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Consumer Credit Reporting Data

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Are you curious about how consumer credit reporting data shape economic research and policy decisions? Discover the transformative role of credit files in understanding borrowing behaviors, economic measures, and the impact on financial markets.

Image courtesy of interviewee. November 15, 2024

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This video discusses the significance and development of consumer credit data, particularly within the context of the United States. It highlights the role of major credit reporting agencies—Equifax, TransUnion, and Experian—in collecting and selling data on individuals' credit habits, which lenders use to assess credit risk.

This video points out that the research into consumer credit data surged following the 2008 global financial crisis, which prominently involved the housing market. Important studies emerged in this field, leveraging credit data to analyze the crisis's impact and ongoing implications for the economy. The speaker emphasizes that while consumer credit data began mainly for lending purposes, its applications have expanded into various research domains, including health and economic mobility.

However, there are limitations and challenges associated with this data. This video notes that the credit reporting system is not comprehensive, missing significant populations like minors and undocumented individuals. Issues arise from the reliance on past credit behavior to assess current creditworthiness, which may overlook individuals who have never needed credit. Additionally, the quality of the data can be inconsistent due to varied reporting practices and changes in regulations, especially highlighted during the pandemic, which affected the accuracy of credit records.

The speaker advocates for better methodologies in using this data for research, noting the need to link it with other datasets for a more complete understanding of financial well-being. Overall, the discussion underscores the complexity and evolving nature of consumer credit data in both academic research and practical applications, pointing to the necessity of ongoing refinement and awareness of its limitations.

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Here are five key points from the video regarding consumer credit reporting data and its significance in economics and finance:

1. **Key Role of Major Credit Bureaus**: In the U.S., consumer credit data is primarily collected and sold by three major firms: Equifax, TransUnion, and Experian. These companies gather detailed information about individuals' credit habits, including their use of mortgages, credit cards, and auto loans. This data is crucial for lenders to assess creditworthiness and make informed lending decisions.

2. **Impact on Economic Research**: Research on consumer credit data has been essential in understanding significant economic events, notably the 2008 global financial crisis. Researchers like Ian and Sufi used credit data to analyze the crisis's causes and implications, leading to a better understanding of consumer behavior in finance. The use of this data has expanded into various economic fields and continues to inform research in the 2020s.

3. **Data Limitations and Challenges**: While consumer credit data provides insights into the financial behavior of a large portion of the U.S. population, it has notable limitations. Not everyone is included in the datasets, particularly vulnerable groups like those without social security numbers or individuals under 18. Additionally, past credit usage does not fully reflect an individual's creditworthiness, as some may be responsible without having a significant credit history.

4. **Linking Credit Data to Other Economic Indicators**: The video emphasizes the need to link consumer credit data with other datasets, such as income and asset records, to gain a comprehensive understanding of individuals' financial situations. Current credit data lacks crucial information about personal finances, which is necessary for a well-rounded analysis of economic dynamics.

5. **Regulatory Impacts on Data Reporting**: Changes in regulations and practices around data reporting have influenced the quality and accuracy of credit data available for research. For instance, medical debt and civil judgments are not consistently reported, which affects the ability to study their impacts on creditworthiness and consumer behavior. The trade-off between consumer privacy and accurate reporting remains a significant concern in the industry.

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Here are the key quotes extracted from the video you regarding consumer credit reporting data and its implications in financing and economics:

1. **Data Utilization**: "Consumer credit reporting data... is a data source that is increasingly become very widely used in financing and economics."

2. **Research Focus**: "We primarily focus on the United States... but similar data sets are available in many other countries."

3. **Key Firms**: "There's three major firms, Equifax, TransUnion and Experian... collecting information about our habits and uses of credits."

4. **Purpose of Data Collection**: "These firms collect this data, and then package it for essentially a huge fraction of the US population, and then resell it on to financial institutions."

5. **Historical Significance**: "The origins of modern research in this field trace back to the use of consumer credit data... the global financial crisis as we know."

6. **Financial Crisis Insight**: "We are still studying the financial crisis of 2008 and 2009... Their original paper was published in 2009 using credit reporting data."

7. **Research Expansion**: "The development of this industry... addressed information asymmetry issues."

8. **Limitations of Data**: "Inherent contradiction... someone may be the most credit worthy if they've never used credit."

9. **Missing Demographics**: "Some people are missing... people without social security numbers... very vulnerable populations."

10. **Holistic Financial Measurement**: "If we really wanna get a full picture of consumers' financial lives... we would want their credit... but we would also want their income and their assets."

11. **Data Quality Improvements**: "Over the 2010s and 2020s data quality has improved dramatically."

12. **Impact of Regulations**: "There are regulations meant to support data quality and also protect consumer privacy."

13. **Consequences of Medical Debt**: "There was a regulatory and industry debate... medical debt is also now not being reported."

These quotes capture the essence of the discussion around consumer credit reporting, the challenges, and implications in research and policy-making.

