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Hurricane Katrina and Mortgage Loan Performance

Ding Du* Office of the Comptroller of the Currency United States Department of the Treasury

400 7th Street SW, Mail Stop 6E-3 Washington, DC 20219 Phone: (202) 649-5543

Fax: (571) 293-4245 E-mail: ding.du@occ.treas.gov

Summary

Although banks and regulators are increasingly concerned about the impact of natural

disasters on bank stability, economic research on disasters and bank stability is still limited. In this paper, we extend the literature by investigating the impact of natural disasters on bank stability with historical performance data from Fannie Mae and Freddie Mac. Empirically, we utilize a difference-in-differences identification strategy and focus on a major natural disaster, namely Hurricane Katrina. Our results suggest that natural disasters can significantly increase loan delinquencies in the short run, and loan losses partly depend on the government policy responses.

* The views expressed in this paper are those of the author, and do not necessarily reflect those of the Office of the Comptroller of the Currency, or the United States Department of the Treasury. Part of this research was conducted while Ding Du was visiting the Robert H. Smith School of Business, University of Maryland at College Park.

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1. Introduction

It is important to study the impact of Hurricane Katrina (Katrina) on bank stability,

because Katrina offers a unique opportunity to assess the effectiveness of the government policy

geared towards assisting existing homeowners. Figure 1 helps motivate the idea. “Katrina

(Treated)” depicts that the normalized employment in the affected counties collapsed by more

than 15% after Katrina that occurred in the third quarter of 2005. In contrast, as shown by “GFC

(Treated)”, the Global Financial Crisis (GFC) depressed the employment by about 5% in the

same affected areas, although for a longer period of time. The policy responses are different. In

the case of Katrina which affected three states with a combined GDP of $396 Billion (or 3% of

the US GDP) as of 2004, the US government as well as insurance companies provided

substantial aid to existing homeowners directly. According to the estimates of Moody’s

(Moody’s, 2017), while the combined economic loss of Katrina including destruction and lost

output was about $174.5 Billion, the total government and insurance aid amounted to $185.7

Billion. In the case of GFC which shocked all the states, American Recovery and Reinvestment

Act of 2009 totaled about $800 Billion, with little direct aid to existing homeowners.

If the aid (from the government and insurance companies) directly assisting existing

homeowners helps prevent loan defaults and therefore stabilize the banking system, we do not

expect to observe substantial increases in foreclosures and loan losses after Katrina in the

affected areas. To test this conjecture, we use historical loan-level data from two government-

sponsored enterprises (GSEs), namely Fannie Mae and Freddie Mac, as it is difficult to identify

the effects of natural disasters with bank-level data (e.g., call reports). First, while individual

disasters have regional or local effects, banks are often geographically diversified particularly

after the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994. Even if

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researchers focus on community banks that are not geographically diversified, it is still difficult

to capture the impact of disasters, as the differences in the loan performance at the bank level

after a disaster could be due to the differences in the composition of bank loan portfolios. For

instance, one possibility is that the banks in the affected areas increase their lending to meet the

higher credit demand (Cortés and Strahan, 2017), and younger loans tend to have higher default

and loss rates. The GSE data helps weigh against this possibility, as with the extensive loan-level

data we can construct and follow the performance a cohort/portfolio of loans originated before

the disaster. Second, bank-level loan performance measures (e.g., charge offs from Call reports)

do not capture actual loan losses, as loan losses depend on not only default loan balances but also

subsequent proceeds and expenses associated with the specific foreclosures (which is difficult to

forecast before the completion of the foreclosures). The GSE data uniquely provides the detailed

information on actual loan losses.

Empirically, we utilize a difference-in-differences (DID) identification strategy. Our

results can be easily summarized. First, consistent with previous observations (e.g., Vigdor,

2008), Katrina drives up delinquency rates substantially. For instance, based on our DID

estimates, while GFC drives up the 180-day delinquency rate by 9.2 basis points (bps) per

quarter (t = 8.41), Katrina pushes up the same rate in the affected areas by 16.6 bps per quarter (t

= 4.36). Second, unlike GFC, Katrina does not lead to increases in foreclosures and loan losses.

Derived from our DID estimates, the increases in loan losses due to GFC and Katrina are 0.6 bps

per quarter (t = 4.03) and 0.2 bps per quarter (t = 1.56), respectively. Our results are thus

consistent with the notion that the aid (from the government and insurance companies) directly

assisting existing homeowners helps prevent loan defaults and therefore stabilize the banking

system.

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Our paper is related with the growing literature on disasters and bank stability. Banks

and regulators are increasingly concerned about the impact of natural disasters on bank stability.

For instance, the Basel Committee recognizes natural disasters as an operational risk (BCBS,

2010). The Bank of England acknowledges that climate events (e.g., storms) can potentially

cause large financial losses (Scott et al., 2017). Economic research on disasters and bank

stability, nevertheless, is rather limited. Steindl and Weinrobe (1983) do not find bank runs after

natural disasters in the US. Using country-level data, Klomp (2014) finds that natural

catastrophes reduce the distance-to-default of banking sectors in developing countries, but not in

developed economies. Our papers adds to the literature by showing that the impact of disasters

on banks may depend on partly how government policy responses.

The remainder of the paper is organized as follows: Section 2 describes data and

empirical framework; Section 3 presents the empirical results; Section 4 concludes the paper

with a brief summary.

2. Data and empirical methodology

2.1 GSE loan data

Individual natural disasters in the US usually have regional or local effects. To illustrate

the idea, we retrieve FEMA Disaster Declarations Summary - Open Government Dataset, which

is a summarized dataset describing all federally declared disasters.1 The data begins with the first

disaster declaration in 1953 and features all three disaster declaration types: major disaster,

emergency and fire management assistance. Two observations emerge from the data. First, the

number of disasters rises dramatically particularly after 1995 (reaching 239 in 2011). This echoes

1 https://www.fema.gov/media-library/assets/documents/28318.

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the growing concern of banks and regulators about financial effects of natural disasters. Second,

the average number of designated counties per disaster, nevertheless, generally stays below 25

(relative to more than 3,000 counties in the US), suggesting that individual disasters usually have

local or regional effects.

