Research Proposal 

Having bad credit is probably an adult’s worst nightmare. You have probably heard that if you have bad credit, you won’t be able to get a car, a house, or even a job. But what does that actually mean? Credit is the ability to borrow money or have access to goods or services with the understanding that you will pay it back later. Your parents, your friends or even yourself have had or currently own a credit card. A credit card is a card that your bank may issue you that allows you to borrow money from them based on the promise that you will pay it back (along with other “agreed” charges). Maybe you really want those new sneakers that came out but you can’t afford it. So, you might use your credit card to be able get the sneakers on the spot and then pay back the money at a later date. In a perfect world, this sounds like a great idea. But sometimes, things happen. What if you don’t pay your bill on time? What if you miss a payment? These things can affect your credit score. Your credit score is a number used to represent your creditworthiness; how likely you are to repay debt. If you have good credit (typically 700 and higher on a 300-850 scale), you can be approved for a loan or credit card, receive better rates for a car and/or a home, help you qualify for lower interest rates and so on. But if you have bad credit, no one will lend you money (or if they do, it won’t be much), you won’t be able to get a credit card, you’ll have higher interest rates and so on. But how much can your credit score affect you? 

People’s credit has always been used against them. Many institutions define you by your credit score which is why it can be nerve-wracking for an adult to have bad credit especially in a time of need. Credit scores can be biased and quite flawed. But how biased can they be? I plan on researching how much of a person’s life can be affected by credit; whether it is good credit or bad. You often hear much about credit as you start getting older and it becomes an important aspect of your life. Bills, wealth, race and age can greatly affect your credit score. But I want to investigate the scope of the impact that credit can have in your life. I believe that credit is almost like a curse. It can have either a negative or positive impact on your life. But at times, there’s little you can do it. 

I chose this topic because now that I am considered an adult in the eyes of the law, I want to be well prepared for what I am getting myself into. Credit has always been something I heard about but never understood. To help me better understand credit, I will be researching different examples of positive or negative credit discrimination (if there is any), responses to price/rate changes and about credit bureaus and credit reports. 

Credit Score Bias: How can it affect you? 

Sofia Castillo 

John Jay College of Criminal Justice 

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Abstract 

Whether you have good credit or bad credit, it can affect your life in many different ways.  But in what ways exactly? A credit score is a three-digit number, typically between 300 and 850, designed to represent your credit risk, or the likelihood you will pay your bills on time. Credit scores are calculated using information in your credit reports, including your payment history, the amount of debt you have, and your credit history. Higher scores mean you have demonstrated responsible credit behavior in the past, which may make potential lenders and creditors more confident when evaluating a request for credit. Having low credit means indicates you’re a riskier borrower than someone with a better credit score. Those with higher credit scores generally receive more favorable credit terms, which may translate into lower payments and less paid in interest over the life of the account. But those with a low credit score will pay more in interest over time than they would if they had better credit and a better interest rate. The more you borrow, the more you’ll pay in interest. There are side effects to having bad credit. You pay higher interest rates on your credit cards and loans, applications for credit cards, loans and apartments may not get approved, you will have to pay security deposits on utilities, you can’t get a cell phone contract, you can get denied for a job, you face difficulties opening a business and it might be difficult to purchase a home and/or car. 

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In society, people get judged based on many things such as their race, color, ethnicity, size, religion and sexual orientation. We see and hear about it every day whether it is in real life or on television. But imagine being judged for something as small as your credit score. Yes, credit score bias exists.  

Different companies have targeted consumers in the past based on where they lived. For example, according to the source, Eveleth, R. (2019, June 13). Credit Scores Could Soon Get Even Creepier and More Biased. Retrieved from https://www.vice.com/en_us/article/zmpgp9/credit-scores-could-soon-get-even-creepier-and-more-biased., it states, “Early credit companies knew that impressionistic records were biased, and introduced a more quantitative score to try and combat the prejudices of credit reporters. In 1935, for example, the Federal Home Owners’ Loan Corporation created a map of Atlanta, showing neighborhoods where mortgage lending was ‘best,’ coded in green, compared to “hazardous,” coded in red. This solution, it turned out, codified the discrimination against minorities by credit companies. Neighborhoods coded red were almost exclusively those occupied by racial minorities. These scores contributed to what’s called ‘redlining,’ a systematic refusal by banks to make loans or locate branches in these ‘hazardous’ areas.” In the past, minorities were targeted based on where they lived. Companies still redline communities to this day. Redlining is very unfair to those that it affects because it makes it hard for them to try to rebuild their credit. Redlining occurs often when companies use “e-scores”. E-scores are made up of data such as our zip codes and internet surfing patterns and are used to determine our “creditworthiness”.  