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Is a data source that is increasingly become very widely

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used in financing and economics and what it comes from.

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And, I think a research paper

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that accompanies this interview on consumer credit reporting

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data, we primarily focus on the United States,

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but I wanna note that similar data sets are available in

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many other countries, including developed countries such

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as the UK or Europe as well as developing markets.

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But my expertise

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and our research paper are gonna focus primarily on the us

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So I will, I will focus on that for now.

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But essentially it's data collected

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by private firms in the US There's three major firms,

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Equifax, TransUnion and Experian.

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And they're collecting, information about our habits

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and, uses of credits such as taking out mortgages,

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credit cards, auto loans,

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and the such, such as any typical type of credit

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that we might think of, gathering that information together

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and then essentially selling it to lenders

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and other institutions that might want to use that data.

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In particular, thinking about if a new bank wants to lend

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a credit card to you

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or a mortgage, they want to see that data

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because they want to know whether

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you're a good credit risk or not.

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So essentially these firms collect this data,

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and then package it for essentially a huge fraction

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of the US population,

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and then resell it on to financial institutions

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and other companies that may be interested in using

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it for business purposes.

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Certainly the research community has researched,

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individuals, experiencing things such

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as the global financial crisis.

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And I would really trace back the origins of modern,

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or let's just say post-crisis household finance itself

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as a research field back to the use of consumer credit data.

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Um, thinking about a seminal papers such as those

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by Ian and Sufi.

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The earliest papers,

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in 2009 really dissecting this crisis is something that

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hit the global economy.

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In some ways, I think we're still suffering the

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repercussions of it politically and economically.

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Um, and, and relatively quick time.

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Their original paper was published in 2009 using credit

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reporting data because the heart

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of the global financial crisis, as we know,

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was the US housing market.

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And we realized that we didn't know much about this sector.

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The household sector in the United States is about two

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thirds of the US economy.

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Of course, the US is key in the global economy

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and the global financial system

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and fragility in the housing sector,

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in the consumer sector was what led

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to the cascading problems globally.

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And, me and Sufi were some of the first researchers put,

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of course, if you wanna study the mortgage

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market, you need credit data.

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And so they used this exact data source to shed some

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of the first light on the crisis.

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And I would say that we are still studying the financial

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crisis of 2008 and 2009.

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Uh, UAN keys is another example looking at refinances

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and equity extraction even into the 2010.

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So the Buddha Keys paper was from 2016

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and even the 2010s,

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I would say there's very important pathbreaking research.

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One example is, Ong

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and Noel from 2020 looking at mortgage restructuring back,

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during the financial crisis.

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So I think we are actually still studying,

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this phenomenon and still using credit data,

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to into this current decade.

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I would say that along with the development

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of consumer finance as a field,

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which again was a bit catalyzed by the financial crisis,

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I think that taking a step back a second,

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consumer credit data did pre-exist from the,

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financial crisis?

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And we have seen development

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of this data source in our research paper on consumer credit

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data this year does seek to advance like

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how we're using this data, help inform researchers about

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what are new ways to use this data

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that haven't really even been used before.

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But I think taking a step back, seminal work

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by the New York Federal Reserve, back

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before the financial crisis in the early two thousands,

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they really helped to broaden all the,

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economists within the Federal Reserve system.

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Hundreds of economists being able to do great work.

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So some of our co-authors, Wilbert Vander Klau

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and Don Lee really pioneered the New York Fed data set,

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which was the first widely available credit data

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set in the two thousands.

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But this type of database all the way back to the 1990s

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and even before with, the Federal Reserve system,

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the FTC and other primarily US government agencies.

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So I wanna make sure to credit these earlier waves

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of researchers, to not say kind of in finance.

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We sometimes have a short memory about economic history

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and even the history of our own field.

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But I think what we seek to do, me

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and my group of of co-authors, we seek to kind of push

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that advancement further,

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into the current era in, in the 2020s.

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And there's a lot of new developments. I would say.

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We are really only scratching the surface of our ability

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to use consumer credit data,

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and, to study a wide variety.

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And you think what we've seen in the past five

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to 10 years is not only using this data to study

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the obvious things such as the housing crisis

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and the financial crisis, but things not even that related

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to finance that you might think of such as health,

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healthcare, health insurance.

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I think one of the great research papers that I can imagine

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so important is linking, credit data

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with abortion records and looking at women

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who were either denied an abortion

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or we're allowed to have an abortion.

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How does that affect their general economic,

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of course health wellbeing as well as financial wellbeing?

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So I think these data have well advanced well

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beyond its origins in studying the financial crisis.

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I think that, the origin of what is consumer credit data

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and the reason this industry existed, it is a kind

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of a private data set.

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Um, these firms, it rose in the private

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market to serve a need.

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And that need was information asymmetry such

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that if you're a new bank

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and you are faced with the consumer, you've never seen

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that person before, it's very

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difficult to lend to that person.

