Keeping Up With the Joneses: Analyzing the Effect of Income Inequality on Consumer Borrowing

Quantitative Marketing and Economics, 3, 145–173, 2005.C© 2005 Springer Science + Business Media, Inc. Printed in The United States.

Keeping Up With the Joneses: Analyzing the Effect ofIncome Inequality on Consumer Borrowing

MARKUS CHRISTEN∗Assistant Professor of Marketing at INSEAD, Fontainebleau, FranceE-mail: [email protected]

RUSKIN M. MORGANDirector at Huron Consulting Group in Chicago, ILE-mail: [email protected]

Abstract. Household debt relative to disposable income increased from 60% in 1980 to 104% at the end of2003. ‘Buying on credit’ has become so popular that an increasing number of firms generate more profit fromfinancing than from selling their products. In this paper, we show that rising income inequality has substantiallycontributed to increased consumer borrowing. Income inequality affects all components of total household debt,but the impact is strongest on non-revolving debt (installment loans), which is used to finance the purchase ofconsumer durables. We argue and provide evidence that the income inequality effect on consumer borrowing isa result of conspicuous consumption. Rising income inequality has forced households with smaller income gainsto use debt to keep up their consumption level relative to households with larger income gains.

Key words. debt puzzle, consumer credit, income inequality, conspicuous consumption

JEL Classification: D12, G29, J31, M30

More and more consumers in the U.S. pay for products and services with debt. This rangesfrom traditional installment loans for ‘big ticket’ items like cars—nine out of 10 new carsand six out of 10 used cars are bought on credit—to credit cards for almost everything else.The average outstanding credit card balance subject to interest charges is over $7,000 perhousehold with credit cards and 23 percent of those households have used up the credit limitsof their cards. But the fastest growing source of credit is home equity loans. Outstandinghome equity debt now exceeds credit card debt. Overall, total household debt relative todisposable personal income soared from 60% in 1980 to 104% in 2003 (see Figure 1).1 Overthe same time period of generally strong economic growth, personal bankruptcy filings percapita quadrupled to over 1.6 million filings in 2003. Thanks to the popularity of ‘buying oncredit’, an increasing number of firms today earn as much or even more profit from offering

∗Corresponding author.1 According to the Federal Reserve’s Flow of Funds Account, household debt totaled $9.3 trillion at the end

of 2003. Of this total, households owed $6.7 trillion in mortgage debt and $2 trillion in consumer credit,which consists of $1.25 trillion in non-revolving debt (e.g., car loans) and $0.75 trillion in revolving debt (e.g.,credit card debt). (The balance over $1/2 trillion includes other loan categories, such as government loans tohouseholds and debt owed by non-profit organizations. We exclude this in our analysis.)

146 CHRISTEN AND MORGAN

Figure 1. Household debt as a percentage of disposable personal income: Total household debt, consumer creditand mortgage debt.

financing services as from selling products.2 Advertising for cars, furniture, electronicgoods, or jewelry more and more promote terms of financing plans rather than productbenefits. Even medical providers, such as dentists, plastic surgeons and ophthalmologistsare offering cosmetic services payable via a monthly payment plan. Why have consumersassumed so much more debt and financial risk over the past 25 years?

This question has attracted the attention of policy makers3 and researchers in economicsand marketing because the rise in household debt is unprecedented and far greater than canbe explained with the standard life-cycle/permanent-income model of Ando and Modigliani(1963) and Friedman (1957) (see for example Barnes and Young, 2003). A number of ex-planations have been proposed including rising household wealth (e.g., Maki and Palumbo,2001) and increased credit supply (e.g., Debelle, 2004). In consumer research, studies focuson the effect of consumer ‘irrationalities’ on credit card borrowing such as hyperbolic timepreferences (Laibson et al., 2000) and overly optimistic income expectations (Soman andCheema, 2002). However, credit card borrowing is a limited part of the phenomenon. Creditcard debt is lower than non-revolving debt (installment loans) and accounts for less than tenpercent of total outstanding household debt (see Figure 2). Moreover, home equity loans andmortgage refinancing, classified as mortgage debt and not consumer credit, are also used to

2 For example, credit income at Circuit City is expected to account for 100% of fiscal 2003 earnings. Theexpectation at Sears, prior to spinning off its credit card operations to Citigroup, was 54% (Stires, 2003). BothFord and GM reported large profit gains in early 2004, almost all of which came from their respective financingarms (The Economist, August 7, 2004).

3 Federal Reserve Board Chairman Alan Greenspan has on several occasions addressed concerns about the healthof household finances (e.g., http://www.federalreserve.gov/boarddocs/speeches/2004/20040223/default.htm).

KEEPING UP WITH THE JONESES 147

Figure 2. Consumer credit as a percentage of disposable personal income: Total consumer credit, non-revolvingdebt and revolving debt.

finance consumption. Credit supply arguments have limited explanatory power because theextra cost from borrowing has remained relatively high even as the cost of funds for bankshas decreased (Ausubel, 1991; Bennett et al., 1998). As a result, the fraction of incomerequired for debt payments has also increased substantially (see Figure 3). In general, thedeterminants of growth in consumer borrowing are not well understood and more researchis needed (Kowalewski, 2000; Maki, 2000).

In this paper, we propose and test a largely overlooked determinant of consumer borrow-ing: income inequality. Income inequality has risen substantially over the last two decades(see Figure 4). Income for high earners relative to medium or low earners has increasedsignificantly for U.S. women since 1984 and for U.S. men since 1980 (Gottschalk, 1997;Katz and Autor, 1999). Existing research on income inequality examines primarily thedeterminants of the increase but has little to say about the consequences (Welch, 1999). In-creasing income inequality and high levels of indebtedness4 are often cited as evidence of aweak overall economy but there is no direct empirical evidence to link income inequality tohousehold indebtedness. Some data and findings are consistent with a positive relationshipbut alternative explanations could also account for them. For example, data from the Surveyof Consumer Finances (see Figure 5) indicate that household debt and debt burdens haveincreased more for households in lower income groups (Aizcorbe et al., 2003). However,this could be a purely supply-driven effect as the number of liquidity constrained house-holds has dropped (Debelle, 2004). Similarly, the finding that rising income inequality didnot result in a corresponding increase in consumption inequality (Krueger and Perri, 2002)

4 We will use the terms indebtedness and debt-to-disposable income ratio interchangeably.


Figure 3. Estimates of household debt payments.

could be explained with the higher savings rate of higher income households (Dynan et al.,2004).

Using aggregate quarterly US data for the years 1980 to 2003 and controlling for variousother determinants of household borrowing, we find a strong positive effect of incomeinequality on household debt relative to disposable income as well as on the componentsof household debt, i.e., mortgage debt, revolving debt (e.g., credit card debt) and non-revolving debt (e.g. car loans). This ‘income inequality’ effect is robust to changes inmodel specification and estimation approach. It is not only statistically significant but alsoeconomically highly relevant. For example, we find that household indebtedness is moresensitive to changes in income inequality than to changes in interest rates. Also, ignoringincome inequality leads to a much lower estimate of the ‘wealth’ effect. Most important formarketers, we find that the ‘income inequality’ effect is strongest for non-revolving debt,which is used to finance consumer durable purchases.

Why should higher income inequality lead to higher household indebtedness? One ex-planation follows from the permanent-income theory (Friedman, 1957), which proposesthat consumers use debt to smooth consumption when suffering temporary income losses.This theory lies beneath the argument that the ‘losers’ of the economic boom are forcedto use debt to maintain their standard of living leading to high levels of debt and financial


Figure 4. Real wages for different income deciles and gini index.

Figure 5. Changes in household finances between 1992 and 2001 by income groups.

distress (Sullivan et al., 2000). In other words, when income inequality increases becausethe poor got poorer, we should observe a positive effect of income inequality on householdindebtedness. This explanation, however, does not conform with aggregate level incomechanges. During the 1980s and in particular the 1990s even the lowest wage earners experi-enced real gains, albeit small ones (see Figure 4 and last panel of Figure 5) indicating that,


on average, income inequality increased because the rich got richer and not because thepoor got poorer.5 Moreover, this argument would predict that debt grows faster during eco-nomic downturns when real incomes are more likely to fall. In fact, consumer credit grewmost quickly when the economy came out of a recession, which contradicts the argumentthat the primary use of consumer credit is to get households through ‘tough times’ (Maki,2000). Hence, the permanent-income theory cannot account for the strong effect of incomeinequality on household indebtedness.

