Open economy, redistribution, and the aggregate impact of ...

OPEN ECONOMY, REDISTRIBUTION,AND THE AGGREGATE IMPACT OF EXTERNAL SHOCKS

Haonan Zhou*

December 3, 2020*Princeton University

MOTIVATION

Two important features of emerging market business cycles:• High consumption volatility (Aguiar and Gopinath, 2007).• Subject to external shocks and international spillovers (Miranda-Agrippino and

Rey, 2020).• Exchange rate: key conduit to propagate external shocks.

To limit exposure to external shocks: exchange rate policies.• Little theoretical and quantitative guidance/justification.

This paper: the role of domestic incomplete markets and household heterogeneity.• Redistributive effect of external shocks has aggregate impact.• Forces are stronger when exposed agents have higher MPCs.

Haonan Zhou Redistribution in the open economy 1

THIS PAPER

Identifies redistribution channels from theory:• Extends Clayton et al. (2018) to the open economy: dollar-denominated bond,

dollar-priced tradable goods.• Amplifying or dampening impact of household heterogeneity summarized by:

Cov(MPCi, Exposurei)

(Foreign currency) Fisher: revaluation of net nominal positionConsumption expenditure: pass-through to consumption basketEarnings heterogeneity: sector-specific income exposure...

Provides measurement of redistribution channels using Uruguayan micro data• Strongest: Foreign-currency Fisher channel.


THIS PAPER

Quantitative exploration: open economy Huggett model in GE• Nonhomothetic preferences; $ and peso-denominated bond; nominal rigidity.• Calibrated using micro and macro moments.

Redistribution substantially amplifies effect of foreign monetary policy tightening• Consumption expenditure channel: 14% larger contraction of initial

consumption relative to homothetic case.• Foreign-currency Fisher channel: 5–30% larger drop relative to

Cov(MPCi,NNP Exposurei) = 0.

In progress: Decomposition of VAR estimates; Two-asset extension


RELATED LITERATURE

Direct extension of closed-economy HANK literature:• Analytical decomposition: Auclert (2019); Slacalek et al. (2020); Kekre and Lenel(2020a); Clayton et al. (2018) (henthforth CJS).

• Heterogeneous incidence for aggregate shocks: Almgren et al. (2019); Alves et al.(forthcoming); Broer et al. (2020).

Heterogeneous agents, distribution, and international macro• Theoretical models: Mendoza et al. (2009); Cugat (2019); Kekre and Lenel (2020b);De Ferra et al. (2020); Ferrante and Gornemann (2020).

• Empirical analyses: Cravino and Levchenko (2017); Drenik et al. (2018); Blancoet al. (2020)

This paper:• Identifies redistribution channels from theory, provides empirical measurement.• Disciplines quantitative model using novel micro-data.


ROADMAP OF TALK

Analytical decomposition

EmpiricsOverview of dataMeasuring redistribution channels

Quantitative model


TWO-PERIOD, PERFECT FORESIGHT, PARTIAL EQUILIBRIUM

Household budget constraints:

pN0cNi0 + e0cTi0 + ωi1 = pN

0γNi (yN0 ) + e0γT

i (yT0) + (1+ i0)ai0 + (1+ i∗0)e0bi0

pN1 cNi1 + e1cTi1 = pN

1 γNi (yN1 ) + e1γT

i (yT1) + (1+ i1)ωi1

cTit/cNit : consumption of tradable (dollar-priced) / nontradable goodset: nominal exchange rateai0/bi0: peso/dollar-denominated nominal legacy bondγsi (·): income sourced from sector s ∈ {T,N}Preferences:• CRRA preferences with RRA σ.• Consumption: Cobb-Douglas aggregator over tradable and nontradable goods:

. In GE model: generalized CES (nonhomotheticity).

cit = (cTit)1−αi(cNit)αi , t ∈ {0, 1}


DECOMPOSITION OF AGGREGATE CONSUMPTION RESPONSEIn response to an external shock – a perturbation at t = 0 in the form of{dq,dpN

0 ,dR,dyN0 ,dyT0}: Simplifying assumptions and definitions Equations

R ≡ (1+ i1/(pN1 /pN0 )), q0 = q1 ≡ q = e/p.

dC0 = MPC0 × E0︸︷︷︸Average exposure

+ Cov(MPCi0, Ei0)︸︷︷︸Redistribution channels

.