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And we've seen an explosion the US credit markets,

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and some would say too much credit, which was what led

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to the, to the crisis.

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But certainly the development of this industry,

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was exactly to address information

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Asymmetry issues. And

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I think one aspect I would point to

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that's very interesting is thinking about linking

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per personal credit

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where we have this very well developed consumer credit

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reporting systems, or essentially any form

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of consumer debt in the US

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and many other countries is reported.

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So if I'm a new lender, whether I'm a large bank

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or a new FinTech firm, if there there's a customer who comes

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to me, I can pull their credit record

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and get very good visibility into their entire history,

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whether they're delinquent or not.

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So the current credit reporting system has solved a lot

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of the information is asymmetries, not

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that they're completely eliminated.

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Uh, so it's still the case that moral hazard,

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if I compare two people with the same credit score,

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there could be differences between them,

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but it's quite well developed.

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But in contrast to the small business le lending market,

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has very little information

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and we know that, the avail available information,

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if I'm a new entrepreneur opening, let's say a restaurant

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or a small manufacturing firm, it's much more difficult

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to get information on that.

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And some of my work, which links the consumer credit data

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to small business credit data, what I find is that in fact,

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small business lenders are using people's consumer records,

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to lend to their businesses

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because of the fact that they don't know, they don't know

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how to, underwrite

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or they don't know how to evaluate the risk

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of those businesses because, essentially that industry

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to manage small business credit risk has not developed

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as much as on the the consumer side.

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So there's actually borrowing a person's personal credit

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record in order to lend to their business.

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And there can be various issues with that,

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that you might imagine.

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My personal credit score may not dictate the quality

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of my new business that I, that I opened.

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But, and, and we know that minorities

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and women tend to have lower credit scores, so then

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that means there had gonna have a harder time starting up

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new businesses and that can impact the broader,

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macro economy and our business dynamism.

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Um, in many countries.

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This was really the core goal of our research paper,

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which is to shed light on ways of measuring and,

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and kind of advance best practices

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and hopefully generalize methodologies

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that we can all then share

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to advance the literature in that way.

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Because essentially what we've seen in the development

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of the literature is that, this

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consumer credit data became first became widespread in the

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two thousands and in the 2010s

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and 2020s, it kind of broadened out

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to many other economic fields, including researchers

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who their main expertise may be in public economics or labor

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or health and not be in credit per se,

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but they're using this type of data.

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And I think what we're hoping in the 2020s is to consolidate

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that and say, okay, we've had this mass proliferation

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of research, now let's, kind

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of compare different methodologies.

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Let's, let's create a frontier best practices

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so then we can all advance starting from the same basis

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of these are the best practices for measurement.

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And there's a few things to remember in that.

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As I mentioned at the very beginning,

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these data were not created for researchers.

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This was, created for business purposes,

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and those might not be exactly the same as

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what we might want from an economic measurement perspective.

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And there's very basic things such as even the definition of

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what is a credit card, they don't have, necessarily,

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not all of these data sets have an indicator

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for this is a credit card versus this is not.

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And a researcher has to do a lot

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of the legwork in creating these measures by themselves.

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So we hope to kind of, both for people

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who are not involved in credit data,

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but maybe reading these research papers,

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including the general public, they may not know

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that even the definition

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of a credit card can be subjective decision.

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And that researchers,

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can differ across two different research groups, in,

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in terms of how they make those subjective decisions.

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One thing to remember is

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that even though it has very broad population coverage in

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the United States, for example, over 200 million us

238
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adults have credit records,

239
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that also means that some people are missing.

240
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For example, obviously anyone under 18 is essentially not

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gonna be covered by this dataset.

242
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So even though they're very interesting questions about

243
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intergenerational, issues with economic

244
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and credit kind of access

245
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and mobility, we would not see the children

246
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until they turn 18.

247
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And of course, the age at which someone first gets a credit

248
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card varies by their parental income.

249
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And so we, even if we do see someone, we may see,

250
00:10:20.925 --> 00:10:22.925
a different selection of people who have credit

251
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by the time they're 18 versus do not enter the credit market

252
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until they're in their twenties.

253
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And so that is very important to take into account.

254
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And for example, this data is missing a lot

255
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of very important populations for policy purposes,

256
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including the most vulnerable populations.

257
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So people without social security numbers.

258
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Um, some data sets explicitly sample off of those

259
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with social security numbers.

260
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So obviously people who are have limited,

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documentation in terms of their, their employment status,

262
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or visa status in the United States,

263
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they might be very important for policy purposes,

264
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they would not be very covered.

265
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Or the other side, I think one of the contradictions

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that we do get into in this research paper is

267
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that the way the the industry has developed essentially over

268
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the course of the 20th century was

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that we measure your credit worthiness by

270
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how you've used credit in the past.

271
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But an inherent contradiction of that was that maybe someone

272
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is the most credit worthy if they've never used credit.

