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Financial Inclusion Through Credit Reporting: Hurdles and Solutions Michael A. Turner, Ph.D., Robin Varghese, Ph.D., Patrick Walker, M.A.

A PERC Policy Brief April 2010 Copyright: © 2010 PERC Press.

Chapel Hill, North Carolina.

USA All rights to the contents of this paper are held by the Political & Economic Research Council (PERC).

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Financial Inclusion Through Credit Reporting: Hurdles and Solutions Michael A. Turner, Ph.D., Robin Varghese, Ph.D., Patrick Walker, M.A.

A PERC Policy Brief April 2010 Table of Contents 1.

Introduction 6 2.

Macro Effects on Development and Finance 8 2.1.

Greater Economic Growth and Stability 8 2.2.

Lowers Average Interest Rates 8 2.3.


Lowers Poverty and Improves the Distribution of Income 10 3. Credit Reporting, Its Structure, and Consequences: The Micro-Level 9 3.1.

Theory and Evidence on How to Structure Credit Payment Data 9 3.1.1.

Full-File Payment Information Versus Negative-Only Data 9 3.1.2.

Comprehensive Reporting Versus Segmented Reporting 10 3.1.3.

Expanding Reporting to Non-Financial “Alternative” Data 10 3.2.

Evidence: The Impact on Access to Credit 10 3.3.


Evidence: The Impact on Loan Performance 11 3.4.

Concerns of Consumer Overextension 12 4.

Ownership Structure: Public v. Private and Financial Inclusion 12 5.

Establishing Financial Identities 13 6.

Conclusion and Policy Recommendations 15 Endnotes 16 GLOSSARY OF COMMONLY USED TERMS A PERC Policy Brief Negative data: Adverse payment data on a consumer.
It consists of Segmented reporting: A system of reporting information, whether full-file or late payments (usually more than 60 days or more negative only, in which only data from one sector or a commonly 90 days past due), liens, collections and limited number of sectors, e.g., retail or banking, are bankruptcies.

contained in reports.


Positive data: Information on the timeliness of payments, including Comprehensive reporting: A system of in which payment and account information, whether payment was on time or was moderately late.

whether full-file or negative-only, are not restricted The payment information may contain the payment by sector, that is, the system contains information date relative to the due date.

Positive information from multiple sectors.


Such a system is in contrast to often includes data on account type, lender, date segmented reporting, in which information in files is opened, inquiries, debt, and can also include credit restricted to one sector such as banking or retail.

utilization rates, credit limits and account balances.

It stands in contrast to negative-only reporting.

Full-file reporting: The reporting of both positive and negative data.
On-time payments and late payments are reported.

Delinquencies are reported at 30 days (sometimes 15 days) following the due date.

Other positive information on an account, such as credit utilization, is also reported.

Negative-only reporting: The reporting of only negative data.

Data furnisher: The supplier of the data, most commonly the supplier of the service to whom a consumer has a payment obligation.

Data user: The end user of the data, usually but not necessarily a financial firm.
In finance, the information is used either manually or in automated computer models to allocate and monitor loans.

Other users include central banks, landlords, cell phone providers, and employers.

5 Financial Inclusion Through Credit Reporting: Hurdles and Solutions 1.

Introduction In the early years of the decade, the United Nations specified the objectives of “Inclusive Finance”.
The UN noted that among the objectives of an inclusive financial system, that is, of a system that does not under-provide the public good of a banking system to the disadvantaged and lower- income segments, are : “The stark reality is that most poor people in the world still lack access to sustainable financial services, whether it is savings, credit or insurance.

The great challenge before us is to address the constraints that exclude people from full participation in the financial sector.
Together, we can and must build inclusive financial sectors that help people improve their lives.” UN Secretary-General Kofi Annan 29 December 2003 Access to a range of “bankable” The sustainability of financial The promotion of competition financial services, comprising, institution that provide in order to lower cost and savings, short term and long access to financial services for provide customer choice.

term credit, insurance, payments underserved segments.
and money transfers, for all households at a reasonable.

6 A PERC Policy Brief In recent years, research has shown that credit information These questions confront policymakers, financial sharing systems, if properly structured, can be very regulators, and others who use credit data, yet they effective and powerful mechanisms for financial inclusion.

are seldom examined systematically in the context of The key qualifier here is “if properly structured”.