Because banks are often geographically diversified particularly after the Riegle-Neal

Interstate Banking and Branching Efficiency Act of 1994, it is difficult to identify financial

effects of natural disasters with bank-level data, as (1) banks may extend loans to both areas

impacted by natural disasters and areas unaffected, and (2) bank-level performance data (for

instance mortgage-loan charge-offs from Call Reports) do not differentiate the performance of

loans banks hold in different areas. Put differently, bank-level data are not able to precisely

capture the variation in bank loan performance driven by natural disasters, making identification

difficult. The same logic suggests that it is even more difficult to identify financial effects of

natural disasters with country-level data.

Furthermore, even if researchers only focus on community banks that are not

geographically diversified, it is still difficult to measure the impact of natural disasters, as the

differences in the loan performance at the bank level after a disaster could be due to the

differences in the composition of loan portfolios. For instance, one possibility is that the banks in

the affected areas make more loans to meet the higher credit demand after a natural disaster

(Cortés and Strahan, 2017), and younger loans may have higher default and loss rates.

Alternatively, the banks in the disaster areas might originate more loans with higher risk after a

disaster. The GSE data helps weigh against these possibilities, as with extensive loan-level data

we can construct and track the performance of a cohort/portfolio of loans originated before the

disaster.

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As part of a larger effort to increase transparency, Fannie Mae and Freddie Mac make

available historical loan performance data on a portion of fully amortizing fixed-rate mortgages

that they purchased or guaranteed from 2000 to 2016 (with some loans originated by sellers in

1999). The availability of the data is to help investors build more accurate credit performance

models in support of ongoing risk sharing initiatives highlighted by their regulator, the Federal

Housing Finance Agency (FHFA), in its 2017 conservatorship scorecard (FHFA, 2016).

The GSE data consist of the acquisition and performance data files.2 The acquisition file

includes static data at the time of a mortgage loan’s origination and delivery to a GSE. More

specifically, the file includes original unpaid principal balance (Orig. UPB), three-digit property

zip codes (which we use to identify the location of the property), and loan and borrower

characteristics, such as the minimum FICO score of the borrower and the co-borrower (FICO),

the combined loan to value ratio (CLTV), the debt to income ratio (DTI), and various other

variables. To parsimoniously capture other loan and borrower characteristics, we define the risk

layer as the sum of three dummy variables, namely Cash-out Refinance (= 1 if loan purpose is

“Cash-out Refinance” and 0 otherwise), Investment (= 1 if occupancy status is “Investment” and

0 otherwise), and One Borrower (= 1 if the number of borrowers is 1 and 0 otherwise).

The performance file contains the monthly performance data of each mortgage loan from

the time of a GSE’s acquisition up until its current status. More specifically, the file includes

current loan delinquency status, zero balance code which indicates the reason the loan’s balance

was reduced to zero (e.g., prepaid, foreclosure), zero balance effective date, various expenses

and proceeds variables associated with dispositions (which allow calculations of actual loan

losses), and et al. The actual loss or the net loss for a loan in the GSE data is defined as Default

2 For more details on the GSE data, please refer to http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html and http://www.freddiemac.com/research/datasets/sf_loanlevel_dataset.html.

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UPB + Accrued Interest + Total Expenses – Total Proceeds, where Total Expenses include

foreclosure costs, property preservation and repair costs, asset recovery costs, miscellaneous

holding expenses and credits, and associated taxes for holding property, and Total Proceeds

account for net sales proceeds, credit enhancement proceeds, repurchase make whole proceeds,

and other foreclosure proceeds.

Panel A of Table 1 presents the summary statistics of the GSE data by origination year.

Three observations emerge. First, the combined GSE data is extensive, including about 59

million mortgage loans originated between 1999 and 2016, with the total original UPB of $11

Trillion. Second, the loss rates are particularly higher for the loans originated between 2005 and

2008, ranging from 1.18% to 3.20% (compared to the average loss rate of 0.65% for all vintage

years). This makes it more challenging to capture the effects of Katrina (which occurred in the

third quarter of 2005) with bank-level data, as the differences in loan performance could be due

to the differences in the composition of loan portfolios. For instance, to meet high credit demand,

the banks in the Katrina-affected areas may increase lending (Cortés and Strahan, 2017),

resulting in their loan portfolios containing more loans originated after 2005 with particularly

higher loss rates. This observation motivates us to focus on the loan cohorts originated before

Katrina. Third, the GSE data has sufficient coverage even for the early vintage years. For

instance, the number of loans in each vintage year between 2000 and 2005 varies from 2 to 7

million. This extensive coverage helps ensure that our results are less likely driven by outliers.

Panel B of Table 1 shows more detailed summary statistics for the loan-level

performance variables. It seems that there are obvious outliers in the data. For instance, although

the size of a GSE conforming loan is generally limited to $424,100 for single family homes in

the continental US, the 99th percentile of the origination UPB is $529,000. To mitigate the effects

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of outliers, we drop the observations where the loan performance variables are below the 1st or

above the 99th percentile.

2.2 Natural disasters and local economic variables

FEMA Disaster Declarations Summary - Open Government Dataset contains 3,473

natural disasters. We use this data to identify the counties affected by Hurricane Katrina in

Alabama, Louisiana, and Mississippi. The loans/properties located in the affected counties are

defined as the treated group. The loans located in the surrounding states (i.e., Arkansas, Florida,

Georgia, Oklahoma, Tennessee, and Texas) are classified as the control group. This is in the

same spirit of the classical study of Card and Krueger (1994), as these neighboring Gulf-coast

states not affected by the hurricane seem to form “a natural basis” for comparison (e.g., the

parallel trends assumption may be more likely to hold).

Following the banking literature, we also include local economic variables in our

regressions. More specifically, we retrieve the county-level annual income and population data

from the Bureau of Economic Analysis (BEA)3, the county-level monthly labor market measures

(e.g., the labor force, the number of the employed, and the number of the unemployed) from the

Bureau of Labor Statistics (BLS)4, and the zip3-level quarterly housing price index (HPI) from

Federal House Finance Agency5.

2.3 2010 ZIP Code Tabulation Area (ZCTA) Relationship File

3 https://www.bea.gov/regional/. 4 https://www.bls.gov/lau/. 5 https://www.fhfa.gov/DataTools/Downloads/Pages/House-Price-Index.aspx.

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Both the FEMA disaster data and the local macroeconomic variables are at the county

level. However, the GSE data only provides the first three-digit zip codes (zip3) of properties.

