Non-Performing Assets and Lending Growth

Amartya Lahiri* Urvi Neelakantan†

*CAFRAL and University of British Columbia

†CAFRAL and FRB Richmond

March 2019

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Questions

I Has bank lending followed standard profitability criteria?

I Have NPAs been driven by borrower productivity?

I How are loans, productivity and NPAs related?

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This Presentation

I Examine questions at the sectoral level

I Use variation in loans, NPAs and total factor productivity(TFP)

I cross-sectional variation across 19 sectors

I time-series variation between 1999-2018

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Data Sources

I Bank Statistical Returns (BSR) data

I amount outstanding, by sector, between 1999-2018

I outstanding loans broken into four groups

I groups 2-4 classified as NPA

I Indian KLEMS data

I sectoral output, input and TFP data

I covers 1981-2015

I PROWESS database for sectoral q

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Pattern 1: Time trend of NPA

Figure: What fraction of each sector’s loans are NPAs?

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1999 2004 2009 2014

Basic Metals

Vehicles

Engineering

Leather

Construction

Agriculture

1999 Average: 12.9% 2018 Average: 7.8%

Mining

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Pattern 2: Sectoral Share of NPAs in 2018I Four sectors currently account for half of all NPAs: Metals,

Construction, Agriculture and Electricity/Gas/Water

Basic Metals & Metal Products

18%

Construction16%

Agriculture -Direct Finance

8%Electricity, Gas &

Water8%

Professional and Other Services

6%

Engineering5%

Textiles5%

Wholesale Trade5%

Food Manufacturing &

Processing4%

Retail Trade3%

Other Industries3%

Vehicles,Vehicle Parts & Transport

Equipments3%

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Pattern 3: Loan share v NPA share in 2018

Figure: Sector Share of Total Lending vs. Sector Share of Total NPAs

Transport Operators

FinanceMining

Textiles

Metals

Engineering

Vehicles

Electricity, Gas & Water

Construction

Personal Loans -Housing

Personal Loans -Others

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

0% 2% 4% 6% 8% 10% 12% 14%

Sect

or S

hare

of T

otal

NPA

s (20

18)

Sector Share of Total Lending (2018)

NPA share 30% greater

NPA share=Loan Share

NPA share 30% lower

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Pattern 4: Sectoral NPAs – Then and NowWhat fraction of each sector’s loans are NPAs (1999 vs. 2018)?

Sector NPA Share Sector NPA Share

1999 2018 1999 2018

Basic Metals & Metal Products 17% 45% Professional and Other Services 13% 10%

Vehicles, Vehicle Parts & Transport Equipment 6% 32% Leather & Leather Products 25% 9%

Engineering 15% 28% All Others 13% 9%

Mining & Quarrying 9% 25% Rubber & Plastic Products 12% 8%

Construction 21% 24% Agriculture - Direct Finance 18% 8%

Paper, Paper Products & Printing 16% 24% Petroleum, Coal Products & Nuclear Fuels 5% 8%

Textiles 17% 21% Retail Trade 10% 8%

Food Manufacturing & Processing 16% 20% Transport Operators 13% 6%

Electricity, Gas & Water 3% 17% Agriculture - Indirect Finance 7% 6%

Other Industries 10% 17% Personal Loans - Consumer Durables 5% 4%

Manufacture of Cement & Cement Products 13% 14% Personal Loans - Others 6% 3%

Beverage & Tobacco 13% 13% Personal Loans - Housing 4% 2%

Chemicals & Chemical Products 17% 12% Finance 5% 1%

Wholesale Trade 6% 11%

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Profit-Based Lending

I Sectors with high TFP should have higher profits

I Profit maximizing lenders should lend more to sectors withhigher profits

⇒ Sectoral loans and TFP should be positively correlated

I High profit sectors should have lower NPAs

⇒ Sectoral TFP and NPAs should be negatively correlated

I Does this show up in the data?

