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A Call for Enterprise in Economic Data Generation and Information Analytics - Dr. Viral V Acharya, Deputy Governor - May 19, 2017 - Presentation at the 9th Indian Chamber of Commerce Banking Summit, Kolkata
May, 23rd 2017
              A Call for Enterprise in

Economic Data Generation and Information Analytics

                  19th May 2017

               Dr. Viral V Acharya
      Deputy Governor, Reserve Bank of India




      Presentation at the 9th Indian Chamber
      of Commerce Banking Summit, Kolkata
  State of Economic Research on India
 A vibrant network is slowly but steadily emerging
   University and business school professors
   Analysts at banks, non-bank finance companies
    (NBFCs), rating agencies, among others
   Researchers at policy institutions and think tanks
   Probing inquiries and fact discovery by media
   Seminars, conferences, forums, panels, deputations
   Global interest in studying India is surging

 More undergraduate and post-graduate (MS, PhD)
 students interested in pursuing Economics and Finance!

 Miles to go before we sleep... on a good, firm trajectory

                                                             2
         How Do We Accelerate?




            The situation seems ripe for

                   Enterprise in

Economic Data Generation and Information Analytics




                                                     3
             A HUGE opportunity!
 Alongside banks and other financial intermediaries,
 need a parallel ecosystem of economic and
 financial data and information services that
   Collects, collates and generates new data points
    on the economy and financial markets
   Disseminates publicly or sells the data
   Analyzes, aggregates and researches data to
    provide information analytics
   Creates information-based business opportunities
   Aids analysis-driven policy-making and thinking

 Given our core human resource strength in
 computing and information systems, this is a low-
 hanging fruit that has not yet been plucked

                                                       4
                        Examples
 Real-time inflation and consumption metrics:
   E-commerce sites
   What are the sustained temporal and geographic
    variations in prices and quantities?
 Employment statistics:
   Payments data; bank and NBFC KYC data
   Can Big Data help us compute quarterly unemployment
    rate?
 Rural and informal economy:
   NBFC and Micro-finance institutions; FMCG companies
   Do omissions of rural and informal economy in formal
    statistics mask economically relevant growth and inflation
    outcomes?
 State finances:
   Implied credit rating/risk using RBI State Finances report
   What is the implied subsidy in borrowing costs?

                                                                 5
                      Examples
 Hot money flows:
   Corporate bond, commercial paper, External
    commercial borrowings, Masala bonds ­ FPI
    investments (maturity/location)
   Which of the flows are "carry trades" and which are
    long-term?
 Governance and corporate finance of pyramids and
 group companies:
   Consolidate individual company/subsidiary filings
   Are internal transfers tunneling or internal capital
    markets in response to credit constraints?
   Are foreign transactions round-tripping / tax-arbitrage
    or genuine investments?
 Bank lending boom and bust cycles:
   Let me elaborate on this as a leading example with
    one of my ongoing research studies and how it could
    be done better

                                                             6
         The Anatomy of a Business Cycle

Presentation at The 2nd Moody's, ICRA and NYU Stern Conference:
                         August 3rd , 2016

  Viral Acharya     Prachi Mishra          N. R. Prabhala
New York University    RBI               CAFRAL, Univ of Maryland
                 Qualifier



            Views are personal.

Not necessarily the official viewpoint of RBI.




                                                 8
                      Context
 We analyze the anatomy of India's economic and
 financial cycle since 2008
   - Cycle is big
   - Cycle is rather sharp

· Understanding and disentangling the channels

   ­ Bank lending channel
      · Supply of credit too low?
      · State-owned (distressed) banks

   ­ Corporate distress channel
      · Demand for credit too low?







                                                  9
 Overview: India's economic and financial cycle


 Investment
   Pick up in investment after GFC
   Slowdown starting 2011-12

 Similar cycle for other real outcomes

 Similar cycle for bank credit

 Credit and real cycles highly correlated




                                                  10
Real and Credit outcomes




                           11
         Firm Sales and Employment
        Growth (Annual average, in %)                    Capital Expenditures
       Employment growth     Sales growth             (Firm-level, average, in %)
18                                            25
16                                            24

14                                            23

12                                            22

10                                            21
                                              20
8
                                              19
6
                                              18
4
                                              17
2
                                              16
0
                                              15
     2008 2009 2010 2011 2012 2013 2014
-2                                                 2008   2009   2010   2011   2012   2013



      Notes. Capital expenditures (t) = (Net fixed assets (t+1) ­Net fixed assets (t)
      + Depreciation)/Net fixed assets

                                                                                             12
            Growth in Credit: By Bank Ownership
                       (Annual, in %)
25                  State-owned   Private
23
21
19
17
15
13
11
 9
 7
 5
     2008    2009         2010              2011   2012   2013