273
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If I've never needed to borrow,

274
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and this does apply to myself,

275
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I've never had to take a student loan.

276
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I've been so fortunate in my life, I've never had

277
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to take a mortgage, so I will never

278
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have a perfect credit score.

279
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So someone who purely lives off

280
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of their savings is extremely responsible.

281
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And I would say that, getting into the idea

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of what's missing, this is very good on credit,

283
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but credit is not all of our economic and financial lives.

284
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So the, the holy grail of research is really we,

285
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we would like, if we really wanna get a full picture

286
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of consumers' financial lives, we would want their credit,

287
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which this data source is very good at.

288
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But then we would also want their income

289
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and their assets, which we essentially

290
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have very little, almost zero.

291
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Um, we have some estimates of income,

292
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but this is not a labor economic data set.

293
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And so we would really not wanna use the measures of income

294
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for measuring someone's labor, mobility or wages.

295
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It could be used as an estimate

296
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of generally are they a six figure income person

297
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or a working class person,

298
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but we wouldn't really wanna use this data

299
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set to measure labor.

300
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And so you need to link those with more established

301
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data sets that that really measure people's wages

302
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and labor, that outcomes.

303
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Uh, so we are really missing huge, huge swaths

304
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of a person's financial life.

305
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Um, and through linking with other data sets, we are able

306
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to get closer to that holy grail.

307
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But I think, starting from the basis

308
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of consumer credit data, in order to use this data properly

309
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for research, you need to be aware of the limitations.

310
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Another thing we do want

311
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to promote in this research paper is

312
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that this is not perfect quality data.

313
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This is collected, from a hodgepodge.

314
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Essentially these three major credit reporting firms in the

315
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United States probably have contracts with dozens,

316
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if not hundreds of other firms

317
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that all focus on specific segments of the market.

318
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For example, they might contract with a company

319
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that essentially only goes to courts

320
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and collects court records including bankruptcies,

321
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tax liens and other things.

322
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And they might outsource to another firm to collect.

323
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Um, and some of these do have payroll data.

324
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So, there are some of these firms

325
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that have higher quality labor wage data,

326
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but it's kind of separate from the, credit side.

327
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But researchers may be able to get the linked version

328
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of both the credit and labor side.

329
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And as I mentioned, only a subset

330
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of these firms have small business data

331
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and we know that that is a very limited coverage compared

332
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with the consumer quality.

333
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So we do wanna emphasize some of these, missing pieces.

334
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And we don't wanna consider this kind of the end all be all

335
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of all data, but I think, over the 2010s

336
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and 2020s data quality has improved dramatically.

337
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So the reporting frequency in the past firms may have only

338
00:13:57.085 --> 00:13:58.485
reported spottily once a quarter,

339
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even longer than that to some extent.

340
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But as of 2024, most of these firms are,

341
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are reporting every single month.

342
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So we get up to date data.

343
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There have been some exceptions to that,

344
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such as during the pandemic.

345
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Uh, there's a little bit of a trade off between,

346
00:14:12.725 --> 00:14:15.565
things like consumer privacy versus the accuracy

347
00:14:15.565 --> 00:14:16.925
of the data consumer privacy as well

348
00:14:16.925 --> 00:14:17.965
as general consumer interest.

349
00:14:17.985 --> 00:14:19.885
So if we think back to 2020,

350
00:14:20.665 --> 00:14:22.485
the many people were becoming unemployed,

351
00:14:22.485 --> 00:14:25.485
really suffering a lot of hardships due to the pandemic.

352
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And so a lot of temporary rules were put in place such

353
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that you are delinquent on your student loan.

354
00:14:30.745 --> 00:14:33.485
It wouldn't necessarily get reported, wouldn't harm you that

355
00:14:33.485 --> 00:14:35.245
because this is essentially an act of God.

356
00:14:35.585 --> 00:14:37.365
Um, global pandemic is not

357
00:14:37.365 --> 00:14:39.845
that you personally were calling delinquent

358
00:14:39.845 --> 00:14:41.965
or not paying your bills on purpose.

359
00:14:42.545 --> 00:14:45.645
Uh, so we were became more lenient on credit reporting.

360
00:14:45.985 --> 00:14:47.925
Of course that affects the data that we see.

361
00:14:47.925 --> 00:14:49.325
So we know that data reported

362
00:14:49.325 --> 00:14:51.605
during the 2020s is not, was not not as accurate.

363
00:14:51.605 --> 00:14:53.925
It was not as up to date. And there were some specific

364
00:14:54.115 --> 00:14:56.645
instances in which mortgages, student loans

365
00:14:56.645 --> 00:14:59.365
and other things were put in a forbearance status so

366
00:14:59.365 --> 00:15:03.325
that everyone was marked as current, artificially so

367
00:15:03.325 --> 00:15:06.045
that we saw, whereas actual payment rates were going

368
00:15:06.045 --> 00:15:08.085
down for obvious reasons due

369
00:15:08.085 --> 00:15:11.485
to the distressed cause the delinquency rate was,

370
00:15:11.625 --> 00:15:12.765
was going down.