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Aggregate DataCorrelations

NPA Share Loan Growth TFP Growth

NPA Share 1

Loan Growth -0.1957 1

TFP Growth -0.5349** 0.5102* 1

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Lending and Productivity: Sectoral DataExpect Positive Correlations

Figure: Lead-Lag Correlation Between TFP Growth and Lending GrowthRates (1999-2015)

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5 C

onst

ruct

ion

Min

ing

& Q

uarr

ying

Bas

ic M

etal

s &

Met

al P

rodu

cts

Tra

de

Pet

role

um, C

oal P

rodu

cts &

…

Veh

icle

s,Ve

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&…

Cem

ent &

Cem

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nd T

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Pla

stic

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Ele

ctric

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as &

Wat

er

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ems,

Oth

er In

dust

ries

Tex

tiles

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eath

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& C

hem

ical

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duct

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nspo

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pera

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aper

Pro

duct

s &

…

Pro

fess

iona

l & O

ther

Ser

vice

s

Fin

ance

-3 -2 -1 0 1 2 3

Note: TFP and lending indexed to 100 in 1999. Certain KLEMS sectors were combined to align with BSR sectorcodes. TFP Growth is anchor variable, Lending Growth Rate’s lag and lead are taken.

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Productivity and NPA: Sectoral DataExpect Negative Correlations

Figure: Lead-Lag Correlation Between TFP Growth and Fraction ofSector Loans that are NPAs (1999-2015)

-1.2-1

-0.8-0.6-0.4-0.2

00.20.40.60.8

1

Eng

inee

ring

Che

mic

als

& C

hem

ical

…

Fin

ance

Pap

er, P

aper

Pro

duct

s &

…

Tra

nspo

rt O

pera

tors

Rub

ber &

Pla

stic

Pro

duct

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icul

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Ele

ctric

ity, G

as &

Wat

er

Pro

fess

iona

l & O

ther

…

Foo

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ge a

nd…

Tex

tiles

& L

eath

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Veh

icle

s,Ve

hicl

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rts

&…

Cem

ent &

Cem

ent…

Tra

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Pet

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cts…

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ems,

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& Q

uarr

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Bas

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etal

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al…

-3 -2 -1 0 1 2 3

Note: TFP and lending indexed to 100 in 1999. Certain KLEMS sectors were combined to align with BSR sectorcodes. TFP Growth is Anchor variable, NPA share in total sector’s lag and lead are taken

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Summary of Sectoral Data Patterns

I Basic Metals, Construction and Mining are commonaberrations

I Collectively account for 36 percent of NPAs

I Account for only 13 percent of loans outstanding

I Are these sectors statistical outliers?

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Econometric Testing

I Formally examine relationship between loans, productivity andNPAs

I Which sectors are econometric outliers?

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Model of Lending

I Baseline specification

∆Lit = F

(NPAit−1

NPAt−1, qit−1,

Dit−1

Ait−1

)+ εit

I q = market valuebook value is Tobin’s q

I Dit−1

Ait−1is debt-to-asset ratio

I Problem: NPA respond to q and debt-to-asset ratio

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Two-Step Process

I Step 1: Estimate

NPAit

NPAt= G

(Lit−1

Lt−1, qit−1,

Dit−1

Ait−1,CPIt−1

)+ ηit

I Step 2: Estimate

∆Lit = F

(η̂it−1, qit−1,

Dit−1

Ait−1, t

)+ εit

I CPI is commodity price index

I η̂it measures NPAs not due to other variables

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Null Hypothesis

I Expect coefficient on loan share to be one

I if NPAs are random then expected NPAs are zero

I sectors with higher loan shares will have higher NPA shares

I Coefficient below one indicates banks doing better thanrandomly allocating loans

I Coefficient above one indicates coin toss would do better

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First-step Result: Sector Share of NPAs(1) (2) (3) (4)

Sector Share 0.863∗∗∗ 0.835∗∗∗ 0.657∗∗∗ 0.612∗∗∗

of Loans Lagged (18.37) (18.65) (10.36) (9.09)

Debt-to-Asset 0.0912∗∗∗ 0.0331∗∗

Ratio Lagged (7.68) (1.98)

Tobin’s Q 0.00542∗∗∗ -0.000457Lagged (3.69) (-0.31)

Comm. Price -0.0000185 0.00000538Index Lagged (-0.41) (0.15)

Sector No No Yes YesFixed Effects (.) (.)

