# RDP 2019-06: The Effect of Mortgage Debt on Consumer Spending: Evidence from Household-level Data 5. Why is There a Negative Effect of Debt on Spending?

The results from our models suggest that there is a significant negative relationship between the level of household debt and spending. However, this need not be driven by the debt overhang mechanism. In this section, we explore possible underlying mechanisms.

## 5.1 The Debt Overhang Channels

We first explore two mechanisms that imply that the link between debt and spending is causal. Following the literature and our theoretical model in Appendix A, these mechanisms are borrowing and liquidity constraints (which we summarise as financing constraints) and precautionary saving motives. To test these hypotheses, we estimate whether the negative effect of debt is driven exclusively by households that we identify as likely to be constrained or have strong precautionary saving motives. Accordingly, we add to our models an interaction term between debt and household characteristics that are plausibly associated with financing constraints or precautionary saving (Zh,t). We focus on the FE model and adjust it to the following form:

$E h,t = β 0 + β 1 D h,t−1 + β 2 Y h,t + β 3 A h,t−1 + π 1 D h,t−1 × Z h,t + π 2 Y h,t × Z h,t + π 3 A h,t−1 × Z h,t + β 4 Z h,t +γ X h,t + δ h + ε h,t$

### 5.1.1 Borrowing and liquidity constraints

To test whether borrowing and liquidity constraints drive the negative relationship between debt and spending, we interact indicators of leverage and liquidity with our balance sheet and income measures. We expect borrowing constraints to be binding for households with a high stock of debt relative to assets. For this purpose, we test if households with loan-to-valuation ratios (LVRs) above 80 per cent adjust their spending by more in response to changes in income, wealth or debt, than households with a low LVR.

We proceed similarly when testing for the importance of liquidity constraints. Here, the first indicator is a dummy variable that is equal to one if the household is hand-to-mouth (adjusted for mortgage prepayments) and zero otherwise. The second indicator is a dummy variable that is equal to one if the household reports being behind on their mortgage payments and zero otherwise. A household that is behind on its repayments will have no prepayment buffer with which to offset unexpected income shocks. The third indicator measures the financial stress of each household. This dummy variable is equal to one if the household reports financial problems, such as the inability to quickly raise emergency funds, and zero otherwise.[15]

If financing constraints drive our results, these interaction terms should be negative. However, we find the debt overhang effect to be pervasive across households and not sensitive to our proxies for borrowing or liquidity constraints (Table 3). All interaction coefficients between debt and our proxies for constraints are statistically insignificant. Furthermore, constrained households also do not appear to be more sensitive to changes in their income or home value. While this appears to suggest that financing constraints are not the main drivers of the negative relationship between debt and spending, it is possible that our proxies do not adequately identify these constraints.

Table 3: Debt Overhang and Financing Constraints
Total spending, 2006–10
Mechanism
Borrowing constraints   Liquidity constraints
Lagged LVR >80% Hand-to-mouth Behind on repayments Financial stress
Lagged mortgage debt −0.03**
(0.02)
−0.03***
(0.01)
−0.03***
(0.01)
−0.05***
(<0.00)
Lagged mortgage debt × constrained −0.02
(0.88)
<0.00
(0.52)
<0.00
(0.78)
0.02
(0.47)
Income 0.11***
(<0.00)
0.09***
(<0.00)
0.09***
(<0.00)
0.13***
(<0.00)
Income × constrained −0.14*
(0.02)
0.03
(0.61)
−0.01
(0.82)
0.01
(0.81)
Lagged home value 0.09*
(0.08)
0.11**
(0.03)
0.08
(0.12)
0.10*
(0.06)
Lagged home value × constrained 0.12
(0.44)
−0.04
(0.48)
0.05
(0.17)
0.02
(0.75)

Note: See notes to Table 1

Sources: Authors' calculations; HILDA Survey Release 17.0

We also estimate these models controlling for household cash flow, measured as income after taxes and mortgage payments rather than after-tax income alone. We find that the estimated effect of debt on spending is smaller, but still statistically significant. This provides further indirect evidence that liquidity (or cash flow) constraints are not the sole explanation for the debt overhang effect. So we next consider the role of precautionary saving motives.

### 5.1.2 Precautionary saving

Unlike most comparable household surveys, the HILDA Survey asks about households' employment type and their expectations of future employment, which helps to identify uncertainty about future income. In this section, we use a household's self-assessed probability of losing their job, as well as the household's casual employment status, to test whether uncertainty significantly influences the effect of debt on spending.[16] Having a high perceived probability of losing a job or low job security as a casual worker should capture households facing high income uncertainty, and therefore having a stronger motive for precautionary saving.

We find that households with lower job security appear to be more sensitive to higher debt levels, but the estimated effects are not statistically significant and the overall negative relationship between debt and spending continues to hold for all households (Table 4). As a result, based on these proxies, we find a persistent negative relationship between debt and spending for all households, and little evidence that uncertainty strengthens the negative relationship between debt and spending.

