Language Frictions in Consumer Credit

Chao Liu

Kellogg School of Management

Northwestern University

FDIC Consumer Research Symposium

March 15, 2024
Introduction

This Paper

One fundamental yet often overlooked friction: language frictions

• Language barriers faced by borrowers with limited English profciency (LEP)

• Nearly one in ten working age adults in the US is LEP

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Introduction

This Paper

One fundamental yet often overlooked friction: language frictions

Question: How do language frictions afect household fnancial decisions?

• Do language frictions afect access to credit?

• How do language frictions afect the price of credit?

• Does reducing language frictions afect the quality of credit?

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Introduction

This Paper

One fundamental yet often overlooked friction: language frictions

Question: How do language frictions afect household fnancial decisions?

Setting: the U.S. mortgage market

• Mortgage balances accounted for 68% of total household debt in 2019 (FRBNY, 20)

• Hard to understand: disclosures (11th grade) vs. reading ability (8th grade)(GAO, 06)

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Introduction

This Paper

One fundamental yet often overlooked friction: language frictions

Question: How do language frictions afect household fnancial decisions?

Setting: the U.S. mortgage market

Data challenge: who are LEP borrowers?

• Survey data: National Survey of Mortgage Originations (NSMO)

• Apply machine learning to predict LEP status

=⇒ Document signifcant descriptive diferences

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Introduction

This Paper

One fundamental yet often overlooked friction: language frictions

Question: How do language frictions afect household fnancial decisions?

Setting: the U.S. mortgage market

Data challenge: who are LEP borrowers?

Identifcation challenge: isolate the role of language

• Natural experiment: phased rollout of translated mortgage documents by FHFA

• Triple-diference: LEP × Hispanic × Post

=⇒ Estimate the causal efect of language frictions

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Data

Data Sources

National Survey of Mortgage Originations (NSMO) 2013-19

• Demographic characteristics

• Perceptions and experiences in the mortgage market (survey response)

• Contract and performance variables (administrative sources)

• LEP status at the individual level

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Data

Assigning LEP Status in the Survey

About 10% are LEP borrowers

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Data

Data Sources

National Survey of Mortgage Originations (NSMO) 2013-19

• Demographic characteristics

• Perceptions and experiences in the mortgage market (survey response)

• Contract and performance variables (administrative sources)

• LEP status at the individual level

Home Mortgage Disclosure Act (HMDA) 2011-2019

• County-level outcomes: application denial rate, origination volume

American Community Survey (ACS) 2011-2019

• LEP share at the county level

• County-level characteristics: population, median income, racial composition

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Stylized Facts

Stylized Facts about LEP Borrowers

• Before application: know less about the mortgage market

≈ 60% of the diferences between borrowers with a college degree and those without

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Stylized Facts

Stylized Facts about LEP Borrowers

• Before application: know less about the mortgage market

≈ 60% of the diferences between borrowers with a college degree and those without

• During application: encounter more problems

5 pp more likely to redo mortgage paperwork

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Stylized Facts

Stylized Facts about LEP Borrowers

• Before application: know less about the mortgage market

≈ 60% of the diferences between borrowers with a college degree and those without

• During application: encounter more problems

5 pp more likely to redo mortgage paperwork

• After application: less familiar with their own mortgage contracts

≈ 2X more likely to be unsure if their own mortgage is an ARM

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Stylized Facts

Stylized Facts about LEP Borrowers

• Before application: know less about the mortgage market

≈ 60% of the diferences between borrowers with a college degree and those without

• During application: encounter more problems

5 pp more likely to redo mortgage paperwork

• After application: less familiar with their own mortgage contracts

≈ 2X more likely to be unsure if their own mortgage is an ARM

• Mortgage outcomes: higher interest rate, same delinquency rate

3 bps ≈ racial discrimination

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Causal Efect

Policy Shock: FHFA Language Access Plan

• Lenders used to face compliance risks (e.g., fair lending risks)

• FHFA provides an online centralized collection of translated mortgage documents

• Phased rollout: Spanish translations in 2018, Chinese translations in 2019

• Triple-diference: LEP × Hispanic × Post

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Causal Efect

Empirical Strategy: Triple-Diference

Dependent variable: 1(redo paperwork) H0: the decrease is smaller than 5 pp

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Causal Efect

Causal Efect of Language Frictions on the Intensive Margin

Efect on access to credit (intensive)?

• Encounter fewer problems: redo mortgage paperwork ↓ 14 pp

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Causal Efect

Efect on Mortgage Rate: Graphical Evidence

H0: pre- and post-policy average interest rates are the same

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Causal Efect

One Potential Mechanism of the Price Efect: Borrower Search

H0: pre- and post-policy distributions are the same

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Causal Efect

Causal Efect of Language Frictions on the Intensive Margin

Efect on access to credit (intensive)?

