Estimation of Net Interest Margin Determinants of the Deposit Banks in Turkey...inventionjournals
Banks, which are the irreplaceable intermediaries of the financial system, are financial institutions that significantly contributeto economic development. The basiccriterion that indicates the efficiency of the intermediation activities of banks is the net interest margins. These costs are assumed to be high for developing countries such as Turkey. The degree to which banks are willing to redeem the funds they collect as credit to the system is directly related to how low their intermediation costs will be. In this paper, it is aimed to estimate the net interest margin determinants of deposit banks in Turkey. Three different panel data models are used for this purpose. These are the Fixed and Random Static models and the GMM (Generalized Moment Models) Dynamic model
Analysis of Deposits on Banks Listed in the Indonesian Stock Exchangesinventionjournals
ABSTRACT :This research objective is to examine empirical an analyzeeffects of interest rate on deposit, inflation, grossdomesticproduct, unemployment, and banks branches on amount of depositscollected by 31 bankswereexamined. This researchisaverificationresearch by testinghypothesisthrough a quantitative approach. Analytical unit uses includesbankslisted in the Indonesian Stock Exchange (IDX). This research uses primary data and secondary data and thenitisprocessed by the RandomEffect Model (REM) to amount of deposits model. In verificationstudies show that : interest rate on deposit and unemployment have a negative impact, meanwhilegrossdomesticproduct and bank branches have a positive impact on deposits. The findings of thisresearch are interest rates on depositis not indicative of the save the funds in the bank, sobanks have more funds and Banks Branches thatspread to the countryside as a major factor in raising public funds
In the business of money, there can be no errors. That goes doubly so for keeping your customers. With PNA's finance data analytics, discover the hidden patterns that customers give you, and learn the language needed to retain them.
A potential objective of every financial organization is to retain existing customers and attain new
prospective customers for long-term. The economic behaviour of customer and the nature of the
organization are controlled by a prescribed form called Know Your Customer (KYC) in manual banking.
Depositor customers in some sectors (business of Jewellery/Gold, Arms, Money exchanger etc) are with
high risk; whereas in some sectors (Transport Operators, Auto-delear, religious) are with medium risk;
and in remaining sectors (Retail, Corporate, Service, Farmer etc) belongs to low risk. Presently, credit risk
for counterparty can be broadly categorized under quantitative and qualitative factors. Although there are
many existing systems on customer retention as well as customer attrition systems in bank, these rigorous
methods suffers clear and defined approach to disburse loan in business sector. In the paper, we have used
records of business customers of a retail commercial bank in the city including rural and urban area of
(Tangail city) Bangladesh to analyse the major transactional determinants of customers and predicting of a
model for prospective sectors in retail bank. To achieve this, data mining approach is adopted for
analysing the challenging issues, where pruned decision tree classification technique has been used to
develop the model and finally tested its performance with Weka result. Moreover, this paper attempts to
build up a model to predict prospective business sectors in retail banking.
Estimation of Net Interest Margin Determinants of the Deposit Banks in Turkey...inventionjournals
Banks, which are the irreplaceable intermediaries of the financial system, are financial institutions that significantly contributeto economic development. The basiccriterion that indicates the efficiency of the intermediation activities of banks is the net interest margins. These costs are assumed to be high for developing countries such as Turkey. The degree to which banks are willing to redeem the funds they collect as credit to the system is directly related to how low their intermediation costs will be. In this paper, it is aimed to estimate the net interest margin determinants of deposit banks in Turkey. Three different panel data models are used for this purpose. These are the Fixed and Random Static models and the GMM (Generalized Moment Models) Dynamic model
Analysis of Deposits on Banks Listed in the Indonesian Stock Exchangesinventionjournals
ABSTRACT :This research objective is to examine empirical an analyzeeffects of interest rate on deposit, inflation, grossdomesticproduct, unemployment, and banks branches on amount of depositscollected by 31 bankswereexamined. This researchisaverificationresearch by testinghypothesisthrough a quantitative approach. Analytical unit uses includesbankslisted in the Indonesian Stock Exchange (IDX). This research uses primary data and secondary data and thenitisprocessed by the RandomEffect Model (REM) to amount of deposits model. In verificationstudies show that : interest rate on deposit and unemployment have a negative impact, meanwhilegrossdomesticproduct and bank branches have a positive impact on deposits. The findings of thisresearch are interest rates on depositis not indicative of the save the funds in the bank, sobanks have more funds and Banks Branches thatspread to the countryside as a major factor in raising public funds
In the business of money, there can be no errors. That goes doubly so for keeping your customers. With PNA's finance data analytics, discover the hidden patterns that customers give you, and learn the language needed to retain them.
A potential objective of every financial organization is to retain existing customers and attain new
prospective customers for long-term. The economic behaviour of customer and the nature of the
organization are controlled by a prescribed form called Know Your Customer (KYC) in manual banking.
Depositor customers in some sectors (business of Jewellery/Gold, Arms, Money exchanger etc) are with
high risk; whereas in some sectors (Transport Operators, Auto-delear, religious) are with medium risk;
and in remaining sectors (Retail, Corporate, Service, Farmer etc) belongs to low risk. Presently, credit risk
for counterparty can be broadly categorized under quantitative and qualitative factors. Although there are
many existing systems on customer retention as well as customer attrition systems in bank, these rigorous
methods suffers clear and defined approach to disburse loan in business sector. In the paper, we have used
records of business customers of a retail commercial bank in the city including rural and urban area of
(Tangail city) Bangladesh to analyse the major transactional determinants of customers and predicting of a
model for prospective sectors in retail bank. To achieve this, data mining approach is adopted for
analysing the challenging issues, where pruned decision tree classification technique has been used to
develop the model and finally tested its performance with Weka result. Moreover, this paper attempts to
build up a model to predict prospective business sectors in retail banking.
AI powered Decision Making in Banks - How Banks today are using Advanced analytics in credit Decisioning, enhancing customer life time value, lower operating costs and stronger customer acquisition
The banking industry is data-demanding with acknowledged ATM and credit processing data. As banks face increasing pressure to stay successful, understanding customer needs and preferences becomes a critical success factor. Along with Data mining and advanced analytics techniques, banks are furnished to manage market uncertainty, minimize fraud, and control exposure risk.
IMPACT ON INDIAN BANKS’ PROFITABILITY INDICATORS – AN EMPIRICAL STUDYIAEME Publication
The Indian banking system consists of 26 public sector banks, 20 private sector banks, 43 foreign banks, 56 regional rural banks, 1,589 urban cooperative banks and 93,550 rural cooperative banks, in addition to cooperative credit institutions. The Indian banking sector’s assets reached US$ 1.8 trillion in FY14 from US$ 1.3 trillion in FY10, with 70 per cent of it being accounted by the public sector. Indian banks are increasingly focusing on adopting integrated approach to risk management. Banks have already embraced the international banking supervision accord of Basel II. According to RBI, majority of the banks already meet capital requirements of Basel III, which has a deadline of March 31, 2019. Most of the banks have put in place the framework for asset-liability match, credit and derivatives risk management.
Consumer Credit Scoring Using Logistic Regression and Random ForestHirak Sen Roy
Project Details: In this study, the concept and application of credit scoring in a German banking environment is
explained. A credit scoring model has been developed using logistic regression and random forest. Limitations of
the model are explained and possible solutions are given with an overview of LASSO.
Guide: Dr. Sibnarayan Guria, Associate Professor and Head of the Department, Department of
Statistics, West Bengal State University
Language Used: R
Historical Credit Data | Total Credit Card SpendExperian
Find out how much your customers are spending on their credit cards with trended data. Understanding consumers’ past behavior is crucial to understanding their credit preferences, their potential risks and for developing a strategy for the future. Trended Solutions: Give your customers credit for their history.
With flickery markets, edgy economy, organizational change and the evolving regulatory landscape, the finance divisions are caught up in a fast increase in the amount of public support and changes. All this while, the need for cost cutting and delivering transparent reports stays stable. Rolta’s Financial Analytics solution CFO Impact helps you bring cost effective and sustainable transformations to financial processes and systems with the help of big data analytic technologies.
https://ijitce.com/index.php
Our journal maintains rigorous peer review standards. Each submitted article undergoes a thorough evaluation by experts in the respective field. This stringent review process helps ensure that only high-quality and scientifically sound research is accepted for publication. Researchers can trust that the articles they find in IJITCE have been critically assessed for validity, significance, and originality.
