Discussion of

Agarwal, Chua, Ghosh and Song:Consumption and Portfolio Rebalancing Response

of Households to Monetary Policy

Jirka Slacalek

European Central Bankwww.slacalek.com

Monetary Economics and Reality, Helsinki, October 2019

The views expressed are mineand do not necessarily reflect those of the ECB.

Key contributions

I Estimate how households rebalance portfolios after cut in interest rates

I Focussing on monetary policy passthrough for term depositors (in India)

I Results

For 100 rupees withdrawn from bank deposits:

I 41 rupees invested in risky assetsI 3 rupees spent on consumptionI Rest: leakage (not covered in the data)I Depositors without auto-renewal accounts and with low liquid savings more

responsive

Key contributions

I Estimate how households rebalance portfolios after cut in interest rates

I Focussing on monetary policy passthrough for term depositors (in India)

I Results

For 100 rupees withdrawn from bank deposits:

I 41 rupees invested in risky assetsI 3 rupees spent on consumptionI Rest: leakage (not covered in the data)I Depositors without auto-renewal accounts and with low liquid savings more

responsive

Key contributions

I Estimate how households rebalance portfolios after cut in interest rates

I Focussing on monetary policy passthrough for term depositors (in India)

I Results

For 100 rupees withdrawn from bank deposits:

I 41 rupees invested in risky assetsI 3 rupees spent on consumptionI Rest: leakage (not covered in the data)I Depositors without auto-renewal accounts and with low liquid savings more

responsive

Key contributions

I Estimate how households rebalance portfolios after cut in interest rates

I Focussing on monetary policy passthrough for term depositors (in India)

I Results

For 100 rupees withdrawn from bank deposits:

I 41 rupees invested in risky assetsI 3 rupees spent on consumptionI Rest: leakage (not covered in the data)I Depositors without auto-renewal accounts and with low liquid savings more

responsive

Key contributions

I Estimate how households rebalance portfolios after cut in interest rates

I Focussing on monetary policy passthrough for term depositors (in India)

I Results

For 100 rupees withdrawn from bank deposits:

I 41 rupees invested in risky assetsI 3 rupees spent on consumptionI Rest: leakage (not covered in the data)I Depositors without auto-renewal accounts and with low liquid savings more

responsive

Key contributions

I Estimate how households rebalance portfolios after cut in interest rates

I Focussing on monetary policy passthrough for term depositors (in India)

I Results

For 100 rupees withdrawn from bank deposits:

I 41 rupees invested in risky assetsI 3 rupees spent on consumptionI Rest: leakage (not covered in the data)I Depositors without auto-renewal accounts and with low liquid savings more

responsive

Key contributions

I Estimate how households rebalance portfolios after cut in interest rates

I Focussing on monetary policy passthrough for term depositors (in India)

I Results

For 100 rupees withdrawn from bank deposits:

I 41 rupees invested in risky assetsI 3 rupees spent on consumptionI Rest: leakage (not covered in the data)I Depositors without auto-renewal accounts and with low liquid savings more

responsive

Flourishing work on MP transmission channels in HANK

Monetary transmission to individual consumption

Direct effects (PE) Indirect effects (GE)

Intertemporal Substitution Income Effects Labor IncomeAsset Prices/Returns

RANK model

• Woodford • Gali • Gertler

Capital Gains Dividends/ProfitsStandard Income Effects through Interest Rates

Income Effects through Mortgage Rates

• Eichenbaum, Rebelo, Wong • Berger, Milbradt, Tourre, Vavra • Wong • Beraja, Fuster, Hurst, Vavra • McKay, Wieland • Greenwald • Hedlund, Karahan, Mitman,

Ozkan • Garriga, Kydland, Sustek

Valuation Effects from Inflation (Fisher Effects)

Fiscal Policy

• Auclert • Sterk,Tenreyro • Doepke,

Schneider • Gottlieb

• Gornemann, Kuester, Nakajima

• Alves, Kaplan, Moll, Violante

• Kekre, Lenel • Werning

• Kaplan, Moll, Violante

• McKay, Nakamura, Steinsson

• Auclert, Rognlie, Straub

• McKay, Reis • Hagedorn,

Manovskii, Mitman

Level

• Kaplan, Moll, Violante

• Luetticke • Auclert • Auclert, Rognlie,

Straub • Werning • Farhi, Werning • Hagedorn, Luo,

Manovskii, Mitman • Bilbiie • TANK model

Risk

• Gornemann, Kuester, Nakajima

• Acharya, Dogra

• Holm • Ravn, Sterk • Werning

• Auclert • Kaplan,

Moll, Violante

• Kaplan, Moll, Violante

• Broer, Hansen, Krusell, Oberg

• Werning • Bilbiie

Moll (2019)

India, May–Oct 2016: 75-bp fall in interest rate“. . . supporting our hypothesis of investors reaching for yield in a period of low interest rate”

But interest rate declines from 7.75% to 7%

35

Figure 1. India’s Interest Rate

Note: This figure shows the bank rate (discount rate) determined by India’s central bank (Reserve Bank

of India), as well as the 1 year Term Deposit rate (with no early withdrawals allowed). The former is

the rate in which the Reserve Bank of India lends money to private bank, while the latter is the rate in

which the private bank pays its term depositors.

