Consumer Bankruptcy

Consumer bankruptcy is a legal process outlined in the U.S. Bankruptcy Code. It is designed to protect households from long term financial destitution. Filing for protection under the U.S. Bankruptcy Code (also known as “filing for bankruptcy”) allows a household to reorganize and/or relieve themselves of all or a portion of their outstanding debts. This allows the household to re-establish itself financially in a reasonable time frame.

Consumer bankruptcy is a costly process. Anytime a household files for bankruptcy protection and debts are discharged, wealth and income are taken from creditors (who are no longer repaid) and reallocated to bankruptcy filers (who no longer have to repay some or all of their outstanding debts). Bankruptcy is an extremely important form of social insurance for the most economically vulnerable households in a community. At the same time, it is also important to ensure that the consumer bankruptcy process is not misused, but rather is used appropriately and judiciously.

The Bankruptcy Filing Process

The consumer bankruptcy process can be complex. Below, we provide a very brief and technically informal introduction to that process. We refer interested readers to the U.S. Bankruptcy Court's website for a more detailed explanation of this process.

With a few exceptions, the majority of households file for bankruptcy protection under one of two chapters of the U.S. Bankruptcy Code: Chapter 7 or Chapter 13. A very small number of filers with unusual circumstances may file under some other chapter of the U.S. Bankruptcy Code, for example, Chapters 11 (for certain small business owners) or 12 (for family farmers/fisherman). In what follows, we focus on Chapter 7 and Chapter 13 filings, as they constitute the vast majority of consumer bankruptcy filings in the United States.

Approximately 60-70 percent of bankruptcies in the U.S. are filed under Chapter 7 each year. When filing under Chapter 7, the household discloses all of its financial information (debts, assets, income, etc.) and relevant demographic characteristics (household size, etc.) to the Court. The Court appoints a trustee, who subsequently prioritizes the household's debts in an order established by the U.S. Bankruptcy Code. Any debts or payments owed to the Court or its personnel (i.e., the trustees and appointed attorneys) receive the highest priority. Priority unsecured creditors (i.e., those owed outstanding child support obligations, alimony obligations, outstanding tax obligations) are next, followed by general unsecured creditors (for example, credit cards), who receive the lowest priority. The trustee liquidates all of the bankrupt household's assets, net of exemptions, if any exist. All proceeds from the liquidation of assets is used to repay creditors in priority order. For debts secured by collateral (mortgages, automobile loans, etc.), the secured creditor has first claim against secured property, especially for the secured portion of the loan. The debtor must either re-affirm the secured claim or release the collateral back to the secured creditor. If there is non-exempt equity in an item of secured property, the trustee will sell the property, pay the secured creditor its' secured claim and the balance would go back into the estate for distribution in accord with the bankruptcy listing of priorities. Once the liquidation process is complete, many (but not all) remaining debts that are eligible are discharged, or simply eliminated. Certain types of debts, including (but not limited to) unpaid child support, student loans, and criminal restitution are not generally dischargeable and will remain with the debtor. At this point, the Chapter 7 bankruptcy process is complete, and the households is immediately able to re-establish itself financially.

Bankruptcy filings under Chapter 7 are extremely beneficial for the bankruptcy household. They also create a sizeable redistribution of wealth from creditors to debtors. To ensure judicious and appropriate use of the bankruptcy process, Congress passed the Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA) in 2005. A critical component of this legislation was the imposition of what is known as a “means test”, which limits eligibility for a Chapter 7 filing. Under the parameters of this test, a household must first demonstrate that it has not previously filed for bankruptcy protection under Chapter 7 (and received a discharge) during the previous 8 years. Next, the household's family income is compared to the median income in the filer's home state, adjusted for the number of people living in the household. If a household's average monthly income is less than the median income (adjusted for family size) in the filer's home state, then the filer is said to have “passed the means test”, and is allowed to file under Chapter 7. Otherwise, the household must file under a different chapter (usually Chapter 13), or use alternative means to resolve its debts.