A Survey on Bigdata Analytics using in Banking Sectorsijtsrd
Current days, banking industry is generating large amount of data. Already, most banks have failed to utilize this data. However, nowadays, banks have starts using this data to reach their main objectives of marketing. By using this data, many secrets can be discovering like money movements, thefts, failure. This paper aims to find out how big data analytics can be used in banking sector to find out spending patterns of customer, sentiment and feedback analysis etc. Big data analytics can aid banks in understanding customer behavior based on the inputs receive from their investment patterns, shopping trends, motivation to invest and personal or financial backgrounds. This data plays a necessary role in leading customer loyalty by designing personalized banking solutions for them. Gagana H. S | Roja H. N | Gouthami H. S "A Survey on Bigdata Analytics using in Banking Sectors" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31016.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31016/a-survey-on-bigdata-analytics-using-in-banking-sectors/gagana-h-s
AI powered Decision Making in Banks - How Banks today are using Advanced analytics in credit Decisioning, enhancing customer life time value, lower operating costs and stronger customer acquisition
The banking industry is data-demanding with acknowledged ATM and credit processing data. As banks face increasing pressure to stay successful, understanding customer needs and preferences becomes a critical success factor. Along with Data mining and advanced analytics techniques, banks are furnished to manage market uncertainty, minimize fraud, and control exposure risk.
IMPACT ON INDIAN BANKS’ PROFITABILITY INDICATORS – AN EMPIRICAL STUDYIAEME Publication
The Indian banking system consists of 26 public sector banks, 20 private sector banks, 43 foreign banks, 56 regional rural banks, 1,589 urban cooperative banks and 93,550 rural cooperative banks, in addition to cooperative credit institutions. The Indian banking sector’s assets reached US$ 1.8 trillion in FY14 from US$ 1.3 trillion in FY10, with 70 per cent of it being accounted by the public sector. Indian banks are increasingly focusing on adopting integrated approach to risk management. Banks have already embraced the international banking supervision accord of Basel II. According to RBI, majority of the banks already meet capital requirements of Basel III, which has a deadline of March 31, 2019. Most of the banks have put in place the framework for asset-liability match, credit and derivatives risk management.
Consumer Credit Scoring Using Logistic Regression and Random ForestHirak Sen Roy
Project Details: In this study, the concept and application of credit scoring in a German banking environment is
explained. A credit scoring model has been developed using logistic regression and random forest. Limitations of
the model are explained and possible solutions are given with an overview of LASSO.
Guide: Dr. Sibnarayan Guria, Associate Professor and Head of the Department, Department of
Statistics, West Bengal State University
Language Used: R
Historical Credit Data | Total Credit Card SpendExperian
Find out how much your customers are spending on their credit cards with trended data. Understanding consumers’ past behavior is crucial to understanding their credit preferences, their potential risks and for developing a strategy for the future. Trended Solutions: Give your customers credit for their history.
With flickery markets, edgy economy, organizational change and the evolving regulatory landscape, the finance divisions are caught up in a fast increase in the amount of public support and changes. All this while, the need for cost cutting and delivering transparent reports stays stable. Rolta’s Financial Analytics solution CFO Impact helps you bring cost effective and sustainable transformations to financial processes and systems with the help of big data analytic technologies.
https://ijitce.com/index.php
Our journal maintains rigorous peer review standards. Each submitted article undergoes a thorough evaluation by experts in the respective field. This stringent review process helps ensure that only high-quality and scientifically sound research is accepted for publication. Researchers can trust that the articles they find in IJITCE have been critically assessed for validity, significance, and originality.
A Survey on Bigdata Analytics using in Banking Sectorsijtsrd
Current days, banking industry is generating large amount of data. Already, most banks have failed to utilize this data. However, nowadays, banks have starts using this data to reach their main objectives of marketing. By using this data, many secrets can be discovering like money movements, thefts, failure. This paper aims to find out how big data analytics can be used in banking sector to find out spending patterns of customer, sentiment and feedback analysis etc. Big data analytics can aid banks in understanding customer behavior based on the inputs receive from their investment patterns, shopping trends, motivation to invest and personal or financial backgrounds. This data plays a necessary role in leading customer loyalty by designing personalized banking solutions for them. Gagana H. S | Roja H. N | Gouthami H. S "A Survey on Bigdata Analytics using in Banking Sectors" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31016.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31016/a-survey-on-bigdata-analytics-using-in-banking-sectors/gagana-h-s
Income and consumption changes did not move in tandem; there was only a slightly positive correlation between changes in income and changes in consumption between 2013 and 2014.
Our global data enables markets to be precisely sized and opportunities to be accurately gauged. We help our clients understand the consumer’s perspective, which we believe is critical to developing a successful product strategy in payments. Our team of consumer payments experts produces insight that provides answers to the questions you don’t know to ask yet.
Presentation on Social Collateral
Paper by Ha Diep-Nguyen and Huong Dang
Presented by Michael-Paul James
Paper uses an experimental design to test the impact of social image on repayment behavior
Predicting Credit Card Defaults using Machine Learning AlgorithmsSagar Tupkar
This is a project that I worked on as a Capstone for my Masters in Business Analytics program at the University of Cincinnati. In this project, I have performed an end-to-end data mining exercise including data cleaning, distribution analysis, exploratory data analysis, model building etc. to identify and predict Credit Card defaults using Customer's data on past payments and general profile. In the process for building Machine Learning models, I have fit and compared the performance of multiple models and algorithms like Logistic Regreesion, PCA, Classification tree, AdaBoost Classifier, ANN and LDA.
- Conduct a job analysis to determine critical behaviors for success in CRM roles (e.g., customer service representatives, sales representatives, account managers).
- Gather input from managers, employees, and customers to identify essential behaviors.
- Align behaviors with company values and CRM goals.
2. Define Performance Levels:
- Establish clear and measurable performance levels for each behavior (e.g., unsatisfactory, needs improvement, meets expectations, exceeds expectations).
- Use specific examples to illustrate each level.
3. Create the Scorecard:
- Develop a visual representation of the scorecard, listing behaviors and performance levels.
- Use a simple and easy-to-understand format.
Supervised and unsupervised data mining approaches in loan default prediction IJECEIAES
Given the paramount importance of data mining in organizations and the possible contribution of a data-driven customer classification recommender systems for loan-extending financial institutions, the study applied supervised and supervised data mining approaches to derive the best classifier of loan default. A total of 900 instances with determined attributes and class labels were used for the training and cross-validation processes while prediction used 100 new instances without class labels. In the training phase, J48 with confidence factor of 50% attained the highest classification accuracy (76.85%), k-nearest neighbors (k-NN) 3 the highest (78.38%) in IBk variants, naïve Bayes has a classification accuracy of 76.65%, and logistic has 77.31% classification accuracy. k-NN 3 and logistic have the highest classification accuracy, F-measures, and kappa statistics. Implementation of these algorithms to the test set yielded 48 non-defaulters and 52 defaulters for k-NN 3 while 44 non-defaulters and 56 defaulters under logistic. Implications were discussed in the paper.
The United States Turns Inward: Thoughts on US Trade Policy and US-Asian Trade Relations by Keith Maskus
http://iems.ust.hk/events/insights/maskus-united-states-turns-inward-thoughts-on-us-trade-policy-and-us-asian-trade-relations
Targeting of Local Government Programs and Voting Patterns in West BengalHKUST IEMS
Targeting of Local Government Programs and Voting Patterns in West Bengal, India by Dilip Mookherjee (Boston University)
More on http://iems.ust.hk/voting
State Absenteeism in India's Reverse Migration? A Comparison with the Chinese...HKUST IEMS
A Didar Singh (Indian Administrative Service - IAS) and David Zweig (HKUST) - State Absenteeism in India's Reverse Migration? A Comparison with the Chinese Experience.