We argue that the effect of income inequality on household indebtedness results fromthe need for consumers to maintain or improve their social position through conspicuousconsumption (Frank and Cook, 1995). Marketers (e.g., Aaker, 1997; Levy, 1959; Soloman,1983) and economists (e.g., Bagwell and Bernheim, 1996; Frank, 1985; Becker, 1974;Veblen, 1899) have long understood that consumers purchase products not only for theirfunctional utility, but also for their social meaning. However, the aggregate level implica-tions of conspicuous consumption have not been examined extensively. The social benefitfrom conspicuous consumption depends on the consumption level and thus on the incomelevel of other consumers. The standards that define acceptable schools, housing, clothing,transportation, and a host of other important items ultimately depend on what others spendon them. Thus, when the income of a neighbor increases, other consumers with no or smallerincome gains need to finance more consumption with debt to “keep up with the Joneses.”Moreover, conspicuous consumption leads to a “positional arms race” as increased conspic-uous consumption constantly establishes new, and more expensive, reference points (Frankand Cook, 1995). This “race” requires consumers to take increasingly more risk as incomeinequality increases, even when they are inherently risk-averse (Gaba and Kalra, 1999), andtherefore may not stop until a party is overextended financially. In other words, the theoryof conspicuous consumption predicts an increase in household indebtedness and financialdistress as income inequality increases even when no consumer experiences a real incomedecline.

This study makes a number of important contributions to our understanding of consumerborrowing to finance consumption. It identifies an important determinant of increased con-sumer borrowing and, at the same time, points to an important consequence of rising incomeinequality. While our aggregate data does not allow us to directly prove, at the level of anindividual household or consumer, that the ‘income inequality’ effect is a result of con-spicuous consumption, findings from additional analyses provide support for our argumentand rule out a number of alternative explanations. This does not mean that other factorscould not have contributed to the observed ‘income inequality’ effect. Moreover, the ‘in-come inequality’ effect does not replace existing explanations for the surge in householdindebtedness. In fact, it complements the argument that competition among credit suppliersexpands credit to riskier borrowers because of the stickiness in interest rates (Ausubel,1991). It suggests that credit suppliers met the needs of consumers who desired to assumemore financial risk to “keep up with the Joneses”. By simultaneously considering all typesof household debt and not just credit card debt, this study also offers a more comprehensive

5 An increase in the educational wage premium is the primary determinant of higher income inequality. Moreover,unearned income (i.e., social assistance) has compensated for the effects of declining wages at the bottom ofthe income distribution (Welch, 1999).


analysis of consumer borrowing than other studies. In particular, our analysis accountsfor the incentives to shift between consumer credit and mortgage debt and among differenttypes of consumer credit. This insight is important to better understand competition betweenproviders of credit and the effect of increasing real estate prices on consumer credit (wealtheffect).

The rest of the paper is organized as follows. First, we discuss existing research related tohousehold borrowing and income inequality. In Section 2, we develop an empirical modeland in Section 3 we present the estimation results for the effect of income inequality. Thisalso includes analyses to assess the robustness of the results and the relative importance ofthe income inequality effect. In support of the suggested effect of conspicuous consumption,we present results from several additional analyses that help rule out alternative explanationsin Section 4. Concluding remarks are provided in Section 5.

1. Income inequality and household borrowing

1.1. Theoretical explanations for household borrowing

The standard model of household debt, the life-cycle/permanent-income model of Andoand Modigliani (1963) and Friedman (1957), assumes that a household chooses a pathof consumption that maximizes utility over its lifetime subject to an intertemporal budgetconstraint. A household cannot consume more than the sum of the present discounted valueof its labor income and its current net worth (asset holdings minus liabilities). Assuming anupward sloping path of income over the working life of the household, households borrowto finance consumption in the early stage of their working life. As the household ages andincome grows, indebtedness generally decreases. Households also use debt (and savings) tosmooth consumption over uncertain temporary income fluctuations. In this standard model,aggregate household indebtedness will depend on demographics, the expected path of futureincome, and real interest rates.

The implications of the life-cycle model for household indebtedness are accentuatedwhen households want to own rather than rent durable goods, especially housing, whichrequires much larger amounts of debt than that needed to smooth consumption of othergoods.6 The tax system can also have an important impact on household indebtedness,particularly the tax treatment of house purchases. For example, if mortgage interest is taxdeductible, but interest payments on other loans are not, households may borrow againsttheir houses to fund other consumption.7

This standard model as presented above focuses on demand-side determinants of house-hold borrowing, effectively treating the supply of funds as perfectly elastic at a given interestrate. In reality, some households face liquidity constraints, i.e., are not able to borrow the

6 While the rise in household indebtedness is closely associated with an increase in mortgage debt (see Figure 1),homeownership actually dropped in the 1980s and only started to rise markedly in 1995.

7 For example, Freddie Mac reports that about 65 percent of refinanced mortgage debt over the last 15 years hasresulted in an increase in the outstanding loan amount and only about 12 percent in a reduction. On the otherhand, the interest rate of refinanced loans was reduced on average by 13 percent.


amount that is optimal according to the standard model (Hall, 1978). As a result, changesin the structure of the lending market have a significant effect on the extent of householdborrowing. Deregulation of financial markets since 1980 has substantially eased existingcredit constraints. Hence a significant part of the growth in household indebtedness mayreflect a move to a higher equilibrium level where households are less liquidity constrained(Debelle, 2004).8

1.2. The effect of income inequality

According to the standard life-cycle model, income inequality can be related to householddebt at an aggregate level in two ways. First, declining incomes (for some income groups)will lead to greater borrowing as households use debt to smooth consumption over tempo-rary income declines. Second, a younger population leads to both higher income inequalityand higher aggregate household indebtedness. However, over the past 20 years, and cer-tainly during the 1990s, the population neither became younger nor experienced real wagedecreases. Under these conditions the standard model does not provide a reason for an effectof income inequality on increased household indebtedness.

A change in the distribution of income affects household indebtedness, independent ofincome levels and demographics, when a consumer’s consumption utility depends on theconsumption level and thus on the income of others. Veblen (1899) argued that all individ-uals crave for status, which is obtained by wasteful displays of wealth: “In order to gainand to hold the esteem of men, wealth must be put in evidence, for esteem is awardedonly on evidence” (p. 24). By social custom, the evidence consists of unduly costly goodsthat fall into the “accredited canons of conspicuous consumption” (p. 71—emphasis isours). Frank (1985) offers an evolutionary perspective for conspicuous consumption. Heargues that preferences are shaped by the forces of natural selection. The success of in-dividuals and their children depends largely on their relative standing in a society. Sincerelevant attributes such as ability are difficult to observe, individuals engage in a signal-ing contest to attain a high relative position and increase the chance of succeeding. Whenthe income of other consumers increases more rapidly, a consumer must resort to debt tokeep up conspicuous consumption relative to those other consumers. This requires greaterrisk taking. Since most consumers are risk averse, one would expect a dampening influ-ence of risk on borrowing. However, in contests where few ‘winners’ obtain most of thebenefits, risk aversion does not restrain risk taking and thus borrowing (Gaba and Kalra,1999). Thus, rising income inequality increases not only the need for more debt to keepup conspicuous consumption, but also the reward from succeeding in society. Frank andCook (1995) provide anecdotal evidence suggesting that the importance of conspicuous

8 Theories of consumer self-control (e.g., Thaler and Shefrin, 1981; Wertenbroch, 1998) provide an alternativeexplanation for the effect of deregulation. Consumers may have treated their houses as a source of saving thatwas not to be accessed until retirement. The illiquidity of home equity was one way of ensuring this. Withthe greater access to home equity loans, consumers may have lost an important ‘device’ to discipline theirconsumption and borrowing. For example, experimental evidence shows that consumers using credit cardsspend more for otherwise identical products than those using cash or checks (e.g., Feinberg, 1986; Soman,2000).


consumption has increased in the US economy, as it has increasingly become a ‘winner-takes-all’ economy.

Conspicuous consumption occurs in all societies but is particularly important in affluentsocieties. When incomes rise, spending on conspicuous goods as a proportion of incomewill increase relative to other goods (Becker, 1974; Hirsch, 1976). Becker’s model of socialinteractions (Becker, 1974) can be extended to analyze the implications of conspicuousconsumption. This analysis shows formally that higher income inequality will lead to higheraggregate indebtedness—even when the lowest income is not decreasing.9

2. Model and data

2.1. Empirical model

The model for our empirical analysis is based on the accounting identity �Dt ≡Ct + St − Yt , where �Dt is the change in debt, Ct consumption, St savings, and Yt dis-posable personal income (DPI), all at time t . As a result, total household debt at time t isDt ≡ �Dt + Dt−1 ≡ Ct + St − Yt + Dt−1. By scaling this identity with income Yt , weobtain the dependent variable of interest, i.e., the debt-to-income ratio, D/Yt . In additionto total household debt (HD), we examine mortgage debt (MD), non-revolving debt (ND),such as car loans, and revolving debt (RD), which consists mostly of credit card debt,10

all relative to DPI. (The sum of the latter two components is called consumer credit (CD),which we do not examine separately). This partition is important because understandingthe determinants of revolving and non-revolving debt to finance consumer goods purchasesis most relevant to marketers. However, as discussed earlier, these two components cannotbe examined independent of mortgage debt. Rising house prices increases households’ bor-rowing capacity, for example through home equity loans, which are used to a large extentto payoff consumer credit and finance consumption that is unrelated to housing. In otherwords, mortgage debt can be used to substitute for consumer credit. The reverse seemsless relevant. On the other hand, revolving and non-revolving credit are substitutable aswell.