                                                                 13
% of Gross Advances                                Stressed Assets of Banks
                                    Restructured Assets %                                           Gross NPA % (RHS)
6.5                                                                                                                                                   8.0
                                                                                                                                                      7.5
6.0                                                                                                                                                   7.0
                                                                                                                                                      6.5
5.5                                                                                                                                                   6.0
                                                                                                                                                      5.5
5.0                                                                                                                                                   5.0
                                                                                                                                                      4.5
4.5                                                                                                                                                   4.0
                                                                                                                                                      3.5
4.0                                                                                                                                                   3.0
      Jun-12

               Sep-12

                        Dec-12

                                 Mar-13

                                          Jun-13

                                                   Sep-13

                                                            Dec-13

                                                                     Mar-14

                                                                              Jun-14

                                                                                       Sep-14

                                                                                                Dec-14

                                                                                                         Mar-15

                                                                                                                  Jun-15

                                                                                                                           Sep-15

                                                                                                                                    Dec-15

                                                                                                                                             Mar-16
                                                                                                                                                      14
                          Credit and Investment Cycle
10                                                                               10

                                                                                 8
 5
                                                                                 6
 0
      2008         2009          2010      2011          2012          2013      4

 -5                                                                              2

                                                                                 0
-10
                                                                                 -2
-15
                                                                                 -4

-20                                                                              -6
       credit growth (annual, in %)     investment (right scale, in mn of Rs)


                                                                                15
           Can we disentangle
     the bank lending (supply) channel
                   from
the corporate demand (demand) channel?


    Should policy resolve bank stress or
        corporate stress or both?



                                           16
        Empirical strategy: Diff-in-diff

· Do weak firms, and firms connected to weak banks,
  respond differently from healthier firms, connected
  to the same banks, when the cycle turned?

   ­ Weak and strong firms

   ­ Firms connected to weak or strong banks

   ­ Use variation pre and post 2012 when cycle
     turned to distinguish bank lending channel from
     corporate channel


                                                        17
                          Data

Firm-level real and financial outcomes
· CMIE Prowess
· 3,000 listed companies

Real outcomes
· Sales, employment, capx

Financial outcomes
· ICR, assets, leverage

Bank-level data
· BSR 2, Reserve Bank of India



                                         18
                  Data (contd.)
 Weak firm
 ­ Interest Coverage Ratio (ICR) < 2

 Weak bank
 ­ Public sector banks
 ­ High Exposure to weak sector
 ­ Higher ex-post NPA

 Firms connected to a weak bank
  ­ At least one bank is a PSB
  ­ Al least one bank has exposure to weak sector
  ­ (Max) non-performing assets: Above and below
    median



                                                    19
              Overview: channels

Bank lending channel helps understand the cycle

 Firms connected to "weak" banks over-invested
 and had better real outcomes in up-cycle, but with
 much weaker outcomes during down-cycle

 Firms with weak corporate balance sheets had
 worse outcomes throughout the sample

 Results provide a strong case for the asset quality
 review and clean-up of banks underway in India


                                                       20
      Employment growth by firm                       Employment growth by bank
               stress                                          stress
         (weak-strong, in pp)                            (weak-strong, in pp)
0                                                1
     2009   2010   2011   2012   2013   2014
                                               0.5
-1
                                                 0
-2                                                     2009   2010   2011   2012   2013   2014
                                               -0.5
-3                                              -1
                                               -1.5
-4
                                                -2
-5
                                               -2.5
-6                                              -3




                                                                                                 21
              Capx by firm stress                          Capx by bank stress
             (weak-strong, in pp)                          (weak-strong, in pp)
 0                                            2.5
      2009     2010   2011    2012   2013
 -2                                             2
                                              1.5
 -4
                                                1
 -6                                           0.5

 -8                                             0
                                                    2009     2010   2011    2012    2013
                                             -0.5
-10
                                               -1
-12                                          -1.5

-14                                            -2
                                             -2.5
-16

      Notes. Capital expenditures (t) = (Net fixed assets (t+1) ­Net fixed assets (t)
      + Depreciation)/Net fixed assets

                                                                                           22
       Interest coverage ratios by firm          Interest coverage ratios by bank
                    stress                                    stress
             (weak-strong, in pp)                      (weak-strong, in pp)
  0                                         0
-0.5    2009 2010 2011 2012 2013 2014              2008 2009 2010 2011 2012 2013
 -1                                       -0.2
-1.5                                      -0.4
 -2
                                          -0.6
-2.5
 -3                                       -0.8
-3.5
                                           -1
 -4
-4.5                                      -1.2




