Replication Codes for: Credit Building or Credit Crumbling? A Credit Builder Loan’s Effects on Consumer Behavior and Market Efficiency in the United States (doi:10.7910/DVN/XXT0GR)

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Document Description
Citation
Title:	Replication Codes for: Credit Building or Credit Crumbling? A Credit Builder Loan’s Effects on Consumer Behavior and Market Efficiency in the United States
Identification Number:	doi:10.7910/DVN/XXT0GR
Distributor:	Harvard Dataverse
Date of Distribution:	2023-03-08
Version:	1
Bibliographic Citation:	Burke, Jeremy; Jamison, Julian; Karlan, Dean; Mihaly, Kata; Zinman, Jonathan, 2023, "Replication Codes for: Credit Building or Credit Crumbling? A Credit Builder Loan’s Effects on Consumer Behavior and Market Efficiency in the United States", https://doi.org/10.7910/DVN/XXT0GR, Harvard Dataverse, V1
Study Description
Citation
Title:	Replication Codes for: Credit Building or Credit Crumbling? A Credit Builder Loan’s Effects on Consumer Behavior and Market Efficiency in the United States
Identification Number:	doi:10.7910/DVN/XXT0GR
Authoring Entity:	Burke, Jeremy (Center for Economic and Social Research, University of Southern California, USA)
	Jamison, Julian (University of Exeter Business School, UK)
	Karlan, Dean (Kellogg School of Management, Northwestern University, USA)
	Mihaly, Kata (RAND Corporation, USA)
	Zinman, Jonathan (Dartmouth College, USA)
Distributor:	Harvard Dataverse
Access Authority:	Karlan, Dean
Depositor:	Ahamed, Hasan
Date of Deposit:	2023-03-07
Holdings Information:	https://doi.org/10.7910/DVN/XXT0GR
Study Scope
Keywords:	Social Sciences
Abstract:	A randomized encouragement design yields null average effects of a credit builder loan (CBL) on consumer credit scores. But machine learning algorithms indicate the nulls are due to stark, offsetting treatment effects depending on baseline installment credit activity. Delinquency on preexisting loan obligations drives the negative effects, suggesting that adding a CBL overextends some consumers and generates negative externalities on other lenders. More favorably for the market, CBL take-up generates positive selection on score improvements. Simple changes to CBL practice, particularly to provider screening and credit bureau reporting, could ameliorate the negative effects for consumers and the market.
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Citation
Bibliographic Citation:	Jeremy Burke, Julian Jamison, Dean Karlan, Kata Mihaly, Jonathan Zinman, Credit Building or Credit Crumbling? A Credit Builder Loan’s Effects on Consumer Behavior and Market Efficiency in the United States, The Review of Financial Studies, 2022;, hhac060, https://doi.org/10.1093/rfs/hhac060
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Label:	00 Run.do
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Label:	01a Clean Sample and Surveys.do
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Label:	01b Create Outcomes.do
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Label:	01c Create Causal Forest Input Dataset.do
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Label:	02 Balance (T1, AT3a, AT3b).do
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Label:	03 Transition Matrix of Having a Credit Score (T2, RT2).do
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Label:	04 Main CBL TE (T3).do
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Label:	05a Causal Forest (T4).do
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Label:	05b Causal Forest Tercile (T5, Fig3).do
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Label:	06a Heterogeneity Driven by Credit Behaviors (T6, AT6).do
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Label:	06b Heterogeneity Driven by Credit Behaviors (T6 col1b2b).do
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Label:	07 Usage of Other SLCCU Products (T7).do
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Label:	08a Selection Effects (T8).do
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Label:	08b Selection Effects by Treatment and Timing (T9).do
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Label:	09 Calculate Mean of CATEs by Treatment (T10, AT8).do
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Label:	10 Randomization N (Fig2).do
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Label:	11 External Validity (AT1).do
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Label:	12 Predict CBL Take-up Univariate (AT2).do
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Label:	13 Treatment Effects by Baseline Installment Borrowing (AT4, RLT1).do
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Label:	14 Causal Forest Tercile by Components (AT5a, AT5b).do
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Label:	15 Usage of other SLCCU products_winsorized outcomes (AT7).do
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Label:	16 Predictive Power (AT9).do
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Label:	17 ROC curves (AFig 1).do
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Label:	18 Response Letter 3_6.do
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Label:	Causal Forest Ficoscore numloans.R
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Label:	Causal Forest scoredf numloans.R
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