Redistribution channels in the open economy:

+Cov(MPCi0, γNi ) · dyN0 ,+Cov(MPCi0, γ

Ti ) · dyT0 [Earnings heterogeneity]

+Cov(MPCi0,bi0) · dq [Fisher (foreign currency)]−Cov(MPCi0, ai0) · dp [Fisher (domestic currency)]−Cov(MPCi0,Ξi0) · dq [Consumption expenditure]+Cov(MPCi0, γ

Ti ) · dq [Foreign-currency earnings]


ROADMAP OF TALK



Quantitative model


OVERVIEW OF DATA

Three datasets from Uruguay:

Continuous Household Survey (ECH)• Basic socio-demographic and labor market information.

Household Financial Survey (EFHU-3)• Add-on module to ECH (2017). Structure is similar to Spanish EFF.• Household balance sheets (assets and liabilities), with currency breakdown.• Oversamples wealthy households.

Consumption Expenditure Survey (ENGIH)• 8-digit product level expenditure survey conducted from late 2016 to 2017.• Purchases with reference period more than one month are standardized into

monthly expenses.• Direct linkage to ECH-EFHU is not available, but allows for imputation.


OVERVIEW OF DATA

• Direct MPC estimation infeasible. Infer (poor and wealthy) hands-to-mouth(HtM) status following Kaplan et al. (2014).

• Impute HtM label in ENGIH dataset using objective characteristics andself-assessment.

• Manually identify tradable goods/industries, following Cravino and Levchenko(2017); Drenik and Perez (2020).

Why Uruguay? Beyond data availability...• Financial dollarization: 70% of all deposits; 50% of all loans. (Toscani, 2018)• Goods-market dollarization: 14% share in CPI basket; tradable goods and

housing more likely to be dollar priced. (Toscani, 2018; Drenik and Perez, 2020)


REDISTRIBUTION CHANNELS: FROM MODEL TO DATA

Covariate Model correspondence Channel Value (s.e.)

Net nominal dollar position Cov(HtMi0,

qbi0E[ci0 ]

)Fisher (foreign currency) -1.064 (0.053)

Net nominal peso position Cov(HtMi0,

p−1i ai0E[ci0 ]

)Fisher (domestic currency) -0.009 (0.058)

Household income due to tradable sectors Cov(HtMi0,

γTi y

T0

E[ci0 ]

)Earnings heterogeneity -0.011 (0.002)

Tradable consumption expenditure Cov(HtMi0,

(1−αi)pici0E[ci0 ]

)Consumption expenditure -0.048 (0.003)

• Data: Households are predominantly hand-to-mouth (>75%, US: 31%).• Large covariance associated with Fisher channel: dollar savings concentrated in the

hands of the wealthy; some liquidity-constrained households leveraged in dollar debt.. Interpreted as elasticity in TANK framework: 1% depreciation ⇒ 1.064% additional ↓ of C0 .

• Consumption expenditure channel (<0) dominated by level effect: low-MPC householdsmore exposed due to high overall expenditure, despite a smaller tradable share.

Hand-to-mouth Balance sheet Consumption Income


ROADMAP OF TALK



Quantitative model


MODEL OVERVIEW

• Discrete time, infinite horizon, small-open-economy populated by ex-postheterogeneous agents.

• Household portfolio: perfectly substitutable dollar and peso nominal bond.

• Consumption bundle: generalized CES with bias in tradable goods:

cit =[(1− α)

1η (cNit)

η−1η + α

1η (cTit − c)

η−1η

] ηη−1

.

• Production: nontradable sector features Rotemberg price ridigity.

• Domestic monetary authority responds to CPI inflation and depreciation:

1+ it+1 = (1+ r̄)(πt)ϕπ · (et/et−1)

ϕe .


EQUILIBRIUM, TRANSITION DYNAMICS

• NFA position well-defined, unlike incomplete-market RA model. (Schmitt-Grohéand Uribe, 2003)

• Unique equilibrium in real terms: normalize e = 1 and recast the model.