Unlike the actual credit scores they resemble, e-scores are arbitrary, unaccountable, unregulated and often unfair. According to a source, O’Neill, C. (2017). Collateral Damage. In Weapons of Math Destruction (pp. 141–160). Great Britian: Penguin., there are customer-targeting services that provide companies with insights about potential consumers based on their location, web browsing and purchasing patterns. For example, the text states, “A Virginia company called Neustar offers a prime example. Neustar provides customer targeting services for companies, including one that helps manage call center traffic. In a flash, this technology races through available data on callers and places them in a hierarchy. Those at the top are deemed to be more profitable prospects and are quickly funneled to a human operator. Those at the bottom either wait much longer or are dispatched into an outsourced overflow center, where they are handled largely by machines. Credit card companies such as Capital One carry out similar rapid-fire calculations as soon as someone shows up on their website. They can often access data on web browsing and purchasing patterns…” A company named Neustar collects data on different customers and sorts them based on what these customers have purchased or searched online. This data is also matched with real estate data. By doing so, they can make inferences on that person’s wealth. Once people are placed at the bottom, it is very hard to get them out of there. Because they have a low score, companies won’t take the time to meet with them.  

Young entrepreneurs are a key source of employment growth in the United States. They already face a lot of bias because they are young and new to the business world. Just like the average person, entrepreneurs face a lot of racial bias with their credit score as well. According to Robb, A., & Robinson, D. (2018). Testing for racial bias in business credit scores. Small Business Economics, 50(3), 429-443., the text states, “Limited access to capital is an especially acute barrier to increased entrepreneurship among minority business founders… found higher loan application rejection rates among otherwise equivalent… minority-owned businesses attempting to borrow. …Black and Hispanic applicants pay higher interest rates on business loans than White borrowers with similar characteristics. In general, minority-owned firms experience loan denial probabilities and higher interest rates then do white-owned businesses even after taking into account differences in creditworthiness and other factors…” Based on their skin tone, minorities face bias. Black and Hispanic entrepreneurs pay more interest than White entrepreneurs.  

Technological advances have altered the process in which banks initiate a loan. Credit scores can have a great impact on housing affordability. Customers with bad credit will have to pay higher mortgage rates. According to Nelson, A. (2010). Credit scores, race, and residential sorting. Journal of Policy Analysis and Management, 29(1), 39-68., credit scores influence residential sorting behavior in Southern California. For example, the text states, “Because black households have significantly lower average credit scores than similarly situated non-black households, the omission of credit scores particularly biases interaction estimates for black households… For example, there are significant positive interactions between household credit score and home value, as well as between credit score and the average district home value. This indicates that households with higher credit scores are able to purchase more expensive homes located in school districts with relatively expensive housing.” Based on your race, residential sorting models provide biased estimates. There are connections between credit score and home value depending on who lives there.  

There is evidence that indicates that credit scores are predictive of future loan performance and suggests that scoring increases the accuracy of risk assessment. This benefits lenders but they can also benefit borrowers by expanding credit opportunities. According to Avery, R., Bostic, R., Calem, P., & Canner, G. (2000). Credit Scoring: Statistical Issues and Evidence from Credit‐Bureau Files. Real Estate Economics, 28(3), 523-547., credit scores are used as a primary screen for applicants seeking credit. For example, the text states, “Bureau scoring models are built on the premise that past performance in repaying debts is the best predictor of future performance. They are designed to (1) rank individuals on the basis of their relative creditworthiness and (2) quantify the likelihood that a given individual will default… Nearly all evaluations of bureau scoring models have focused on their ability to predict relative creditworthiness. Uniformly, this research has found that their powerful predictors of default this research has found that their powerful predictors of default and delinquency.” It has been researched that these models have been used to rank people based on their credit. These models determine a person’s credibility using their zip codes.  

In conclusion, credit scores can have a huge impact on you. Whether you have good credit or bad credit, companies use your credit score to rank you on your likelihood that you will pay money back in time. This can be devastating for those with bad credit because no matter how hard you try to rebuild your credit, some companies won’t give you the chance. Scoring models use your purchase history, search history and your location to determine what kind of customer you will be without giving you a chance first.  

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References 

Nelson, A. (2010). Credit scores, race, and residential sorting. Journal of Policy Analysis and Management, 29(1), 39-68. 