Table 4: Debt Overhang and Precautionary Saving
Total spending, 2006–10
Precautionary saving motives
Casual worker Probability lose job >0
Lagged mortgage debt −0.03**
(0.01)
−0.05***
(0.01)
Lagged mortgage debt × uncertain −0.06
(0.15)
0.02
(0.27)
Income 0.11**
(0.01)
0.15***
(<0.00)
Income × uncertain −0.02
(0.79)
−0.10**
(0.04)
Lagged home value 0.11*
(0.08)
0.11
(0.10)
Lagged home value × uncertain 0.16
(0.11)
0.03
(0.53)

Note: See notes to Table 1

Sources: Authors' calculations; HILDA Survey Release 17.0

## 5.2 Non-causal Explanations for a Negative Effect of Debt on Spending

### 5.2.1 The spending normalisation hypothesis

Andersen et al (2016) suggest that the presence of a negative effect of debt on spending could be due to ‘spending normalisation’ rather than the causal debt overhang mechanism. Households take on debt to finance a large purchase and subsequently reduce their spending back to normal levels (as highlighted by the event study earlier). The macroeconomic policy implications of spending normalisation are quite different to those for debt overhang, so it is important to examine this.

To test the spending normalisation hypothesis, we introduce a lagged dependent variable into the FE model and control for any bias introduced by this lag using the Arellano-Bond estimation procedure (Arellano and Bond 1991). The lagged variable should capture any negative effect due to ‘spending normalisation’ between the previous and current period. We refer to this as the ‘dynamic model’.

$E h,t = β 0 +α E h,t−1 + β 1 D h,t−1 + β 2 Y h,t + β 3 A h,t−1 +γ X h,t + δ h + ε h,t$

In addition, we also follow the approach used in Andersen et al (2016) and separately introduce the lagged growth in household debt into our FE and IV models. The advantage of this approach over the dynamic model is that it maintains a larger sample size, since the dynamic model requires multiple lags to be used as instruments. The lagged growth in debt should capture whether the negative effect of debt is due to previous changes in debt (e.g. spending normalisation) or the level of debt (e.g. debt overhang).

The results from the dynamic model, as well as the models controlling for the growth in debt, suggest that spending normalisation cannot explain the negative relationship between debt and spending. This is consistent with the statistically significant negative effect of lagged debt on non-durables spending in Table 1. Non-durables spending is less likely to be affected by spending normalisation since non-durable items tend to be less lumpy and smaller in size. The link between lagged debt and contemporaneous spending persists even after including lagged spending or changes in debt. Moreover, the estimated coefficient remains significant even when using the Arellano-Bond estimation procedure which reduces the sample size considerably. In contrast to Andersen et al (2016), we find no evidence that lagged spending or lagged changes in debt explain lower spending today.

Table 5: Spending Normalisation
Total spending, 2006–10
Model
Dynamic Arellano-Bond (1991) FE Andersen et al (2016) IV Andersen et al (2016)
Lagged mortgage debt −0.01**
(0.04)
−0.03*
(0.08)
−0.27***
(<0.00)
Lagged spending −0.09
(0.23)

Lagged growth in mortgage debt   >–0.00
(0.78)
<0.00***
(<0.00)
Income 0.12***
(<0.00)
0.13***
(<0.00)
0.35***
(<0.00)
Lagged home value 0.07
(0.25)
0.08
(0.18)
0.43***
(<0.00)
Observations 4,134 5,494 5,494

Note: See notes to Table 1

Sources: Authors' calculations; HILDA Survey Release 17.0

### 5.2.2 The housing preferences hypothesis

Next, we consider whether a shift in household preferences towards owner-occupier housing might be driving the correlation between mortgage debt and spending. Note that so far our measure of spending relates specifically to non-housing goods and services. A negative correlation between mortgage debt and non-housing spending could be due to households shifting preferences towards housing and away from other goods and services (with this increased housing consumption at least partly financed through mortgage debt).

To address this, we adjust our measures of household spending and income to include the imputed rent on owner-occupier housing.[17] If the increase in debt reflects a shift towards housing spending alone, we should see no effect of debt when we include owner-occupier housing spending in total spending.

Table 6 presents the results from the FE and IV models using the adjusted measure of household spending and income. In both models, the effect of mortgage debt on total spending is weaker than previously. However, the negative effect of debt persists, suggesting that the shift in household preferences is not the only driver of the relationship between debt and spending. Our estimates suggest that the preference shift accounts for at most one-third to half of the total negative effect of debt on spending.

Table 6: Housing Preferences Hypothesis
Total spending including imputed rent, 2006–10
Model
FE IV
Lagged mortgage debt −0.02***
(<0.00)
−0.14***
(<0.00)
Income 0.16***
(<0.00)
0.34***
(<0.00)
Lagged home value 0.17***
(<0.00)
0.49***
(<0.00)
Observations 6,622 6,622

Note: See notes to Table 1

Sources: Authors' calculations; HILDA Survey Release 17.0

## Footnotes

See La Cava and Simon (2003) for more details on the measure of financial stress. [15]

Our proxies for high job uncertainty are the maximum probability of a household member losing their job (equal to one for any positive probability of job loss) and whether the household reference person is employed as a casual worker. Since most households assign a zero probability of losing their job, we identify job-insecure households as households with any positive probability of losing their job. Our results are robust to setting a higher threshold. [16]

We estimate imputed rent as 5 per cent of the estimated home value less mortgage repayments. [17]