Credit regulatory reform.


information sharing systems are key elements of modern financial sectors infrastructure.

As with other kinds of An information sharing based agenda for financial inclu- infrastructure, its scope determines in large measure the sion can be advanced by: extent to which different social segments in an economy can participate within the financial system.
broadening the scope of information gathering; expanding the set of sectors sharing information within the system through the reduction of regulatory, technological and economic barriers; and taking measures that can help credit bureaus establish financial identities.

This brief quickly outlines the logic of information sharing as it relates to financial inclusion and suggests some policy concerns and policy targets designed to promote a financial inclusion agenda.
In order to understand how the scope the information sharing system affects financial inclusion, it is important to note how credit bureaus help to solve a key problem that is inherent in lending, namely, imprecise knowledge of a bor- rower’s likelihood of repaying.

The lender must infer the risk profile of the borrower so that some low-risk borrowers are not mistaken as high-risk, and some high-risk borrow- ers are not mistaken as low-risk.
The mistakes lead low-risk borrowers to face high interest rates that act as subsidies for high-risk borrowers.

In presenting information about potential borrowers to a lender, credit-reporting agencies (CRAs) reduce these asymmetries, allowing for: (i) greater lending through reduced rationing; and, (ii) lower rates of delinquency and default.
Few disagree that consumer credit and other information allow lenders to make smarter decisions, but this consensus sidesteps additional important questions, including: What types of information should be reported?

Which sectors should be encouraged to report?

What steps are needed to expand the coverage of the information sharing system?

7 Financial Inclusion Through Credit Reporting: Hurdles and Solutions 2.

Macro Effects on Development and Finance 2.2.


Lowers Average Interest Rates Without information on borrowers’ risk profiles, a lender will mistake good risks for bad, and vice versa.

The portfolio, there- fore, will consist of more risky loans and, over time, as interest rates adjust to reflect loan performance, higher rates.

Addition- ally, higher rates create incentives to engage in riskier projects, as lower-risk projects will not yield the return to compensate for the costs of the loan.
Risky projects come to account for a larger share of the portfolio, thereby driving up the average Three spheres of economic life are strongly shaped, rate.
When information is shared, the ability to screen out riskier directly and indirectly, by the structure of credit reporting: borrowers improves the portfolio’s performance and allows (1) economic growth and stability; (2) the price of credit; and lenders to offer lower rates to less-risky borrowers who would not have borrowed otherwise.3 (3) income distribution, as it relates to both poverty and equality.

2.3.
2.3.

Lowers Poverty and Improves the Distribution of Income These macro effects are achieved most commonly through Beck, Demirgüç-Kunt, and Levine examined the impacts of a sustainable expansion of lending that comes with better greater private-sector borrowing on risk assessment.

2.1.
2.1.

Greater Economic Growth and Stability Multi-country estimates show that economies with larger financial sectors (under various measurements) have higher rates of growth, greater productivity increases, and faster growing capital stock.
1 (1) income inequality as measured by the Gini coefficient (a standard measure of income inequality; higher values mean greater income inequality); (2) relative poverty, in terms of the income share of the poorest quintile; and (3) absolute poverty, in terms of the share of the population that lives on less than US$1 per day.4 Beck and colleagues found that greater private-sector lending: Ross Levine estimated that an increase in private-sector lowers the growth of the Gini coefficient; lending by 30% of GDP should lead to an increase in GDP growth by one percent per annum, and an increases in productivity and capital stock by 0.75% per annum.
2 lowers the growth of the percentage of the population living under $1 per day; and, increases the growth of the lowest (poorest) quintile’s income share.

8 A PERC Policy Brief benefit of the information inclusion can then be measured in terms of economic trade-offs between extending credit and worsening loan performance.

Smaller trade-offs are to the benefit of all.

3.1.
Theory and Evidence on How to Structure Credit Payment Data Here, we elaborate three features of an information sharing system that can bring the financially underserved into consumer credit markets: 1. the inclusion of timely payments (full-file) and not simply only delinquencies (negative-only); 2. the inclusion of information from across all credit granting sectors (comprehensive) instead of a single sector such as banking (segmented); and 3. whether non-financial, or “alternate”, information is included or not.
We then discuss some result from supporting research and propose some policy recommendations.

3.1.1.