Credit scores have a profound impact on home purchasing power and mortgage pricing, yet little is known about how credit scores influence households’ residential location decisions. This study estimates the effects of credit scores on residential sorting behavior using a novel mortgage industry data set combining household demographic, credit, and financial data with property location information and detailed community attribute data. 

Robb, A., & Robinson, D. (2018). Testing for racial bias in business credit scores. Small Business Economics, 50(3), 429-443. 

The article develops a novel empirical test of racial bias based on comparisons between forward-looking, expectations-based credit scores and backward-looking, repayment-history-based credit scores. Businesses founded by disadvantaged minorities have much lower average business credit scores, but these scores show no evidence of racial bias. If anything, forward-looking credit-score models under-predict the rate of payment delinquency among minority-owned businesses. 

Swanton, Mary. (2010). Background bias: EEOC steps up pressure on employers that reject applicants based on criminal records and credit scores.(LABOR). InsideCounsel, 26. 

Pope, D., & Sydnor, J. (2011). Implementing Anti-Discrimination Policies in Statistical Profiling Models. American Economic Journal: Economic Policy, 3(3), 206-231. 

In many settings, factors such as race, gender, and age are prohibited. However, the use of variables that correlate with these omitted characteristics is often contentious. The article provides a framework to address these issues and propose a method that can eliminate proxy effects while maintaining predictive accuracy relative to an approach that restricts the use of contentious variables outright. The article illustrates the value of our proposed method using data from the Worker Profiling and Reemployment Services system. 

O’Neil, C. (2016). Weapons of math destruction : How big data increases inequality and threatens democracy (First ed.). 

Avery, R., Bostic, R., Calem, P., & Canner, G. (2000). Credit Scoring: Statistical Issues and Evidence from Credit‐Bureau Files. Real Estate Economics, 28(3), 523-547. 

Although credit scoring offers benefits to lenders and borrowers, its use raises important statistical issues that may affect the ability of scoring systems to accurately quantify an individual’s credit risk. The evidence from a national sample of credit‐bureau records suggests that concerns about omitted‐variable bias may be justified, as local economic factors show significant correlations with credit scores. 

Scripted Interview 

Sofia: How were you able to investigate your topics? 

Avery: We used actual files from Credit-Bureau files to demonstrate statistical issues that may affect the ability of scoring systems to accurately quantify an individual’s credit risk 

Robb: We developed tests of racial bias based on comparisons between expectations-based credit scores and repayment-history-based credit scores. Then, we tested for racial bias using data from the Kauffman Firm Survey. Businesses founded by disadvantaged minorities have much lower average business credit scores, but these scores show no evidence of racial bias 

Nelson: I used a novel mortage industry data set that combines household demographic, credit, and financial data with property location information and detailed community attribute data. Results showed that increases in credit score are associated with increases in the consumption of higher priced homes in more expensive school districts, higher-quality public schools and proximity to urban/metropolitan areas. 

Sofia: Wow, that’s interesting! Why did you choose to research this topic? 

Avery: Bureau scores are increasingly being used as an initial and sometimes primary screen for applicants seeking credit and are also a prescreening tool for credit solicitations. The growing reliance on bureau scores in underwriting decisions raises statistical issues. 

Robb: Credit scores are predictive of future loan performance and suggest that scoring increases the accuracy of risk assessment. This benefits lenders but they can also serve interest of borrowers by expanding credit opportunities and improving the efficiency of the credit review process. I wanted to investigate why this was. 

Nelson: Similar to what Robb is saying, we know little about how credit scores influenced residential location decisions. By linking homebuyer attributes from a mortgage industry data set, to housing-specific community attributes, we can analyze the effects of credit scores on residential sorting across housing choices with varying public amenities. 

Sofia: In your opinion, how are people’s lives impacted by bad credit? 

Avery: Bureau scores are constructed only from information on the individual’s credit history. If you have bad credit, your life can be greatly impacted. You won’t be able to rent/buy homes, apply for credit cards and so much more. 

Robb: Like Avery said, a person’s life can be negatively impacted in many ways by bad credit. In an entrepreneurial perspective, minority owned businesses pay higher interest rates on business loans than white borrowers with similar characteristics.  

Nelson: Credit scores have a profound impact on home purchasing power and mortgage pricing. Individuals with poor credit scores will pay higher mortgage interest rates and will qualify for lower mortgage amounts. 

Outline 

Introduction 

• Thesis : Credit score bias is REAL 
• Include sources (explain the main idea of each) 
• What is credit score? 