Does the flow of financing respond to changes in productive opportunities even for the world's poor? We answer this question by examining the response of private bank financing to a shock to the rural road network in India, which brought road access to hitherto unconnected villagers.
http://iems.ust.hk/roads
China has achieved remarkable poverty reduction since the reform began in late 1970s. More than 800 million people living under US$1.9 a day has been lifted out of poverty and China’s contribution to reducing the rate of global poverty exceeded 70 percent. However, with the slowdown of economic growth and increase of income inequality, China needs to reform its targeted poverty reduction strategies to enable the poor benefit more from poverty reduction interventions. In November 2013, President Xi Jinping proposed the strategy of “precision poverty alleviation” during his visit to western Hunan, and the strategy has become a significant part of China’s fight against poverty with the objective to end extreme poverty by 2020 in China. This presentation will summarize the main policies and practices implemented under this strategy in recent years. Progress and challenges will also be discussed to give the audience a better understanding of China’s efforts in helping the poor.
China Employer-Employee Survey Report (June 2017) - English VersionHKUST IEMS
The “China Employer-Employee Survey”, jointly initiated by researchers from Hong Kong University of Science and Technology, Stanford University, Wuhan University, and the Chinese Academy of Social Sciences, is one of the most comprehensive surveys of its type in China. It surveyed more than 1200 companies and 11300 employees in the Guangdong and Hubei provinces in 2015 and 2016, in order to study how Chinese firms are coping with business challenges, and the implications for Chinese workers. Find out more about the survey at http://iems.ust.hk/cees
The “China Employer-Employee Survey”, jointly initiated by researchers from Hong Kong University of Science and Technology, Stanford University, Wuhan University, and the Chinese Academy of Social Sciences, is one of the most comprehensive surveys of its type in China. It surveyed more than 1200 companies and 11300 employees in the Guangdong and Hubei provinces in 2015 and 2016, in order to study how Chinese firms are coping with business challenges, and the implications for Chinese workers. Find out more about the survey at http://iems.ust.hk/cees
Richard Freeman: Work and Income in the Age of AI RobotsHKUST IEMS
This talk is a part of the HKUST IEMS & IPP – EY Hong Kong Emerging Market Insights Series. It is presented by HKUST IEMS with support by Institute for Public Policy and EY.
Will the next AlphaGo beat you at your job?
Will artificial intelligence overwhelm companies that rely on human decision-makers?
Or is the concern over robots and automation largely media hype?
This talk will offer evidence-driven insights about the on-going and likely future effects of the “robo-lution” on the global economy.
Find out more at Iems.ust.hk/insights
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Digital Artefact 1 - Tiny Home Environmental Design
Financial Inclusion and Contract Terms
1. MITIGATING THE RISKS OF FINANCIAL INCLUSION
EXPERIMENTAL EVIDENCE FROM MEXICO
Sara G. Castellanos1 Diego Jiménez Hernández2 Aprajit Mahajan3 Enrique Seira4
1
Banco de México
2
Stanford University
3
University of California, Berkeley
4
Instituto Tecnológico Autónomo de México
Oct 5, 2018
HKUST SEMINAR
2. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Motivation
− Increased interest in expanding access to financial services.
− Considerable work on innovative approaches to inclusion – e.g. Micro-Finance.
− Much less known about inclusion by large financial institutions whose scale
potentially important for expanding financial access:
◦ In 2009 Mexico had 2.3M total MF clients.
◦ Study financial product by large Mexican bank with 1.3M clients at the time.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 1/34
3. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Today
− Use large Mexican bank’s experience to describe challenges of financial inclusion.
− Examine formal sector credit for borrowers with limited credit history.
◦ Combine observational & experimental data in Mexican credit card market.
− Large scale RCT on population identified as marginal borrowers by the bank.
◦ Results representative of large population (> 1M) of borrowers by construction.
◦ Discuss relevance for broader population.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 2/34
4. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Research Questions and Preview of Findings
(1) New to Formal Credit Borrowers (NTB) are credit constrained (3X relative to U.S.).
(2) How much risk do NTB represent?
◦ High turnover: 1/3 default or cancel over study period.
◦ Large revenue variation.
◦ Default, revenue difficult to predict.
(3) Can changes in interest rates and minimum payments reduce default?
◦ Reducing interest rates or increasing minimum payments (experimentally)
• Have small impacts on default ( R = +0.20 MP = +0.02)
• But decrease bank revenues significantly.
◦ Substantively small effects on default.
◦ Coda: Bank discontinued card; moved away from NTB borrowers.
(4) What explains high default? (in progress)
◦ Large negative shocks.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 3/34
5. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Presentation Outline
1 Motivation
2 Related Literature
3 Data
4 Market Facts
5 Experiment
6 Explaining Default
7 Conclusions
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 4/34
6. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Presentation Outline
1 Motivation
2 Related Literature
3 Data
4 Market Facts
5 Experiment
6 Explaining Default
7 Conclusions
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 5/34
7. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Related Literature
− Asymmetric Information and Credit Constraints:
Gross and Souleles (2002), Karlan and Zinman (2009),Adams et al (2009), Einav et al (2012),
Dehejia et al (2012); Attanasio et al (2008), Karlan and Zinman (2016).
− Sub-optimal Contract Choice and Consumer Protection:
Bar-Gill (2004), Ausubel (1999), Durkin (2000); Kőszegi & Heidhues (2010), Meier and Sprenger
(2010); Laibson (2006b); Melzer (2011), Bertrand and Morse (2011) Agarwal et al (2015).
− Financial Inclusion:
Demirguc-Kunt et al (2012); Dabla-Norris et al (2015); Dupas et al (2018).
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 6/34
8. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Presentation Outline
1 Motivation
2 Related Literature
3 Data
4 Market Facts
5 Experiment
6 Explaining Default
7 Conclusions
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 7/34
9. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Datasets
(1) Bank Data (Experimental Sample):
◦ Monthly card level data from 03/07 to 05/09.
• Basic demographics, stratum indicators.
• Credit limit, debt, purchases, payments, fees, card status.
(2) Credit Bureau Data:
◦ Loan level data matched to experimental sample annually from 06/07 to 06/09.
◦ Loan level data for 06/10 representative of the entire credit bureau population.
• Credit limit, payment history, opening and closing dates for loans, amount due, amount in
arrears, closure reasons, credit score and demographics.
(3) Social security Data:
◦ Individual-level, monthly information (from 10/10 to 05/15).
◦ IMSS-reported monthly wage, employment status in the formal sector.
◦ Limited matching (∼ 18%) with experimental data.
(4) Survey Data: ENIGH, MxFLS
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 8/34
10. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Experimental Sample: Product
− Product: Store credit card offered by large Mexican bank (“Bank A”) for borrowers
with limited credit history.
◦ Started in 2002 – 1.3 million clients nationwide by 2009.
◦ Accounts for 14% of all first-time formal sector loan products in 2010.
Type of first loan (Credit bureau 2010)
0.25.5.75
Fractionofindividuals
Credit cardPersonal loan Credit line Real estate Auto Other
Experimental type of cards
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 9/34
11. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Experimental Sample: Description
− Sample frame: Cardholders who had paid at least minimum payment for past 6
months up to 01/07. (∼ 1M)
◦ Stratified random sample of 162,000 cardholders.
◦ First Credit Card (57%); First formal sector credit product (47% ).
− Strata: Partitioned borrowers into 9 strata.
◦ Card Tenure (01/07)∈ { 6-11 months, 12-23 months, 24+ months}.
◦ Repayment History (01/07)∈ {“minimum-payers”, “medium payers”, “full-payers”}.
◦ Sample 18,000 borrowers from each stratum.
◦ Use stratum weights to make population statements.
− Experiment: Varied interest rates and minimum payments for 144,000
cardholders.