Quantitative experiment: 25bps surprise hike of i∗1 announced at t = 0. (MIT shock)

−1 0 1steady state: ai0

i∗1 announced

q0, r0 jumps

ai1 set t

• At t = 0: unexpected capital gains/losses due to jumps in r0,q0.• s(a): Dollar share in net worth. Ex-post real portfolio return with steady-statewealth a: (qt ≡ et/pt)

1+ ra0(a) = (1+ r0) · (1− s(a))︸︷︷︸parameterized exogenously

+(1+ r∗0)q0q−1

· s(a).

• Parametrize s(a) in different ways to isolate the effect of redistribution channels.


CALIBRATION

Common parameters across specifications: standard in the literature. Table

• Borrowing limit, s.d. of idiosyncratic productivity: (non-reserve) NFA/GDP = -0.49.

• Consumption bias/weight: tradable consumption share (average/top/bottomincome decile).

Calibrate s(a): two approaches –• Match dollar share in NNP for conditional quantiles of wealth distribution. Detail

• Match Cov(MPCi,Dollar exposure of NNP) = −0.166. Implied covariances


GE IMPACT: CONSUMPTION EXPENDITURE CHANNEL

IRF (1) (2) (3)PE decomposition Homothetic Nonhomothetic Nonhomothetic

c = 0 c = 0.28 c = 0.28s(a) = 0.25 s(a) = 0.25 s(a) matches EI[aiF0], CovI

(MPCi,

aiF0(EI [ci ])−1

)in (1)

Model-implied moments:MPC 0.127 0.116 0.116EI[q−1aiF0] -0.180 -0.155 -0.180CovI

(MPCi,

q−1aiF0(EI [ci ])−1

)-0.009 -0.011 -0.009

CovI(MPCi,

q−1cTi

(EI [ci ])−1

)-0.0046 -0.0037 -0.0037

Time-0 change relative to steady state (bps):Consumption (c) -37.54 -42.71 -42.78Tradable consumption (cT) -50.96 -44.81 -44.87Nontradable consumption (cN) -27.16 -39.03 -39.10RER (q) 26.83 28.85 28.89


GE IMPACT: FOREIGN-CURRENCY FISHER CHANNEL

Variable/Statistic/Variant (1) (2) (3) (4)

s(a) targeting: Conditional quantiles MPC covariance EI[q−1aiF0] of (1) EI[q−1aiF0] of (2)

Model-implied moments:

EI[q−1aiF0] 0.056 -3.17 0.056 -3.17

CovI(MPCi,

q−1aiF0(EI [ci ])−1

)-0.017 -0.166 0.003 -0.000

Time-0 change relative to steady state (bps):

Consumption (c) -36.31 -84.04 -34.51 -64.75

Tradable consumption (cT) -49.56 -102.01 -47.62 -81.25

Nontradable consumption (cN) -26.07 -70.14 -24.36 -52.00

RER (q) 26.49 35.93 26.23 33.00

PE decomposition


CONCLUSION

This paper:• Identifies redistribution channels from theory.

• Evaluates the empirical and quantitative relevance of redistribution channels.

Practical implications:• Targeted macroprudential policies in the financial market and de-dollarization policies in

the goods market help mute the redistribution channels.

Possible extensions:• Two-asset HANK. (Better fit of wealth distribution)

• Endogenous portfolio choice. (Allow for gross positions)

• Financial and nominal frictions. (The role of banks and downward wage rigidity)

• Welfare and optimal policy. (Cost/benefit of “fear for floating”)


ANALYTICAL DECOMPOSITION: SIMPLIFYING ASSUMPTIONS AND DEFINITIONS

Assume:• i0 = i∗0 = 0.• γj

i(yjt) = γj

i · yjt, j ∈ {T,N} (Werning, 2015; Clayton et al., 2018).

Present-value budget constraint: (qt ≡ et/pNt )

cN0 + q0cT0 +cN1 + q1cT1

R = γNi yN0 + q0γ

Ti yT0 +

γNi yN1 + q1γ

Ti yT1

R +ai0pN0+ q0bi0︸︷︷︸

yi

(1)

R ≡ (1+ i1/(pN1 /pN

0 )): real interest rate from t = 0 to 1.

For perturbation:• Follow CJS to consider period 1 as the long run, to abstract away from

perturbations to long-run output yN1 and yT1 .