Full-File Payment Information Versus Negative-Only Data Systems that only report serious delinquencies do not capture many moderately late payments (30 to 60 days past due) that are often indicative of a borrower’s risk.
In addition, positive credit information provides a low-cost way of gathering data on applicants who have paid in a timely fashion, and it provides information on those who may often face discrimination, such as lower-income 3. Credit Reporting, Its Structure, and Consequences: The Micro-Level The research suggests that (a) full-file, comprehensive credit reporting increases lending to the private sector more than other reporting regimes; (b) the presence of private bureaus with comprehensive data is associated with greater lending to the private sector; and (c) full-file, comprehensive reporting results in better loan performance than segmented and negative-only reporting.
The evidence for these three claims is extensive.

The impacts of credit reporting and its structures have been examined in two ways.

The first approach statistically estimates the impact of different systems of credit reporting worldwide, controlling for factors such as wealth and the legal system (particularly rights in collateral, bankruptcy, and property rights).
The second approach uses individual credit files from an economy that engages in full-file reporting and simulates a restricted system by removing certain information.

Credit scores (predictions of default/ delinquencies) made using the restricted and full data sets borrowers, women, racial minorities, and the young.
Full-file are then compared with actual outcomes in the observation reporting also allows creditors to measure a borrower’s period, the year or years following the timing of the credit capacity to carry a loan by revealing the individual’s existing scoring.

The cost of the information restriction or the lines of credit, associated balances, and credit limits.

9 Financial Inclusion Through Credit Reporting: Hurdles and Solutions 3.1.2.
Comprehensive Reporting Versus Segmented Reporting In many ways, the issue of comprehensive reporting versus segmented reporting is akin to that of full-file versus negative-only reporting.

More information allows for better predictions.

In addition, comprehensive reporting provides a low-cost way of gathering data on those who apply for loans in another sector.

3.1.3.
3.1.3.

Expanding Reporting to Non- Financial “Alternative” Data Expansion of the scope of credit information sharing to non-financial payment obligation histories has been shown to be perhaps the most effective avenue to financial inclusion in terms of access to short and long term credit.
In those economies where only or mostly credit payment data is shared, the problem of access to short and long term credit becomes a ‘Catch-22’ for populations without collateral: one needs to have credit to build a credit history but cannot have a credit history without previously having obtained credit.
Information from non-financial services, such as payments and prepayments on rent, gas, electric, insurance, telecommunications and other recurring obligations, can serve to evaluate the risk of a those potential borrowers that are outside the credit mainstream.

Payment histories from rental housing, municipal services, and prepaid public and private services (including power, water, and telecommunications) especially may be able to serve as a proxy for income stability and credit behavior.
A PERC study measured the impact on access to credit with the inclusion of energy utility and telecom payment data in U.S. consumer credit files.

(Some 35 to 54 million US consumer lack credit files or have too little information to assess risk and thereby remain outside the credit mainstream.)
PERC’s simulations found that when energy utility and telecom payment are included in credit files, 10 there were greater rises in those that become credit eligible (assuming a three percent target default rate) among ethnic minorities, lower income households, younger individuals, and older individuals.

That is, those least likely to be in the credit mainstream, not having had multiple credit accounts in the past, are those most likely to benefit from the inclusion of non-financial data in credit files.
3.2.

Evidence: The Impact on Access to Credit Several simulations have used anonymous, individual level credit files from several different economies to gauge the impact on credit of wider access to information.

Some elements of the credit file are kept while others are purged, thereby mimicking the information content from more restricted cases.

The researchers then apply decision (credit scoring) models to the two (or more) sets of files.
Thus for a simulation of negative-only reporting, positive information is purged.

The scores produced are predictions of the likelihood of serious delinquency, bankruptcies, and other outcomes.

The predictions are then compared with actual outcomes in the “observation” period, the year or years following the timing of the score.
The results of these simulations consistently indicate a sizable reduction in the ability of lending systems to identify the good risks from bad risks with shifts from a comprehensive full-file data to negatively only or segmented data.
Barron and Staten, using US data, compared the findings of a simulated negative-only reporting system with a full-file, comprehensive system and found that for a three percent default target of three percent, a negative-only reporting system accepts 39.8% of the applicant pool, whereas a full-file system would accept 74.8% of the pool.