Therefore, to merge the FEMA and local macro data with the GSE data, we need a mapping

between zip codes and counties. The particular mapping we use is 2010 ZIP Code Tabulation

Area (ZCTA) Relationship File from US Census Bureau.6 To assign ZCTA codes, the Census

Bureau first examined all of the addresses within each census block to define the list of ZIP

Codes by block. Next, the most frequently occurring ZIP Code within each block was assigned

to the entire census block as a preliminary ZCTA code. After all of the census blocks with

addresses were assigned a preliminary ZCTA code, blocks were aggregated by code to create

larger areas. ZCTAs were created using residential and nonresidential ZIP Codes that are

available in the Census Bureau’s MAF/TIGER database. In most instances, the ZCTA code is the

same as the ZIP Code for an area.

We use the ZCTA relationship file for two reasons. First, the data also contains the

number of house units in each zip code. This allows us to examine if the GSE data’s geographic

coverage is representative. This is particularly important in our case, as disasters (e.g., Katrina)

are associated with specific regions. If the GSE data has particularly lower coverage in specific

regions, the results inferred from such data may not be reliable. Figure 2a shows the distribution

of house units collapsed at the zip3 level in the US, and Figure 2b presents the distribution of the

mortgage loans in the GSE data at the zip3 level in 2010. It seems that the geographic coverage

of the GSE data is representative. Second, besides house units, the ZCTA relationship files also

has the population and area information, which allows us to use different weight to aggregate

county-level macroeconomic variables to assess the robustness of our results.

6 https://www.census.gov/geo/maps-data/data/zcta_rel_download.html.

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2.4 Merged data

We first use the ZCTA relationship file to identify the zip3’s that are affected by Katrina,

based on the declared counties in the FEMA data. Because one zip3 area includes multiple

counties, we define a zip3 as an affected area only when all the counties in the zip3 are eligible

for both public and individual assistances (i.e., eligible to receive the government aid directly

assisting existing homeowners). Figure 3 provides a visualization of the affected and unaffected

areas (or zip3’s).

We next use the house units, population, area, and land area data in the ZCTA

relationship file as weights to construct the zip3-level macroeconomic variables, based on the

county-level macroeconomic data from BEA and BLS. For instance, to construct the zip3-level

employment with population as the weighting variable, we first calculate the percentage of

population in a county living in a zip3, then compute the zip3 population as the weighted average

of the population of the associated counties. The same logic applies to other weighting variables.

We then collapse the monthly loan-level GSE performance data to the quarterly zip3

level. The idea of collapsing the data to the quarterly frequency is to ensure that we have

relatively large number of loans in each zip3 portfolio to reliably measure the average loan loss

rates. We finally merge the zip3-level disaster, HPI, and macroeconomic data with the GSE loan-

performance data.

2.5 Main variables

We look at delinquency rates (DQ) at the zip3 level, such as, 90-day, 120-day, and 180-

day DQ rates, weighted by the origination UPB. For instance, the 90-day DQ rate in a quarter

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captures the percentage of loans in a zip3 that first become 90-day delinquent in that quarter,

weighted by the origination UPB of the loans in the zip3.

We also examine various loan loss measures. The probability of default (PD) is defined

as the total default UPB of the foreclosure loans divided by the total origination UPB of all the

loans in a zip3. Again, we focus on the completed foreclosures to be able to capture actual losses.

The loss given default (LGD) is defined as the total actual loss divided by the total default UPB

of the foreclosure loans in a zip3, and the loss rate (Loss) is the total actual loss divided by the

total origination UPB of all the loans. To be compatible to the loss rate, we also normalize the

proceeds, the expenses, and the accrued interest associated with the foreclosure loans by the

origination UPB of all the loans.

2.6 Similarity between the treated and control groups

Table 2 presents the summary statistics of the merged sample over the two year period

before the event (i.e., Katrina). We focus on the comparison between the treated and control

groups, as our DID methodology requires the similarity between two groups before the event. As

Imbens and Wooldridge (2009) suggest, we use the normalized differences to compare the

similarity between two groups. Normalized differences are calculated as “the difference in

averages by treatment status, scaled by the square root of the sum of the variances" (Imbens and

Wooldridge, 2009, p. 24). Imbens and Wooldridge (2009) imply that the groups are considered

as sufficiently equal if normalized differences are in the range of ± 0.25.

The summary statistics reported in Table 2 suggest that the groups of affected and

unaffected loans are relatively similar before the event. In Panel A, we focus on the loan-level

characteristics. The characteristics of the loans that exist in the two year period prior to Katrina

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at the origination (e.g., FICO, LTV, DTI, and risk layers) are weighted by the origination UPB.

As we can see, the control group includes around 3.5 million loans, and the treated group has

about 350,000 loans. The normalized differences are in the range of ± 0.25. The loss rate in the

control group is 84 basis points, and that in the treated group is 53 basis points. The normalized

difference is 0.04.

In Panel B, we compare the macroeconomic conditions and HPI appreciation between the

treated and control zip3’s. Overall, there are 28 treated zip3’s and 163 control zip3’s. The

average economic conditions between the two groups in the two year period before the event are

similar. For instance, the population-weighted employment annual growth in the control zip3’s is

1.20%, and that in the treated zip3’s is 1.83%. The normalized difference is 0.12. Furthermore,

the economic variables based on different weights produce very similar results. Therefore, in our

empirical tests, we use the population-weighted economic variables. However, the annual HPI

growth in the control zip3’s is higher than that in the treated zip3’s, with a normalized difference

of 0.34. We address this issue in two ways. First, we include the HPI growth in our regressions to

control for this difference. Second, we also run the DID regressions based on kernel propensity

score matching. In all the cases, our results do not change materially.

2.7 Empirical methodology

We employ a difference-in-differences (DID) identification strategy. The baseline DID

model is as follows:

tik

ktikititti xTreatedPostTreatedPosty ,,3210, )( εγββββ ++×+++= ∑ (1)

where tiy , is a loan performance measure of zip3 i in quarter t, Postt is equal to 1 if the

observation is from the post treatment period and 0 otherwise, Treatedi equals 1 if the

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observation is from the treated group (i.e., the zip3’s affected by the hurricane) and 0 otherwise,

and x’s are covariates. As for the covariates, we account for the zip3-level HPI and employment

annual growth. Furthermore, we also estimate Eq. (1) with and without kernel propensity score

matching.

