Discussion of Credit Growth and the Financial Crisis: A New Narrative, by Albanesi et al.

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1 Discussion of Credit Growth and the Financial Crisis: A New Narrative, by Albanesi et al. Atif Mian Princeton University and NBER March 4, / 24

2 Main result high credit growth attributed to low credit score individuals captures life cycle demand for borrowing, as well as life cycle affects in the evolution of credit scores. Contrast with Mian and Sufi (2015a), Household debt and defaults from 2000 to 2010: Facts from credit bureau data. 2 / 24

3 Replicating Figure 1 (b): Sort by 1999 Credit Score 3 / 24

4 Replicating Figure 1 (a) : Sort by 8Q-lagged Credit Score 4 / 24

5 Replication Differences Replication using Mian and Sufi (2015a) data is qualitatively similar One data difference: MS15a data is limited to zip codes for which Case-Shiller zip code level house price index existed. (essentially urban areas) One important methodological difference MS15a data follows the same set of individuals over time. New entrants into credit bureau are not included. Hence credit growth is computed over the same set of individuals. The authors are not looking at credit growth of the same set of individuals over time. The credit balance of new entrants in any credit score quartile, should not be compared with individuals in the same quartile in previous period. This is especially problematic for lower quartile bins as new entrants may start with low total credit and lower score on average. 5 / 24

6 Comment # 1: Proper way to compute score-specific credit growth Define, y it = total $ borrowing of individual i in period t. Sort individuals by their base period (t = 0) credit score bin q 0, and define, Y qt = i q 0 y it Then credit-score-bin-specific credit growth from t j to t can be computed as: CG qt = jy qt Y qt j (1) The methodology avoids the issue of dividing by zeros or very small numbers 10.38% of individual observations have zero total debt 6 / 24

7 Is CG qt spuriously correlated with life-cycle trends? The authors argue yes. They argue that CG qt is correlated with age in the data, and that controlling for age changes the ranking of CG qt by q0. Comment # 2: MS15a did test for this issue: Yes, age is correlated with initial credit score. But within the same age-cohort shows the same qualitative pattern 7 / 24

8 Comment #2: MS15a age-score correlation 8 / 24

9 Comment #2: MS15a within age-cohort robustness 9 / 24

10 Comment # 3: Comparing CG qt within the same age-year bin I compute CG aqt for each age-year cohort separately. We can test for differential credit growth across credit-score bins by regressing: CG aqt = Q (β qt I q ) + ɛ aqt (2) q=1 We can control (almost) non-parametrically for age by regressing: CG aqt = Q 99 (β qt I q ) + (β at I a ) + ɛ aqt (3) q=1 a=19 All regressions are value($)-weighted, results same if we weigh by number of individuals in each observation. 10 / 24

11 Comment # 3: Unadjusted 11 / 24

12 Comment # 3: Age-adjusted 12 / 24

13 Comment # 4: One should not over control using age While controlling for age does not change the main message. There are good reasons not to control for age. Age is also correlated with behavioral traits. e.g. Agarwal, Driscoll, Gabaix and Laibson (2009): young and very old make more financial mistakes, with 53 as age of reason Mian and Sufi (2011): Young homeowners more responsive to credit expansion through housing collateral channel. Under Fed s Reg. B (Equal Credit Opportunity), a credit-scoring model can use age (as long as empirically based ), but cannot use prohibited information including race, ethnicity, national origin, religion, sex, and marital status. [See: Report to Congress On Credit Scoring, Fed BoG] 13 / 24

14 Comment # 4: One should not over control using age Suppose the true equation is, Y i = α + β 1 X 1i + β 2 X 2i + ɛ i (4) where X 1i is inverse of individual i s credit score, X 2i is a life cycle variable, β 1 > 0, and β 2 > 0. We do not observe X 2i, but are concerned that it might be positively correlated with X 1i biasing ˆβ 1 upwards. We use a proxy for X 2i, X 2i, which is also correlated with X 1i, Plugging (5) in (4), we end up regressing, X 2i = π 1 X 1i + π 2 X 2i (5) π 1 Y i = α + (β 1 β 2 )X 1i + β 2 X 2i + ɛ i (6) π 2 ˆβ 1 is biased downwards due to over-controlling. π 2 14 / 24