To examine the effect of income inequality, we also need to account for the variousfactors that follow from the standard life-cycle/permanent-income model. Indebtednesswill tend to be greater when income is expected to increase faster or when householdwealth increases. The effect of changes in real interest rates on indebtedness dependson the relative size of income and substitution effects. A decline in interest rates re-duces the return on the household’s asset holdings, but decreases the cost of borrow-ing and increases the present value of future income. After controlling for expectedchanges in real income and changes in household wealth, higher interest rates shouldlead to lower indebtedness. The real cost of borrowing also depends on the tax treat-ment of interest payments. The tax deductibility of interest payments should increase in-

9 Details are available from the authors.10 The second largest item is overdraft protection on checking accounts.


debtedness. Finally, a reduction in the expected ‘cost’ of default will allow householdsto increase indebtedness and assume more risk. On the other hand, this effect couldbe compensated for by lenders that become more cautious. All these factors will af-fect borrowing through the consumption-to-income ratio, C/Yt , of the above accountingrelationship.

2.2. Measures, data and estimation

Income inequality is the independent variable of central interest. A variety of mea-sures have been proposed to determine the amount of inequality in an income distri-bution, including income classes, income shares, percentile ratios, and indexes. Fre-quently used measures in the earnings and wage inequality literature are the Gini-Index,11 and the 90th/50th or the 90th/10th percentile ratios (Ryscavage, 1999). Incontrast to the percentile ratios, the Gini-Index is based on the entire income dis-tribution. We thus use the Gini-Index as the measure of income inequality, but ex-amine whether the results are sensitive to the use of a particular income inequalitymeasure.

The other independent variables are measured as follows. For expected future incomegrowth, we use past growth of real disposable personal income, �Yt−d , measured as anannualized percent.12 Similarly, we use past per capita asset holdings, APCt−d , to accountfor the ‘wealth’ effect. As discussed earlier, ownership of houses has a significant impacton household debt. Higher house prices require more mortgage debt but also enable homeowners to borrow more against that asset and reduce consumer credit. Hence, it is necessaryto separately account for housing (non-financial) assets, ANFPCt−d .

We use the following interest rates, rt , for different debt components. For mortgage debt,we use the 30-year standard mortgage rate. For non-revolving debt, we use the interestrate for car loans charged by non-financial institutions (e.g., car companies). For revolvingdebt, we use the interest rate for personal loans charged by banks. For total household debt,we use a weighted average of these three interest rates. To account for the ‘competitive’effect of alternative borrowing options, qt , we add the mortgage rate to the equation fornon-revolving debt. For revolving debt, we add the interest rate for car loans used for non-revolving debt. We capture the tax effect of mortgage debt with the marginal tax benefitfrom deducting mortgage interest payments, mt . Finally, we follow existing research (e.g.,Fay et al., 2002) and use past bankruptcy rates, BRt−d , i.e., the number of filings per capita,as a proxy for the expected cost of bankruptcy.

11 The Gini-Index is defined as follows. Consider a plot of the cumulative income distribution where all house-holds are sorted by income starting with the lowest income household. The Gini-Index is then calculatedas the area between the diagonal line for an even income distribution and the actual cumulative incomedistribution divided by the total area below the diagonal line. A value of zero implies that everybody hasthe same income; a value of 1 or 100% implies that one single household earns all the income. The quar-terly Gini-index used in our analysis is constructed from the income deciles calculated from the CurrentPopulation Survey and thus not affected by the methodology changes in the reported annual Gini Index in1992.

12 We denote an unspecified lag as d. The detailed lag structure is determined during estimation.


Assuming a linear functional form leads to the following estimation model:

Dt

Yt= α0 + α1t + α2

Dt−1

Yt−1+ β1

Ct−1

Yt−1+ β2rt + β3qt + β4mt

+∑

j

β5 j�Yt− j +∑

k

β6kAPCt−k +∑

l

β71ANFPCt−1

+∑

m

β8mBRt−m +∑

i

γi GINIt−i + εt . (1)

This specification also includes a trend variable and the past consumption rate, C/Yt−1.From the accounting identity follows that α2 should be equal to 1. When controlling forpast borrowing, D/Yt−1, the effect of past consumption, C/Yt−1, should reduce currentconsumption and thus borrowing (β1 < 0). The higher past consumption relative to income,all else equal, the fewer resources households have to pay for or finance future consumption.Overall, we estimate four such debt equations—one for total household debt and one foreach debt component. The exact lag structure for all variables will be based on the estimationresults.

We estimate our empirical model using quarterly U.S. data covering the years from 1980to 2003.13 We focus on this time period for the following reasons. First, as shown in Figure 1,this is the period during which most of the growth in household indebtedness occurred. Sec-ond, it corresponds with the era of deregulation of financial markets and covers the periodafter important changes in bankruptcy law in 1979. There are three important estimationissues to consider. First, since interest rates are in equilibrium determined by credit demandand supply factors, we need to control for shifts in credit supply to properly estimate thedifferent debt (demand) equations. As instruments, we use the adjusted monetary base,which is a direct measure of the credit supply in the monetary system provided by theFederal Reserve, together with 1-period lagged interest rates, rt−1. Our primary interestis in understanding shifts in demand for debt but it is important to properly control forconcurrent supply-side shifts that occurred due to deregulation. Second, since the threeindividual debt components are likely related with each other, we use SUR to estimate theeffect of income inequality. Finally, the use of macroeconomic data poses two problemswith respect to attributing the income inequality effect to conspicuous consumption. First,our empirical analysis could be subject to an aggregation bias since aggregate debt relativeto disposable income is a ratio of two aggregate numbers rather than an aggregation ofratios. Second, there is heterogeneity in social networks as well as in its effect on consump-tion. Outside an experimental setting, it is all but impossible to obtain measures for theactual networks and the kinds of products that are considered conspicuous consumptionin each network. A theoretical analysis of these two issues indicates that our estimationmore likely underestimates the effect of income inequality on household indebtedness (seeAppendix B).

13 Debt, consumption, income, asset and interest data are readily available from various government sources (seeAppendix A for details).


Table 1. Augmented Dickey-Fuller test results for different debt-to-income ratios.

Parameter estimates F-test statistics for restrictionsLags

y k a0 a2 γ a0 = a2 = γ =0 a2 = γ = 0 a0 = γ = 0

D/Y 8 7.364 (3.460) 0.097 (4.793) −0.167 (4.056) 8.995 11.954 10.342

CD/Y 4 1.296 (4.200) 0.014 (3.872) −0.111 (4.460) 6.925 9.953 10.002

ND/Y 4 0.836 (3.594) 0.004 (2.434) −0.087 (3.923) 5.274 7.694 7.701

RD/Y 4 0.119 (3.290) 0.008 (3.790) −0.083 (3.667) 6.563 7.212 6.870

MD/Y 8 4.577 (3.501) 0.052 (3.903) −0.128 (3.572) 6.133 8.002 6.486

Results are based on the estimation model:

�yt = a0 + γ yt−1 + a2t +k∑

i=2

βi �yt−i+1 + εt .

Note: The numbers in parentheses are t-values. The critical value for a 95% confidence interval for the t-test ofγ is 3.47. The respective critical values for a 95% confidence interval for the restrictions are 4.88 for the 3-wayrestriction and 6.49 for the two-way restrictions.

3. Estimation results

3.1. Specification tests

We start our analysis by assessing whether the different debt-to-income ratios are stationaryor not. Figures 1 and 2 suggest a more or less strong growth trend for the estimation period1980 to 2003 for all four debt measures. We use the augmented Dickey-Fuller (ADF) testto test for unit roots in these variables, as described in Enders (1995). The results of thistest are presented in Table 1. In all cases we can reject the hypothesis of a unit root. (This isalso true for the Gini-Index.) However, non-revolving debt has a drift but no trend and thesignificance of the unit root test for mortgage debt is not very strong. We examine thereforethe sensitivity of our results from level (i.e., non-transformed) data by estimating all debtequations also with differenced data.

A second estimation issue concerns the choice of lags for the different explanatoryfactors. We start our estimation with the inclusion of lags ‘t − 1’ and ‘t − 2’ for allvariables (except interest rates) and then decide which lags to retain based on model fit.(Longer lags were not significant). Considering the statistical significance of effects, wealways retain the second lag for income inequality and the bankruptcy rate. Appendix Cprovides a summary of results from this process for total household debt. These results yieldthree important insights regarding the ‘wealth’ effect. First, excluding income inequalityleads to a substantially smaller estimate of the ‘wealth’ effect on aggregate householdindebtedness. Second, different lags of asset holdings have opposing effects and we thusinclude both lags. Finally, the ‘wealth’ effect is different for non-financial assets. Changes innon-financial asset holdings, primarily housing, has a stronger positive effect on householdindebtedness.