The mean performance of the control group prior to the treatment is captured by β0, and

that for the treated group is β0 + β2. The difference estimate, β2, captures the cross-sectional

difference between two groups. The mean performance after the treatment for the control group

is β0 + β1, and that for the treated group is β0 + β1 + β2 + β3. The difference estimate, β2 + β3,

differences away the common trend but still depends on the cross-sectional difference between

two groups. Thus, the DID estimate, β3, identifies the effect of the treatment by differencing

away both the cross-sectional difference between the control and treated groups and the time-

series common treads.

To account for the concern of Bertrand and et al. (2004) regarding the standard errors in

DID tests, we also collapse our data and take average over two periods, before and after the

event, and estimate the following first-difference specification:

tik

ktikiti xTreatedy ,,3, ηγβα +∆++=∆ ∑ (2)

where ∆y is the change in the loan performance around the event. In all the cases, we cluster

standard errors by zip codes to account for serial correlations within same zip codes.

As we find in Table 2, the HPI growth between the treated and control groups in the two

year period prior to the hurricane is not very similar, with a normalized difference of 0.34.

Therefore, we combine the DID regression with the kernel propensity score matching. More

specifically, we estimate three versions of the propensity-score matching DID. In the first

version, we use the annual growth in the zip3-level HPI to match the treated and control zip3’s.

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In the second version, we also add the annual growth in the zip3-level employment. In the third

version, we use logit estimation of the propensity score instead of probit estimation.

Additionally, we estimate the DID on the common support of propensity scores.

We define eight quarters prior to Hurricane Katrina (i.e., 2003q3 - 2005q2) as the pre-

treatment period, and eight quarters following the hurricane (including the hurricane quarter) as

the post-treatment period (i.e., 2005q3 – 2007q2). One concern is whether the post-treatment

window is long enough to allow nonperforming loans to go through the foreclosure process. To

address this concern, we examine the number of months between the first credit event (180-day

delinquency or foreclosure) and the foreclosure, and report the summary statistics in Table 3. As

we can see, for the Fannie Mae sample, there are 518, 722 foreclosures from 2000 to 2016.

Although the average number of months between the first credit event (FCE) and the foreclosure

over the whole sample period is 16 months, that before the Global Financial Crisis is 6 months.

In fact, for the pre-crisis sample, the number of months between FCE and foreclosure for 75% of

foreclosures is equal to or below eight months. Similar patterns are also found in the Freddie

Mac sample. The evidence thus suggests that a two-year post-treatment window should be

sufficient to capture the effects of the hurricane on loan losses.

“GFC (Treated)” and “GFC (Control” in Figure 1 show that in both Katrina affected and

unaffected areas the employment drops during the GFC. However, the employment decrease is

smaller relative to that caused by Katrina in the Katrina-affected areas (“Katrin (Treated)”. As

we have pointed out, the government responses are different: substantial aid assisting exiting

homeowners directly is provided in the case of Katrina, but not in the case of GFC. Therefore,

comparing the loan performance outcomes following the two events may shed some light on the

effectiveness of the government policy. Therefore, we repeat the same exercises on the same

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cohorts of the mortgage loans located in the Katrina affected and unaffected areas that are

originated before Katrina, except that we look at the GFC period. To separate from the Katrina

period, the pre-GFC is over the one year period before the collapse of Lehman Brothers (i.e.,

2007q3 – 2008q2), and the post-GFC is the two year period from 2008q3 to 2010q2.

3. Empirical results

3.1 Delinquency rates

3.1.1 Benchmark DID regressions

Previous research has found that the delinquency rates increase dramatically in New

Orleans (e.g., Vigdor, 2008). We provide evidence here that the delinquency rates on average

increase substantially in all the affected areas.

Panel A of Table 4 reports the results based on the benchmark specification, Eq. (1), for

various delinquency rates. The estimates for the 90-day DQ rate are presented in Columns (1) to

(3). In Column (1), we estimate the DID model for the 90-day DQ rate without any covariates,

The diff-in-diff estimate (i.e., the coefficient on Post × Treated) is 73.9 basis points (bps) per

quarter with a robust t-statistic of 3.43, suggesting that the event (i.e., Hurricane Katrina) has

statistically significant impact on the 90-day DQ rates. In Columns (2) and (3), we account for

the covariates (i.e., the zip3-level HPI and employment annual growth). As we can see, the

results are materially unchanged. The HPI growth does not enter with a significant coefficient,

but the employment enters with a significantly negative coefficient. This is plausible, as

employment growth should help reduce delinquency rates on mortgage loans. The coefficient on

Post × Treated drops to 55.3 bps (t = 3.94). This is expected, as Katrina also causes large

movements in local economic variables in the affected areas. For instance, as Figure 1 shows,

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although the employment in the control group in Graph “Katrina (Control)’ still increases after

Katrina, the employment in the affected areas collapses by 15% in Graph “Katrina (Treated)”.

Thus, local economic variables would subsume some explanatory power of Katrina.

In Columns (4) – (9), we report the results for the 120-day and 180-day DQ rates. In all

the cases, Katrina is associated with substantial increases in DQ rates. For instance, for the 180-

day DQ rates, the diff-in-diff estimate is 16.6 bps per quarter even with the presence of the

covariates.

We next test the parallel trends assumption by plotting the mean DQ rates for the treated

and control groups over the pre- and post-treatment periods in the top three panels of Figure 4.

To test the parallel trends assumption, we focus on the pre-treatment period, as the counterfactual

of the treated group in the post-treatment period is not observable. Across all three outcome

variables (i.e., the various DQ rates), the treated and control groups seem to move in a very

similar fashion in the pre-treatment period, consistent with the parallel trends assumption.

3.1.2 First-difference DID regressions

To account for the standard-error concern of Bertrand and et al. (2004), we also collapse

our data and estimate the first-difference specification of Eq. (2) for the various DQ rates. The

results are reported in Panel B of Table 4. As we can see, the results are materially the same. For

instance, for the 180-day DQ rate regression, without the covariates of the annual growth in the

zip3-level HPI and employment, the coefficient on Treated suggests that Katrina is associated

with a 19.6 bps increase (t = 3.32) in the DQ rate in the affected areas; with the covariates, the

increase is 12.9 bps (t = 4.73). Again, the decrease in the estimate is due to that local economic

variables could absorb some explanatory power of Katrina.