◦ Treatments: Full-block design of 4 interest rate levels and 2 minimum payment levels.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 10/34
12. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Summary Statistics
Panel A. Experimental Sample
Payments in pesos (March 2007) 711 (1,473)
Purchases in pesos (March 2007) 338 (1,023)
Debt in pesos (March 2007) 1,198 (3,521)
Credit limit in pesos (March 2007) 7,879 (6,117)
(%) Cardholders for whom experiment card is first card 57
(%) Cardholders who default between Mar/07 - May/09 17
Panel B. Matched Credit bureau Data
Credit score (June 2007) 645 (52)
Amount in arrears given that it is positive (June 2007) 9,738 (49,604)
(%) Cardholders with any arrears (June 2007) 22
Panel C. Demographics
(%) Male 52
(%) Married 62
(%) Cardholders matched in SS data 18
Age (March 2007) 39 (6)
Monthly income in pesos (10/11)a
13,855 (11,244)
Observations 162,000
a Income only available for formal sector workers (∼ 18%).
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 11/34
13. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Presentation Outline
1 Motivation
2 Related Literature
3 Data
4 Market Facts
5 Experiment
6 Explaining Default
7 Conclusions
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 12/34
14. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Market Facts: NTB Borrowers
(1) NTB borrowers are Credit Constrained.
◦ Use Gross and Souleles, (2002) methodology: Sample credit constrained (3X U.S.).
(2) High and Unpredictable Exit Rates:
◦ 1/3 of sample exits (defaults or cancellations) during 26 month study.
◦ Default hard to predict
• Use ML tools.
• Observables at application.
• Observables in the beginning of experiment (March 2007).
(3) Variable and Unpredictable Bank Revenues:
◦ Construct revenue measure per card, and show that it is hard to predict using:
• Use ML tools.
• Observables at application.
• Observables in the beginning of experiment (March 2007).
(4) Client “poaching” consistent with first lender externality (Stiglitz, 1993).
◦ NTB borrowers ex post revealed as good clients are “poached”.
◦ Rough calculation: first lender loses around twice the mean revenue per switcher.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 13/34
15. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
NTB Borrowers Are Credit Constrained
− Use Gross and Souleles, (2002) to document credit constraints. Card i in month t:
∆Debti,t = δt +
12
j=0
βj∆Limiti,t−j + γ Xi,t + i,t
− Object of Interest in θ ≡
12
j=0
βj.
− Weaknesses:
◦ Only observe formal sector debt.
◦ {∆Limiti,t−j}j likely endogenous.
• Banks use “timing rules” to evaluate accounts for credit limit (time since last revision).
• Need to assume: Time since last revision affects debt only through change in credit limits.
• =⇒ Instrument for {∆Limiti,t−j }j using months since last change (dummies).
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 14/34
16. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
NTB borrowers are credit constrained
6-11 months 24+ months
All Minimum Full
(1) (2) (10)
Panel A. Bank’s debt and limit
ˆθOLS 0.32*** 0.69*** 0.03**
(0.04) (0.06) (0.01)
ˆθIV 0.73*** 2.14*** -0.08
(0.14) (0.32) (0.14)
Observations 1366035 118687 186338
Mean dependent variable 70 184 23
(2292) (3631) (1272)
Amount Due/Credit Limit 0.52 0.72 0.3
(2.96) (0.34) (2.82)
Median utilization 0.5 0.81 0.2
Notes: Errors are clustered at the individual level. Each cell represents a different regression. Column (1) estimates incorporate stratum weights. All
regressions include time fixed effects and the total number of credit line increases and decreases. The first row shows the “baseline” estimates; the second
row shows the instrumental variable estimates. *: p < .05; **: p < .01; ***: p < .001 respectively.
− ˆθUS ≈ 0.13
− Large variation across strata.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 15/34
17. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Significant Card Exit
Cumulative Exits: Control group
0.1.2.3.4.5
Proportionofaccounts
Jan/07 Jul/07 Jan/08 Jul/08 Jan/09 Jul/09
Fecha
Client cancelled Revoked by bank Other
Note: Other includes lost cards and cardholder deaths
− 19% of control group defaulted over experiment.
− Another 16% cancelled card.
− Similar rates for similar populations in other data.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 16/34
18. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Measuring Bank Revenues
− Using monthly purchases, payments, debt to construct bank revenue measure.
− Define revenue for card i:
Revi ≡ PV(Pay - Buy)i − Debt03/07,i + αiPV(Debt05/09,i) (1)
− Strong assumptions on borrower behavior outside the experiment window.
◦ Subtract March 2007 debt.
◦ Assume fraction of May 2009 debt is repaid (adjusting for card exit).
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 17/34
19. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Bank Revenues: High Variability
− Non-monotonic relationship between credit scores and revenue proxy.
0.02.04.06.08.1
Fractionofcardholders
-20 -10 0 10 20
NPV of Revenue (MXN thousand pesos)
25thpercentile
50thpercentile
75thpercentile
1234
NPVofRevenue(MXNthousandpesos)
525 575 625 675 725
Credit Score in June 07
95% CI lpoly smooth
kernel = epanechnikov, degree = 0, bandwidth = 6.99, pwidth = 10.48
Revenue by strata Credit score distribution
− Average (median) monthly revenue per card (over study): 168 (144) pesos (s.d. 282 pesos).
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 18/34
20. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Revenue and Default are Difficult to Predict
Table 1: Predicting Revenues and Default
Revenue Bank Revocations
Benchmark Linear Regression Random Forest Benchmark Random Forest
(1) (2) (3) (4) (6)
Panel A. Public information available at the moment of application
ρ(predicted, realized) 0.00 0.09 0.15 0.00 0.29
Out of sample root MSE 6201 6180 6140 0.43 0.41
Out of sample MAE 4354 4370 4364 0.18 0.17
Out of sample R-squared 0.00 0.01 0.02 0.00 0.01
AUC - ROC Curve - - - 0.50 0.58
Panel B. March 2007 public information
ρ(predicted, realized) 0.00 0.08 0.28 0.00 0.30
Out of sample root MSE 6201 6642 6399 0.43 0.41
Out of sample MAE 4354 4595 4364 0.19 0.17
Out of sample R-squared 0.00 0.00 0.08 0.00 0.03
AUC - ROC Curve - - - 0.50 0.58
Panel C. March 2007 public and private information
ρ(predicted, realized) 0.00 0.42 0.51 0.00 0.36
Out of sample root MSE 6201 6049 5779 0.43 0.39
Out of sample MAE 4354 4066 3732 0.18 0.15
Out of sample R-squared 0.00 0.17 0.24 0.00 0.05
AUC - ROC Curve - - - 0.50 0.62
− Caveat: Sample is only successful applicants.
− Estimation Details
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 19/34
21. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Presentation Outline
1 Motivation
2 Related Literature
3 Data
4 Market Facts
5 Experiment
6 Explaining Default
7 Conclusions
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 20/34
22. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Experimental Design
− Given limited screening, can ex-post contract terms to affect behavior?
◦ Use RCT by Bank A to answer this.
− Treatment arms: Bank ran 8 Arm (+C) RCT with 18,000 borrowers per arm. 8
interest rate and minimum payment combinations:
◦ Annual Interest rate: r ∈ {15%, 25%, 35%, 45%}
◦ Monthly Minimum payment: MP ∈ {5%, 10%} of Amount Due.
− Borrowers informed about new contract terms in March 2007 statement . No
other information; not informed about RCT or when terms would end.
◦ Borrower awareness: Initial non-response to MP changes.
− 26 Month Experiment: Consumers remained in assigned arm March 2007 – May
2009. No pre-announced end of terms.
− Control: Interest rates and MP varied at mkt conditions (∼ 55%, 4%).
− (r = 45%, MP = 5%) arm most similar to mkt contract . Show two contrasts to
simplify exposition:
◦ Effect of interest rate decrease: (r = 45%, MP = 5%) vs. (r=15%, MP = 5%).
◦ Effect of minimum payment increase: (r = 45%, MP = 5%) vs. (r = 45%, MP=10%).
− Use sampling weights to be representative of eligible population in the bank.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 21/34
23. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Effect on Default
− Large ∆ in contract terms =⇒ small ∆ in default relative to base rate.
− Interest rate decrease (45% ↓ 15%) =⇒ ↓ 2.6 percentage points over 26 months
( = +0.20).
◦ Smaller than previous studies.