3.2. Income inequality effect

The final estimation results for the three individual debt components and total householdindebtedness are reported in Table 2. In all cases, we find a strong positive relationshipbetween income inequality and debt. The size of the income inequality effect varies sig-nificantly across debt components. Relative to their fraction to total household debt, theincome inequality effect is strongest for non-revolving debt and weakest for mortgage debt.Non-revolving debt accounts for about 14% of total household debt but accounts for 25%of the effect of income inequality on household indebtedness.14

To assess the robustness of the income inequality effect, we repeat the analysis by varyingmodel assumptions and estimation approach. First, we also report in Table 2 the estimatesfor income inequality for the three individual debt components obtained with OLS ratherthan SUR. As expected, these estimates are less efficient but otherwise very similar.

Next, we report in Table 3 the effect of income inequality on each debt component foreight different cases (of which seven are new). They result from 2 types of data transforma-tion (level data and differenced data) ×2 measures of income inequality (Gini-Index and the90th/10th percentile ratio) ×2 model specifications. The alternative model does not sepa-rately account for non-financial asset holdings. Moreover, it neither accounts for the interestrate of a ‘competing’ debt component nor for the tax benefit of mortgage debt. Finally, werepeat the analysis with annual data. In this case, we use a longer time period (1953 to2003) but a simpler lag structure (1 year) and include only one interest rate (1-year T-billrate).

Table 3 reports a total of 36 estimates for the effect of income inequality: 32 based onquarterly data and 4 based on annual data.15 The results in the first row of Table 3—leveldata with Gini-Index—correspond to the results reported in Table 2. All estimates have theexpected positive sign, 33 are significant at the 0.05-level or better and 2 more are significantat the 0.1-level. The only insignificant estimate is obtained for revolving debt with level datawith the 90th/10th ratio as a measure of income inequality. Moreover, the results in Table 3show that the parameter estimates are quite consistent. Finally, the R2-values obtained withdifferenced data for the ‘base’ specification (1) are quite high. For non-revolving debt, ourmodel explains about 50% of the variation; for mortgage debt, it explains about 34%; andfor revolving debt about 26%. For total household debt, the model explains about 35%.When eliminating income inequality, the R2 drops by 2% to 3% for each debt component.Moreover, the effects of household asset holdings and bankruptcy rate are considerablysmaller (i.e., closer to zero).

The longer time series available with annual data allows us to examine the argumentthat the importance of conspicuous consumption has increased in recent times (Frank andCook, 1995). If the effect of income inequality is related to conspicuous consumption

14 Given the presence of autoregressive terms in our estimation equations, we carefully examined the estimationerrors for moving average terms. In most cases we find a slightly elevated moving average term for lag 2. Theresults did not change when removing these effects.

15 Detailed estimation results for this sensitivity analysis are available from the authors. Since we have a priorhypothesis for the effect of income inequality, we use a 1-tail t-test when evaluating the income inequalityestimates.


Table 2. Estimation results for extended model.

Non-revolving debt Revolving debt Mortgage debt Household debt

Constant 0.884(2.669)

0.801(1.546)

9.101(8.868)

9.408(12.536)

Trend 0.014a

(0.006)0.007b

(0.003)0.001

(0.021)0.016

(0.029)

Debt ratioD/Yt−1

0.992a

(0.021)1.000a

(0.022)0.934a

(0.029)0.930a

(0.053)

ConsumptionC/Yt−1

−0.087a

(0.018)−0.038a

(0.011)−0.338a

(0.070)−0.453a

(0.115)

Interest rate†

rt

−0.051a

(0.010)−0.033a

(0.013)−0.154a

(0.042)−0.236a

(0.070)

Competing rate††

qt

0.027b

(0.013)0.021a

(0.006)

Mortgage tax savingmt

0.027b

(0.013)0.112a

(0.043)0.171b

(0.068)

Income growth�Yt−1

0.0071c

(0.0040)−0.0176

(0.0144)0.0064

(0.0232)

Income growth�Yt−2

−0.0037c

(0.0021)−0.0203

(0.0135)−0.0154

(0.0219)

Assets per capitaA/POPt−1

−0.0092c

(0.0048)−0.0056b

(0.0028)−0.0548a

(0.0174)−0.0672a

(0.0239)

Assets per capitaA/POPt−2

0.0145a

(0.0052)0.0092a

(0.0030)0.0587a

(0.0197)0.0836a

(0.0280)

Non-financial assets per capitaANF/POPt−1

−0.0236a

(0.0087)−0.0150a

(0.0035)0.1386a

(0.0268)0.1201b

(0.0532)

Bankruptcy rateBR/POPt−2

−0.935a

(0.170)−0.469a

(0.102)−0.001

(0.632)−1.383(0.928)

Income inequalityGINIt−2

0.168a

(0.050)0.076a

(0.027)0.422b

(0.191)

Income inequalityGINIt−2 (OLS Results)

0.161a

(0.054)0.073b

(0.029)0.340c

(0.211)0.675b

(0.268)

Durbin-Watson 1.939 2.319 2.274 2.218

Box-Ljung Q (8) 8.23(0.411)

15.64(0.048)

8.17(0.417)

6.46(0.596)

Box-Ljung Q (12) 12.86(0.379)

18.36(0.105)

12.98(0.370)

13.38(0.342)

Note: Numbers in parentheses are standard errors. Results from 94 quarterly observations for 1980:3 to 2003:4.Individual debt components estimated with SUR; household debt estimated with OLS.aSatistically significant at a 0.01-level; b statistically significant at a 0.05-level; c statistically significant at a 0.1-level (2-tail t-test).†Non-revolving debt: car loan rate; revolving debt: personal loan rate; mortgage debt: 30-year standard mortgagerate; household debt: weighted average of all 3 rates.††Non-revolving debt: 30-year standard mortgage rate; revolving debt: car loan rate.


Table 3. Summary of income inequality estimates.


Level Data†

GINIt−2

Base Model0.168a

(0.050)0.076a

(0.027)0.422b

(0.191)0.675a

(0.268)

GINIt−2

Alternative Model0.165a

(0.055)0.049b

(0.029)0.660a

(0.188)0.699a

(0.251)

90th/10tht−2

Base Model0.452a

(0.134)0.227a

(0.068)1.691a

(0.448)2.387a

(0.637)

90th/10tht−2

Alternative Model0.180c

(0.139)0.016

(0.066)1.848a

(0.339)2.087a

(0.561)

Differenced Data††

GINIt−2

Base Model0.117b

(0.064)0.085b

(0.040)0.450b

(0.237)0.636b

(0.314)

GINIt−2

Alternative Model0.116b

(0.064)0.087b

(0.040)0.410b

(0.250)0.552b

(0.327)

90th/10tht−2

Base Model0.352b

(0.180)0.254b

(0.119)1.561b

(0.686)2.153b

(0.925)

90th/10tht−2

Alternative Model0.330b

(0.181)0.261b

(0.119)1.457b

(0.720)2.029b

(0.897)

Annual Level Data†††

1953–2003 0.305a

(0.080)0.158b

(0.094)0.176c

(0.134)0.535a

(0.179)

1953–1984 0.295b∗(0.119)

-0.243∗(0.222)

0.220c

(0.146)0.471b∗

(0.229)

1985–2003 0.649a∗(0.225)

0.235b∗(0.087)

0.599b

(0.320)1.078a∗

(0.387)

Note: Numbers in parentheses are standard errors. Results for the first two sections from 94 quarterly observationsfor 1980:3 to 2003:4 and for the third section from 51 annual observations for 1953 to 2003.astatistically significant at a 0.01-level; b statistically significant at a 0.05-level; c statistically significant at a0.1-level (1-tail t-test).∗Difference between estimates for the two time periods is statistically significant at the 0.05-level.†The Base Model corresponds to the specification as shown in Table 2. The Alternative Model corresponds tomodel (4) in Appendix B, expect for the addition of the ratio of the housing price index to the overall consumerprice index (HPI/CPIt−2) to capture rising real estate value. In addition to the GINI ratio, the 90th/10th incomeratio is used as a measure of income inequality.††Estimation with first differenced data using the same model specifications as for level data.†††The model includes the following variables: D/Yt−1, C/Yt−1, rt , �Yt−1, A/POPt , ANF/POPt , and GINIt−1.For all debt components the standard 1-year T-bill rate is used for rt . For the series 1953 to 2003 we allowed fordifferent intercept and trend for the years 1985–2000. For revolving credit, the data series starts in 1967. Resultsbased on OLS estimation.

and conspicuous consumption has increased, the effect of income inequality on house-hold borrowing should have also increased over time. We test this conjecture with an-nual data from 1953 to 2003. The results of this test are shown in the third part ofTable 3: For all debt components we find an increase in the effect of income inequality


for the past two decades. The χ2-test statistics show that all differences are significantat a 0.10-level or better (non-revolving debt: χ2 = 4.52, p = 0.034; revolving debt:χ2 = 6.77, p = 0.009; mortgage debt: χ2 = 2.79, p = 0.095; total debt: χ2 = 4.15,p = 0.042).