371
00:15:12.825 --> 00:15:15.645
So it made it seem like people's financial,

372
00:15:15.645 --> 00:15:17.165
health was getting better even though

373
00:15:17.165 --> 00:15:18.405
people are falling delinquent.

374
00:15:18.405 --> 00:15:20.165
And so with the exception of the pandemic,

375
00:15:20.395 --> 00:15:21.725
data quality has improved.

376
00:15:21.905 --> 00:15:24.765
But I would point out to two other examples of regulations

377
00:15:25.035 --> 00:15:28.005
that are meant to support data quality

378
00:15:28.185 --> 00:15:29.845
and also protect consumer privacy

379
00:15:30.025 --> 00:15:33.325
and protect consumers from things like having an incorrect

380
00:15:33.325 --> 00:15:35.805
credit report merged to your record.

381
00:15:35.915 --> 00:15:36.965
That can be devastating

382
00:15:36.965 --> 00:15:39.925
and could take years for somebody to correct a mistake

383
00:15:39.925 --> 00:15:42.765
that then you cannot get credit due to somebody else

384
00:15:42.765 --> 00:15:44.165
with the same name or something like that,

385
00:15:44.165 --> 00:15:47.245
which can be really, really challenging and devastating.

386
00:15:47.705 --> 00:15:50.725
But as a result in the 2010s, in the past five

387
00:15:50.725 --> 00:15:53.965
to 10 years or so, things like civil judgements,

388
00:15:53.975 --> 00:15:57.045
which are essentially being sued in a civil court

389
00:15:57.145 --> 00:15:59.685
by a creditor, for example, if you, fall

390
00:15:59.685 --> 00:16:01.645
behind on your credit card bill,

391
00:16:01.645 --> 00:16:03.325
that bank can eventually sue you.

392
00:16:03.545 --> 00:16:04.765
And there are records of this.

393
00:16:04.765 --> 00:16:06.965
This is a less commonly known phenomenon,

394
00:16:06.965 --> 00:16:09.085
but there's twice as many of them as bankruptcies.

395
00:16:09.105 --> 00:16:11.125
So, there were questions about,

396
00:16:11.185 --> 00:16:12.525
how accurate that data were.

397
00:16:12.585 --> 00:16:14.805
So essentially, today

398
00:16:15.315 --> 00:16:17.445
they're not really being reported in credit records.

399
00:16:17.545 --> 00:16:18.965
So they're still happening,

400
00:16:18.965 --> 00:16:20.325
people are still getting sued in court, but

401
00:16:20.325 --> 00:16:22.245
because there were questions about the quality of that data,

402
00:16:23.385 --> 00:16:25.885
the result was, due to regulatory

403
00:16:25.885 --> 00:16:26.925
and market equilibrium

404
00:16:26.925 --> 00:16:28.445
that now we're not reporting them at all

405
00:16:28.445 --> 00:16:30.685
because we don't know how accurate they are.

406
00:16:30.945 --> 00:16:33.485
But that means that as researchers we're not really able

407
00:16:33.485 --> 00:16:35.845
to observe them, we're not able to do research to say, well,

408
00:16:35.845 --> 00:16:38.405
what is the impact of a civil judgment on a consumer?

409
00:16:38.435 --> 00:16:42.645
It's probably not great to be sued by,

410
00:16:42.785 --> 00:16:45.965
by a creditor, but now we can't see what's happening to them

411
00:16:45.965 --> 00:16:47.125
because it's not showing up.

412
00:16:47.565 --> 00:16:49.805
A similar story with medical debt where there were concerns

413
00:16:49.805 --> 00:16:52.485
that, medical debt was not being,

414
00:16:52.625 --> 00:16:56.125
act was not really, an accurate predictor

415
00:16:56.125 --> 00:16:57.365
of your future credit worthiness and

416
00:16:57.365 --> 00:16:59.405
therefore it shouldn't really be part of your credit score.

417
00:16:59.705 --> 00:17:01.485
And there was a regulatory

418
00:17:01.705 --> 00:17:03.605
and industry debate on this such

419
00:17:03.605 --> 00:17:06.485
that medical debt is also now not being reported.

420
00:17:06.485 --> 00:17:07.525
Essentially very little

421
00:17:07.525 --> 00:17:09.725
of it is being reported in the data source.

422
00:17:09.865 --> 00:17:12.565
And then as can, as researchers,

423
00:17:12.625 --> 00:17:14.205
we cannot see what's going on.

424
00:17:14.205 --> 00:17:16.405
People are still having medical debt accumulated

425
00:17:16.405 --> 00:17:17.645
and still affecting their lives.