Table 2: ATE on Default
Sep/07 May/09
(1) (2)
r = 15, MP = 5 0.000 -0.026*
(0.001) (0.008)
r = 45, MP = 10 -0.000 0.005
(0.000) (0.007)
Constant (r = 45, MP = 5) 0.016*** 0.193***
(0.000) (0.006)
Observations 143,916 143,916
R-squared 0.000 0.001
*: p < .05; **: p < .01; ***: p < .001 respectively. Regressions include
stratum dummies and stratum weights.
− No substantive (or stat sig) effect of minimum payment increase on default.
− Doubling minimum payment (5% ↑ 10%) =⇒ ↑ default .5 pp over 26 months
( = .02).
◦ Smaller than previous studies (all observational).
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 22/34
24. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Effect on Default: Variation Over Time
-0.04
-0.02
0.00
0.02
TreatmentEffect(prop)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: interest rate
Dependent variable: cumulative default
-0.04
-0.02
0.00
0.02
TreatmentEffect(prop)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: minimum payment
Dependent variable: cumulative default
− Effect of ∆r ≈ 0 first year.
− Decline small, only statistically significant in last months.
− Effect of ∆ MP relatively constant (≥ 9 months).
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 23/34
25. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Effect on Default: Variation Across Strata
Table 3: Stratum Treatment Effects on Default (May 2009)
Min.Pay, 6-11M Full Pay,24+M Min.Pay,24+M
(3) (4) (5)
r = 15, MP = 5 -0.018 -0.001 -0.037**
(0.015) (0.006) (0.012)
r = 45, MP = 10 0.018 0.001 -0.004
(0.015) (0.006) (0.012)
Constant (r = 45, MP = 5) 0.346*** 0.040*** 0.182***
(0.011) (0.004) (0.009)
Observations 15,978 16,000 15,987
R-squared 0.001 0.000 0.001
Columns (3),(4) and (5) estimate the endline regressions for three different strata – (a) “Min Payers, 6-11M” borrowers
who were with the bank for between 6 and 11 months in January 2007 and were in the lowest payment category ;(b) “Full
Payers,≥24M” who had been with the bank for more than 2 years by January 2007 and had were in the highest payment
category; (c) “Min Payers,≥24M” borrowers who had been with the bank for more than 2 years by January 2007 and were
in the lowest payment category. *: p < .05; **: p < .01; ***: p < .001 respectively.
− Oldest borrowers & best baseline repayment history (least constrained): No effects.
− Newest borrowers & poorest baseline repayment history (most constrained): No effects.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 24/34
26. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Effect on Cancellations
Table 4: Effects on Cancellations
Sep/07 May/09
(1) (2)
r = 15, MP = 5 -0.008** -0.035***
(0.002) (0.004)
r = 45, MP = 10 0.007 0.017**
(0.003) (0.005)
Constant (r = 45, MP = 5) 0.051*** 0.134***
(0.002) (0.002)
Observations 143,916 143,916
R-squared 0.001 0.002
*: p < .05; **: p < .01; ***: p < .001 respectively. Regressions include
stratum fixed effects and stratum weights. Estimation Details
− Cancellations: 13% over 26 month study (default: 19%).
− Larger reductions in r =⇒ card more attractive to borrowers.
◦ Interest rate decrease (45% ↓ 15%) =⇒ ↓ 3.5 percentage points (pp) over 26 months ( = +0.39).
− Ambiguous apriori effect of ∆MP on cancellations.
◦ Doubling minimum payment (5% ↑ 10%) =⇒ ↑ cancellations 1.7 pp over 26 months ( = +0.12).
− Effect of ∆r, ∆MP on cancellations much stronger than on default.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 25/34
27. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Effect on Cancellations: Variation Over Time
-0.04
-0.02
0.00
0.02
0.04
TreatmentEffect(prop)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: interest rate
Dependent variable: cumulative cancellations
-0.04
-0.02
0.00
0.02
0.04
TreatmentEffect(prop)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: minimum payment
Dependent variable: cumulative cancellations
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 26/34
28. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Effect on Cancellations: Variation Across Strata
− Oldest borrowers with best baseline repayment history (least constrained): No effects.
− Newest borrowers with poorest baseline repayment history (most constrained): Largest effects.
Min.Pay, 6-11M Full Pay,24+M Min.Pay,24+M
(3) (4) (5)
r = 15, MP = 5 -0.039*** -0.011 -0.040***
(0.008) (0.011) (0.010)
r = 45, MP = 10 0.002 0.022 0.017
(0.009) (0.012) (0.011)
Constant (r = 45, MP = 5) 0.095*** 0.150*** 0.142***
(0.007) (0.008) (0.008)
Observations 15,978 16,000 15,987
R-squared 0.003 0.001 0.003
Columns (3),(4) and (5) estimate the endline regressions for three different strata – (a) “Min Payers,
6-11M” borrowers who were with the bank for less than six months in January 2007 and were in the
lowest payment category ;(b) “Full Payers,≥24M” who had been with the bank for more than 2 years
by January 2007 and had were in the highest payment category; (c) “Min Payers,≥24M” borrowers
who had been with the bank for more than 2 years by January 2007 and were in the lowest payment
category.*: p < .05; **: p < .01; ***: p < .001 respectively.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 27/34
29. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Effect on Revenues
− Revenues increasing in interest rate ( = +1.54).
− Revenues decreasing in minimum payments ( = −0.16).
− =⇒ Departures from (45, 5) arm ↓ bank revenues
− =⇒ Bank A’s standard terms maximize profits
Table 5: Treatment Effects on Bank Revenues
Standard dependent variable Selected strata in May/09
May/09 Min.Pay, 6-11M Full Pay,24+M Min.Pay,24+M
(1) (2) (3) (4)
r = 15, MP = 5 -2,859*** -3,426*** -514*** -3,113***
(212) (222) (123) (164)
r = 45, MP = 10 -469*** -488* -23 -522**
(41) (245) (130) (176)
Constant (r = 45, MP = 5) 2,768*** 1,708*** -185 3,291***
(110) (172) (96) (133)
Observations 143,916 15,978 16,000 15,987
R-squared 0.035 0.027 0.003 0.042
− Best paying stratum generates zero revenues.
− Largest revenues from long-term borrowers with poorest baseline repayment history.
− Revenue Graph
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 28/34
30. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Effect on Proximate Determinants of Revenues
− Can dig deeper into revenue effects by examining (monthly) data on purchases,
payments and debt.
− Account for attrition (card exit)
◦ Use Lee, (2009) bounds. Assumptions For Lee Bounds
◦ Lee bounds after imputing zero for all outcomes for cancelled cards.
• Imputing zeros for defaulted cards less defensible.
• Details for Zero Imputations
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 29/34
31. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Proximate Effects
− Interest rate reductions had
◦ mixed effects on purchases: ∈ [−0.37, +0.25].
◦ small negative effects on payments: ∈ [+0.04, +0.39].
◦ a modest negative effect on debt: ∈ [+0.35, +0.74].
− Doubling the minimum payment had
◦ small positive effect on purchases: ∈ [+0.15, +0.68]
◦ small positive effects on payments: ∈ [+0.01, +0.37]
◦ small negative effect on debt: [−0.44, −0.01]
− Detailed Analysis for Purchases
− Detailed Analysis for Payments
− Detailed Analysis for Debt
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 30/34
32. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Presentation Outline
1 Motivation
2 Related Literature
3 Data
4 Market Facts
5 Experiment
6 Explaining Default
7 Conclusions
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 31/34
33. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Explaining Default
− Previous sections document that:
◦ NTB borrowers default at high rates.
◦ Large experimental changes in contract terms have muted effects on default.
− What explains underlying default rates?
− We document:
(1) Default reduces subsequent access to formal sector credit.
(2) Formal sector terms (interest rates and duration) Informal sector terms.
(3) Default correlated with unemployment (controlling for individual FE)
• Use monthly employment status from IMSS (≈ 20% subsample).
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 32/34
34. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Presentation Outline
1 Motivation
2 Related Literature
3 Data
4 Market Facts
5 Experiment
6 Explaining Default
7 Conclusions
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 33/34
35. Motivation Related Literature Data Market Facts Experiment Explaining Default Conclusions
Summary and Conclusions
− Increasing emphasis on expanding financial access.