Constant 0.00794∗∗∗ -0.0264∗∗∗ 0.00588 -0.00346(3.57) (-4.45) (1.25) (-0.47)

Observations 418 418 418 418R2 0.448 0.520 0.737 0.739

t statistics in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

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Actual and Predicted Share of NPAs by Sector

0.1

.2.3

0.1

.2.3

0.1

.2.3

0.1

.2.3

0.1

.2.3

2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020

2000 2005 2010 2015 2020 2000 2005 2010 2015 2020

Transport Operators Prof & Other Services Finance Agriculture Mining & Quarrying

Food Mfg & Proc Beverage & Tobacco Textiles Paper & Printing Leather

Rubber & Plastic Chemicals Petrol, Coal, Nuclear Cement Metals

Engineering Vehicle & Trans Equip Wood, Gems, Other Electricity, Gas, Water Construction

Wholesale Trade Retail Trade

Actual Predicted

Sect

or S

hare

of E

cono

my

NPA

s

year

Graphs by bsr_ind_code

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Actual and Predicted Share of NPAs: Mining

0.0

05.0

1.0

15.0

2Se

ctor

Sha

re o

f Eco

nom

y N

PAs

2000 2005 2010 2015 2020year

Actual Predicted

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Actual and Predicted Share of NPAs: Metals

.05

.1.1

5.2

.25

Sect

or S

hare

of E

cono

my

NPA

s

2000 2005 2010 2015 2020year

Actual Predicted

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Actual and Predicted Share of NPAs: Vehicles

0.0

1.0

2.0

3.0

4Se

ctor

Sha

re o

f Eco

nom

y N

PAs

2000 2005 2010 2015 2020year

Actual Predicted

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Actual and Predicted Share of NPAs: Electricity, Gas,Water

0.0

2.0

4.0

6.0

8Se

ctor

Sha

re o

f Eco

nom

y N

PAs

2000 2005 2010 2015 2020year

Actual Predicted

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Actual and Predicted Share of NPAs: Construction

0.0

5.1

.15

Sect

or S

hare

of E

cono

my

NPA

s

2000 2005 2010 2015 2020year

Actual Predicted

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Second-step Result

Lending Growth

Residual -0.560∗

Lagged (-1.89)

Tobin’s Q 0.00742Lagged (0.84)

Debt-to-Asset -0.250∗∗∗

Ratio Lagged (-2.63)

Year YesFixed Effects

Sector YesFixed Effects

Observations 396R2 0.371

t statistics in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

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Actual and Predicted Lending Growth

-.50

.5-.5

0.5

-.50

.5-.5

0.5

-.50

.5

2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020

2000 2005 2010 2015 2020 2000 2005 2010 2015 2020

Transport Operators Prof & Other Services Finance Agriculture Mining & Quarrying

Food Mfg & Proc Beverage & Tobacco Textiles Paper & Printing Leather

Rubber & Plastic Chemicals Petrol, Coal, Nuclear Cement Metals

Engineering Vehicle & Trans Equip Wood, Gems, Other Electricity, Gas, Water Construction

Wholesale Trade Retail Trade

Actual Predicted

Lend

ing

Gro

wth

year

Graphs by bsr_ind_code

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Conclusion

I NPAs have risen secularly since 2011, and spiked over the pastthree years

I A few sectors have been outliers, construction and metalsbeing two such

I NPAs of outliers have exceeded the amount that fundamentalscan explain

I Banks, on average, did well: sectoral NPA shares < sectoralloan shares

I Problem appears to be specific misallocation, not generalinefficiency

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Sector Share of NPAs and Sector Share of Risky Firms

Sector Share of NPAs Debt-to-Asset Ratio Sector Share of Risky FirmsSector Share of NPAs 1.0000Debt-to-Asset Ratio 0.3562*** 1.0000Sector Share of Risky Firms 0.5035*** 0.1757*** 1.0000

Risky firms refers to firms in the top quartile of fitted probability of default.