426
00:17:18.225 --> 00:17:20.485
Um, and so there can be a little bit of a trade off

427
00:17:20.485 --> 00:17:21.645
between consumer privacy

428
00:17:21.745 --> 00:17:25.205
and consumer welfare concerns with, just being able

429
00:17:25.205 --> 00:17:27.205
to observe, this information at all.

430
00:17:27.205 --> 00:17:28.525
And sometimes it just ends up that

431
00:17:28.525 --> 00:17:31.005
that information becomes excluded all together

432
00:17:31.425 --> 00:17:33.925
and we can think about is that good or bad?

433
00:17:33.925 --> 00:17:35.325
Would we rather have bad information

434
00:17:35.545 --> 00:17:37.485
or would we rather have no information?

435
00:17:37.755 --> 00:17:40.725
It's difficult, but I think we always found way, find ways

436
00:17:40.725 --> 00:17:42.605
around it and find other data sources.

437
00:17:43.025 --> 00:17:45.125
Uh, so I think we can be resourceful,

438
00:17:45.265 --> 00:17:47.685
but at the same time there are legitimate public policy

439
00:17:47.725 --> 00:17:50.445
concerns for, for the general public about this as well.

440
00:17:50.445 --> 00:17:51.445
So it's a tricky, it's a very tricky.

Understanding Consumer Credit Reporting Data

00:00:00 - Is a data source that is increasingly become very widely used in financing and economics and what it comes from. And, I think a research paper that accompanies this interview on consumer credit reporting data, we primarily focus on the United States, but I wanna note that similar data sets are available in many other countries, including developed countries such as the UK or Europe as well as developing markets. But my expertise and our research paper are gonna focus primarily on the us So I will, I will focus on that for now.

00:00:32 - by private firms in the US There's three major firms, Equifax, TransUnion and Experian. And they're collecting, information about our habits and, uses of credits such as taking out mortgages, credit cards, auto loans, and the such, such as any typical type of credit that we might think of, gathering that information together and then essentially selling it to lenders and other institutions that might want to use that data. In particular, thinking about if a new bank wants to lend

00:01:05 - or a mortgage, they want to see that data because they want to know whether you're a good credit risk or not. So essentially these firms collect this data, and then package it for essentially a huge fraction of the US population, and then resell it on to financial institutions and other companies that may be interested in using it for business purposes. Certainly the research community has researched, individuals, experiencing things such as the global financial crisis. And I would really trace back the origins of modern,

Consumer Credit Data and the Ongoing Impact of the Financial Crisis

00:01:38 - as a research field back to the use of consumer credit data. thinking about a seminal papers such as those by Ian and Sufi. The earliest papers, in 2009 really dissecting this crisis is something that hit the global economy. In some ways, I think we're still suffering the repercussions of it politically and economically. and, and relatively quick time. Their original paper was published in 2009 using credit reporting data because the heart of the global financial crisis, as we know,

00:02:08 - And we realized that we didn't know much about this sector. The household sector in the United States is about two thirds of the US economy. Of course, the US is key in the global economy and the global financial system and fragility in the housing sector, in the consumer sector was what led to the cascading problems globally. And, me and Sufi were some of the first researchers put, of course, if you wanna study the mortgage market, you need credit data. And so they used this exact data source to shed some

00:02:41 - And I would say that we are still studying the financial crisis of 2008 and 2009. UAN keys is another example looking at refinances and equity extraction even into the 2010. So the Buddha Keys paper was from 2016 and even the 2010s, I would say there's very important pathbreaking research. One example is, Ong and Noel from 2020 looking at mortgage restructuring back, during the financial crisis. So I think we are actually still studying, this phenomenon and still using credit data,

Advancements in Consumer Credit Data Research

00:03:13 - I would say that along with the development of consumer finance as a field, which again was a bit catalyzed by the financial crisis, I think that taking a step back a second, consumer credit data did pre-exist from the, financial crisis? And we have seen development of this data source in our research paper on consumer credit data this year does seek to advance like how we're using this data, help inform researchers about what are new ways to use this data that haven't really even been used before.

00:03:45 - by the New York Federal Reserve, back before the financial crisis in the early two thousands, they really helped to broaden all the, economists within the Federal Reserve system. Hundreds of economists being able to do great work. So some of our co-authors, Wilbert Vander Klau and Don Lee really pioneered the New York Fed data set, which was the first widely available credit data set in the two thousands. But this type of database all the way back to the 1990s and even before with, the Federal Reserve system, the FTC and other primarily US government agencies.

00:04:18 - of researchers, to not say kind of in finance. We sometimes have a short memory about economic history and even the history of our own field. But I think what we seek to do, me and my group of of co-authors, we seek to kind of push that advancement further, into the current era in, in the 2020s. And there's a lot of new developments. I would say. We are really only scratching the surface of our ability to use consumer credit data, and, to study a wide variety. And you think what we've seen in the past five

Linking Consumer Credit Data to Broader Economic Health Issues

00:04:48 - the obvious things such as the housing crisis and the financial crisis, but things not even that related to finance that you might think of such as health, healthcare, health insurance. I think one of the great research papers that I can imagine so important is linking, credit data with abortion records and looking at women who were either denied an abortion or we're allowed to have an abortion. How does that affect their general economic, of course health wellbeing as well as financial wellbeing?