− Little known about expanding credit by large formal sector financial organizations
whose size suggests important role in expanding access.
− Examine large Mexican bank’s effort at catering to NTB population with credit
card – constituted 14% of all first-time formal sector loan products in 2010.
− NTB population: credit-constrained, high default and cancellation rates.
− Construct measure of bank revenue per borrower: low and variable.
− Used ML methods to argue that screening borrowers ex-ante only weakly
predictive of default and subsequent revenue.
− Next, use large national level RCT and find that large changes in interest rates and
minimum payments have muted effects on default.
− Bank discontinued card.
− Work in Progress: Explaining large baseline default rates. Using matched
individual level employment data.
Castellanos, Jiménez Hernández, Mahajan, Seira − Financial Inclusion and Contract Terms 34/34
37. Related Literature: Asymmetric Information and Credit Constraints
− Gross and Souleles (2002)
− Karlan and Zinman (2009)
− Adams et al (2009)
− Einav et al (2012)
− Dehejia et al (2012
− Attanasio et al (2008)
− Karlan and Zinman (2016)
38. Related Literature: Consumer Protection
− Melzer (2011): Evaluated welfare effects of payday loans by using distance to a
state that allows payday lending as a source of exogenous variation. Finds that
access to payday loans leads to difficulty in paying other bills (rent, utilities,
mortgage) so welfare effects are likely low.
− Bertrand and Morse (2011): Information intervention RCT with payday borrowers.
Borrowers informed of fees (in dollar terms) accumulated for typical repayment
profiles reduced borrowing by 11% four months after treatment.
− Agarwal et al (2015): Find that the CARD act regulation that limited fees led to a
decline in borrowing costs for lower credit score borrowers.
39. Consumer Protection and Sub-Optimal Choice
− Bar-Gill (2004), Warren (2008): Policy pieces arguing for
− Ausubel (1999): Evidence of adverse selection from RCT on solicitation for
pre-approved credit cards. Also, some evidence of behavioral issues – the
“underestimation hypothesis” – consumers underestimate current and future
borrowing.
− Ausubel and Shui (2005): Use RCT on solicitations; estimate β/δ model, find
β = .8
− Koszegi and Heidhues (2010): Model of firms interacting with possibly
time-inconsistent agents. Equilibrium contracts will have front-loaded payments
and high fees and penalties.
− Meier and Sprenger (2010): Find positive correlations between survey elicited
measures of time preferences and credit card borrowing on both the extensive
and intensive margins.
− Gabaix and Laibson (2006): Argue that hidden costs (“shrouding”) may be an
equilibrium phenomenon in an economy with myopic (or unaware) consumers.
41. Sampling Weights
Cardholder’s payment behavior
Total
Minimum payer Part-balance payer Full-balance payer
(1) (2) (3) (4)
Months of credit card use
6 to 11 months 9.8 1.6 0.6 12
12 to 23 months 10.7 1.7 0.7 13
24+ months 61.5 9.8 3.8 75
Total 82 13 5 100
Return to slide Return to Study Design Slide
42. Bank Revenue Calculation
− Define
Amount Due[t, t + 1] =Amount Due[t − 1, t] − Payments[t − 1, t]
+ Purchases[t − 1, t] + Fees[t − 1, t] +
r
12
Debt[t − 1, t]
− Manipulating,
Payments[t − 1, t] − Purchases[t − 1, t] =Amount Due[t − 1, t] − Amount Due[t, t + 1]
+ Fees[t − 1, t] +
r
12
Debt[t − 1, t]
and summing card inception (t = 0) to exit (t = T) and discounting each period by β
T
t=0
βt
Payments[t − 1, t] − Purchases[t − 1, t]
= Amount Due[−1, 0] − βT
Amount Due[T, T + 1]
+ (β − 1)
T −1
t=0
βt
Amount Due[t, t + 1]
+
T
t=0
βt
Fees[t − 1, t] +
r
12
Debt[t − 1, t]
− Adjust since (a) T (card exit) not observed for all cards; (b) 0 corresponds to start of experiment, not
card exit.
Return to slide
43. Large variance in revenue
6 to 11 months, minimum payers
0.02.04.06
Fractionofcardholders
-20 -10 0 10 20
NPV of Revenue (MXN thousand pesos)
25thpercentile
50thpercentile
75thpercentile
-4-20246
NPVofRevenue(MXNthousandpesos)
525 575 625 675 725
Credit Score in June 07
95% CI lpoly smooth
kernel = epanechnikov, degree = 2, bandwidth = 42.79, pwidth = 64.18
24+ months, full payers
0.1.2.3.4
Fractionofcardholders
-20 -10 0 10 20
NPV of Revenue (MXN thousand pesos)
25thpercentile
50thpercentile
75thpercentile
-20246
NPVofRevenue(MXNthousandpesos)
525 575 625 675 725
Credit Score in June 07
95% CI lpoly smooth
kernel = epanechnikov, degree = 2, bandwidth = 33.04, pwidth = 49.56
44. Credit score of experimental sample (2007) and market (2016)
0.02.04.06.08.1
Fractionofindividuals
400 500 600 700 800
Credit score
Market data (PL) Experiment cards
45. Estimation Details for Table 1
− Note: Each column in each Panel is a different prediction method. The first row in each
panel represents the correlation between the predicted value and the realized value for a
test sample. The R-squared is 1 minus the ratio of the variance of the prediction errors
relative to the variance of the dependent variable.
− Variables: Panel A uses variables measured at the moment of application. The prediction
variables are the state, zip code, marital status, sex, date of birth, number of prior loans,
number of prior credit cards, number of payments in the credit bureau, number of banks
interacted with, number of payments in arrears, number of payments in arrears for credit
cards, the length in months of the relationship in the credit bureau, the date of last time in
arrears, and the date of last time in arrears for a credit card. Panel B uses all variables from
Panel A, but measured in March 2007. In addition, we use the credit score which is
measured in June 2007 (this is our oldest credit score measure). Panel C uses all variables in
Panel B, and in addition it uses purchases, payments, debt, and amount due, all measured
in March 2007.
− Overview: We we separate the control group into two samples: the test sample (25%) and
the training sample (75%). We construct different predictors using the training sample, and
evaluate predictive success by comparing the predicted outcome to the true observed
outcome for the test sample.
Return to slide
46. Credit limit and duration of the card in the market
Meaninitialcreditlimitfortheexperiment
.15
.2
.25
.3
.351(cardclosesbefore27months)
0 30,000 60,000 90,000 120,000
Credit limit in pesos
95% CI lpoly smooth
kernel = epanechnikov, degree = 3, bandwidth = 4396.17, pwidth = 6594.25
Return to slide
47. Quantifying The First Lender Externality
− Regress realized revenues on June 2007 credit scores for all cards that did not
attrit during the experiment.
− Predict revenues for the entire sample of cards using the estimates above and
compute the difference between predictions and realized values for the entire
sample.
− The average of this difference for the sub-sample that cancelled cards during the
experiment is our estimate of the revenue lost by the bank over the 27 months.
Return to slide
48. Other Elasticities
− Elasticity of loan demanded with respect to the interest rate.
− D. Karlan and Zinman, (2016) Mexico: = −2.9 (29 Months)
− Dehejia, Montgomery, and Morduch, (2012) Bangladesh: ∈ (−.73, −1.04)
− Attanasio, Goldberg, and Kyriazidou, (2008) USA: ≈ 0 (poorer households)
− D. S. Karlan and Zinman, (2008) South Africa: = −0.32
− Gross and Souleles, (2002) USA: = −1.3
− Return to (Debt, Purchase) Slide.
49. Other Default Elasticities
− Elasticity of Default with respect to the Interest rate. +0.20
◦ Lower than the delinquency elasticity of 1.8 implied by D. Karlan and Zinman, (2016).
No default elasticities shown.
◦ Lower than the default elasticity of 0.39 implied by the interventions in D. S. Karlan and
Zinman, (2009).
− Elasticity of Default with respect to minimum payment increase: +0.02
◦ Smaller than = .20 for delinquency in Keys and Wang, (2016)
◦ Smaller than = .06 in d’Astous and Shore, (2015) revocation rates.