3.3. Effect of other factors

The estimates of other factors provide further evidence regarding the appropriateness ofour estimation model and thus the robustness of the income inequality effect. Overall,these estimates are as expected and consistent with theoretical predictions or other studies.For example, last period’s consumption relative to disposable income (C/Yt−1) has thenegative sign as reported elsewhere (Maki, 2000). Except for mortgage debt, we cannotreject α2 = 1. Importantly, the pattern of parameter estimates remains the same whenchanging model specification or estimation approach, as was done to obtain the resultsreported in Table 3.

Two of the factors deserve a closer examination. First, we observe that all parameterestimates for interest rates have the expected sign and are highly significant: mortgage debtdecreases with the 30-year mortgage rate, revolving debt with the interest rate for personalloans charged by banks, and non-revolving debt with the interest rate for car loans chargedby ‘non-banks’. (Alternative rates yield similar but less efficient results.) In addition, wefind that non-revolving debt increases significantly with a higher mortgage rate. Revolvingdebt, on the other hand, is not affected by changes in the mortgage rate. Rather it increaseswhen the rate for car loans increases. These two ‘cross’ interest rate effects are quite strongrelative to the respective ‘own’ interest rate effects. As expected, mortgage debt is notsignificantly affected by any consumer credit interest rate.16

Second, we find interesting effects of household asset holdings on debt, i.e., for the‘wealth’ effect. We find the expected opposite effects of non-financial asset holdings ondifferent debt components: an increase in non-financial asset holdings increases mortgagedebt but decreases revolving and non-revolving debt. The result for total household debtindicates that in addition to this shift, an increase in non-financial assets leads to higherindebtedness. Less obvious is the result for other, i.e., financial, assets. We find that inthe short run an increase in financial assets reduces indebtedness. This result holds forall debt components and suggests that some of these gains are used to pay down debtbecause financial assets tend to be more liquid than houses. In the longer run, we findthe expected positive ‘wealth’ effect on all debt components. In other words, there is nouniform ‘wealth’ effect on consumer credit components. Different assets have differenteffects.

16 We also estimated a model that included the spread between the interest rate of a debt component and the T-billrate, which is a proxy of lenders’ profit margins (Ausubel, 1991). These variables have the expected positivesign—the higher the margin, the larger is the credit supply, but were only significant for mortgage debt andmarginally significant for total household debt. In these two cases, the effect of income inequality becomesmarginally higher. Other variables to capture the substitution effects between debt components such as thedifference in price indices for houses, durable goods and nondurable goods lead to the same conclusions.


Table 4. Comparison of effects.

Sensitivity† Effect size††

ND RV MD HD ND RV MD HD

Income inequality 0.583 0.436 0.295 0.356 0.712 0.322 1.789 2.862

Interest rate −0.037 −0.061 −0.020 −0.025 0.424 0.182 1.095 1.751

Competing rate 0.017 0.025 −0.192 −0.175

Income growtht−1 0.002 −0.001 0.000 −0.015 0.038 0.014

Income growtht−2 −0.002 −0.001 −0.001 0.008 0.044 0.034

Assetst−1 −0.104 −0.105 −0.125 −0.116 −0.786 −0.478 −4.680 −5.739

Assetst−2 0.165 0.173 0.134 0.144 1.238 0.786 5.013 7.139

Non-fin. assetst−1 −0.088 −0.093 0.104 0.068 −0.690 −0.438 4.050 3.509

Bankruptcy rate −0.075 −0.062 0.000 −0.017 −0.979 −0.491 −0.001 −1.448

Change in debt-to-income ratio 1983–2003 2.00 6.12 39.02 47.14

Note: Calculations based on parameter estimates from Table 2.†Numbers indicate point elasticities calculated at the average values for 1991:1 to 2000:4. In other words, a onepercent increase in income inequality increases non-revolving debt (ND) by 0.58%, revolving debt (RD) by 0.44%,mortgage debt (MD) by 0.3%, and household debt (HD) by 0.36%.††Numbers indicate the amount of debt in percent of disposable personal income added or subtracted due to changesof the different factors in between 1983:1 and 2003:4. The bottom row indicates the total change in different debtcomponents over this period. In other words, the increase in income inequality over the past 20 years accounts for2.9 points of the increase in household indebtedness of 47.1 points.

3.4. Comparison of effect sizes

The estimates of the income inequality effect on the various debt components say little aboutthe economic importance of income inequality relative to other determinants. To be able tocompare effect sizes, we convert the parameter estimates from Table 2 into point elasticityestimates based on the average value for each parameter for 1991 to 2000. The results arereported in the first four columns of Table 4. These point elasticity estimates suggest thathousehold indebtedness is much more sensitive to changes in income inequality than tochanges in other factors such as interest rates or asset holdings. The two consumer creditcomponents are particularly sensitive to changes in income inequality.

In a second analysis, we examine how much of the increase in debt relative to disposableincome between 1983 and 2003 can be attributed to changes in different factors. The resultsfor different factors are shown in the last four columns of Table 4. The ‘wealth’ effectis the most significant contributor to the rise in household indebtedness. Combining thedifferent estimates for household asset holdings, we find that the ‘wealth’ effect accountsfor 4.9 points of the 47.1-point increase in the household debt-to-income ratio. This effect islargely due to theincrease in mortgage debt (4.4 points of 39.0 points). In contrast, our resultssuggest that the ‘wealth’ effect on consumer credit is mixed because of the substitution effectof mortgage debt. The increase in real estate values allowed households to assume moremortgage debt in place of either consumer credit component.


Rising income inequality has been the largest contributor to the increase in consumercredit. For non-revolving debt, it accounts for 0.7 points of the 2.0-point increase or 36%. Forrevolving debt, income inequality ‘only’ accounts for 0.3 points of the 6.1-point increase or5%. Overall, income inequality accounts 2.9 points (or 6.1%) of the increase in householdindebtedness of 47.1 points. This is larger than the increase due to falling interest rates,which accounts for 1.8 points (or 3.7%). Again, the effect of interest rates on the twoconsumer credit components has been limited due to the substitution effect from fallingmortgage rates in the case of non-revolving debt and from falling car loan rates in the caseof revolving debt.

While this analysis of the relative contribution of various determinants of householdindebtedness is somewhat rudimentary, it does provide an indication of the economic im-portance of income inequality compared to other factors, especially for consumer creditsuch as car loans or credit card debt. For overall household indebtedness, the effect of in-come inequality is second only to the ‘wealth’ effect but still larger than the ‘interest rate’effect.

4. Further analyses

The results presented so far provide strong evidence for a positive effect of income inequalityon household indebtedness. While this result is consistent with the argument that householdshave borrowed more to maintain consumption relative to those with larger income gains aspredicted by the theory of conspicuous consumption, there exist alternative explanations forthis positive effect of income inequality. In this section, we examine a number of alternativeexplanations and then provide more direct evidence for the ‘conspicuous consumption’argument.

4.1. Alternative explanations for the effect of income inequality

It follows from the standard life-cycle model that the age distribution affects aggregatehousehold indebtedness. The larger the proportion of ‘young’ households in the overallpopulation, the higher should be the debt-to-income ratio. Thus, the entry of ‘baby-boomers’into the ‘household formation’ stage starting in the 1970s should have contributed to theincrease in household indebtedness and income inequality. We control for this possibleexplanation by adding the fraction of the population in the 18-to-45-age bracket to thedifferent debt equations as shown in Table 2. The results in the first two rows of Table 5indicate that the inclusion of this variable does not substantially alter the income inequalityestimates.

A more refined argument, presented by Morgan and Toll (1997), is that the demographicshift due to ‘baby boomers’ impacted indebtedness by amplifying the ‘wealth’ effect. Asshown in rows three to five of Table 5, we find some support for this argument: Thereis a positive interaction effect between the size of the 18-to-45 age bracket and householdasset holding for revolving debt and mortgage debt. The former result is consistent with datashowing that younger households are more likely to hold credit card debt. Their outstandingcredit card balances have also increased faster during the 1990s (Aizcorbe et al., 2003). The


Table 5. Assessing alternative explanations.


%Population 18–45P1845t

−0.059c

(0.034)0.088

(0.099)−0.164(0.109)

−0.191(0.155)

Income InequalityGINIt−2

0.167a

(0.050)0.075a

(0.027)0.410b

(0.190)0.656b

(0.268)

%Population 18-45P1845t−1

0.026c

(0.182)−0.302a

(0.104)−1.214c

(0.657)−3.069b

(1.170)

Assets X %Population 18–45APCxP1845t−1

−0.0006(0.0014)

0.0024a

(0.0008)0.0080c

(0.0046)0.0216b

(0.0087)


0.166a

(0.051)0.093a

(0.026)0.421b

(0.187)0.650b

(0.261)

%�Wage(90th decile)t−2

−0.0015(0.0030)

0.0030c

(0.0017)0.0104

(0.0103)0.0130

(0.0157)


0.181a

(0.053)0.060b

(0.028)0.381c

(0.197)0.582b

(0.291)

%Self employedt−1 −0.101(0.101)

0.063(0.045)

−0.312(0.322)

−0.355(0.446)


0.178a

(0.052)0.068b

(0.027)0.442b

(0.193)0.702b

(0.271)

Recession† 0.026(0.054)

0.034(0.031)

0.013(0.195)

0.315(0.283)


0.182a

(0.057)0.092a

(0.030)0.440b

(0.207)0.791a

(0.287)

1986 Tax revision†† −0.085c

(0.060)0.051c

(0.030)0.184

(0.268)0.329

(0.421)


0.156a

(0.050)0.080a

(0.027)0.418b

(0.190)0.702a

(0.271)

Note: Numbers in parentheses are standard errors. Model specification and data as in Table 2; additional factorswere included sequentially.astatistically significant at a 0.01-level; b statistically significant at a 0.05-level; c statistically significant at a0.1-level (2-tail t-test).†Indicator variable for quarter with recession as defined by NBER.††Indicator variable for quarters post 1986:2.

latter effect is consistent with the need to finance home purchases by younger households.Importantly though, the income inequality effect remains unchanged.