Similar simulations conducted in a number of countries with comparable results verify the robustness of such findings.
A PERC Policy Brief Two studies have examined how different systems of as 0.3 percentage points (or a 10% increase), which is still reporting affect the distribution of credit by various a considerable degradation of portfolio performance, to demographic characteristics.

The first uses U.S. credit files as much as 1.84 percentage points (a 170% increase) in and the second Colombian files.

Three results are notable.

cases restricted to financial accounts only.


Majnoni and Ethnic minorities, the young, and low-income groups in the colleagues’ simulation using Brazilian files reveals that even United States experience greater increases in acceptance at an extremely high acceptance target of 80%, the default rates with full-file information than do their counterparts.

rate increases by 0.86 percentage points (or 30%).


At a 60% The increase in acceptance rates for Caucasians was 21.8% acceptance target, the default rate nearly doubles (an 83% while for minorities it was 35.5%.

Similar differences were increase) under negative-only reporting compared with full- found for low-income groups vis-à-vis richer segments.

file reporting.
file reporting.

These effects are significant for a lender and, The Information Policy Institute’s study of Latin America moreover, as aggregated they can have a significant effect found an increase in the share of women among the pool on an economy’s financial stability and growth.

(For more of borrowers when switching to a full-file system; women information see Section 3.)
Comparisons using segmented accounted for 33% of the borrower pool under a negative- and comprehensive files show similar shifts in performance only system compared to 47% under a full-file system.

These as were evident in the shift from full-file to negative-only.

findings strongly suggest that individuals in underserved Loan performance is also greatly improved by the addition social segments are the most likely to benefit from of alternative data.
The PERC study found that the inclusion expanded information sharing.

of fully reported energy utility and telecommunications trade lines (i.e., different accounts) in traditional consumer The addition of alternative data shows greatest promise credit reports measurably improves the performance of for financial inclusion.

Another PERC study found that the loans for a target acceptance rate.
For example, by integrat- addition of the alternative data (utility and telecom) can ing fully reported energy utility data, a lender’s default rate move 10% of the population from being unscoreable to (percentage of outstanding loans 90 days or more past due) scoreable, that is from the segment characterized by difficult declines 29%, given a 60% target acceptance rate.
Similarly, access to credit to the credit mainstream.5 This access to adding telecommunications data reduces the default rate credit was witnessed in practice as well as in simulations.

by 27%.

These reductions allow lenders to make more capi- Over a one-year period, 16% of thin-file borrowers whose tal available and improves their margins, capital adequacy, credit report included nontraditional data opened a and provisioning requirements.
Such improvements could new credit account compared with only 4.6% of thin-file have further positive economy-wide effects.

borrowers with only traditional data in their credit reports.

3.3.

Evidence: The Impact on Loan Performance The counterpart to greater acceptance rates at a given default rate is lower default rates at a given acceptance rate.
The four negative-only to full-file simulations restricted to financial accounts (i.e., all but the Colombia simulations) show the default rate increasing by as little 11 Financial Inclusion Through Credit Reporting: Hurdles and Solutions 3.4.
Concerns of Consumer Overextension A concern that some may have regarding the improvement and increase in information in consumer credit files is that precisely since access to credit and financial services will be expanded there may be a problem with consumers overextending themselves.

There are a number of reasons why we believe this should not be a major concern.
First, the expansion of information in credit files should not lead to simply to easier credit for consumers but to better credit decisions by lenders.

It is the lenders’ increased ability to efficiently identify good risks from bad risks that increases the availability of credit.

This is something very different from, say, a relaxation in lending rules increasing access to credit.
Second, evidence from the U.S. market indicates that there is no rush to obtain credit when consumers gain access to the credit system via new data entering their credit files or becoming scoreable with new data.6 (The credit crisis appears to be driven by the failure to use information, the failure to underwrite owing to changes in market structure, rather than the use of information to expand credit.)
Third, while in many cases consumers without sufficient information in their credit files have little access to mainstream credit they usually have access to high cost credit, predatory lenders, informal financial services, and the like.

And so, it is not that they are suddenly introduced to the concept of credit as much as they are able to utilize affordable credit.

PUBLIC BUREAU PRIVATE BUREAU 4.


Ownership Structure: Public v. Private and Financial Inclusion The third aspect of a credit reporting system—in addition to full-file to negative-only and comprehensive vs. segmented reporting—has only recently begun to gain attention.