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3.1.3 Propensity-score matching DID regressions

The results based on the propensity-score matching DID regressions for the various DQ

rates are presented in Panel C of Table 4. As we can see, the results are qualitatively consistent

with those based on the benchmark DID regressions. For instance, in the case of the 180-day DQ

rate, using the annual growth in the zip3-level HPI to match the treated and control zip3’s, the

Diff-in-diff estimate is 19.5 bps (t = 3.32) in Column (7); using both the annual growth in HPI

and employment to match, the Diff-in-diff estimate is 14.4 bps (t = 3.30); using logit instead of

probit estimation of the propensity score, the Diff-in-diff estimate is 19.5 bps (t = 3.29). All the

results suggest that Katrina significantly drives up the DQ rates in the affected areas.

3.1.4 Global Financial Crisis

We report the benchmark DID regression results for the same loan portfolios in the

Katrina affected and unaffected areas around the GFC in Panel A of Table 7. We focus on the

coefficient on Post, as this coefficient measures the effects of the GFC on all the loans located in

the Gulf-coast states. Across different specifications and DQ rates, the results are consistent. For

instance, in the case of the 180-day DQ rate, the coefficient on Post without the local economic

covariates in Column (7) is 16.8 basis points (bps) per quarter with a robust t-statistic of 15.94,

suggesting that the GFC has statistically significant impact on the 180-day DQ rates. In Columns

(8) and (9), we account for the local economic covariates (i.e., the zip3-level HPI and

employment annual growth). As we can see, the results are materially unchanged. Both the HPI

and employment growth enter with a significantly negative coefficient. This is plausible, as HPI

appreciation and employment growth should help reduce delinquency rates on mortgage loans.

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The coefficient on Post drops to 9.2 bps (t = 8.41). This is expected, as the GFC causes large

fluctuations in local economic variables, which should subsume some explanatory power of

Katrina.

We also verify the parallel trends assumptions by plotting the DQ rates for the Katrina

treated and control groups over the pre- and post-GFC periods in the bottom three panels of

Figure 4. Across all three outcome variables (i.e., the various DQ rates), the treated and control

groups generally move in a similar fashion in the pre-treatment period, consistent with the

parallel trends assumption.

Comparing the DQ rates following the two events, we can see that both Katrina and the

GFC are associated with substantial increases in delinquencies. Recall that the 180-day DQ rate

increased by 16.6 bps per quarter in the case of Katrina in Column (9) of Table 1, and 9.2 bps in

the case of the GFC in Column (9) of Table 7, respectively. This suggests that banks and

regulators should be concerned with natural disasters.

3.2 PD, LGD, and Loss rates

We repeat the similar exercises with PD, LGD, and loss rates around Katrina. Our

maintained hypothesis is that if the government aid is geared towards assisting existing

homeowners directly, loan defaults and losses may not increase significantly after a disaster. The

results in Table 5 are largely consistent with this conjecture. For the probability of defaults (PD),

the coefficient on Post × Treated is insignificant in all cases, suggesting that the probability of

defaults is not higher after Katrina. For the loss rate, in all cases except one, the coefficient on

Post × Treated is all insignificant.

19

In Panel B of Table 7, we repeat the same exercises for the GFC period. Again, we focus

on the coefficient on Post to capture the effects of the GFC on loan performance. Different from

the case of Katrina, the GFC drives up PD and loss rates significantly. The differences in loan

performance between two events suggest that the government policy geared towards assisting

existing homeowners may be effective in preventing loan defaults and losses and therefore help

stabilize the banking sector.

We also test the parallel trends assumption for the both events and report the results in

Figure 5. As we can see, it seems that the parallel trend assumption generally holds in both cases.

3.3 Proceeds, expenses, and accrued interests

To shed more empirical light, we further look at the loss components in Table 6 around

Katrina and Panel C of Table 7 around the GFC. We also test for the parallel trends assumption

in Figure 6. The evidence suggests that LGD increases after Katrina, mainly due to increases in

accrued interest. This is expected, as the foreclosure processing time increases in the affected

areas after Katrina. In contrast, not only accrued interests but also foreclosure expenses increase

substantially after the GFC.

4 Conclusion

Although banks and regulators are increasingly concerned about the impact of natural

disasters on bank stability, economic research on disasters and bank stability is still limited. In

this paper, we extend the literature by investigating the impact of natural disasters on bank

stability with historical performance data from Fannie Mae and Freddie Mac. Empirically, we

utilize a difference-in-differences identification strategy and focus on a major natural disaster,

20

namely Hurricane Katrina. Our results suggest that natural disasters can significantly increase

loan delinquencies in the short run, and loan losses partly depend on the government policy

responses.

21

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22

Steindl, F., Weinrobe, M., 1983. Natural hazards and deposit behavior at financial institutions. J.

Bank. Finance 7, 111–118.

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Perspectives 22, 135-154.

23

Figure 3 Employment movements during the Katrina and Global Financial Crisis (GFC) periods

24

Figure 1 Geographic distribution of houses and GSE loans in the US as of 2010 1a The distribution of house units

1b The distribution of the GSE mortgage loans

25

Figure 2 Treated and control areas

26

Figure 4 Delinquency rates during the Katrina and Global Financial Crisis (GFC) periods

Treated - the dashed line Control – the solid line

27

Figure 5 PD, LGD and loss rates during the Katrina and Global Financial Crisis (GFC) periods

Treated - the dashed line Control – the solid line

28

Figure 6 Components of loss rates during the Katrina and Global Financial Crisis (GFC) periods

Treated - the dashed line Control – the solid line

29

Table 1 Summary statistics of the GSE data Panel A: Summary statistics by vintage years

Origination

Year Loan Count Total Orig.