Chapter 13 filings typically comprise approximately 30-40 percent of U.S. consumer bankruptcy filings each year. The Chapter 13 bankruptcy filing process is quite different from a Chapter 7 filing. When a household files for Chapter 13 bankruptcy protection, it discloses all of its assets, liabilities, income and demographics to the Court. The household is able to retain all of its assets, so long as they are properly disclosed. The Court then assigns a trustee, who prioritizes all of the household's debts in a manner identical to that utilized in a Chapter 7 filing. The trustee subsequently uses the number of people living in the household and officially approved cost of living information in that community to establish an allowable level of (monthly) household expenses. Any average monthly income exceeding these expenses are considered “excess income” which can be used to repay creditors, in order of priority. The court typically requires households with excess income to establish a repayment plan - which lasts between 36 and 60 months - to repay creditors in priority order. Any debts that cannot be funded under the repayment plan are typically discharged.

Chapter 13 filings create a different set of tradeoffs. A household is able to retain all of its assets, but is asked to repay at least some portion of its debts. It takes a household filing under Chapter 13 much longer to re-establish itself financially than under a Chapter 7 filing. Chapter 13 filings are also more creditor friendly, in that the household may repay some or all of its outstanding debts. However, the degree to which general unsecured creditors are repaid depends largely on the financial position of the household at the time of filing. The less excess income a filer has, the fewer debts that are actually repaid. The BAPCPA legislation is generally regarded as “creditor friendly” because it attempts to force households who are deemed to have an ability to repay some or all of the debts, to repay them over time.

Consumer Bankruptcy and Local Economic Development

The legal and economic disciplines have extensively analyzed the causes and outcomes of the consumer bankruptcy process. However, most of these studies have been conducted at a state, regional, or national level. Much less attention is given to the impact of bankruptcy on the well-being of households in individual communities, or at a local level. Still fewer studies have attempted to not only understand consumer bankruptcy at a local level, but also to tie bankruptcy into community development initiatives.

This is extremely problematic, because consumer bankruptcy is an important form of social insurance, and it provides a “lower bound” on financial well-being among the community's most economically vulnerable residents. Reduced use of the consumer bankruptcy process indicates greater economic prosperity in the community, especially among its most economically vulnerable populations. On the other hand, greater use of the bankruptcy process indicates both lower overall economic prosperity and greater variability in economic prosperity within that community. Moreover, an analysis of the characteristics of the households filing for bankruptcy (their incomes, different types of debts, etc.) provides insights into identifying those specific individuals in the community who experience the greatest risk of financial insolvency. Analysis of filer incomes and household sizes also sheds light on the reasons a specific community may have an unusually high or low proportion of cases filed under Chapter 7 or Chapter 13. This helps identify new local initiatives to improve economic well-being for these households.

It is also important to assess bankruptcy filings over time. Such longitudinal assessments are useful in assessing the degree of progress, stagnation, or regress, in number (and characteristics) of the community's economically vulnerable residents.


The purpose of this webpage is to conduct a longitudinal, evolutionary assessment of bankruptcy filings in Spokane, Washington, and more specifically its Logan Neighborhood. The contribution of this project is twofold:

  1. It informs policy makers in Spokane about the characteristics of its most economically vulnerable residents;
  2. It provides a template for researchers and policy makers in other communities to follow in conducting their own assessments.

Our goal for the project is to inform local policy making. This requires us to present our data in as simple, and in as visually appealing a manner as possible. Rather than utilize complicated regression analysis and statistical tests, this analysis uses simple graphs - powered through data analytics - to provide visual depictions of trends in bankruptcy filings.


The data used in the case study come from the Public Access to Court Electronic Records (PACER) database maintained by the U.S. Bankruptcy Court's Eastern District of Washington. We use interval random sampling techniques to draw either a 10% sample from the population of (Chapter 7 and Chapter 13) consumer bankruptcy filings each year, or 400 observations, whichever is the lesser of the two.

Current and former faculty members in the Gonzaga University School of Business have worked with Court personnel to access the PACER database for the purposes of academic research. This allowed for random samples to be drawn for the years 2003, 2005, 2007, 2009, 2011, 2014, 2016, and 2020 over the entirety of the District. Because the focus of this project was specifically on Spokane, we eliminated any filing whose primary residence was not located in one of the 13 zip codes comprising the community of Spokane, Washington. Given the use of interval random sampling techniques, this, should lead to a sample that is also approximately 10% of the filings that occurred each year in the city of Spokane.