India, May–Oct 2016: 75-bp fall in interest rate“. . . supporting our hypothesis of investors reaching for yield in a period of low interest rate”

But interest rate declines from 7.75% to 7%

Identification

I Exploit within-borrower variation. . .

I . . . of term deposits around IR adjustment (after large monetary policy cut)

I Similar to Di Maggio et al. (2017)

I IR change at expiry of term deposit unexpected when they start deposit

Estimation method: Diff in diff

I Compare behavior of depositors with early vs late maturity

I eg, for May 2016:

I Treatment group: Depositors with expired term deposits in MayI Control group: Depositors with expired term deposits in June, July, August

Estimation results

I Interest rate elasticity of C = −1 [Lower end of typical estimates]⇒ Modest direct effects of MP

I Elasticity of rebalancing into risky assets = −6.5⇒ Larger indirect effects of MP

I Stronger reaction of depositors without auto-renewals

I Interest elasticity declines from −3 to −0.5 across deciles of liquid savings

Estimation results

I Interest rate elasticity of C = −1 [Lower end of typical estimates]⇒ Modest direct effects of MP

I Elasticity of rebalancing into risky assets = −6.5⇒ Larger indirect effects of MP

I Stronger reaction of depositors without auto-renewals

I Interest elasticity declines from −3 to −0.5 across deciles of liquid savings

Estimation results

I Interest rate elasticity of C = −1 [Lower end of typical estimates]⇒ Modest direct effects of MP

I Elasticity of rebalancing into risky assets = −6.5⇒ Larger indirect effects of MP

I Stronger reaction of depositors without auto-renewals

I Interest elasticity declines from −3 to −0.5 across deciles of liquid savings

Estimation results

I Interest rate elasticity of C = −1 [Lower end of typical estimates]⇒ Modest direct effects of MP

I Elasticity of rebalancing into risky assets = −6.5⇒ Larger indirect effects of MP

I Stronger reaction of depositors without auto-renewals

I Interest elasticity declines from −3 to −0.5 across deciles of liquid savings

Relation to other work

I Risk-taking channel of monetary policy

I Existing work on banks, mutual funds

I Little evidence on household sector

I Estimation similar to Di Maggio et al. (2017): Effects of ARM mortgagepayments on car purchases

I Artavanis, Paravisini et al. (2019): Effects of (large) uncertainty shocks on earlydeposit withdrawals (Greece), daily data

Relation to other work

I Risk-taking channel of monetary policy

I Existing work on banks, mutual funds

I Little evidence on household sector

I Estimation similar to Di Maggio et al. (2017): Effects of ARM mortgagepayments on car purchases

I Artavanis, Paravisini et al. (2019): Effects of (large) uncertainty shocks on earlydeposit withdrawals (Greece), daily data

Relation to other work

I Risk-taking channel of monetary policy

I Existing work on banks, mutual funds

I Little evidence on household sector

I Estimation similar to Di Maggio et al. (2017): Effects of ARM mortgagepayments on car purchases

I Artavanis, Paravisini et al. (2019): Effects of (large) uncertainty shocks on earlydeposit withdrawals (Greece), daily data

Relation to other work

I Risk-taking channel of monetary policy

I Existing work on banks, mutual funds

I Little evidence on household sector

I Estimation similar to Di Maggio et al. (2017): Effects of ARM mortgagepayments on car purchases

I Artavanis, Paravisini et al. (2019): Effects of (large) uncertainty shocks on earlydeposit withdrawals (Greece), daily data

Relation to other work

I Risk-taking channel of monetary policy

I Existing work on banks, mutual funds

I Little evidence on household sector

I Estimation similar to Di Maggio et al. (2017): Effects of ARM mortgagepayments on car purchases

I Artavanis, Paravisini et al. (2019): Effects of (large) uncertainty shocks on earlydeposit withdrawals (Greece), daily data

Comment 1: How representative of population?

I Sample: Clients of top-four Indian bank

I Nine (large) cities

I How does your sample compare to Indian population by income, wealth, . . . ?

I Is it skewed toward rich, older, urban individuals? More likely to rebalance?

Comment 2: What is the aggregate size of this effect?

I 80 percent of households in India have bank accounts

I What fraction has term deposits?

I How much of total assets do term deposits make up?

I What fraction of accounts expires every quarter?