Although there is no theoretical reason why a public bureau cannot behave like a private one, there are practical reasons.
Public bureaus have been set up largely and primarily for supervisory purposes, to monitor the safety and soundness of the financial sector and determine whether reserves are sufficient.

Unlike private bureaus, they are not established primarily to facilitate greater and sustainable lending.

The primary objective of private bureaus is reducing information asymmetries and to improve risk assessment in lending.

By this account, private bureaus are complements to public bureaus.
Three separate studies have estimated the impact.

First, Djankov, McLiesh, and Shleifer examined private credit and credit reporting in 129 countries.7 In estimations that examined all countries, private bureaus increased lending by 21% (vs. seven percent for public bureaus, although the latter was not a statistically significant increase).

In estimations that restricted the data to poorer economies, private bureaus increased lending by 14.5% compared with 10.3% for public bureaus.
Second, The Information Policy Institute found that 100% coverage of credit-eligible 12 A PERC Policy Brief adults by a full-file private bureau can be expected to increase private-sector lending by more than 45% of GDP (all else being equal).8 In other words, after removing these observations, lending increased by more than 45% percent of GDP with a shift to 100% coverage from no coverage.
Third, the Inter-American Development Bank (IADB) measured the impact of information-sharing and ownership on loan performance using data from 170 banks across Latin America.9 It found that banks that loaned primarily to consumers and small businesses and that used private bureau data had nonperformance rates that were 7.75 percentage points lower than banks that did not.

The 5.
The 5.

Establishing Financial Identities authors found no such effect of any magnitude for the One challenge, especially in emerging markets, is the impact of public bureaus.
perceived and real difficulties in matching an individual to their various accounts, or correctly identifying that one set It should also be noted that privately owned credit bureaus of obligations belongs to a specific person by connecting are more likely to seek data outside the banking and the different account information from different credit financial sectors.

While there is no theoretical reason why and other service providers to a shared consumer.
This publicly owned bureaus cannot also capture alternate data, problem of matching is especially felt as a great challenge but incentive to do so appear to be greater in bureaus that in societies without national identification numbers and are privately owned.

Moreover, public bureaus generally stable address systems.
The challenge becomes one limit their data collection to those sectors that financial of the availability of a set of identifying factors that can authorities (central banks and ministries of finance) have be uniquely assigned to one individual.

These factors regulatory purview over.

enable the creation of a financial identity, that is one that connects a person to their financial behavior and other non-financial transaction histories.
13 Financial Inclusion Through Credit Reporting: Hurdles and Solutions Those inside the financial system can be more or less easily The factors that can be used in establishing an individual’s identified as the know your customer recommendations identity for the sake of establishing a financial identity of the Financial Action Task Force specify that financial can be varied: utility accounts tied to an address, as institution should: defined by the utility provider; biometric identifiers; voter identification cards; digital photos; welfare basket “undertake customer due diligence measures, including allotment cards; and account information from ongoing identifying and verifying the identity of their customers, non-financial service providers such as cell-phones.
A when: establishing business relations; wider arrays of sources helps to establish unique identities better.

Credit bureaus and other information service firms can build solutions to establishing an identity based on carrying out occasional transactions: the information topography of a society.
(i) above the applicable designated threshold; or (ii) that are wire transfers … The customer due diligence (CDD) measures to be taken are as follows: a) Identifying the customer and verifying that customer’s identity using reliable, independent source documents, data or information.” 10 This requirement for “reliable, independent source documents, data, or information” is seen as constraining when it is understood narrowly, as limited to national identification cards, passports and other government identity documents.
It should be noted that, identity matching is never limited to national identification numbers and addresses even in developed markets.

German law, for example, prohibits the use of national identification numbers in credit reporting.

Consequently, Schufa, the German credit bureau, matches an individual on a number of factors.
There are two policy issues that should be addressed: Guidelines should be developed to allow credit bureaus access to a wider set of information sources for establishing identities; and A data protection framework that guards individuals’ data by limiting its uses and access (as specified in the OECD Fair Information Practices guidelines) must be adopted.
Finally, the creation of a body of case studies in how different economies have proceeded technically and regulatorily in the establishment of individual identities for credit reports can help policy makers and practitioners expand financial access by extending the information sharing system and thereby the financial infrastructure to a wider segment of the population.