UPB ($M)L Loss rate FICO CLTV DTI

1999 1,253,518 156,902 0.14 712 78 33 2000 2,049,941 264,019 0.15 712 78 35 2001 5,121,628 731,809 0.19 715 74 33 2002 5,533,180 825,878 0.25 720 71 33 2003 7,025,050 1,088,115 0.36 724 69 33 2004 2,866,237 461,397 0.88 718 73 36 2005 3,128,818 539,900 2.12 723 72 37 2006 2,329,390 422,148 3.20 722 73 38 2007 2,454,896 464,533 3.10 722 75 38 2008 2,657,652 551,200 1.18 741 73 38 2009 4,334,152 944,492 0.13 762 68 33 2010 3,220,785 697,340 0.04 765 68 32 2011 2,614,443 565,393 0.02 764 70 32 2012 4,009,077 903,646 0.01 766 70 31 2013 3,506,453 766,623 0.00 759 73 33 2014 2,414,978 522,967 0.00 749 77 34 2015 3,163,652 719,298 0.00 751 76 34 2016 1,886,334 447,640 0.00 751 75 34

Average 3,309,455 615,183 0.65 738 73 34 Total 59,570,184 11,073,300

Panel B: Summary statistics of loan losses

Net loss Severity Proceeds Costs Expenses Interest

Cost Foreclosure

UPB Orig. UPB

min -365,371 -26.35 -328,200 -169,915 -491,394 0 0 1,000 p1 -17,850 -0.13 2,126 35,943 0 1,057 26,707 39,000 p5 -2,778 -0.02 16,391 56,210 520 2,116 44,095 60,000 p50 49,538 0.41 107,000 163,393 11,079 9,731 136,222 161,000 p95 172,428 1.06 294,930 391,008 42,953 44,778 334,143 400,000 p99 249,735 1.35 398,229 483,350 70,608 82,815 403,555 529,000 max 855,788 4,697,300.00 1,051,040 1,185,394 515,065 521,156 790,929 1,470,000

30

Table 2 Comparison of the treated and control groups prior to 2005q3 Panel A: Loan-level variables

Control Treated Norm. Diff Mean SD

Mean SD

Origination UPB

129,056 64,519

113,803 58,644

0.17 Note Rate

6.14 0.79

6.01 0.80

0.11

FICO

715.01 56.93

709.53 58.72

0.07 LTV

74.91 15.97

74.93 15.72

0.00

DTI

32.97 13.02

31.08 13.19

0.10 Risk Layers

0.73 0.70

0.73 0.71

0.00

PD 0.35 5.93 0.33 5.77 0.00 EAD 98.09 2.55 97.73 3.35 0.08 LGD

11.00 20.59

15.06 23.03

-0.13

Loss Rate

0.84 6.92

0.53 5.71

0.04 Loan Count

3,499,402

348,205

Panel B: Local economic variables

Control Treated Norm. Diff Mean SD

Mean SD

HPI 153.86 24.27 149.15 11.16 0.18 HPI growth

6.05 5.00

4.27 1.68

0.34

Employment growth (population)

1.20 2.21

0.84 1.83

0.12 Employment growth (house units)

1.21 2.21

0.85 1.83

0.13

Employment growth (area)

1.20 2.14

0.85 1.81

0.13 Employment growth (land area)

1.20 2.13

0.84 1.81

0.13

Income growth (population)

4.37 2.47

4.71 2.36

-0.10 Income growth (house units) 4.37 2.46 4.71 2.37 -0.10 Income growth (area) 4.37 2.47 4.68 2.29 -0.09 Income growth (land area) 4.36 2.47 4.64 2.33 -0.08 Labor force growth (population) 0.85 2.07 0.56 1.59 0.11 Labor force growth (house units) 0.86 2.07 0.57 1.59 0.11 Labor force growth (area) 0.86 1.98 0.57 1.58 0.11 Labor force growth (land area) 0.85 1.98 0.57 1.58 0.11 Zip3 Count 163 28

Orig. UPB = original unpaid principal balance; FICO = the minimum FICO score of the borrower and the co-borrower; CLTV = the combined loan to value ratio; the risk layers = the sum of four dummy variables, namely Cash-out Refinance (= 1 if loan purpose is “Cash-out Refinance”), Investment (= 1 if occupancy status is “Investment”), Debt to Income (= 1 if original debt-to-income ratio is above 45%), and One Borrower (= 1 if the number of borrowers is 1).; Not Matched = the number of loans with zip codes not in the ZCTA Relationship File.

31

Table 3 Summary statistics of the number of months between the first credit event and foreclosure Fannie Mae Freddie Mac 2000-2016 2000-2007 2008-2016 2000-2016 2000-2007 2008-2016 N 518,722 62,801 455,921 529,925 59,863 470,062 Mean 16 6 17 17 8 19

P1 0 0 0 0 0 0 P5 0 0 0 0 0 0 P25 3 1 3 5 1 6 P50 9 4 10 12 7 13 P75 21 8 23 24 12 25 P95 57 22 59 56 24 58 P99 83 39 85 79 40 81

We examine the number of months between the first credit event and the foreclosure, and report the summary statistics in Table 3.

32

Table 4 DID regressions for delinquency rates during the Katrina period Panel A: Benchmark DID regressions

90 DQ 90 DQ 90 DQ 120 DQ 120 DQ 120 DQ 180 DQ 180 DQ 180 DQ (1) (2) (3) (4) (5) (6) (7) (8) (9) Post×Treated 0.739*** 0.743*** 0.553*** 0.461*** 0.465*** 0.342*** 0.224*** 0.227*** 0.166*** (3.43) (3.46) (3.94) (3.38) (3.41) (4.10) (3.33) (3.38) (4.36) Treated 0.004 0.000 -0.000 0.004 0.000 -0.000 0.006 0.003 0.003 (0.57) (0.02) (-0.05) (0.45) (0.01) (-0.00) (0.87) (0.51) (0.51) Post 0.017 0.019 0.027** 0.009 0.011 0.016** -0.001 -0.000 0.001 (1.45) (1.50) (2.10) (1.28) (1.46) (2.01) (-0.36) (-0.12) (0.35) ∆HPI -0.002 -0.000 -0.002 -0.001 -0.001 -0.001 (-0.95) (-0.17) (-1.10) (-0.41) (-1.18) (-0.62) ∆Employment -0.009*** -0.005*** -0.002*** (-7.51) (-7.90) (-7.90) N 2,745 2,730 2,723 2,745 2,730 2,723 2,745 2,730 2,723 Adj-R2 0.049 0.049 0.135 0.080 0.080 0.198 0.061 0.062 0.152

Panel B: Difference specification regression

(1) (2) (3) (4) (5) (6) (7) (8) (9) treated 0.643*** 0.640*** 0.431*** 0.403*** 0.404*** 0.267*** 0.196*** 0.201*** 0.129*** (3.41) (3.05) (4.13) (3.38) (3.01) (4.26) (3.32) (3.01) (4.73) HPA 0.002 0.023** -0.000 0.013** -0.002 0.005 (0.12) (2.09) (-0.03) (2.05) (-0.38) (1.55) Emp -0.030 -0.018 -0.011 (-1.19) (-1.22) (-1.48) N 183 182 181 183 182 181 183 182 181 Adj-R2 0.243 0.239 0.311 0.239 0.235 0.320 0.233 0.233 0.341