− Return to Default Slide.
50. Documenting Credit Constraints
−
∆Debti,t = δt +
T
j=0
βj∆Limiti,t−j + γ Xi,t + i,t (2)
− θ ≡
T
j=0
βj
Table 6: Documenting Credit Constraints: θ
6-11 months 24+ months
(1) (2) (4) (8) (10)
All Minimum Full Minimum Full
Panel A. Bank’s debt and limit
Baseline 0.32 0.69 0.23 0.33 0.03
(0.04) (0.06) (0.03) (0.06) (0.01)
IV 0.73 2.14 0.47 0.62 -0.08
(0.14) (0.32) (0.37) (0.19) (0.14)
Observations 1366035 118687 170791 146291 186338
Mean dependent variable 70 184 59 95 23
SdDep 2292 3631 1756 2863 1272
Mean changes in limit -104 -141 -105 -100 -120
SdInd 1460 1532 1486 1446 1956
53. Effect on Purchases
-.5
0
.5
1
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Interest rate
Dependent variable: purchases
-.5
0
.5
1
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Minimum payment
Dependent variable: purchases
− Treatment effect coefficient normalized by control mean in each period.
− Return to Purchase slide.
54. Effect on Purchases: Across Strata and Time-.50.511.5
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Min. Payers w/ 6-11 M Full Payers w/ 24+ M
Dependent variable: purchases
Treatment: Interest rate
-.50.511.5
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Min. Payers w/ 6-11 M Full Payers w/ 24+ M
Dependent variable: purchases
Treatment: Minimum payment
− Treatment Effect Coefficient normalized by Control Mean in each period.
− Minimal response from long-term “full payers”.
− Return to Purchases Slide
55. Effect on Purchases
-.2
0
.2
.4
.6
.8
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Interest rate
Dependent variable: purchases
-.2
0
.2
.4
.6
.8
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Minimum payment
Dependent variable: purchases
− Bounds with purchases for all borrowers who cancelled set to 0.
− Return to Purchase slide.
56. Effect on Purchases/Amount Due
Monthly Purchases
Amount Due
(1) (3)
Short Term (6m) Long Term (27m)
r = 15, MP = 5 0.0194*** -0.0025
(0.0031) (0.0040)
r = 45, MP = 10 0.0211*** 0.0150**
(0.0037) (0.0032)
Constant (r = 45, MP = 5) 0.0762*** 0.0888***
(0.0021) (0.0019)
Observations 123,009 81,519
R-squared 0.003 0.005
Lee Bounds IR [0.0168, 0.0194] [-0.0542, 0.0046]
Lee Bounds MP [0.0203, 0.0393] [0.0088, 0.0770]
Lee Bounds IR [ -0.38, -0.33] [ -0.08, 0.92]
Lee Bounds MP [ 0.27, 0.52] [ 0.10, 0.87]
− Using Purchases
Amount Due
as outcome.
− Dropping ≈ 5% of observations with 0 amount due.
Return to Purchases Slide
57. Effect on Purchase/Amount Due Across Time
− Point Estimates and Lee Bounds.
-.05
0
.05
.1
TreatmentEffect(Ratio)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Interest rate
Dependent variable: purchases / amount due
-.05
0
.05
.1
TreatmentEffect(Ratio)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Minimum payment
Dependent variable: purchases / amount due
− Using Purchases
Amount Due
as outcome.
− Return to Purchases Slide
58. Effect on Fraction Paid
Monthly Payment
Amount Due
(1) (3)
Short Term (6m) Long Term (27m)
r = 15, MP = 5 -0.0024 -0.0113***
(0.0012) (0.0016)
r = 45, MP = 10 0.0289*** 0.0249***
(0.0011) (0.0015)
Constant (r = 45, MP = 5) 0.1152*** 0.1053***
(0.0016) (0.0011)
Observations 125,152 79,612
R-squared 0.009 0.013
Lee Bounds IR [-0.0055, -0.0021] [-0.0435, -0.0027]
Lee Bounds MP [0.0277, 0.0402] [0.0173, 0.0609]
Lee Bounds IR [ 0.03, 0.07] [ 0.04, 0.62]
Lee Bounds MP [ 0.24, 0.35] [ 0.16, 0.58]
− Dropping ≈ 5% of observations with 0 amount due.
Return to Payments Slide
59. Effect on Fraction Paid Across Time
− Point Estimates and Lee Bounds.
-.05
0
.05
.1
TreatmentEffect(Ratio)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Interest rate
Dependent variable: paym_amt_due
-.05
0
.05
.1
TreatmentEffect(Ratio)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Minimum payment
Dependent variable: paym_amt_due
− Persistent, constant effect of MP change.
− Limited effect of r changes.
− Return to Payments Slide
60. Effect on Normalized Monthly Payments
− Point Estimates and Lee Bounds
-.4
-.2
0
.2
.4
.6
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Interest rate
Dependent variable: payment
-.4
-.2
0
.2
.4
.6
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Minimum payment
Dependent variable: payment
− Treatment Effect Coefficient normalized by Control Mean in each period.
− Return to Payments Slide
61. Effect on Monthly Payments: Across Strata and Time-.20.2.4.6.8
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Min. Payers w/ 6-11 M Full Payers w/ 24+ M
Dependent variable: payment
Treatment: Interest rate
-.20.2.4.6.8
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Min. Payers w/ 6-11 M Full Payers w/ 24+ M
Dependent variable: payment
Treatment: Minimum payment
− Treatment Effect Coefficient normalized by Control Mean in each period.
− Return to Payments Slide 1
− Return to Payments Slide 2
62. Effect on Payments (Cancellations set to 0)
-.2
0
.2
.4
.6
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Interest rate
Dependent variable: payment
-.2
0
.2
.4
.6
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Minimum payment
Dependent variable: payment
− Bounds informative for most of experiment.
− Return to Payments Slide
63. Cancellation Estimation Details
− Column (1) is estimated for client-initiated cancellations 6 months after the start
of the intervention and the remainder are for cancellations at the end of the
experiment (27 months). Columns (3),(4) and (5) estimate the endline regressions
for three different strata – (a) “Min Payers, <12” borrowers who were with the bank
for less than six months in January 2007 and were in the lowest payment category
;(b) “Full Payers,>24M” who had been with the bank for more than 2 years by
January 2007 and had were in the highest payment category; (c) “Min
Payers,>24M” borrowers who had been with the bank for more than 2 years by
January 2007 and were in the lowest payment category.
Return to Cancellation Table
64. Assumptions for Lee, (2009) Bounds
− (YA, YB, ) potential outcomes under treatments A and B .
− (SA, SB) potential sample selection indicators. e.g. If card remains in sample under
treatment A but exits sample under treatment B then (SA = 1, SB = 0).
− Need to assume SA ≥ SB
− In our context, need card exit to be more likely under B than A. Reasonable e.g. when
S(r%,m) ≥ S(45%,m) ∀ r < 45%, ∀m
but not necessarily others.
− If SA ≥ SB, then obtain sharp bounds on ATE for the “always in sample” sub-population
E (YA − YB|SA = 1, SB = 1) = E (YA − YB)
− Bounds on ATE for sub-population of cards that would not exit under treatment A or B.
− Compute these period-by-period (t = 1 . . . 27).
− Return to Proximate Determinants Slide
65. Imputing Zeros for Card Exits
− For purchases and payments in period t impute Yt = 0 for all periods t ≥ s after
card cancels (St = 0 ∀ t ≥ s).
− Eliminates attrition by cancellers.
− Since card has been closed with no outstanding balance , plausible to set
outcomes to zero (purchases, payments and debt).
− Setting revoked cards to zero less defensible.