• Further restricted to the case in which q0 = q1 = q, so that the response of realexchange rate to the external shock is permanent. Back


PROPOSITION: CONSUMPTION DECOMPOSITION

Proposition 2Under the posited perturbation:

dC0 =

Substitution︷︸︸︷−σ−1E[ci0(1− κ−1

i q1−αiMPCi0)]dRR

+p−1[MPC · E[ωi1] +

Unhedged interest rate exposure︷︸︸︷Cov(MPCi0, ωi1) ]

dRR

+ MPC · (dyN0 + qdyT0)︸︷︷︸Aggregate income

+ Cov(MPCi0, γNi )dy

N0 + qCov(MPCi0, γT

i )dyT0︸︷︷︸

Earnings heterogeneity

+[q(MPC · E[bi0] + Cov(MPCi0, bi0)︸︷︷︸

Fisher (foreign currency)

)dqq

− p−1(MPC · E[ai0] + Cov(MPCi0, ai0)︸︷︷︸Fisher (domestic currency)

)dpp

]

+ (MPC+ Cov(MPCi0, γTi )︸︷︷︸

Price of earnings

)q(yT0 + R−1yT1 )dqq

− [MPC · Ξ + Cov(MPCi0,Ξi,0)︸︷︷︸Consumption expenditure

]dqq

(2)

where Ξi,0 ≡ κ−1i q1−αi(1− αi)(ci0 + R−1ci1).

Back


HAND-TO-MOUTH HOUSEHOLDS: SUMMARY STATISTICS

ECH-EFHU dataset:Non-HtM Poor HtM Wealthy HtM

Population share (%) 23.35 27.14 49.51Average age 50.23 44.79 53.01Median monthly income (USD) 2904 1590 1939Average monthly income (USD) 3661 1832 2317Has credit cards (% within group) 84.55 46.44 57.64

ENGIH dataset:Non-HtM Poor HtM Wealthy HtM

Population share (%) 24.11 25.28 50.61Average age 49.10 43.55 54.03Median monthly income (USD) 2817 1287 1548Average monthly income (USD) 3395 1411 1743Tradable share in consumption expenditure (%) 41.80 45.36 44.03

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BALANCE SHEET AND CURRENCY BREAKDOWN

Non-HtM Poor HtM Wealthy HtM

Median illiquid debt (if nonzero, in USD)All 5000 1800 3300Peso 4074 1650 3000Dollar 3500 1312.5 3000

Median net liquid wealth (in USD)All 3000 0 0Peso 375 0 0Dollar 2625 0 0

Average net liquid wealth (in USD)All 11400 11.27 48.10Peso 2350 -14.14 -13.12Dollar 9049 25.41 61.22

Large degree of wealth inequality:• Nearly 70% of all households: zero

liquid asset.

• <15% exceeds one month’s income.

Leverage:• Non-HtM hold much more debt than

poor-HtM.

• Conditional on leveraged in dollar,wealthy-HtM has similar medianindebtedness to non-HtM.

Local linear regressions

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LOCAL LINEAR REGRESSIONS0

.2.4

.6.8

Prop

ensi

ty to

hol

d do

llar

0 2 4 6 8 10Monthly income decile

Asset Debt

(a) Unconditional

0.2

.4.6

.8Pr

open

sity

to h

old

dolla

r

0 2 4 6 8 10Monthly income decile

Asset Debt

(b) Conditional on nonzero wealth

Figure: Propensity to hold dollar asset and debt by income decile

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CONSUMPTION INEQUALITY ALONG THE WEALTH DISTRIBUTION

Category / Group Non-HtM Poor HtM Wealthy HtM Aggregate

Food and non-alcoholic beverages 14.62 21.24 20.42 18.57Alcoholic beverages and tobacco 0.98 1.36 1.10 1.10Apparel 3.04 3.91 3.09 3.23Housing and utilities 26.63 29.11 28.96 28.27Furniture and home appliances 5.02 2.96 3.33 3.86Medical care 10.00 12.56 12.44 11.57Transportation 15.07 7.98 10.02 11.40Communications 3.11 4.04 3.74 3.57Culture and recreation 7.28 5.91 5.74 6.29Education 4.40 1.93 2.18 2.87Hotels and restaurants 4.72 3.76 3.27 3.87Other goods and services 5.12 5.25 5.72 5.41

