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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. 433-448. Their reported impacts were larger.

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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)

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Estimations: Private Full-File Coverageand Private Sector Borrowing

* p < 0.1** p < 0.05***p < 0.01

Source: IMF International Financial Statistics; World Bank, Doing Business database

VARIABLE Model I Model IV (reduced)

Constant -142.40*** (35.31)

-130.80*** (32.20)

Log of GDP per capita (adjusted for PPP)

20.31*** (4.65)

16.85*** (3.87)

Avg. Change in GDP (1995-2004)

-1.20* (0.70)

Legal Rights of Creditors (from 0 to 10)

4.55** (2.07)

4.80** (1.97)

Credit Information1 (from 0 to 6)

-3.87 (2.88)

Private Full-file Coverage (0 to 100, as percentage of adults)

0.72*** (0.20)

0.67*** (0.16)

Private Negative-only Coverage (0 to 100, as percentage of adults)

-0.02 (0.86)

Public Full-file Coverage (0 to 100, as percentage of adults)

-0.11 (0.41)

Public Negative-only Coverage (0 to 100, as percentage of adults)

0.16 (0.46)

R squared 0.7075 0.6883 F-stat (p value)

16.93 (1.88e-012)

44.9 (1.887e-015)

Residual Standard Error 29.45 29.12 N 65 65

Lesson: what matters?• Wealth• Creditor Rights• Reporting

o privateo full-fileo 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.)

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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)

*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

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Private Sector Lending in Honduras

Source: Hong Kong Monetary Authority

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%

GDP growth (in 1978 prices) 3.5% 5.0% 4.2%

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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

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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?

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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)ACCEPTANCE RATES BY TARGET DEFAULTS,

UNDER DIFFERING LEVELS OF PARTICIPATION Share of furnishers providing full-file information (remainder provides negatives only)

Target Default rate 100% 75% 50% 25% 0%

3% 10.00% 6.64% 4.73% 4.80% 2.56% 5% 41.35% 28.96% 19.28% 9.69% 5.15% 7% 58.82% 45.59% 36.42% 25.71% 13.60% 10% 73.06% 68.09% 68.08% 68.09% 54.97% 12% 77.80% 77.21% 76.49% 75.06% 72.26%

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Change in Non-Financial Acceptance Rates for a Performance Target

Source: Hong Kong Monetary Authority

Non-Financial Acceptance Rates, by Scenario (Colombia) Share of tradelines consisting of both positive and negative information

Tar get Default rate 100% 75% 50% 25% 5% 5.50% 4.00% 2.95% 1.96% 7% 37.30% 29.95% 17.96% 10.07%

10% 61.03% 49.36% 43.14% 36.01% 12% 69.75% 63.27% 57.70% 50.43%

Full sample (3.1 million files)

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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.

DEFAULT RATES BY TARGET ACCEPTANCE, UNDER DIFFERING LEVELS OF PARTICIPATION

Share of furnishers providing full-file information (remainder provides negatives only) Target

Acceptance Rate 100% 75% 50% 25% 0% 20% 3.52% 3.72% 4.66% 5.91% 8.46% 30% 4.12% 4.62% 5.74% 6.78% 9.06% 40% 4.89% 5.66% 6.67% 7.52% 13.85% 50% 5.86% 6.70% 7.49% 8.22% 14.40% 60% 7.20% 7.73% 8.49% 9.25% 15.30%

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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%.

Source: Hong Kong Monetary Authority

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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

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Acceptance-Default Trade-Offs

Furnishers can reduce losses.Furnishers can reduce losses.

0%

3%

6%

9%

12%

15%

0% 15% 30% 45% 60% 75% 90%

Acceptance Rates

Default Rates

100% Reporting Full File 75% Reporting Full File 50% Reporting Full File

25% Reporting Full File 0% Reporting Full File

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Change in Share Accepted by Gender

Women are hit disproportionately; thinner files.

Women are hit disproportionately; thinner files.

25% 0%

100% 75% 50%

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Change in Share Accepted by Age

Young also hit disproportionately; thinner files.

Young also hit disproportionately; thinner files.

25% 0%

100% 75% 50%

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Loss of Information Means More Mistakes are Made

Issues of giving credit where credit is due. Issues of giving credit where credit is due.

CHANGES IN ERROR RATES (MEASURED AS A PERCENT OF ALL CREDIT-ELIGIBLE ADULTS)

Share of tradelines consisting of both positive and negative information 75% 50% 25% Type I (false positives, or mistaking a high risk borrower for a low risk one) +1.00% +2.22% +3.31% Type II (false negatives, or mistaking a low risk borrower for a high risk one) +3.81% +5.32% +7.53%

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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 socio-demographic categories.

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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

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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 40.5 Costa Rica Complete 49.3

Colombia Negative Only 54.2 Colombia Full-File (ACIERTA) 67.3

Issue 2: why the difference is starting points (40.5 vs. 54.2)?

Accuracy For better predictions For matching (also reduces mistakes)

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Introduction

Findings

Conclusion

Agenda

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Lessons

Reporting positive payment data enables growth in lending to private sectoro PERC 2006--up to 45% of GDP (moving from 0% to 100% participation in private bureaus) o 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 Improves mainstream access for the under-served (developed & emerging economies).o 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

POLITICAL & ECONOMIC RESEARCH COUNCIL

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Chapel Hill, NC 27517

www.infopolicy.org

Phone: (212) 629 -4557