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Probability of Default Series: MethodologyA. Sample ConstructionI Data regarding listed firms characteristics and defaults is

collected for the period 2002 – 2018 as follows:I A1. Firm characteristics data : Accounting and stock

market data gives a daily panel of all listed firmsI A2. Firm level default data : Sample of firms that have

defaulted on their debt payment obligations in each quarter

I A ‘quarterly default dummy’ is created, which is set to 0 if fora given quarter a firm appears in the characteristic datasetbut not the default dataset.If a firm appears in both thedatasets, the variable is set to 1.

B. Variable DescriptionI B1. Dependent Variable: YDD (Yearly Default Dummy)

I If for firm i in year t, the sum of ‘quarterly default dummy’from Q1 to Q4 is greater than 0, then YDDit is 1, else 0.

I After the first occurrence of YDD being 1 for a company, allsubsequent observations for the company are dropped.

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Probability of Default Series: Methodology

I B2. Independent VariablesThe independent variables are calculated using the datasetdescribed in A1. For obtaining an annual measure of each ofthese variables, the value is taken as of the 1st of Jan forcompany i in year t in the yearly panel.I Distance to Default (DTD) : Using the KMV Merton

distance-to-default methodologyI Interest Expense to PBDITA (Int2PB) : Interest Expense

PBDITAI Book-to- Market Ratio (Bk2Mkt) :

Total AssetsMarket Capitalization +Total Liabilities

I Liquidity (Lqdty) : Cash and Cash EquivalentsTotal Assets

I Profitability (Pft) : Net IncomeTotal Assets

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Probability of Default Series: Methodology

C. Logistic Regression

I For obtaining the coefficients required for calculating thefitted PDs for year t, a rolling regression of the following formis estimated on the yearly panel over the last four years i.e.over window r ranging from t-4 to t-1:

YDDr = α+β1DTDr +β2Int2PBr +β3Bk2Mktr +β4Lqdtyr+

β5Pftr + εr ....(1)

I Thus, for the period 2002 to 2018, we get 13 coefficients (onefor each year, starting in 2006 and ending in 2018).

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Probability of Default Series: MethodologyD. Daily Fitted PDsBase data is the daily panel of listed firms as created in A1. Thedaily fitted PD are calculated as follows:I For each firm i for each day j in a given year t, the Sum is

calculated using the coefficients obtained in equation (1) asfollows:

Sumitj = α+β1DTDitj+β2Int2PBitj+β3Bk2Mktitj+β4Lqdtyitj

+β5Pftitj ....(2)

I For each day j, the fitted PD (FittedPD) is calculated byusing Sumitj in a logistic function as follows:

FittedPDj =eSumitj

1 + eSumitj

E. Annual Fitted PDsFor each firm i in a given year t, we obtain the annual fitted PDvalue by taking an average across the daily values in that year.

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Measure Of Riskiness : MethodologyA.Classification Of Firms Based on RiskI Based on the distribution of fitted PD variable, a threshold

value is defined to classify each of the firms as high risk or lowrisk. For a firm i in a year t, if the fitted PD value is greaterthan the threshold, then the variable ‘high risk dummy’(HRDit) takes the value 1, otherwise 0.

I Two different ‘high risk dummy’ variables are generated usingthe threshold as 75th and 80th percentile value of fitted PDrespectively.

B.Calculating Industry RiskTo generate the risk measure at an industry level, we define a newvariable which is calculated as follows:

I For an industry m in year t,

RMmt =No of firms in industry m with HRDit = 1

Total no. of firms with HRDit = 1

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