Next, people may form expectations about their future income from the income gains ofhigh earners. Such overoptimistic expectations would cause them to borrow more than theirown income growth can support, leading to higher household indebtedness and subsequentlyto more bankruptcy filings. We account for this effect by including lagged income growthfor the 90th-decile income group. The results, shown in rows six and seven of Table 5, lend


some limited support to this argument. There is a positive and marginally significant effectin the case of revolving debt. Including this factor slightly reduces the income inequalityeffect except for non-revolving debt.

Third, anecdotal evidence indicates that people who start their own business increasinglyuse non-commercial debt, especially credit card debt, to finance the start-up of their business.If entrepreneurial activity leads to higher income inequality due to an unequal distribution ofsuccess and failure, this factor could also provide an alternative explanation for our results.We test a number of variables to capture this effect. In rows eight and nine of Table 5,we report the results when adding the percentage of self-employed people in non-farmingpositions. We find a positive but insignificant effect for revolving debt and a small declinein the effect of income inequality.

Fourth, income inequality and debt-to-income ratios tend to decline during economicdownturns. We thus examine whether the income inequality effect could actually be theresult of a shift to more cautious behaviors by both lenders and consumers during economicdownturns. We test this explanation by adding a dummy variable to indicate quarters duringwhich the economy was in recession as identified by the NBER. As shown in rows ten andeleven of Table 5, we find no significant effect of economic downturns (beyond what iscaptured by income growth) and the income inequality effect remains unchanged.

Finally, there are a number of structural changes that influenced demand for and supply ofcredit. For example, one such structural change is the 1986 tax law revision, which removedthe deductibility of consumer credit interest payments. To control for this change and anypotential effect on the income inequality results, we add a dummy variable for the quarterspost 1986:2. The results are shown in the last two rows of Table 5. Again, we find that theincome inequality effect remains unchanged.

There still remain other alternative explanations. The constraints imposed by the regula-tion of financial markets prior to 1980 resulted in extensive credit rationing of households.Removing these constraints substantially eased the liquidity constraints of many house-holds, which had a significant effect on the extent of household borrowing. Overall, it isdifficult to completely control for supply-side changes. However, the effect of credit supplyhas been restricted by the fact that the extra cost from financing has remained relativelyhigh despite competition among credit suppliers and a declining federal funds rate (Ausubel,1991; Bennett et al., 1998). In our model, we account for supply shifts by instrumentinginterest rates using an aggregate level measure of credit supply, the Federal Reserve’s ad-justed monetary base. Adding this measure directly to the model does not change the incomeinequality estimates. Removing liquidity constraints enables greater household borrowingand may have an additional effect. Higher borrowing limits are also used as a positive signalof future income prospects (Soman and Cheema, 2002).17

4.2. Consumption effect of income inequality

We next directly examine the consumption effect of income inequality. If the income in-equality effect is the result of conspicuous consumption, income inequality should have

17 While this is an interesting question we do not have appropriate measures for credit card or other debt limitsto examine whether this could in any way explain the positive effect of income inequality.


different effects on the consumption of conspicuous and non-conspicuous goods. To testthis conjecture, we estimate the effect of income inequality on two different categories ofconsumption—automobiles and food. These two categories are not perfect indicators of con-spicuous and non-conspicuous consumption, respectively, but we contend that more peopleuse automobiles as status symbols than food items. Thus, we should expect a positive effectof income inequality on automobile consumption as a percentage of total consumption.Food consumption relative to total consumption should not directly be affected by incomeinequality. However, due to the substitution effect from higher automobile consumption,we could see a negative but smaller effect of income inequality on food consumption.

Since we cannot reject the hypotheses of a unit root in the two dependent consumptionvariables, we use an error-correction model to estimate the consumption effect of incomeinequality. As expected, we find a positive and significant effect of income inequality(lagged by one quarter) on consumption of automobiles relative to total consumption forboth measures of income inequality (Gini: 0.187, p < 0.05; 90th/10th: 0.542, p < 0.05).Conversely, the effect on relative consumption of food is not significant (Gini: 0.030; p =0.49; 90th/10th: 0.075, p = 0.59). (Detailed estimation results are provided in Appendix D.)This pattern of results remains the same when changing model specifications. The natureof the data and analysis for food and automobiles limits the strength of any claims we canmake here, but these results are consistent with the ‘conspicuous consumption’ argument.

5. Conclusions

In this paper, we examined whether rising income inequality had contributed to the record-high household debt relative to disposable income in the US. Despite the attention paid byWall Street, the business press, and consumer advocacy groups to the growing mountainof household debt, research that explains the popularity of home equity loans, installmentplans and ‘plastic money’ among U.S. consumers has remained limited (Kowalewski, 2000;Soman and Cheema, 2002).

5.1. Contributions and implications

This paper makes a number of important contributions. First, it confirms the existence ofa positive relationship between income inequality and household indebtedness—beyondwhat is predicted by the standard life-cycle/permanent-income model. Both rising incomeinequality and household indebtedness are important economic trends, not just in the US,but in many industrialized nations (Debelle, 2004). These two trends are often used as indi-cators of declining economic health. However, there has been little research that links them.In confirming this link, this paper also adds to the still limited literature on implications ofincome inequality (Welch, 1999; Krueger and Perri, 2002). This paper differs from existingstudies concerning consumer decisions to finance consumption in marketing (Prelec andLoewenstein, 1998; Soman, 2000; Soman and Cheema, 2002). We use aggregate economicdata rather than experimental data. This enables us to take a comprehensive look at de-terminants of various components of household debt and highlight the overall economicimportance of the income inequality effect. On the other hand, it makes it impossible todirectly examine individual household decisions.


Second, the paper provides evidence to support the assertion that conspicuous consump-tion is a primary source of the income inequality effect. The social role of consumption isan important research topic in marketing (e.g., Aaker, 1997; Levy, 1959; Soloman, 1983),economics (e.g., Frank, 1985; Veblen, 1899) and sociology (e.g., Hirsch, 1976; Rae, 1834).The effect of income inequality shows an important role for relative income in understand-ing consumption behavior as suggested by Champernowne and Cowell (1998) in the prefaceto their book on economic inequality and income distribution, (p. xvii),

“. . . what is improvement for some almost always seems to others to be detrimental. Evenif the change in distribution stimulates the growth of income in total, this is unlikely tobe perceived by those who are losing or who are falling behind in the race; and evenif it is perceived, they will still feel aggrieved that they are getting less benefit than theothers.”

We cannot provide direct evidence for a causal link between conspicuous consump-tion and household indebtedness. In fact, the unobservability of a consumer’s networkof influence makes it very difficult to provide such direct evidence even in a laboratorysetting. We would like to highlight four results from our analysis that provide strong,indirect support for this link. First, the effect of income inequality is strongest on non-revolving debt, which is used to finance durable goods consumption. Durable goods aremore expensive and more visible and are thus much more suited for conspicuous con-sumption than other goods. Second, our results regarding the effect of income inequalityon automobile and food consumption further support this argument. Third, the increasein the effect of income inequality over time is consistent with the increase in conspic-uous consumption in recent years (Frank and Cook, 1995). Finally, we are able to ruleout a number of alternative explanations for the effect of income inequality on householdindebtedness.

Third, by separately analyzing individual debt components, our paper identifies importantdifferences between mortgage debt and consumer credit. It shows the strong impact thatfalling mortgage rates and rising real estate values have had on revolving and non-revolvingdebt, which explains why consumer credit relative to income has not risen as much astotal household debt. The role of refinancing mortgages and taking out home equity loansin fueling consumption has long been suggested but little empirical research exists thatsupports this effect. This finding also has important implications for durable goods sellers:The real ‘competitor’ to their financing options is not a credit card but real estate lending.On the other hand, the analysis did not identify such an effect for credit card borrowing.Here the competition is more likely the financing plans offered by sellers of products andservices.