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EMPLOYMENT AND INCOME FROM TRADABLE SECTOR

Decile Employment share Income share

1 0.197 0.1802 0.222 0.2023 0.225 0.1894 0.247 0.2085 0.263 0.2106 0.239 0.1707 0.252 0.1728 0.255 0.1619 0.231 0.14010 0.223 0.144

Back


CALIBRATION: COMMON PARAMETERS

Parameter Description Value Target/Source

β Time preference 0.975σ EIS−1 2 Stockman and Tesar (1995)ϕ Inverse of Frisch elasticity 0.5η Tradable/nontradable goods elasticity of substitution 0.5 Cugat (2019)κ Tradable labor disutility 0.3ρzT Persistence of tradable productivity 0.95ρzN Persistence of nontradable productivity 0.95δ Curvature in tradable production 0.57 Cugat (2019)ε Elasticity of substitution of nontradable varieties 11 De Loecker and Eeckhout (2020)i∗ Foreign interest rate 0.01 Neumeyer and Perri (2005)θ Price adjustment cost parameter 100 De Ferra et al. (2020)ϕπ Interest rate sensitivity to inflation 1.5ϕe Interest rate sensitivity to depreciation 0.5a Borrowing limit -1.2 (NFA-Reserve)/GDP = -0.49

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CALIBRATING s(a): MATCH WEALTH DISTRIBUTION

-4.204

-2.321-1.3 -.6 -.3

0

.5

3

10

22.5 50

0.2

.4.6

.8Sh

are

of d

olla

r in

net w

orth

34 46 57 71 79 88 92 96 98 99 100Percentile of stationary distribution

sJ :=∑

i∈J[($ Assets)i − ($ Liabilities)i]∑i∈J Net Nominal Positioni

• Well-defined except for zero net worthhouseholds.

• Also drop households with offsettingdollar/peso positions.

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MPC COVARIANCES WITH EXPOSURE: DATA VS. MODEL

Channel Model correspondence Data Model, targeting wealth distribution:Conditional quantiles MPC covariances

Consumption expenditure Cov(MPCi0,

(1−αi)pici0E[ci0 ]

)-0.007 -0.005 (Homothetic) / -0.004 (Non-homothetic)

Foreign-currency Fisher Cov(MPCi0,

qbi0E[ci0 ]

)-0.166 -0.017 -0.166

• MPC is assigned to each identified household group based on baseline estimates ofKaplan et al. (2014). MPCNon-HtM = 0.127,MPCPoor-HtM = 0.243,MPCWealthy-HtM = 0.3.

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IMPULSE RESPONSE FUNCTIONS

0 50 1000

10

20

0 50 1000

50 Homothetic

Nonhomothetic

0 50 100-20

0

20

40

0 50 1000

0.1

0.2

0 50 1000.5

1

1.5

0 50 100

0

0.2

0.4

0 50 1000

1

2

0 50 100

0

0.5

1

0 50 100

-1

-0.5

0

0 50 100

Horizon

-0.4

-0.2

0

0 50 100

Horizon

-0.4

-0.2

0

Homothetic

Nonhomothetic

0 50 100

Horizon

-0.4

-0.2

0

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IRF DECOMPOSITION: CONSUMPTION EXPENDITURE CHANNEL

(1) (2) (3)

Homothetic Nonhomothetic Nonhomothetic

c = 0 c = 0.28 c = 0.28

s(a) = 0.25 s(a) = 0.25 s(a) matches EI[aiF0], CovI(MPCi,

aiF0(EI [ci ])−1

)in (1)

IRF decomposition of c0 (%):

q 2.28 2.38 2.43

r 56.16 55.57 55.53

wN/p 65.14 64.67 64.65

wT/p 2.92 3.09 3.09

Transfer -25.37 -24.86 -24.85

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IRF DECOMPOSITION: FOREIGN-CURRENCY FISHER CHANNEL

Variable/Statistic/Variant (1) (2) (3) (4)

s(a) targeting: Conditional quantiles MPC covariance EI[q−1aiF0] of (1) EI[q−1aiF0] of (2)

IRF decomposition of c0 (%):

q 1.16 24.52 -0.76 18.14

r 57.16 35.56 58.76 41.70

wN/p 65.58 56.14 66.65 58.89

wT/p 2.69 7.55 2.19 6.12

Transfer -25.46 -23.36 -25.68 -24.02

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