Extracting information from Big Data –BCB Research Department´s experience

Sérgio Mikio Koyama

The views expressed here are those of the author and do not necessarily reflect those of the Central Bank of Brazil or its members.

… to make research of great importance, we need think in “old

questions… some of the most important questions in

economics and social science have not yet been fully

answered, and the recent availability of big data of various

types allows us, for the first time, to tackle those classic

questions”

Raj Chetty

Three V’s

• Volume

• Velocity

• Variety

Big Data Concept

August, 2017

Aggregated data:

- 756 million credit operations (100%)

Granular data (loan-by-loan):

- 614 million credit operations (567,5 million individuals, 46.5 million enterprises)

- 105.5 million borrowers (101 million individuals, 4.5 million enterprises)

about 180 millions new records per month

Credit Information System (SCR)

System that performs procedures related to the processing and settlement of funds transfer operations, operations with foreign currency or with financial assets and securities.

Having started in 2002, it presents more than 1 billion records, with over 189 million records over the last year

Payment System (SPB)

• RECOR / SICOR (of rural credit)• SELIC (Treasury Bonds Custody)• CETIP• Foreign Exchange Operations• Bank’s financial information (COSIF)• Census of foreign capital• Expectations system

• RAIS (formal employment record)• CadÚnico (unified registry of social programs)• etc

Databases in BCB

Cross-subsidy in Credit Markets: Micro Level Evidence from Earmarked Rules in Brazil

Carlos V. de Carvalho (BCB/PUC-Rio)Bruno Martins (BCB-Depep)

Motivation

• The participation of regulated lending in banks credit

portfolio has increased

30%

32%

34%

36%

38%

40%

42%

44%

46%

0%

2%

4%

6%

8%

10%

12%

14%

16%

Mar

/11

Jul/

11

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12

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13

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/17

Credit Portfolio Allocation: Official Banks

Regulated Loans: Rural BNDES: OnlendingsRegulated Loans: Real Estate Non-earmarked Loans (right-axis)

Motivation

• The participation of regulated lending in banks credit

portfolio has increased

70%

72%

74%

76%

78%

80%

82%

84%

0%

2%

4%

6%

8%

10%

12%

14%

Mar

/11

Jul/

11

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12

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14

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15

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/17

Credit Portfolio Allocation: Private Banks

Regulated Loans: Rural BNDES: OnlendingsRegulated Loans: Real Estate Non-earmarked Loans (right-axis)

Motivation

• And the return of regulated lending is much lower

than that of non-earmarked credit.

0

5

10

15

20

25

30

Mar

-11

Jul-1

1

Nov

-11

Mar

-12

Jul-1

2

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Jul-1

3

Nov

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-14

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Gross Credit Return: Non-financial Firms

Non-earmarked Credit Regulated Real Estate CreditSelic Regulated Rural Credit

%

Government Intervention in Credit Markets: Potential Adverse Effects

• Bank competition, market segmentation and

financial instability

• Prevents the development of capital markets

• Weakens the credit channel of monetary policy

• Fiscal imbalances

• Misallocation of funds

• Cross-subsidy

Objective

• Using loan level data from SCR, they investigate the

impact of credit regulation on the cost of free-market

loans (non-financial firms): the cross-subsidy effects

• They also explore banks heterogeneous responses:

regulation vs. ownership

Conclusions

•Regulated lending is subsidized by free loans

Regulated rural credit: positive and relevant impact onmarket based corporate loans rate

around 3.4 p.p. reduction for a 1 p.p. decreasein outstanding rural banks loans to total banksdebt ratio

Conclusions

smaller effects found for official banks (1.4 p.p. reduction)

negative effects of regulated rural rates on the estimated impact

Regulated real estate credit: not statistically significant effects

Do Credit Unions Provide Access to Credit in Dire Times?

Leila Aghabarari, World Bank GroupAndre Guettler, Ulm University and IWH

Mahvish Naeem, Ulm UniversityBernardus Van Doornik, Central Bank of Brazil

Motivation

• Interruptions on bank lending activity can transmitnegative shocks to the real sector

• Transmission of liquidity shocks in the Brazilian bankingmarket using the financial crisis of 2008/2009

• As of 2015, CUs in Brazil have 7.8 million members. Thenetwork of CUs represents around 20% of bank branchesin Brazil

• Use data on firm-bank-quarter level to investigate theimpact on the intensive margin of the same firm that, in thesame point of time, has credit in Credit Unions versus non-Credit Unions.

2

Conclusions

• Insurance effect seems to dominate the equity effect

• CUs seem to provide insurance to their members during crisis by reducing lending to a lesser extent compared to non-Cus

• If the CUs are better capitalized, they are able to manage risk better

• CUs may be able to decrease the propagation of negative effects from transmission of negative liquidity shocks to the real economy

14

Capital Allocation Across Regions, Sectors and Firms: evidence from a commodity boom in Brazil

Paula Bustos – CEMFIGabriel Garber – Depep BCB

Jacopo Ponticelli – Northwestern

Does capital generated in agriculture flow to other sectors inthe economy?

Does is relocate regionally? What is the role of the bankingsystem?