1^st Paragraph 

• Key point about credit score 
• Supporting Source 1 : Analyze the source and include evidence 
• Supporting Source 2 : Analyze and include evidence 

2^nd Paragraph 

• Another key point about credit score  

• Supporting Source 3 : Analyze 
• Supporting Source 4 : Analyze 

3^rd Paragraph 

• Another key point 
• Supporting source 5 : Analyze 

• Supporting source 6 : Analyze 

4^th Paragraph 

• Another key point 
• Supporting source 7 : Analyze 
• Supporting source 8 : Analyze 

Conclusion 

• Restate intro 
• Provide results 
•  

References 

Nelson, A. (2010). Credit scores, race, and residential sorting. Journal of Policy Analysis and Management, 29(1), 39-68. 

Credit scores have a profound impact on home purchasing power and mortgage pricing, yet little is known about how credit scores influence households’ residential location decisions. This study estimates the effects of credit scores on residential sorting behavior using a novel mortgage industry data set combining household demographic, credit, and financial data with property location information and detailed community attribute data. 

Robb, A., & Robinson, D. (2018). Testing for racial bias in business credit scores. Small Business Economics, 50(3), 429-443. 

The article develops a novel empirical test of racial bias based on comparisons between forward-looking, expectations-based credit scores and backward-looking, repayment-history-based credit scores. Businesses founded by disadvantaged minorities have much lower average business credit scores, but these scores show no evidence of racial bias. If anything, forward-looking credit-score models under-predict the rate of payment delinquency among minority-owned businesses. 

Swanton, Mary. (2010). Background bias: EEOC steps up pressure on employers that reject applicants based on criminal records and credit scores.(LABOR). InsideCounsel, 26. 

Pope, D., & Sydnor, J. (2011). Implementing Anti-Discrimination Policies in Statistical Profiling Models. American Economic Journal: Economic Policy, 3(3), 206-231. 

In many settings, factors such as race, gender, and age are prohibited. However, the use of variables that correlate with these omitted characteristics is often contentious. The article provides a framework to address these issues and propose a method that can eliminate proxy effects while maintaining predictive accuracy relative to an approach that restricts the use of contentious variables outright. The article illustrates the value of our proposed method using data from the Worker Profiling and Reemployment Services system. 

O’Neil, C. (2016). Weapons of math destruction : How big data increases inequality and threatens democracy (First ed.). 

Avery, R., Bostic, R., Calem, P., & Canner, G. (2000). Credit Scoring: Statistical Issues and Evidence from Credit‐Bureau Files. Real Estate Economics, 28(3), 523-547. 

Although credit scoring offers benefits to lenders and borrowers, its use raises important statistical issues that may affect the ability of scoring systems to accurately quantify an individual’s credit risk. The evidence from a national sample of credit‐bureau records suggests that concerns about omitted‐variable bias may be justified, as local economic factors show significant correlations with credit scores. 

I was 15 and in love. Now, I know what you must be thinking, “That’s too young,” or “You don’t know what love is.” Well, you’re right. I don’t. At least I thought I did. To me, “love” was sticking through “thick and thin”. But what is “thick and thin” exactly? How do you know what you can handle and what you can’t? I certainly didn’t know. I thought I was the strongest woman ever at 15. I thought I could handle anything life threw at me. I thought I had everything figured out. I thought I knew what love was. But what I didn’t know was what love is not.  

I would often hear that you should stick by your significant other’s side no matter what. That you should always make them happy. That you should always be there when they need you. And that’s exactly what I was doing. So, why did everything go wrong? 

When I was a freshman in high school, a new kid had transferred to my school. Everyone knew him except me. He was the talk of the school. I remember when he walked into my classroom; I could hear all the whispers and the gasps. I had no clue who this kid was. He was cute but way out of my league. All of a sudden, I just kept seeing him everywhere. I couldn’t stop running into him. Until one day, he came over to me and introduced himself. I was confused … and nervous … and happy … and nervous again. I didn’t know how to react. He was a pretty cool guy. Before I knew it, we became best friends. We were always together. Always hanging out. But it was solely platonic, I promise. People thought we were dating. I never really saw him like that. I only saw him as a friend. But then, he asked me out. Obviously, I said yes. He was my best friend, why would I not? I thought it was the perfect love story. Only this story doesn’t have a happy ending. 

