Destabilizing Effects of Consumer Bankruptcy ∗
3.2 Empirical evidence
with monetary policy.
The remainder of this paper is organized as follows. In section 3.2 we present empirical evidence on the comovement of household income risk and credit condi-tions and estimate the effect of income risk shocks on economic activity and credit conditions. Section 3.3 describes the model environment, the solution method, and the calibration. Section 3.4 presents the numerical results under our baseline cali-bration. In section 3.5 we study role of stabilization policy by the central bank as well as the transmission of monetary policy shocks. Section 3.6 concludes.
3.2 Empirical evidence | 105
Figure 3.1: Income risk and credit card lending
.01.1logscale
1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1 2020q1
(a): Income risk level
46810%
1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1 2020q1
(b): Unemployment rate
-200204060%
1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1 2020q1
(c): Credit card lending standards
246810%
1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1 2020q1
(d): Credit card charge-offs
Notes:Income risk level denotes the standard deviation of persistent income shocks from Bayer et al.
(2019). The remaining series come the FRED database. Shaded areas denote NBER recessions.
charge-off rates show a build-up over the course of recessions, similar to the unem-ployment rate. This indicates that banks anticipate future financial distress of their borrowers and tighten credit standards preemptively. By contrast, consumers seem to take advantage of the option to discharge debt only at later stages, once the drop in income and employment, coupled together with limited borrowing opportunities, has made declaring bankruptcy either optimal or inevitable. The preemptive tight-ening of lending standards is also at the heart of models of consumer bankruptcy such as ours in which financial intermediaries price unsecured loans based on future repayment probabilities.
Next, we turn to the impact of income risk shocks on macroeconomic variables.
To this end, we estimate impulse response functions with Local Projections (Jordà, 2005) using the income risk shocks series from Bayer et al. (2019) as our impulse variable. More specifically, we estimating the following set of regressions for each horizonh= 0, . . . , H:
yt+h=αh+θhϵt+ Γhyt−1+ut+h (3.1) yt+h denotes the dependent variable of interest h steps ahead, and ϵt denotes the income risk shock. Equation (3.1) also includes the past value of the dependent variable to control for potential nonstationarity of the respective variable2. The
2This specification includesyt−1 only to control for nonstationarity. We have also estimated the
Figure 3.2: Responses to income risk shocks
-1-.50.511.5%
0 3 6 9 12
Quarter Real GDP
-1-.50.51%
0 3 6 9 12
Quarter Real Consumption
-.50.51ppts.
0 3 6 9 12
Quarter Unemployment Rate
-1.5-1-.50.5ppts.
0 3 6 9 12
Quarter Federal Funds Rate
Notes: Income risk shocks come from Bayer et al. (2019), the remaining series come the FRED database. Shaded areas denote 90% error bands constructed using Newey-West standard errors.
effect of income risk shocks on the outcome variable are then directly given by the coefficientsθh. To improve the efficiency of the point estimates, forh >1we include the residual from the previous regression as an additional regressor (Jordà, 2005).
In the following, all variables except interest rates, the unemployment rate, and the net percentage of banks increasing lending standards, are transformed into 100 times the natural logarithm of that variable.
Figure 3.2 largely replicate the empirical evidence from Bayer et al. (2019) and shows that both real GDP and real consumption fall within the first year and reach their trough around 4 quarters after the shock. GDP falls by about 0.5% at the trough, and the consumption response is slightly smaller. Similarly, the unemploy-ment rate rises by 0.5 percentage points, while the nominal interest rate declines by about 0.8 percentage points within the first year, before returning to the pre-shock level.
Figure 3.3 adds to this and plots responses of variables related to availability and the cost of consumer credit: the charge-off rates and lending standards from Figure
model in cumulated first differences without controls as well as with a richer control set, with very similar results. These can be found in Appendix 3.B.
3.2 Empirical evidence | 107
Figure 3.3: Responses of financial variables to income risk shocks
-1-.50.51ppts.
0 3 6 9 12
Quarter
Charge-Off Rate - Credit Cards
-20-1001020ppts.
0 3 6 9 12
Quarter Lending Standards
-.50.51ppts.
0 3 6 9 12
Quarter
Credit Card Interest Spread
-3-2-101%
0 3 6 9 12
Quarter Consumer Credit
Notes: Income risk shocks come from Bayer et al. (2019), the remaining series come the FRED database. Shaded areas denote 90 % error bands constructed using Newey-West standard errors.
3.1 as well as the difference between interest rates charged on credit card plans and the federal funds rate, intended to capture premia on unsecured credit, as well as total liabilities from consumer credit of households and nonprofit organizations.
Evidently, following income risk shocks, we find an increase in credit card default rates and tighter lending conditions. In line with the evidence in Figure 3.1, we observe that lending standards jump upward on impact and increase to their peak much faster than charge-off rates do, while charge-off rates build up slowly over time.
Additionally, while the interest rate charged on credit card plans falls (not shown), the nominal central bank interest rate declines faster, leading to an increased spread between the two. Lastly, there is a prolonged reduction in consumer credit, which falls by 2% after seven quarters before slowly recovering to trend.
Taken together, the patterns established point to the following mechanisms as a possible explanation. Banks rationally price the increased income and default risk and retract lending to higher risk households, thus leading to an increased spread between risky and safe borrowing rates as well as tighter lending standards.
At the same time, households also cut back on consumption so as to increase their precautionary savings to accumulate more buffer stock savings against future income
shocks. The decline in aggregate demand depresses production and employment, to which the central bank endogenously responds by cutting interest rates to offset some of the economic decline. Low income households may then find it optimal or necessary to not repay their debts, increasing default rates in the economy. The endogenous increase in savings, higher credit costs, and higher default rates then leads to the observed decline in consumer credit. In the following we explore these mechanisms in a quantitative model that incorporates unsecured credit, time-varying household income risk, and sticky prices.