Panel C: Propensity-score matching DID regressions

(1) (2) (3) (4) (5) (6) (7) (8) (9) Diff-in-diff 0.636*** 0.496*** 0.635*** 0.398*** 0.305*** 0.397*** 0.195*** 0.147*** 0.195*** (3.36) (3.66) (3.35) (3.33) (3.79) (3.32) (3.30) (4.00) (3.29) Observations 308 308 310 308 308 310 308 308 310 R-squared 0.236 0.270 0.236 0.231 0.276 0.230 0.223 0.282 0.222 Mean control t(0) 0.17 0.18 0.17 0.14 0.14 0.14 0.08 0.08 0.08 Mean treated t(0) 0.17 0.17 0.17 0.13 0.13 0.13 0.08 0.08 0.08 Diff t(0) 0.00 -0.01 -0.01 0.00 -0.01 -0.01 0.00 0.00 0.00 Mean control t(1) 0.19 0.19 0.19 0.15 0.15 0.15 0.08 0.08 0.08 Mean treated t(1) 0.82 0.69 0.82 0.54 0.45 0.54 0.27 0.23 0.27 Diff t(1) 0.63 0.49 0.63 0.39 0.30 0.39 0.19 0.15 0.19

Robust t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

33

Table 5 DID regressions for PD, LGD, and loss rates during the Katrina period Panel A: Benchmark DID regressions

PD PD PD LGD LGD LGD Loss Loss Loss (1) (2) (3) (4) (5) (6) (7) (8) (9) Post×Treated 0.001 0.007 0.008 2.803 3.641** 3.794** 0.001 0.001 0.002 (0.17) (0.95) (1.07) (1.58) (2.00) (2.03) (0.53) (1.39) (1.56) Treated -0.010** -0.016*** -0.016*** 1.497 0.715 0.713 -0.001 -0.002** -0.002** (-2.06) (-3.56) (-3.56) (1.27) (0.61) (0.61) (-1.35) (-2.43) (-2.43) Post -0.007*** -0.001 -0.001 2.018*** 2.658*** 2.672*** 0.001 0.002*** 0.002*** (-3.34) (-0.34) (-0.34) (3.25) (4.26) (4.28) (1.62) (3.44) (3.46) ∆HPI -0.003*** -0.003*** -0.425*** -0.424*** -0.000*** -0.000*** (-15.44) (-15.43) (-6.62) (-6.61) (-10.97) (-10.96) ∆Employment 0.000 -0.012* -0.000 (0.80) (-1.82) (-0.59) N 2,745 2,730 2,723 2,410 2,410 2,403 2,745 2,730 2,723 Adj-R2 0.006 0.085 0.085 0.012 0.033 0.033 0.000 0.026 0.026

Panel B: Difference specification regression

(1) (2) (3) (4) (5) (6) (7) (8) (9) treated 0.003 0.006 0.007 3.983** 4.211** 4.576** 0.001 0.002 0.002* (0.38) (0.86) (0.92) (2.02) (2.05) (2.09) (0.84) (1.29) (1.75) HPA -0.002*** -0.002*** -0.121 -0.156 -0.000*** -0.000*** (-3.17) (-3.13) (-0.73) (-0.90) (-2.65) (-3.09) Emp 0.001 0.055 0.000 (0.60) (0.17) (1.24) N 183 182 181 180 180 179 183 182 181 Adj-R2 -0.004 0.022 0.018 0.020 0.016 0.013 -0.002 0.012 0.018

Panel C: Propensity-score matching DID regressions

(1) (2) (3) (4) (5) (6) (7) (8) (9) Diff-in-diff 0.001 0.001 0.001 4.353** 3.614* 3.671* 0.001 0.001 0.001 (0.15) (0.14) (0.18) (2.06) (1.80) (1.82) (0.62) (0.62) (0.58) Observations 308 308 310 302 308 306 308 308 310 R-squared 0.086 0.087 0.089 0.063 0.069 0.068 0.033 0.033 0.034 Mean control t(0) 0.07 0.07 0.07 13.23 12.68 12.74 0.01 0.01 0.01 Mean treated t(0) 0.05 0.05 0.05 12.76 12.79 12.79 0.01 0.01 0.01 Diff t(0) -0.02 -0.02 -0.02 -0.47 0.11 0.05 0.00 0.00 0.00 Mean control t(1) 0.06 0.06 0.06 14.66 14.65 14.65 0.01 0.01 0.01 Mean treated t(1) 0.04 0.05 0.04 18.55 18.37 18.37 0.01 0.01 0.01 Diff t(1) -0.02 -0.02 -0.02 3.89 3.73 3.73 0.00 0.00 0.00

t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

34

Table 6 Proceeds, expenses, and accrued interests Panel A: Benchmark DID regressions

Proceeds Proceeds Proceeds Expenses Expenses Expenses Interest Interest Interest (1) (2) (3) (4) (5) (6) (7) (8) (9) Post×Treated 0.001 0.007 0.008 -0.000 0.000 0.000 0.001 0.001** 0.001** (0.12) (0.87) (1.00) (-0.83) (0.10) (0.48) (1.21) (2.03) (2.21) Treated -0.010** -0.017*** -0.017*** -0.001 -0.001*** -0.001*** -0.001** -0.001*** -0.001*** (-2.05) (-3.50) (-3.51) (-1.49) (-2.73) (-2.73) (-2.10) (-3.33) (-3.33) Post -0.008*** -0.002 -0.002 -0.000 0.000** 0.000** -0.001*** -0.000 -0.000 (-3.89) (-1.01) (-1.01) (-0.34) (2.56) (2.58) (-3.81) (-1.06) (-1.05) ∆HPI -0.003*** -0.003*** -0.000*** -0.000*** -0.000*** -0.000*** (-15.68) (-15.68) (-14.04) (-14.02) (-13.89) (-13.92) ∆Employment 0.000 -0.000 0.000 (1.29) (-1.29) (0.00) N 2,745 2,730 2,723 2,745 2,730 2,723 2,745 2,730 2,723 Adj-R2 0.008 0.088 0.087 0.003 0.065 0.064 0.003 0.058 0.057