− Return to Proximate Determinants Slide
66. Effect on Purchases
Purchases
(1) (3) (5)
Short Term (6m) Long Term (27m) Long Term w/Zeros
r = 15, MP = 5 99*** 65*** 75***
(15) (7) (6)
r = 45, MP = 10 75*** 92*** 62***
(9) (9) (6)
Constant (r = 45, MP = 5) 401*** 415*** 341***
(6) (10) (8)
Observations 134,385 87,093 105,180
R-squared 0.002 0.004 0.003
Lee Bounds IR [ 49, 101] [ -192, 104] [ -56, 85]
Lee Bounds MP [ 75, 107] [ 65, 352] [ 51, 231]
Lee Bounds IR [ -0.38, -0.18] [ -0.38, 0.69] [ -0.37, 0.25]
Lee Bounds MP [ 0.19, 0.27] [ 0.16, 0.85] [ 0.15, 0.68]
− ↓ interest rates =⇒ ↑ purchases somewhat, bounds wide (include zero).
◦ Low relative to other elasticities.
− ↑ minimum payments =⇒ ↑ purchases.
◦ Robust, unexpected.
67. Effect on Purchases: Variation Across Time
− Monthly Point Estimates and Lee Bounds.
-200
0
200
400
TreatmentEffect(MXN)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Interest rate
Dependent variable: purchases
-200
0
200
400
TreatmentEffect(MXN)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Minimum payment
Dependent variable: purchases
− Bounds relatively tight for initial 6 months.
− Persistent positive effect of MP on purchases.
− Even upper bounds suggest relatively small effects.
− Results (Regressions, Graphs) with purchases as fraction of amount due as LHS.
− Results normalized by control mean in each period.
− Variation across strata and time.
− Bounds with cancellations set to zero.
68. Effect on Monthly Payments
Monthly Payments
(1) (3) (5)
Short Term (6m) Long Term (27m) Long Term (w/ Zeros)
r = 15, MP = 5 -27* -64*** -26*
(12) (9) (8)
r = 45, MP = 10 154*** 53* 25
(13) (18) (15)
Constant (r = 45, MP = 5) 638*** 628*** 515***
(8) (5) (5)
Observations 134,385 87,093 105,180
R-squared 0.003 0.003 0.002
Lee Bounds IR [ -103, -24] [ -267, -17] [ -134, -14]
Lee Bounds MP [ 153, 184] [ 9, 301] [ 7, 193]
Lee Bounds IR [ 0.06, 0.24] [ 0.04, 0.64] [ 0.04, 0.39]
Lee Bounds MP [ 0.24, 0.29] [ 0.01, 0.48] [ 0.01, 0.37]
− ↓ interest rates =⇒ ↓ payments (debt related).
− ↑ minimum payments =⇒ ↑ payments.
◦ No heterogeneity in signs (unlike Keys and Wang, 2016).
69. Effect on Monthly Payments: Variation Across Time
− Monthly Point Estimates and Lee Bounds.
-400
-200
0
200
400
TreatmentEffect(MXN)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Interest rate
Dependent variable: payment
-400
-200
0
200
400
TreatmentEffect(MXN)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Minimum payment
Dependent variable: payment
− Limited response to MP increase in first two months (consistent with inattention).
− Results (Regressions, Graphs) with amount paid as fraction of amount due as LHS.
− Results normalized by control mean in each period.
− Variation across strata and time.
− Bounds with cancellations set to 0.
70. Effect on Debt
Debt
(1) (3) (5)
Short Term (6m) Long Term (27m) Zeros
r = 15, MP = 5 -270* -604*** -417***
(83) (62) (42)
r = 45, MP = 10 25 -789*** -691***
(46) (89) (69)
Constant (r = 45, MP = 5) 1,409*** 2,114*** 1,732***
(11) (49) (35)
Observations 134,385 87,093 105,180
R-squared 0.001 0.005 0.004
Lee Bounds IR [ -397, -266] [-1,576, -474]
Lee Bounds MP [ 22, 106] [ -971, 326]
Lee Bounds IR [ 0.28, 0.42] [ 0.34, 1.12] [0.34, 0.74]
Lee Bounds MP [ 0.02, 0.08] [ -0.46, 0.15] [-0.44,-0.00]
− ↓ interest rates =⇒ ↓ debt.
◦ Recall ↓ interest rates =⇒ purchases ↑ (?), payments ↓
◦ Debt compounds at lower rates.
◦ Compare to other papers
− ↑ minimum payments =⇒ ↓ debt.
◦ Larger than Keys and Wang, (2016) (and less heterogeneity)
71. Effect on Debt: Variation Across Time
-1
-.5
0
.5
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Interest rate
Dependent variable: debt
-1
-.5
0
.5
TreatmentEffect(ε)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Treatment: Minimum payment
Dependent variable: debt
− Normalized by control mean.
− Interest rate effects robustly negative for most of experiment.
72. Effect on Debt: Variation Across Strata and Time
-1000
-500
0
500
1000
1500
Treat.Effect(MXN)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Min. Payers w/ 6-11 M Full Payers w/ 24+ M
Dependent variable: debt
Treatment: Interest rate
-1000
-500
0
500
1000
1500
Treat.Effect(MXN)
Mar/07 Sep/07 Mar/08 Sep/08 Mar/09
Min. Payers w/ 6-11 M Full Payers w/ 24+ M
Dependent variable: debt
Treatment: Minimum payment
− No evidence of perverse differential responses (contra Keys and Wang, 2016)
73. NPV of bank revenue
-3000-15000150030004500
NPVofrevenue
I:15%
P:5%
I:15%
P:10%
I:25%
P:5%
I:25%
P:10%
I:35%
P:5%
I:35%
P:10%
I:45%
P:5%
I:45%
P:10%
Mean Std. Deviation
74. NPV of bank revenue
6 to 11 months minimum payers
-3000
-1500
0
1500
3000
4500
NPVofrevenue
I:15%
P:5%I:15%
P:10%
I:25%
P:5%I:25%
P:10%
I:35%
P:5%I:35%
P:10%
I:45%
P:5%I:45%
P:10%
Mean Std. Deviation
24+ months full payers
-3000
-1500
0
1500
3000
4500
NPVofrevenue
I:15%
P:5%I:15%
P:10%
I:25%
P:5%I:25%
P:10%
I:35%
P:5%I:35%
P:10%
I:45%
P:5%I:45%
P:10%
Mean Std. Deviation
75. Probability of getting a loan against default
New credit card between t and t + 6 New credit between t and t + 6
OLS OLS
(3) (6)
Default -0.1145*** -0.1466***
(0.0035) (0.0045)
Constant 0.1498*** 0.2126***
(0.0014) (0.0016)
R-squared 0.0048 0.0060
Observations 258,102 258,102
Dependent Variable Mean 0.1443 0.2056
− Strong negative effect of default on subsequent credit (≈ 70% decline).
− Back to Explaining Default
76. Formal Sector Terms Dominate Informal Terms
Interest rate Loan amount Loan duration in years
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Formal credit -94*** -108** -7.08 6,184.3*** 4,926*** 3,934*** 0.554*** 0.544*** 0.491***
(31) (48) (38) (288) (484.3) (659.3) (0.034) (0.058) (0.104)
Age -0.483 97.86*** 0.005***
(1.45) (10.73) (0.002)
Monthly expenditure 0.014* 0.382*** 0.000
(0.007) (0.060) (0.000)
Car -26 -760*** -0.059***
(16) (130) (0.020)
Washing machine -43 110 0.007
(36) (226) (0.040)
Appliances 28 -364* -0.023
(31) (198) (0.034)
Constant 291*** 336*** 152*** 3,658*** 564 4699*** 0.520*** 0.333** 0.436***
(19) (125) (41) (134) (960) (762) (0.021) (0.149) (0.122)
Education dummies No Yes No No Yes No No Yes No
Sample dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes
Individual FE No No Yes No No Yes No No Yes
Dependent variable mean 254 254 231 5022 5022 5061 0.732 0.732 0.732
Dependent variable SD 503 503 423 6,938 6,938 7,023 0.757 0.757 0.757
Observations 2,427 880 202 8,810 2,992 423 4,257 1,522 301
R-squared 0.006 0.036 0.860 0.063 0.171 0.661 0.083 0.119 0.646
− Back to Explaining Default
77. Unemployment Increases Default
default
j
it = αi + γs,t +
k≥1
βj
k × 1( months unemployedit = k) + εit (3)
-.050.05.1
increaseinprobability(β)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Months since last employed
dep. var >1m >2m >3m >6m
− Back to Explaining Default