Event study: legalization of genetically engineered (GE) soyin Brasil (2003)Classical problem of disentangling supply and demand

Motivation

Geographic reallocationIdentification strategy

Conclusions

• Increase deposits in affected municipalities without credit increase

In non-soy producing municipalities:• Intensive margin

• Increase of credit for all sectors• Increase in the number of employees and the wage bill in

industry• Extensive margin

• Inclusion of new customers, especially between micro and small enterprises

• Concentration in industry and services

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Countercyclical Macroprudential Policy:Evidence from an Emerging Country

Rodrigo B Gonzalez (BCB) Bernardus Van Doornik (BCB)

João Barata R Barroso (BCB)José-Luis Peydró (UPF)

Motivation

Identification Strategy

∆𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝒕𝒕𝒃𝒃= 𝟏𝟏𝟏𝟏𝟏𝟏 ∗ ∆

𝑪𝑪𝑪𝑪𝑹𝑹𝑹𝑹𝑹𝑹𝑪𝑪𝒕𝒕𝒕𝒕𝒃𝒃 − 𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝒕𝒕𝑹𝑹𝑹𝑹𝑪𝑪𝑪𝑪𝑪𝑪𝒕𝒕𝑪𝑪𝑪𝑪𝑪𝑪𝒕𝒕

𝒃𝒃

𝑳𝑳𝑳𝑳𝑪𝑪𝒃𝒃𝑳𝑳𝑪𝑪𝑳𝑳𝒕𝒕𝑳𝑳𝑹𝑹𝑹𝑹𝒕𝒕𝒃𝒃

∆𝑪𝑪𝑪𝑪 𝑪𝑪𝑹𝑹𝑹𝑹𝑪𝑪𝑳𝑳𝒕𝒕𝑪𝑪,𝒕𝒕−𝟏𝟏,𝒕𝒕+𝟐𝟐𝒃𝒃

= ∆𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝒕𝒕𝒃𝒃 + ∆𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝒕𝒕𝒃𝒃 + 𝑿𝑿𝑪𝑪,𝒕𝒕−𝟏𝟏𝒃𝒃 + 𝜶𝜶𝑪𝑪

∆𝑪𝑪𝑪𝑪 𝑪𝑪𝑹𝑹𝑹𝑹𝑪𝑪𝑳𝑳𝒕𝒕𝑪𝑪,𝒕𝒕−𝟏𝟏,𝒕𝒕+𝟐𝟐

= 𝒘𝒘∆𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑪𝑪,𝒕𝒕 + 𝒘𝒘𝑿𝑿𝑪𝑪,𝒕𝒕−𝟏𝟏𝒃𝒃 + 𝑿𝑿𝑪𝑪,𝒕𝒕−𝟏𝟏 + 𝜶𝜶𝑹𝑹∗𝑪𝑪

• Existence of compulsory effect on credit

• Asymmetry

Conclusions

Measuring systemic risk under monetary policy shocks: a network approach

Thiago Christiano Silva (BCB)Solange Guerra (BCB)

Michel Alexandre (BCB)Benjamin Tabak (UCB)

Motivations• How do monetary policy shocks affect the real sector

and the economy’s financial stability

• What is the measure of systemic risk in face ofmonetary policy shocks?

• Which economic sectors have larger systemic riskimplications?

• What is the role of interconnectedness in transmittingmonetary policy shocks to different financial andnonfinancial firms?

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Bank-policymaker

layer

Financial sector

Bank-firmlayer

Real sector

Policymakerlayer

Data

• All banks• Unsecured financial assets(Cetip, BMF&Bovespa, ...)

• Supervisory data

• Only firms listed in stock exchanges

• Brazilian credit register

• Do NOT have thecorporate trade network

Results - Direct impact of interest shock on the financial sector

• State-owned banks have small sensitiveness regardless of size• Most sensitive banks are small/medium domestic private, particularly

investment banks• Among large banks, foreign private banks are the most sensitive

Large banks Small/medium banks

Results - Indirect impact (contagion) on the financial sector

• Although large state-owned banks are the least affected to direct impactsof monetary policy shocks, they turn out to be the most affected in terms ofindirect impacts via financial contagion

Large banks Small/medium banks

Results - Indirect impact (contagion) on the real sector

Macroprudential analysis – Operation Car Wash

• “The Impact of Government-Driven Loans in the Monetary Transmission Mechanism: what can we learn from firm-level data?” by Bonomo & Martins, WPS 419• Effects of Government-driven credit on monetary

transmission mechanism and employment• Data from SCR, banks financial information, # employers by

firm

• “Collateral after the Brazilian Creditor Rights Reform” by Van Doornik & Capelletto, WPS 404• Effects of the bankruptcy law in Brazil on corporate debt

structure, collateral liquidity, and collateralization rate. • Data from SCR, banks financial information

Research in the Central Bank of Brazil with Big data

• “Loan-to-value policy and housing loans: effects on constrained borrowers” by Araujo, Barroso & Gonzales• LTV cap is effective in reducing credit risk , while also

generating less favorable contracts terms to borrowers• Data from SCR, banks financial information, employment data

Research in the Central Bank of Brazil with Big data

Technological advances are making possible the use of a hugeamount of data to:

perform econometric studies

build statistics with a deeper level of detail

This is playing a fundamental role in the supervision framework, andcontributes to support policy decisions.

Final Remarks

Thank you!