Panel B: Difference specification regression

(1) (2) (3) (4) (5) (6) (7) (8) (9) treated 0.002 0.005 0.006 -0.000 0.000 0.000 0.001 0.001* 0.001** (0.29) (0.75) (0.78) (-0.51) (0.01) (0.55) (1.48) (1.93) (2.06) HPA -0.002*** -0.002*** -0.000*** -0.000*** -0.000*** -0.000*** (-3.07) (-2.96) (-2.86) (-3.29) (-2.80) (-2.94) Emp 0.001 0.000 0.000 (0.44) (1.10) (0.72) N 183 182 181 183 182 181 183 182 181 Adj-R2 -0.005 0.017 0.012 -0.004 0.022 0.029 0.012 0.025 0.024

Panel C: Propensity-score matching DID regressions

(1) (2) (3) (4) (5) (6) (7) (8) (9) Diff-in-diff 0.001 0.001 0.001 -0.000 -0.000 -0.000 0.001 0.001 0.001 (0.07) (0.07) (0.10) (-0.81) (-0.64) (-0.81) (1.22) (1.23) (1.23) Observations 308 308 310 308 308 310 308 308 310 R-squared 0.096 0.098 0.099 0.080 0.079 0.082 0.055 0.055 0.057 Mean control t(0) 0.07 0.07 0.07 0.01 0.01 0.01 0.00 0.00 0.00 Mean treated t(0) 0.05 0.05 0.05 0.00 0.00 0.00 0.00 0.00 0.00 Diff t(0) -0.02 -0.02 -0.02 0.00 0.00 0.00 0.00 0.00 0.00 Mean control t(1) 0.06 0.06 0.06 0.01 0.01 0.01 0.00 0.00 0.00 Mean treated t(1) 0.04 0.04 0.04 0.00 0.00 0.00 0.00 0.00 0.00 Diff t(1) -0.02 -0.02 -0.02 0.00 0.00 0.00 0.00 0.00 0.00

t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

35

Table 7 DID regressions around the Global Financial Crisis Panel A: Delinquency rates

90 DQ 90 DQ 90 DQ 120 DQ 120 DQ 120 DQ 180 DQ 180 DQ 180 DQ (1) (2) (3) (4) (5) (6) (7) (8) (9) Post×Treated -0.059*** -0.048*** -0.073*** -0.052*** -0.042*** -0.064*** -0.056*** -0.046*** -0.061*** (-3.50) (-2.90) (-4.41) (-3.14) (-2.61) (-3.90) (-3.91) (-3.33) (-4.22) Treated -0.039*** -0.008 0.004 -0.032*** -0.002 0.008 -0.009 0.016* 0.023** (-2.61) (-0.46) (0.27) (-3.13) (-0.20) (0.70) (-1.26) (1.77) (2.45) Post 0.189*** 0.119*** 0.090*** 0.187*** 0.122*** 0.097*** 0.168*** 0.109*** 0.092*** (14.95) (8.97) (6.84) (15.62) (9.75) (7.75) (15.94) (10.04) (8.41) ∆HPI -0.016*** -0.014*** -0.015*** -0.013*** -0.013*** -0.012*** (-13.18) (-11.37) (-13.08) (-11.34) (-13.98) (-12.39) ∆Employment -0.012*** -0.010*** -0.007*** (-6.97) (-6.27) (-4.49) N 2,005 2,002 2,002 2,005 2,002 2,002 2,005 2,002 2,002 Adj-R2 0.171 0.391 0.418 0.183 0.396 0.419 0.186 0.402 0.416

Panel B: PD, LGD, and loss rates

PD PD PD LGD LGD LGD Loss Loss Loss (1) (2) (3) (4) (5) (6) (7) (8) (9) Post×Treated -0.030*** -0.029*** -0.032*** -3.091 -2.847 -4.253 -0.008*** -0.008*** -0.009*** (-3.85) (-3.64) (-3.99) (-1.13) (-1.04) (-1.52) (-3.17) (-2.91) (-3.44) Treated 0.006 0.008 0.010 4.606** 5.181** 6.006*** 0.002 0.003 0.004* (0.84) (1.16) (1.43) (2.24) (2.47) (2.82) (0.96) (1.52) (1.83) Post 0.025*** 0.019*** 0.015*** 6.240*** 5.086*** 3.548*** 0.010*** 0.007*** 0.006*** (6.43) (4.56) (3.38) (6.48) (4.94) (3.25) (7.23) (5.25) (4.03) ∆HPI -0.001*** -0.001*** -0.242*** -0.171** -0.001*** -0.001*** (-3.41) (-2.75) (-3.79) (-2.55) (-4.45) (-3.75) ∆Employment -0.002** -0.623*** -0.001*** (-2.44) (-3.26) (-2.96) N 2,005 2,002 2,002 1,684 1,684 1,684 2,005 2,002 2,002 Adj-R2 0.034 0.051 0.056 0.023 0.030 0.038 0.040 0.073 0.079

Panel A: Proceeds, Expenses, and Accrued Interest

Proceeds Proceeds Proceeds Expenses Expenses Expenses Interest Interest Interest (1) (2) (3) (4) (5) (6) (7) (8) (9) Post×Treated -0.027*** -0.026*** -0.029*** -0.002*** -0.002*** -0.003*** -0.002*** -0.002*** -0.002*** (-3.73) (-3.57) (-3.79) (-3.91) (-3.67) (-3.77) (-3.78) (-3.58) (-3.64) Treated 0.006 0.008 0.009 0.001 0.001** 0.001** 0.001*** 0.002*** 0.002*** (0.96) (1.19) (1.39) (1.60) (1.98) (2.18) (2.71) (3.02) (3.14) Post 0.019*** 0.015*** 0.013*** 0.003*** 0.003*** 0.002*** 0.002*** 0.001*** 0.001*** (5.95) (4.31) (3.28) (9.16) (6.95) (5.78) (6.57) (4.76) (3.97) ∆HPI -0.001*** -0.001** -0.000*** -0.000*** -0.000*** -0.000*** (-2.81) (-2.22) (-3.21) (-2.70) (-3.96) (-3.49) ∆Employment -0.001* -0.000 -0.000 (-1.93) (-1.45) (-1.13) N 2,005 2,002 2,002 2,005 2,002 2,002 2,005 2,002 2,002 Adj-R2 0.028 0.037 0.040 0.049 0.062 0.065 0.031 0.051 0.052

Robust t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1