I started seeing a side of him I was not expecting. A side I did not like. It all started when he would “joke” about telling me what to wear and what not to wear. I always brushed it off like who are you to tell me what to wear (lol). That was one red flag I chose to ignore. Then, he started keeping me from seeing my friends. He would constantly question me about all my male friends and tell me that, “it doesn’t look good for him when I am hanging out with boys.” Another red flag I chose to ignore. He would pick a fight with anyone who looked my way and make a scene. He would also pick fights with me over hypothetical situations. Things that didn’t even happen, isn’t that crazy? He would pick fights with me in front of our families. Straight up embarrass me. He would go through my phone and question me about every little thing to try and catch me lying. He would grab me inappropriately in public even though he knew I didn’t like it. When we would argue, he would often get in my face and say, “Do you think I’m scared of you?” He would argue with me in school and yell at me in front of everyone. Make me cry in front of everyone. He would look at me in my face while I’m crying and he’d say, “This crying act of yours doesn’t do anything to me.” I eventually stopped caring about crying in front of people in school.  

We would fight every single day. He constantly tried controlling me. He hated the fact that I would fight back. He was used to these weak bitches who would do anything and everything he said. Unfortunately, it worked. Nothing was ever his fault. He turned everything on me somehow. And he was pretty damn good at it. Everything was on his terms. He got whatever he wanted all the time. He made me his personal little servant. He made me clean his room, do his laundry, wash his dishes, make his bed, make him food. He always claimed that since he works a lot, then I should want to do those things for him (even though he worked 1-2 days a week) *rolls eyes*. I had “PENDEJA”* written across my forehead.  

Eventually, I felt myself change. I felt myself getting used to this “special treatment”. It was a while before I stopped arguing with him. And if you know me, you know that is a very hard thing for me to do; I love to argue lol. But I found myself thinking about him and his feelings over my own. One day, he got so angry with me that when we got in the car, he just started yelling at me, cursing at me, threatening me saying how he just wants to crash the car with the both of us in it. He was also saying how lucky I was to be a girl because if I was a boy he would’ve “rocked me”. I just sat there, silent AF, ignoring him. Then, he had THE NERVE to get mad at me for not wanting to give him a kiss goodbye. Yup. That’s roughly what I dealt with for a year and a couple of months. It wasn’t always bad though. We did basically everything together. He bought me clothes, shoes, makeup, food. He even bought me a “promise ring” after dating for 3 months. We were meant to be. I met his entire family. He met mine. He became my world. You’re probably thinking, “OMFG why didn’t you just leave?” I wish it was that easy. I couldn’t leave him. I couldn’t be the one to end it after everything he has done for me. Everyone has a bad day or two. He just had really bad anger issues. He was only protecting me. He was only looking out for me. He was only showing me what love is. He loved me. (Please tell me you caught the sarcasm…) 

Isn’t that what love is supposed to be? Accepting each other’s flaws and insecurities? Loving your significant other for who they are no matter what? That’s what I did. Love him for who he is but hope he’d change. Why wasn’t I happy? Why was I the one being treated like crap? Why should I have to accept them even if I don’t want to? Deep down, I knew this wasn’t it but I still stuck around. Society made me feel like you should stay by someone’s side if you love them. You should love them unconditionally. I didn’t even know what that meant but remember I thought I knew everything. Now when I look back, I get angry at myself for allowing this toxicity in my life during my entire high school experience. I wish I listened to those that told me not to date in high school. 

 But like I said before, I was 15 and in love. I thought I knew everything. I thought I could change him. After we broke up and I found out about things he did behind my back like cheat and lie, I was really depressed for about a month or so. I was scared to talk to any boy. I was crying 24/7. My eyes were permanently swollen. I skipped class to cry on my teacher’s shoulder. I never ate. I wasn’t starving myself, I just wasn’t hungry. I felt nothing but everything at the same time. Even after we broke up, he still had a piece of my heart even after everything he did. I do admit that he has traumatized me. But over time, it got easier without him. I started gaining control of my life again. I reignited the fire he had put out in me. I became happier. It felt as though a weight was slowly lifting off my shoulders. I felt great. People were even starting to take notice of the “new me”.  

 Eventually, I realized he never loved me. I didn’t know everything. I didn’t know what love was. I knew what love was not and that wasn’t love. I realized that you don’t have to put up with anyone’s bullshit. I no longer agree with the phrase, “Accept and love people for who they are.” I’m sorry but I don’t have to. I don’t have to associate with anyone that I KNOW is not good for me. I don’t have to accept your toxic behavior just because I love you. I don’t have to ignore the red flags. I don’t have to put your needs above mine. I don’t have to stay.  

*Pendejo/a: stupid, or idiot in spanish*