14 A PERC Policy Brief 6.


Conclusion and Policy Recommendations Findings are consistent across a wide body of research examining information-sharing and related finance and growth, as well as finance and equality.

Information-sharing expands access to credit overall and disproportionately expands access among the underserved.

Information-sharing improves loan performance by reducing delinquency rates for any given target.
Both are achieved by accurately identifying good credit risks that otherwise would have been misidentified as bad risks and, therefore, would have been denied credit.

At the same time, bad risks, given credit because they were thought to be good risks, now have credit denied to them or are no longer subsidized by lower-risk individuals.

In the aggregate, lending is increased, leading to greater economic growth, rising productivity and greater capital stocks.

Average interest rates decrease.


Poverty and income inequality are alleviated.

This is especially true of full-file, comprehensive reporting, comprising non- financial obligations as well, to private bureaus.

In addition to the recommendations regarding financial identity noted above, policy reform and credit reporting standards should allow the collection of non-financial payment and other non-financial data.
As shown above, the inclusion of this information can greatly expand financial services to the underserved.

15 Financial Inclusion Through Credit Reporting: Hurdles and Solutions Endnotes 1 Walter Bagehot believed that England beat out its 7 Simeon Djankov, Caralee McLiesh, Andrei Shleifer, competitors not because it had more capital but because “Private Credit in 129 Countries” (National Bureau of it could mobilize it better.

Also see R. G. King and Ross Economic Research working paper no.
11078, January Levine, “Finance, Entrepreneurship, and Growth: Theory 2005).

Available at http://papers.nber.org/papers/w11078.

and Evidence,” Journal of Monetary Economics 32 (1993): 513-542; R. Levine and S. Zervos, “Stock Markets, Banks, 8 From Turner and Varghese, The Economic Impacts, and Economic Growth,” American Economic Review 88 Table 3, p. 18.
(1998): 537-558; Ross Levine, “Financial Development and Economic Growth: Views and Agenda” Journal of Economic 9 Inter-American Development Bank, IPES 2005: Unlocking Literature 25 (June 1997): 688–726.

Credit: The Quest for Deep and Stable Bank Lending 2 Levine, “Financial Development and Economic Growth,” iadb.org/res/ipes/2005/index.cfm.

p. 706, and King and Levine, “Finance, Entrepreneurship, and Growth,” find similar outcomes.
10 FATF Recommendation 5: Customer due diligence and (Washington, DC: IADB, 2004), p. 178.

Available at www.

3 Turner et al., The Fair Credit Reporting Act, Table 6., p. 30.

343,en_32250379_32236920_43642938_1_1_1_1,00.html record-keeping.

http://www.fatf-gafi.org/document/58/0,3 4 The Gini, which is a ratio that takes values between 0 and 1, or 0 and 100 when indexed, measures income distribution with higher values indicating greater inequality.
Thorsten Beck, Asli Demirgüç-Kunt, and Ross Levine, “Finance, Inequality, and the Poor” (National Bureau of Economic Research working paper no.

10979, January 2007).

Available at www.econ.brown.edu/fac/ Ross_Levine/Publication/Forthcoming/Forth_3RL_Fin%20 Inequalily%20Poverty.pdf 5 Michael Turner, Alyssa Lee et al., Give Credit Where Credit is Due (Washington, DC: The Brookings Institution, December 2006).
6 See rate of opening new accounts in Michael Turner, Alyssa Lee et al., Give Credit Where Credit is Due (Washington, DC: The Brookings Institution, December 2006).
16 A PERC Policy Brief About the APCC The Asia-Pacific Credit Coalition (APCC) is a non-profit organization dedicated to educating lawmakers, regulators, and other stakeholders in the Asia-Pacific Economic Cooperation (APEC) member economies about the economic and social benefits of full-file, comprehensive credit reporting systems and private credit bureaus.

For more information, or to learn how to join APCC or support our efforts, visit our Web site at www.apeccredit.org.
17 Results and solutions POLICY & ECONOMIC RESEARCH COUNCIL Dr. Michael Turner President and Senior Scholar PERC 100 Europa Drive, Suite 403 Chapel Hill, NC 27517, USA Tel: +1 (919) 338-2798 x801 Skype: +1 (919) 975-5565 Mobile: +1 (919) 265-3466 Fax: +1 (919) 640-8881 Email: turner@perc.net URL: www.perc.net