Finally, this paper contributes to our understanding of the success of offering financingoptions. As firms have likely discovered, the provision of financing options is necessarynot only to attract customers from lower income brackets, it may be even more importantin attracting more affluent customers who are more likely exposed to escalating conspic-uous consumption. The income inequality effect also implies that marketers need to payclose attention to understand consumers not only in isolation but also within their socialnetworks. The sensitivity to the terms of a financing plan likely depends on the motivation


for a consumer to finance a purchase. When it comes to “keeping up with the Joneses”,a consumer may be less sensitive to the ‘cost’ of credit. Similarly, financial institutions,which need to assess the credit risk of loan applicants, should also pay attention to a bor-rower’s social network. Here, conspicuous consumption suggests that a borrower in therace with “the Joneses” may constitute a higher risk because of a likely increased willing-ness to borrow. Higher income or education alone may be no assurance of the capacity torepay loans. It is interesting to note that the typical bankruptcy filer is a member of themiddle class, and filers with 6-digit annual incomes are not uncommon (Sullivan et al.,2000).

5.2. Limitations and future research

To our knowledge this study is a first attempt to empirically validate the link between incomedistribution and household borrowing. Including income inequality into debt forecastingmodels should significantly improve prediction accuracy. Developing such models andtesting this conjecture is one obvious area of future research.

This study raises a number of questions. For example, the success of offering financ-ing plans and consumer credit will likely increase competition to finance consumption.We find asymmetric competitive effects between different types of credit. Future researchshould try to substantiate and further refine this pattern. The relationship among differentdebt components also suggests that it may be misleading to examine competitive inter-actions among providers of just one type of credit, for example credit cards. Our resultsalso suggest different ‘wealth’ effects for different types of assets. In particular, it seemsthat financial and non-financial assets can have opposing effects. This also warrants moreresearch.

An interesting extension of this study concerns the question of whether consumers areaware of the escalating effect of conspicuous consumption. Wertenbroch (1998) showsconsumers engage in activities to limit current consumption of items that could have negativeconsequences in the future. For example, there are anecdotes of consumers cutting up creditcards or putting them in the freezer to limit consumption. Following the work on cigaretteaddiction (Becker et al., 1994), one could test whether consumers are forward looking andanticipate the escalating effect of current conspicuous consumption.

Our analysis focused on the United States, but future research should consider to whatextent this is a uniquely American phenomenon. There is indication of very similar debttrends in many industrialized countries, especially in the U.K. and Australia. The the-ory of conspicuous consumption is independent of nationality but it does hinge criti-cally on the degree of income inequality and the importance of conspicuous consump-tion. A question that arises from this is whether the income inequality effect is strongerin the U.S., where more people have a chance to be among the economic winners. Insocieties where lineage and relationships are more important for economic success, theneed for conspicuous consumption could be lower and hence the effect of income in-equality on household debt weaker. A preliminary analysis of data from Switzerland


points in this direction.18 Thus, a cross-cultural examination of these two factors would beinteresting.

Finally and most importantly, we have argued that the primary reason for the incomeinequality effect is the need to maintain or improve social status through conspicuousconsumption. We test the aggregate-level implication of this theory but have not been ableto directly trace the effect of income inequality on conspicuous consumption at the individuallevel. Our analysis of automobile and food consumption is only suggestive. So, we inviteother researchers to present evidence that can either reject the proposed explanation forthe income inequality effect on consumer borrowing or provide direct evidence for thehypothesized effect of conspicuous consumption on household borrowing.

Appendix A: Description of data and their sources

Debt MeasuresMortgage Debt

Federal Reserve Statistical Release Z.1, Federal Reserve Flow of Funds Accounts of theUnited States-Report L.218.

Consumer Credit—Revolving and Non-revolvingFederal Reserve Statistical Release G.19.

Interest RatesMortgage Rates

Federal Reserve Statistical Release H.15.

Credit Card Interest RatesFederal Reserve Statistical Release G.19.

Benchmark One-year U.S. Government SecuritiesFederal Reserve Statistical Release G.13.

Consumer Price Index (CPI)Data is deflated using the CPU-U deflator, 1982 − 1984 = 100. U.S. Department of

Labor, Bureau of Labor Statistics.

Population DataUS Census Bureau. Census population estimates for 2000 were determined by a different

methodology than all prior years (April 1 decennial census), requiring the authors to estimatestate populations in 2000 based on annual growth rates experienced over the prior 10 years.

18 Unfortunately, the Swiss data set is not rich enough to allow for an analysis of comparable details.


Income Inequality DataAll inequality measures were constructed from microdata from the National Bureau of

Economic Research Extracts of the Current Population Survey (CPS) Outgoing RotationGroup Earnings Files. Hourly wages were available as a directly reported rate of pay or,for non-hourly workers, constructed from a report of usual weekly earnings divided byusual weekly hours. Data only for respondents age 18–64 who were not self-employed.

Per Capita Consumer Bankruptcy DataTotal bankruptcies were compiled by the Administrative Office of the U.S. Courts. This

includes filings under Chapters 7, 11, and 13 which were relatively stable as a percentageof the total at approximately 70%, <1%, and 30%, respectively.

GDP & Disposable Personal IncomeU.S. Department of Commerce, Bureau of Economic Analysis, National Accounts Data.

Value of Household AssetsFederal Reserve Statistical Release Z.1, Federal Reserve Flow of Funds Accounts of the

United States-Report B.100.

Appendix B: Aggregation and household heterogeneity

The use of macroeconomic data poses two problems with respect to attributing the incomeinequality effect to conspicuous consumption. First, our empirical analysis could be subjectto an aggregation bias. Both variables of primary interest are measured with error relativeto the individual level model due to aggregation. Our dependent measures—debt relative todisposable income—are ratios of two aggregate numbers. This measures indebtedness ofthe population but likely overestimates the average debt-to-income ratio for an individualhousehold. Moreover, income differences in the social network of an individual householdare most likely smaller than in the entire economy. Second, the theory of conspicuousconsumption allows for heterogeneity in how individuals are influenced by their socialnetworks. Unfortunately, it is virtually impossible outside an experimental setting to obtainmeasures for the strength of influence, i.e., the actual networks, and the kinds of productsthat are considered conspicuous consumption in each network.

To assess whether aggregation and heterogeneity issues could account for our re-sults, we use the following analysis. Let us assume that the individual-level model isyit = Dit/Yit = a + (b + νi )xit + eit and the aggregate level model is yt = a + bxt + et ,where yt = 1/n

∑n Dit/Yit , xt = 1/n

∑n xit and ν = 0. Due to measurement error from

aggregation and ignoring consumer heterogeneity, we actually estimate y′t = α +βx ′

t +ωt ,wherey′

t = yt − ηt , x ′t = xt + Nt and ωt = e − βNt − ηt . The question then is whether

the nuisance factors ηt and Nt are correlated with the aggregate income inequality mea-sure x ′

t (or other factors that are included in the empirical model). As the average levelof debt increases, it is reasonable to assume that its variance across households increases,too. This increases the nuisance factor ηt . If income inequality, x ′

t , has a positive effect on


debt, then ηt increases, which leads to a negative correlation between x ′t and the error term,

ωt . Similarly, the nuisance term Nt also induces a negative correlation. Negative correla-tions, however, suggest that by using aggregate data we actually underestimate the effectof income inequality on household indebtedness.

Appendix C: OLS results for household debt

(1) (2) (3) (4) (5) (6)

Constant34.180a

(9.212)8.145

(11.490)0.671

(10.515)4.312

(10.483)14.923

(12.486)11.802

(11.859)

Trend0.026

(0.019)−0.022(0.022)

−0.047b

(0.021)−0.011(0.027)

0.008(0.030)

0.026(0.029)

Debt RatioHD/Yt−1

0.997a

(0.031)1.014a

(0.029)1.010a

(0.026)0.925a

(0.049)0.956a

(0.053)0.924a

(0.051)

ConsumptionC/Yt−1

−0.376a

(0.107)−0.439a

(0.103)−0.451a

(0.093)−0.335a

(0.108)−0.401a

(0.115)−0.441a

(0.110)

Interest Rate†

rt

−0.168b

(0.079)−0.144c

(0.075)−0.200a

(0.069)−0.178b

(0.068)−0.190a

(0.068)−0.258a

(0.068)

Income Growth�Yt−1

0.0229(0.0250)

0.0050(0.0241)

0.0010(0.0218)

0.0043(0.0215)

0.0084(0.0214)

0.0012(0.0205)

Assets per CapitaA/POPt−1

0.0054(0.0109)

0.0243b

(0.0116)−0.0743a

(0.0243)−0.0685a

(0.0283)−0.0608b

(0.0243)−0.0665a

(0.0232)

Assets per CapitaA/POPt−2

0.1132a

(0.0251)0.0852a

(0.0283)0.0806a

(0.0282)0.0765a

(0.0268)

Non-Financial Assets perCapita ANF/POPt−1

0.1020b

(0.0503)0.0791

(0.0520)0.1324b

(0.0521)

Bankruptcy RateBR/POPt−2

−1.442(0.933)

−1.589c

(0.886)

Mortgage Tax Savingmt

0.196a

(0.061)


0.722a

(0.209)0.938a

(0.195)0.659a

(0.235)0.514b

(0.252)0.586b

(0.240)

Durbin-Watson1.557 1.882 1.969 2.038 2.048 2.218

Box-Ljung Q (8)26.26(0.0009)

7.05(0.531)

6.41(0.601)

5.63(0.689)

5.03(0.754)

7.38(0.496)

Box-Ljung Q (12)37.60(0.0002)

16.33(0.176)

13.41(0.340)

14.75(0.256)

11.98(0.447)

15.21(0.230)

Note: Numbers in parentheses are standard errors. Results from 94 quarterly observations for 1980:3 to 2003:4.aStatistically significant at a 0.01-level; bStatistically significant at a 0.05-level; cStatistically significant at a 0.1-level (2-tail t-test).†Weighted average of car loan rate, personal loan rate and 30-year standard mortgage rate, based on relative amountof non-revolving, revolving and mortgage debt.