                                                                                    23
          Empirical specification




Key Hypotheses: 1. Firms connected to weak banks
had poorer real outcomes once the cycle turned



Key Hypotheses: 2. Weaker firms had poorer real
outcomes through the cycle




                                                   24
              Economic significance
                Counterfactual exercise:
    Losses from a firm's association with a weak bank
= How much higher would economic outcomes be if firms
         were NOT associated with weak banks

(1) Overall change 2011-14       (2) Weak bank induced contraction
        (% of 2011)                         (% of 2011)




(3) Real loss = (2)/[(1)+(2)] (in %)


                                                                     25
Results




          26
         Economic significance
            Counterfactual exercise:
Losses from a firm's association with a weak bank
                        Employment
(1) Overall change       (2) Weak bank        (3) Real loss =
2011-14 (% of 2011)   induced contraction   (2)/[(1)+(2)] (in %)
                           (% of 2011)
        6.3                   5.5                  46.3


                            Sales
       38.1                   7.5                  16.4

                            Capx
       34.8                   7.8                  18.4




                                                                   27
         Conclusions from the Study

 Bank lending channel important in explaining the cycle

    Real outcomes stronger for firms connected to weak
    banks in the up-cycle; but decline during down-cycle

    Firms connected to weak banks have weak balance
    sheets throughout the sample
      lower ICR, higher leverage, are larger in size

 Firms with weak corporate balance sheets had worse
 outcomes throughout the sample

 Results provide strong case for clean-up of stressed bank
 balance-sheets by resolving heavily indebted firms







                                                             28
            Corroborating Evidence
 RBI Monetary Policy Report (MPR, April 2017) finds
 supporting evidence using only bank-level data

 Banks with greater stressed assets and worse capital
 ratios / provision cover:

    Lend at higher rates earning greater net interest
    margins, but as a result

    Show weaker credit growth

 Bank-level analysis, however, makes it hard to rule out a
 demand-based explanation that the bank became
 stressed due to risky borrowers, which in turn are facing
 higher rates and are not demanding credit any more



                                                             29
         Questions Left Unanswered

Did healthier banks in a consortium lend more to
healthier firms compared to weaker banks?



Did stressed banks that responded with
recapitalization and provisioning lend healthily?



Did under-capitalized and under-provisioned banks
evergreen their bad loans lending to stressed
borrowers at over-subsidized rates to roll over debt?


                                                        30
         Questions Left Unanswered

Did banks and firms that did restructure experience
better outcomes?



Did stressed banks have poor transmission of
accommodative monetary policy during 2015-16?



What did stressed banks do with excess liquidity during
demonetization compared to healthier banks?



                                                          31
  Could we have done this better? YES!
1. Bank-firm loan-level matched data w/ loan terms at
    time of origination and corporate finance data
   - Should this be a public credit registry? Public good?
   - All creditors, e.g., trade creditors also?
    - E.g.: RBI BSR-RBI CRLIC-CMIE Prowess integration
2. Bank-firm loan-level ratings data
    - Internal / external ratings and their evolution
    - Market-based measures of firm and sector credit risks
3. Bank-firm loan-level restructuring data w/ details
    - Augmented CRLIC
4. Platform for secondary loan sales and price discovery
5. Firm-debt level Default and Recovery (LGD) data
    - Rating agencies should track and provide this

Such data could also help "lean against the wind" of a
lending cycle, e.g., with risk- and sector-based provisioning

                                                                32
 Such datasets exist in many other countries

UNITED STATES, for example:

1. Deal Scan: syndicated loan origination
2. Shared National Credit Program: originations and draw
   downs
3. Capital IQ: draw downs
4. FDIC Call Reports: bank statistics
5. SNL Financial: bank statistics
6. Dealogic: mergers and acquisitions
7. LSTA: secondary loan sales
8. Prowess/Losscalc: default and recovery rates

HMDA (mortgages), Survey of Small Business Finance, ...


                                                           33
                      Key Players
1. Large banks in commercial and mortgage lending, and
   large NBFCs and micro-finance institutions in rural and
   MSME lending can set data standards

2. RBI can play an aggregating role to collate data at
   source from all financial firms and disseminate with
   appropriate lags, if any

3. Data vendors and information analytics firms,
   potentially housed as arms of large banks and rating
   agencies, can distribute data and analysis

4. Vibrant research community I referred to at the outset
   can be its consumer

5. Private financial firms can use analytics to undertake
   analysis-aided enterprise and financial transactions

                                                             34
                    Summing Up

"Not everything that counts can be counted; and not
everything that can be counted counts."

                         -   Albert Einstein


It is a sobering thought for economists!

It should induce innovations to count better what really
counts!!

Time ripe for taking giant strides in
Economic Data Generation and Information Analytics!!!


                                                           35

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