As noted above, we focus on filings in Spokane, as defined by zip code. We are specifically interested in the Logan neighborhood which is primarily (but not exclusively) covered by the 99207 zip code. This neighborhood is interesting to study because it is economically diverse in nature. The Logan neighborhood is home to Gonzaga University, and many of its students and employees live in the neighborhood. Its remaining residents' incomes are typically employed in blue-collar occupations, and whose incomes are at or below the mean for Spokane as a whole. The other remaining zip codes utilized include: 99021, 99026, 99202, 99203, 99205, 99206, 99208, 99212, 99217, 99218, 99223, and 99224.

In this page, we will investigate the distribution of different variables, one at a time.

Distribution of variable of interest using a histogram

A histogram provides information on how values of a variable are distributed across the range of values. The height of a bar in the graph indicate how many observations fall within a specific interval of the range. For more on how to interpret histograms, please see this video from Khan Academy.

Click here for the Khan Academy video!

Distribution of variable of interest using box plot

Box plots provide information on the minimum, maximum, 25th percentile, 50th percentile (median), and the 75th percentile of a distribution. Please see the following tutorial for more information.

Click here to learn more about box plots!

How did different variables change over the years for all Zips

Please select a variable to see how its average (mean) changed over the years

In this page, we will investigate different variables, one at a time.

From the list of variables given below, please select a variable to study in-depth.

Comparing Zip codes on the mean of the variable of interest

Please note that the red line denotes the mean for all Zips together

Comparing distribution of variable of interest across Zip codes using box plots

Box plots provide information on the minimum, maximum, 25th percentile, 50th percentile (median), and the 75th percentile of a distribution. Please see the following tutorial for more information.

Click here to learn more about box plots!

Geographically mapping the average of the variable of interest in different zip codes

Scatter plot between two variables

Relationship between variables

Interpreting correlations

Correlation coefficients are statistics that are used to measure the statistical relationship (or the “association”) between two variables. Correlation coefficients between two variables can take any value between -1 and +1. Normally, correlation coefficients are interpreted in two steps.

  1. Assess the sign of the correlation coefficient. Negative values indicate a negative relationship; that is, as one variable increases, the other variable decreases (for example, as individuals spend more of their income, they save less of it). Positive values indicate a positive relationship; that is, as one variable increases, the other variable also increases (for example, as individuals earn greater income, they tend to spend more on goods and services).

  2. Determine the size of the correlation coefficient, regardless of sign (or in absolute value). If we ignore the sign, all correlation coefficients (in absolute value) take values between 0 and 1. If the absolute value of the correlation coefficient is close to 0, then there is virtually no statistical relationship between the two values. If the absolute value of the correlation coefficient is close to 1, then there is a nearly perfect, 1-to-1, statistical relationship between the two values. For example, one might expect the correlation coefficient between the average height of 12 year old children in Spokane and the price of wool in Scotland in any given day of the year to be close to zero. These two variables should not be related to each other at all. On the other hand, we might expect the price for gasoline in Spokane on any given day to be highly correlated (i.e., have a correlation much closer to 1, than to 0, in absolute value) with the price for a barrel of crude oil on the same day, because gasoline is refined from crude oil.

Correlation coefficients are typically represented numerically, but they can also be represented graphically using a scatterplot. More specifically, it is possible to draw a line through the middle of the data, which is called a “trend line”. If the correlation between the two variables is positive, the trend line has a positive slope. If the correlation coefficient is negative, the trend line slopes downward. Similarly, if the absolute value of the correlation coefficient is close to 1, all of the data points in the scatterplot lie on or very near to the trend line. If the correlation coefficient is close to zero, the data points all lie very far from the trend line.

Statisticians typically conduct hypothesis tests under the maintained assumption (or null hypothesis) that the correlation coefficient between two variables (over an entire population) is zero. This means that there is no statistical relationship (measured over the entire population) between the two variables. That is, the two variables are completely unrelated to each other. If this null hypothesis is incorrect (or if we reject the null hypothesis), then we know that there is a meaningful statistical relationship (measured over the entire population) between the two variables. The test is operationalized using a p-value, which equates to running a two-sided hypothesis test with 95% confidence (or 5% significance). If the p-value is less than 0.05, then we reject our null hypothesis. We are 95% sure that there is some meaningful relationship between the two variables. If the p-value is greater than 0.05, then we “fail to reject” our null hypothesis. That is, we are 95% confident that our prior assumption of no relationship between the two variables is reasonably correct.

Bankruptcy explorer

This application is a shiny application that is built in the R statistical programming environment. The list of packages it uses are listed below.