Consumer Credit Trends by Income and Geography in 2001-12

By Amromin, Gene; McGranahan, Leslie et al. | Chicago Fed Letter, January 1, 2015 | Go to article overview

Consumer Credit Trends by Income and Geography in 2001-12


Amromin, Gene, McGranahan, Leslie, Schanzenbach, Diane Whitmore, Chicago Fed Letter


In this Chicago Fed Letter, we present information on credit growth rates at the zip code level for different types of consumer debt (mortgages, student loans, and other credit). We show how the level and composition of debt changed during the credit run-up period (2001:Q4-2008:Q3) and also during the credit retrenchment period (2008:Q3-2012:Q4). To better understand whose credit use changed over time, we show how the credit cycle played out across income classes by grouping zip codes by their decile rank in the national income distribution. In addition, to understand where credit use changed over time, we show how the credit cycle played out across states. We then cross-tabulate both measures, which allows us to show the connection between credit growth during the credit cycle's boom years and the subsequent credit retrenchment across the income distribution in different parts of the nation. This exercise may be particularly relevant to those interested in understanding the impact of the Great Recession on low-income families.3

Data

We use three primary sources of data to investigate credit patterns across zip codes with different average incomes in the 50 states (and the District of Columbia). First, in order to group zip codes by income, we use Internal Revenue Service (IRS) zip-code-level data on annual adjusted gross income per tax return for 2001 and 2012. Second, we use 2001-12 data from the Federal Reserve Bank of New York Consumer Credit Panel/Equifax (CCP) database to construct quarterly credit aggregates for zip codes.4 Third, we use annual data on zip code population from the U.S. Census Bureau. We restrict our analysis to zip codes that consistently have data from all three sources over time and whose boundaries and position in the income distribution have been fairly stable.5 The result is a balanced panel of 25,946 zip codes. We group zip codes into population-weighted deciles (i.e., each decile has the same number of individuals) based on their average adjusted gross income per tax return in 2001, and hold each zip code's income category assignment constant over time.

We analyze credit measures for zip code income deciles, states (which we group according to the pace of state-level credit run-up in 2001:Q4-2008:Q3), and state groups by deciles. We measure all credit values in per capita terms-defined as total credit for a group of zip codes divided by total U.S. Census population in those zip codes.

Debt patterns by income decile

We begin by looking at the increase in debt across the income distribution in the period 2001:Q4-2008:Q3. We start in 2001:Q4 because the CCP data stabilize by that date. Prior to late 2001, some of the patterns appear to be driven by improvements and refinements in data collection. We end our calculation in 2008:Q3 because that is the quarter in which national aggregate consumer credit peaked.6 In figure 1, panel A, we display average annual percentage growth in real per capita debt, by zip code income decile, for the following: mortgage debt (defined as mortgages plus home equity installment loans), student loans, total debt excluding student loans, and total debt excluding mortgage debt and student loans (thus primarily composed of revolving home equity, auto, and credit card debt).7

In figure 1, panel A, we show what we and others have noted previously8- namely, that mortgage debt growth rates are highest at the bottom of the income distribution during the run-up period while nearly monotonically declining across the deciles.9 Student loan growth rates (captured on the right-hand scale) display a similar pattern. However, looking at growth rates in debt excluding mortgage debt and student loans, we find the opposite pattern: The growth rates for such debt are highest at the top of the income distribution. Large increases in revolving home equity lines of credit-which play more of a central role in the debt profile of higher-income individuals-drive this pattern. …

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Consumer Credit Trends by Income and Geography in 2001-12
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