Appendix D: Effect of income inequality on automobile and food consumption

Relative Automobile Relative FoodConsumption (CA/Ct ) Consumption (CF/Ct )

GINI 90th/10th GINI 90th/10th

Constant −0.061c

(9.035)−0.061c

(0.034)−0.016(0.017)

−0.014(0.017)

Past Consumption††

CX/Ct−1

−0.187c

(0.101)−0.192c

(0.074)0.041

(0.103)0.043

(0.103)

Interest Rate†

rt

−0.107a

(0.021)−0.103a

(0.022)0.034b

(0.015)0.033b

(0.015)

Income Growth�Yt−1

0.015b

(0.007)0.015b

(0.007)−0.009b

(0.004)−0.010b

(0.004)

Consumer CreditCD/Yt−1

0.209b

(0.094)0.241b

(0.096)−0.065c

(0.041)−0.066c

(0.041)

AssetsA/Yt−1

0.0002(0.0019)

0.0019(0.0029)

−0.0008(0.0008)

−0.0008c

(0.0008)

et−1 −0.594a

(0.106)−0.594a

(0.107)−0.288a

(0.094)−0.294a

(0.094)

et−3 −0.273b

(0.113)−0.256b

(0.113)


0.187b

(0.091)0.542b

(0.276)0.030

(0.045)0.075

(0.125)

Durbin-Watson 1.995 2.016 2.016 2.006

Box-Ljung Q (12) 4.97(0.959)

6.14(0.909)

3.65(0.989)

3.72(0.988)

Note: Numbers in parentheses are standard errors. Results from 96 quarterly observations for 1980:2 to 2004:1.aStatistically significant at a 0.01-level; bStatistically significant at a 0.05-level; cStatistically significant at a 0.1-level (2-tail t-test).†Automobiles: car loan rate. Food : 1-year T-bill rate.††Automobile: past automobiles consumption (CA/Ct−1). Food: past food consumption (CF/Ct−1).

References

Aaker, J. (1997). “Dimensions of Brand Personality.” Journal of Marketing Research 34(August), 347–357.Aizcorbe, A.M., A.B. Kennickell, and K.B. Moore. (2003). “Recent Changes in U.S. Family Finances: Evidence

from the 1998 and 2001 Survey of Consumer Finances.” Federal Reserve Bulletin.Ando, A. and F. Modigliani. (1963). “The ”Life Cycle” Hypothesis of Saving: Aggregate Implications and Tests.”

The American Economic Review 53(1), 55–84.Ausubel, L.M. (1991). “The Failure of Competition in the Credit Card Market.” The American Economic Review

81(March), 50–81.Bagwell, L.S. and B. Douglas Bernheim. (1996). “Veblen Effects in the Theory of Conspicuous Consumption.”

American Economic Review 86(3), 349–373.


Barnes, S. and G. Young. (2003). “The Rise in US Household Debt: Assessing its Causes and Sustainability.”Bank of England Working paper no. 206.

Becker, G.S. (1974). “A Theory of Social Interactions.” Journal of Political Economy 82(6), 1063–1093.

Becker, G.S., M. Grossman, and K.M. Murphy. (1994). “An Empirical Analysis of Cigarette Addiction.” TheAmerican Economic Review 84(3), 396–418.

Bennett, P., R. Peach, and S. Peristiani. (1998). “Structural Change in the Mortgage Market and the Propensity toRefinance.” Federal Reserve Bank of New York Staff Reports 45.

Champernowne, D.G. and F.A. Cowell. (1998). Economic Inequality and Income Distribution. Cambridge. U.K.:Cambridge University Press.

Debelle, G. (2004). “Macroeconomic Implications of Rising Household Debt.” Bank of International SettlementsWorking Paper 153.

Dynan, K.E., J. Skinner, and S.P. Zeldes. (2004). “Do the Rich Save More?” Journal of Political Economy 112(21),397–444.

Enders, W. (1995). Applied Econometric Time Series. New York, NY: John Wiley & Sons, Inc.Fay, S., E. Hurst, and M. White. (2002). “The Household Bankruptcy Decision.” The American Economic Review

92(3), 706–718.Feinberg, R.A. (1986). “Credit Cards as Spending Facilitating Stimuli: A Conditioning Interpretation.” Journal of

Consumer Research 13(3), 348–356.Frank R.H. (1985). “The Demand for Unobservable and Other Nonpositional Goods.” American Economic Review

75(March), 101–116.Frank R.H. and P.J. Cook. (1995). The Winner-Take-All Society. New York, NY: Penguin.Friedman, M.F. (1957). A Theory of the Consumption Function. Princeton, NJ: Princeton University Press.Gaba, A. and A. Kalra. (1999). “Risk Behavior in Response to Quotas and Contests.” Marketing Science 18(3),

417–434.Gottschalk, P. (1997). “Inequality, Income Growth, and Mobility: The Basic Facts.” Journal of Economic Perspec-

tives 11(Spring), 21–40.Hall, R. (1978). “Stochastic Implications of the Life-Cycle Permanent Income Hypothesis: Theory and Evidence.”

Journal of Political Economy 96, 971–987.Hirsch, F. (1976). Social Limits to Growth. Cambridge, MA: Harvard University Press.Katz, L.F. and D.H. Autor. (1999). “Changes in the Wage Structure and Earnings Inequality.” In O. Ashenfelter

and D. Card (eds.), Handbook of Labor Economics, Vol. 3a, Chapter 26. Elsevier: Netherlands.Kowalewski, K. (2000). “Personal Bankruptcy: A Literature Review.” Congressional Budget Office report.Krueger D. and F. Perri. (2002). “Does Income Inequality Lead to Consumption Inequality? Evidence and Theory.”

NBER Working Papers 9202.Laibson, D., A. Repetto, and J. Tobacman. (2000). “A Debt Puzzle.” NBER Working Paper 7879.Levy, S.J. (1959). “Symbols for Sale.” Harvard Business Review 37(4), 117–124.Maki, D.M. (2000). “The Growth of Consumer Credit and the Household Debt Service Burden.” Federal Reserve

Board Finance and Economics Discussion Series 2000-12.Maki, D.M. and M.G. Palumbo. (2001). “Disentangling the Wealth Effect: A Cohort Analysis of Household

Savings in the 1990s.” Federal Reserve Board Finance and Economics Discussion Series, 2001-21m.Morgan, D.P. and I. Toll. (1997). “Bad Debt Rising.” Current Issues in Economics and Finance 3(4), 1–6.Prelec, D. and G. Loewenstein. (1998). “The Red and the Black: Mental Accounting of Savings and Debt.”

Marketing Science 17(1), 4–28.Rae, J. (1834). The Sociological Theory of Capital. London: McMillan.Ryscavage, P. (1999). Income Inequality in America. Armonk, NY: M. E. Sharpe.Soloman, M.R. (1983). “The Role of Products as Social Stimuli.” Journal of Consumer Research 10(3), 319–

329.Soman, D. (2000). “Effects of Payment Mechanism on Spending Behavior: The Role of Rehearsal and Immediacy

of Payments.” Journal of Consumer Research 27(March), 460–474.Soman, D. and A. Cheema. (2002). “The Effect of Credit on Spending Decisions: The Role of Credit Limit and

Credibility.” Marketing Science 21(Winter), 32–53.Stires, D. (2003). “Is Your Store a Bank in Drag?” Fortune, March 17, 38.


Sullivan, T.A., E. Warren, and J.L. Westbrook. (2000). The Fragile Middle Class: Americans in Debt. New Haven:Yale University Press.

Thaler, R. and H.M. Shefrin. (1981). “An Economic Theory of Self Control.” Journal of Political Economy 89(2),392–406.

Veblen, T. (1899). The Theory of the Leisure Class. New York: Modern Library.Welch, F. (1999). “In Defense of Inequality.” American Economic Review 89(May), 1–17.Wertenbroch, K. (1998). “Consumption Self-Control by Rationing Purchase Quantities of Virtue and Vice.”

Marketing Science 17(4), 317–337.

Keeping Up With the Joneses: Analyzing the Effect of Income Inequality on Consumer Borrowing

Documents

Transcript of Keeping Up With the Joneses: Analyzing the Effect of Income Inequality on Consumer Borrowing