15 Comment # 5: Dynamic Credit Score Sort Is Problematic The authors argue that MS15a choice of sorting by q 0 is misleading because initial credit scores naturally evolve for life-cycle reasons. They argue we should hence sort on t minus 8 quarter credit score. I have already shown that the premise of this logic is not correct: We observe the same credit growth pattern even when fully adjusting for potential age-cohort effects. Nonetheless there are additional (serious) problems with dynamically sorting on credit scores If people entering lower credit score bins are either new entrants, or have recently defaulted, their credit growth would be naturally slower. If credit growth is (partly) driven by lax lending standards, change in credit score is endogenous. (e.g. ever-greening of consumer loans while house prices continue to go up) 15 / 24

16 Comment # 5: Dynamic Credit Score Sort Is Problematic While the level of credit score in 2006 is negatively correlated with the likelihood of default in subsequent years, change in credit score during the credit boom positively predicts default! Ideally, the current credit score should be a sufficient statistic for predicting default. Clearly not the case for the Great Recession period. Dynamically sorting on credit scores is problematic! We had already mentioned this point in MS15a 16 / 24

17 Comment # 5: Dynamic Sort Effect. Dependent variable: Default in year X? (0/100) (1) (2) (3) (4) (5) (Credit Score) (0.061) (0.068) (0.069) (0.068) (0.070) (Credit Score) (Credit Score) (Credit Score) (Credit Score) (Credit Score) (0.079) (0.084) (0.083) 2.28 (0.12) 3.65 (0.13) 4.77 (0.13) 5.72 (0.13) Constant (0.53) (0.58) (0.58) (0.58) (0.59) R Observations / 24

18 Comment # 5: Conditioning on no default in 2006 The dynamic sort effect is even more pronouned now! Dependent variable: Default in year X? (0/100) (1) (2) (3) (4) (5) (Credit Score) (0.070) (0.077) (0.080) (0.080) (0.082) (Credit Score) (Credit Score) (Credit Score) (Credit Score) (Credit Score) (0.073) (0.080) (0.081) 2.14 (0.11) 3.61 (0.12) 5.51 (0.12) 6.94 (0.12) Constant (0.62) (0.66) (0.69) (0.69) (0.71) R Observations / 24

19 Comment # 5: Partial Plot Credit Score 19 / 24

20 Comment # 5: Partial Plot Credit Score Change 20 / 24

21 Comment # 6: HELOCS vs Mortgage Refinancing 21 / 24

22 Comment # 7: Misunderstanding of prior work Example: First paragraph A broadly accepted narrative about the financial crisis is based on the findings in Mian and Sufi (2009) and Mian and Sufi (2015a) suggesting that most of the growth in credit during the boom was concentrated in the subprime segment and most of the new defaults during the crisis were also concentrated in this segment. 22 / 24

23 Comment # 7: Misunderstanding of prior work MS2009 was about marginal homebuyer, not overall credit. The scope of MS2009 was limited to new home purchase loans, and 65% of households already owned a home. Mian and Sufi (2011) shows the quantitative importance of broad-based credit growth through the increase in leverage of existing homeowners. For the macro channel, credit score / income correlation with credit growth is not necessarily relevant. What matters is that lending and borrowing households are different in terms of their discount rate, or MPC. (e.g. Eggertsson and Krugman (QJE 2012), Korinek and Simsek (AER forthcoming)) 23 / 24

24 Summary of Comments 1. Credit growth should be properly computed without composition effects 2. MS15a had shown the age-score correlation & robustness 3. Credit growth pattern is robust to fully saturated age controls 4. One should be careful not to over control using age 5. Dynamic credit sort is problematic 6. Helocs vs mortgage refinancing 7. Misunderstanding of prior work 24 / 24

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