Household portfolios, India: Low share of (total) financial assets

12 CHAPTER 1. AN INTERNATIONAL COMPARATIVE ANALYSIS

Figure 1.1Country comparison: Allocation of household assets and liabilities

As documented in Badarinza, Balasubramaniam and Ramadorai (2016b), we compare the average alloca-

tions of household assets across countries. The data sources are the All India Investment and Debt Survey

(2012 wave), the Chinese Household Finance Survey (CHFS, 2012 wave), the Townsend Thai Survey (TTS,

2012), the US Survey of Consumer Finances (SCF, 2010 wave), the UK Wealth and Assets Survey (WAS, 2012

wave), the Australian Household, Income and Labour Dynamics Survey (HILDA, 2010 wave), and the Eu-

rosystem Household Finance and Consumption Survey (HFCS). Real estate includes land, buildings and

other constructions owned by the household, for residential, commercial or vacation purposes. Durable

goods include vehicles and other transport equipment, valuables, works of art and carpets, livestock and

poultry, agricultural machinery, and other farm and non-farm equipment. Gold includes jewellery, bullion,

ornaments and coins. Financial assets include bank deposits, publicly traded shares and government se-

curities, mutual funds, managed accounts, and loans receivable by the household. Retirement accounts

include private pension accounts, provident funds, annuity certificates, and life insurance accounts. Mort-

gage debt includes loans using land or real estate as collateral. Other secured debt includes loans secured

by a third party, and loans using crops, shares of companies, government securities, or insurance policies

as collateral. Gold loans use bullion and ornaments as collateral. Unsecured debt includes all loans classi-

fied as personal security, which are not backed up by any collateral, such as unsecured loans from money

lenders, loans from family and friends, credit cards, and overdraft facilities. We compute weighted pop-

ulation averages across households using representative population weights, as indicated in each survey.

0%

20%

40%

60%

80%

100%Assets

India

China

Thaila

ndUSA UK

Austra

lia

Germ

any

Real estateDurable goodsGoldFinancial assetsRetirement accounts

0%

20%

40%

60%

80%

100%Liabilities

India

China

Thaila

ndUSA UK

Austra

lia

Germ

any

Mortgage debtGold loansOther secured debtUnsecured debt

Ramadorai et al., 2017: “Indian Household Finance” report

Household portfolios, India: Low share of fin assets over life cycle

F I G U R E 4 . Allocation of Household Assets and Liabilities Along the Life Cycle

This figure reports the average shares of different assets and debt classes, relative to total household wealth and total household liabilities, respectively. The data sources are the AIDIS (2012 wave), the Eurosystem’s HFCS (2010 wave), the US SCF (2010 wave), the CHFS (2012 wave), the Australian HILDA (2010 wave), and the UK WAS (2012 wave). For consistency, in the CHFS, we classify business loans originated with banks as secured loans and informal private loans as unsecured. We compute averages across households using population weights, as indicated in each survey.

Panel A The assets side of the household balance sheet

0%< 35 35–44 45–54 55–65 > 65

20%

40%

60%

80%

100%Fin. assets and pensions

Perc

ent

Perc

ent

Perc

ent

Age (Years)

IndiaChinaUSA

UKAustraliaGermany

0%< 35 35–44 45–54 55–65 > 65

20%

40%

60%

80%

100%Durable goods and gold

Age (Years)

IndiaChinaUSA

UKAustraliaGermany

0%< 35 35–44 45–54 55–65 > 65

20%

40%

60%

80%

100%Real estate

Age (Years)

IndiaChinaUSA

UKAustraliaGermany

(Figure 4 Continued)Badarinza, Balasubramanian, Ra-madorai, 2017: “Indian FinancialLandscape”

I Financial assets make up 5% oftotal

I Term deposits (small?) fractionof financial assets

I Can still be important forsubpopulations

I Or as fraction of consumption

Figure taken from: Badarinza, Balasubra-

manian, Ramadorai, 2017: “Indian Financial

Landscape”

Comment 3: Do more on heterogeneity across households

1. Which households hold automatic renewal accounts?

2. Size of shocks

I Does eg deposit–income ratio matter?I Do 25 bp policy changes matter less than 75 bp? [Inattention, . . . ]

3. Role of adjustment costs

I How do they vary across households?

I Term deposits: Low adjustment costs (compared to other assets)Which households do / do not adjust?

I Pervasive evidence on inertia in household portfolios, e.g., Ameriks and Zeldes(2004), Brunnermeier and Nagel (2008), Andersen et al. (2018) and others.

Comment 4: Measurement of budget constraint items

I Incomplete measurement of budget constraint: Accounts of individuals (not all Hhmembers) with one bank

I Cover subset of budget constraint items:ATM withdrawals, credit/debit card spending, investments in mutual funds andequities

I 44 % of withdrawals are accounted for, 56 % leakage

I 5 % Leakage for auto-renewals, huge leakage for rest

I Can you do more on to what extent this incomplete measurement matters forestimates?

Summary

I Nice, topical paper on portfolio rebalancing of households

I Cool dataset

I Would be nice to do more work on:

I How the results matter in aggregate

I Household heterogeneity