• Encounter fewer problems: redo mortgage paperwork ↓ 14 pp

Efect on the price of credit?

• Lower interest rates: ↓ 15 bps, save $22 per month and $1800 after 8 years

• One possible channel: borrower search ↑ 16 pp

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Causal Efect

Causal Efect of Language Frictions on the Intensive Margin

Efect on access to credit (intensive)?

• Encounter fewer problems: redo mortgage paperwork ↓ 14 pp

Efect on the price of credit?

• Lower interest rates: ↓ 15 bps, save $22 per month and $1800 after 8 years

• One possible channel: borrower search ↑ 16 pp

Efect on the quality of credit?

• Minimal efect on mortgage delinquency rate

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Causal Efect

Additional Results on the Intensive Margin

Data limitations of the survey data

• No lender or location information

• No up-front costs (e.g., discount points)

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Causal Efect

Additional Results on the Intensive Margin

Data limitations of the survey data

1. A new loan-level data: HMDA+

• Merge HMDA with Fannie Mae, Freddie Mac, and Ginnie Mae data

• Include borrower, lender, property, mortgage contract, mortgage performance information

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Causal Efect

Additional Results on the Intensive Margin

Data limitations of the survey data

1. A new loan-level data: HMDA+

2. Use machine learning to predict LEP status in HMDA+

• Solve a binary classifcation problem

• Training sample: micro-level American Community Survey

• 99% accuracy in the test sample

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Causal Efect

Additional Results on the Intensive Margin

Data limitations of the survey data

1. A new loan-level data: HMDA+

2. Use machine learning to predict LEP status in HMDA+

3. Run triple-diference regressions in HMDA+

• Misclassifcation brought by ML =⇒ Attenation bias

• Use ML performance to bound measurement error

• Recover the lower bound of the average treatment efect on the treated (ATT)

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Causal Efect

Additional Results on the Intensive Margin

Data limitations of the survey data

1. A new loan-level data: HMDA+

2. Use machine learning to predict LEP status in HMDA+

3. Run triple-diference regressions in HMDA+

Revisit the price efect

• Interest rate decreases by at least 5 bps

• Lower total borrowing costs (interest rate ↓ + discount points →)

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Causal Efect

LEP Consumers Excluded From the Mortgage Market?

Estimate the efect on credit access on the extensive margin

• Data: county-level HMDA

• Regression: diference-in-diferences

Yct = α + βDct + γXct + δc + δst + εct

▶ c, s, t: county c, state s, year t

▶





0, if t ≤ 2017

Dct = Hispanic LEP sharec , if t = 2018

 Hispanic LEP sharec + Chinese LEP sharec , if t = 2019

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Causal Efect

Causal Efect of Language Frictions on the Extensive Margin

# Applications Share of # Originations Dependent variable Denial rate (10K) incomplete app. (10K)

(1) (2) (3) (4)

LEP share × Post 0.121** -0.062*** -0.155*** 0.089**

(0.060) (0.022) (0.041) (0.044)

Sample mean 0.090 0.117 0.175 0.067

Observations 25,225 25,225 25,225 25,225

County FEs ✓ ✓ ✓ ✓

Year × State FEs ✓ ✓ ✓ ✓

Additional controls ✓ ✓ ✓ ✓

Application incomplete and denial rate ↓ by 6 pp and 16 pp

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Causal Efect

Causal Efect of Language Frictions on the Extensive Margin

# Applications Share of # Originations Dependent variable Denial rate (10K) incomplete app. (10K)

(1) (2) (3) (4)

LEP share × Post 0.121** -0.062*** -0.155*** 0.089**

(0.060) (0.022) (0.041) (0.044)

Sample mean 0.090 0.117 0.175 0.067

Observations 25,225 25,225 25,225 25,225

County FEs ✓ ✓ ✓ ✓

Year × State FEs ✓ ✓ ✓ ✓

Additional controls ✓ ✓ ✓ ✓

4 pp ↑ in the local share of LEP people =⇒ + 48 applications and 36 originations

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Causal Efect

Causal Efect of Language Frictions on the Extensive Margin

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Conclusion

Conclusion

Main takeaway: Reducing language frictions can lead to

• increased availability of credit

• a streamlined application process

• lower borrower costs

• no deterioration of credit quality

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Conclusion

Conclusion

Main takeaway: Reducing language frictions can lead to

• increased availability of credit

• a streamlined application process

• lower borrower costs

• no deterioration of credit quality

Policy implications

• Reduce compliance risks for fnancial institutions

• A cost-efective policy

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