00:05:19 - beyond its origins in studying the financial crisis. I think that, the origin of what is consumer credit data and the reason this industry existed, it is a kind of a private data set. these firms, it rose in the private market to serve a need. And that need was information asymmetry such that if you're a new bank and you are faced with the consumer, you've never seen that person before, it's very difficult to lend to that person. And we've seen an explosion the US credit markets,

00:05:49 - to the, to the crisis. But certainly the development of this industry, was exactly to address information Asymmetry issues. And I think one aspect I would point to that's very interesting is thinking about linking per personal credit where we have this very well developed consumer credit reporting systems, or essentially any form of consumer debt in the US and many other countries is reported. So if I'm a new lender, whether I'm a large bank or a new FinTech firm, if there there's a customer who comes

Challenges in Credit Reporting: Information Asymmetry and Small Business Lending

00:06:21 - and get very good visibility into their entire history, whether they're delinquent or not. So the current credit reporting system has solved a lot of the information is asymmetries, not that they're completely eliminated. so it's still the case that moral hazard, if I compare two people with the same credit score, there could be differences between them, but it's quite well developed. But in contrast to the small business le lending market, has very little information and we know that, the avail available information,

00:06:53 - or a small manufacturing firm, it's much more difficult to get information on that. And some of my work, which links the consumer credit data to small business credit data, what I find is that in fact, small business lenders are using people's consumer records, to lend to their businesses because of the fact that they don't know, they don't know how to, underwrite or they don't know how to evaluate the risk of those businesses because, essentially that industry to manage small business credit risk has not developed

00:07:25 - So there's actually borrowing a person's personal credit record in order to lend to their business. And there can be various issues with that, that you might imagine. My personal credit score may not dictate the quality of my new business that I, that I opened. But, and, and we know that minorities and women tend to have lower credit scores, so then that means there had gonna have a harder time starting up new businesses and that can impact the broader, macro economy and our business dynamism.

Advancing Methodologies in Consumer Credit Research

00:07:54 - This was really the core goal of our research paper, which is to shed light on ways of measuring and, and kind of advance best practices and hopefully generalize methodologies that we can all then share to advance the literature in that way. Because essentially what we've seen in the development of the literature is that, this consumer credit data became first became widespread in the two thousands and in the 2010s and 2020s, it kind of broadened out to many other economic fields, including researchers who their main expertise may be in public economics or labor

00:08:29 - but they're using this type of data. And I think what we're hoping in the 2020s is to consolidate that and say, okay, we've had this mass proliferation of research, now let's, kind of compare different methodologies. Let's, let's create a frontier best practices so then we can all advance starting from the same basis of these are the best practices for measurement. And there's a few things to remember in that. As I mentioned at the very beginning, these data were not created for researchers. This was, created for business purposes,

00:09:01 - what we might want from an economic measurement perspective. And there's very basic things such as even the definition of what is a credit card, they don't have, necessarily, not all of these data sets have an indicator for this is a credit card versus this is not. And a researcher has to do a lot of the legwork in creating these measures by themselves. So we hope to kind of, both for people who are not involved in credit data, but maybe reading these research papers, including the general public, they may not know

Limitations and Subjectivity in Credit Data Coverage

00:09:31 - of a credit card can be subjective decision. And that researchers, can differ across two different research groups, in, in terms of how they make those subjective decisions. One thing to remember is that even though it has very broad population coverage in the United States, for example, over 200 million us adults have credit records, that also means that some people are missing. For example, obviously anyone under 18 is essentially not gonna be covered by this dataset. So even though they're very interesting questions about

00:10:04 - and credit kind of access and mobility, we would not see the children until they turn 18. And of course, the age at which someone first gets a credit card varies by their parental income. And so we, even if we do see someone, we may see, a different selection of people who have credit by the time they're 18 versus do not enter the credit market until they're in their twenties. And so that is very important to take into account. And for example, this data is missing a lot

00:10:36 - including the most vulnerable populations. So people without social security numbers. some data sets explicitly sample off of those with social security numbers. So obviously people who are have limited, documentation in terms of their, their employment status, or visa status in the United States, they might be very important for policy purposes, they would not be very covered. Or the other side, I think one of the contradictions that we do get into in this research paper is

Rethinking Credit Worthiness: Beyond Traditional Metrics

00:11:07 - the course of the 20th century was that we measure your credit worthiness by how you've used credit in the past. But an inherent contradiction of that was that maybe someone is the most credit worthy if they've never used credit. If I've never needed to borrow, and this does apply to myself, I've never had to take a student loan. I've been so fortunate in my life, I've never had to take a mortgage, so I will never have a perfect credit score. So someone who purely lives off of their savings is extremely responsible. And I would say that, getting into the idea

00:11:38 - but credit is not all of our economic and financial lives. So the, the holy grail of research is really we, we would like, if we really wanna get a full picture of consumers' financial lives, we would want their credit, which this data source is very good at. But then we would also want their income and their assets, which we essentially have very little, almost zero. we have some estimates of income, but this is not a labor economic data set. And so we would really not wanna use the measures of income

00:12:11 - It could be used as an estimate of generally are they a six figure income person or a working class person, but we wouldn't really wanna use this data set to measure labor. And so you need to link those with more established data sets that that really measure people's wages and labor, that outcomes. so we are really missing huge, huge swaths of a person's financial life. and through linking with other data sets, we are able to get closer to that holy grail.

Limitations and Improvements in Consumer Credit Data

00:12:41 - of consumer credit data, in order to use this data properly for research, you need to be aware of the limitations. Another thing we do want to promote in this research paper is that this is not perfect quality data. This is collected, from a hodgepodge. Essentially these three major credit reporting firms in the United States probably have contracts with dozens, if not hundreds of other firms that all focus on specific segments of the market. For example, they might contract with a company that essentially only goes to courts

00:13:14 - tax liens and other things. And they might outsource to another firm to collect. and some of these do have payroll data. So, there are some of these firms that have higher quality labor wage data, but it's kind of separate from the, credit side. But researchers may be able to get the linked version of both the credit and labor side. And as I mentioned, only a subset of these firms have small business data and we know that that is a very limited coverage compared with the consumer quality. So we do wanna emphasize some of these, missing pieces.

00:13:47 - of all data, but I think, over the 2010s and 2020s data quality has improved dramatically. So the reporting frequency in the past firms may have only reported spottily once a quarter, even longer than that to some extent. But as of 2024, most of these firms are, are reporting every single month. So we get up to date data. There have been some exceptions to that, such as during the pandemic. there's a little bit of a trade off between, things like consumer privacy versus the accuracy

Credit Reporting During the Pandemic: Impact and Inaccuracies

00:14:16 - as general consumer interest. So if we think back to 2020, the many people were becoming unemployed, really suffering a lot of hardships due to the pandemic. And so a lot of temporary rules were put in place such that you are delinquent on your student loan. It wouldn't necessarily get reported, wouldn't harm you that because this is essentially an act of God. global pandemic is not that you personally were calling delinquent or not paying your bills on purpose. so we were became more lenient on credit reporting.

00:14:47 - So we know that data reported during the 2020s is not, was not not as accurate. It was not as up to date. And there were some specific instances in which mortgages, student loans and other things were put in a forbearance status so that everyone was marked as current, artificially so that we saw, whereas actual payment rates were going down for obvious reasons due to the distressed cause the delinquency rate was, was going down. So it made it seem like people's financial, health was getting better even though

00:15:18 - And so with the exception of the pandemic, data quality has improved. But I would point out to two other examples of regulations that are meant to support data quality and also protect consumer privacy and protect consumers from things like having an incorrect credit report merged to your record. That can be devastating and could take years for somebody to correct a mistake that then you cannot get credit due to somebody else with the same name or something like that, which can be really, really challenging and devastating.

Challenges in Reporting Civil and Medical Debt

00:15:50 - to 10 years or so, things like civil judgements, which are essentially being sued in a civil court by a creditor, for example, if you, fall behind on your credit card bill, that bank can eventually sue you. And there are records of this. This is a less commonly known phenomenon, but there's twice as many of them as bankruptcies. So, there were questions about, how accurate that data were. So essentially, today they're not really being reported in credit records. So they're still happening,

00:16:20 - because there were questions about the quality of that data, the result was, due to regulatory and market equilibrium that now we're not reporting them at all because we don't know how accurate they are. But that means that as researchers we're not really able to observe them, we're not able to do research to say, well, what is the impact of a civil judgment on a consumer? It's probably not great to be sued by, by a creditor, but now we can't see what's happening to them because it's not showing up. A similar story with medical debt where there were concerns

00:16:52 - act was not really, an accurate predictor of your future credit worthiness and therefore it shouldn't really be part of your credit score. And there was a regulatory and industry debate on this such that medical debt is also now not being reported. Essentially very little of it is being reported in the data source. And then as can, as researchers, we cannot see what's going on. People are still having medical debt accumulated and still affecting their lives. and so there can be a little bit of a trade off

Navigating Information Gaps: Balancing Data Access and Consumer Welfare

00:17:50 - and consumer welfare concerns with, just being able to observe, this information at all. And sometimes it just ends up that that information becomes excluded all together and we can think about is that good or bad? Would we rather have bad information or would we rather have no information? It's difficult, but I think we always found way, find ways around it and find other data sources. so I think we can be resourceful, but at the same time there are legitimate public policy concerns for, for the general public about this as well. So it's a tricky, it's a very tricky.

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