Domestic Financial Participation and External

Domestic Financial Participation and ExternalVulnerability in Emerging Economies1

Alan Finkelstein ShapiroTufts University

Victoria NuguerIADB-RES

IT Conference, BCBMay 24, 2019

1The views in this paper are solely the responsibility of the authors and should not beinterpreted as representing the views of the Inter-American Development Bank, itsExecutive Boards, or its Management.

Finkelstein Shapiro and Nuguer Dom FinPart and Ext Vulnerability in EMEs 1 / 35

Introduction

Firm financial participation in EMEs is very low: only 20% of firmshave access to and use bank credit (vs. 70% in AEs)

In general, models do not include the extensive margin of financialparticipation; effects shown might be different when financialinclusion varies

Additionally, EME are vulnerable to external shocks

How does financial participation of firms / financial development ofan economy affect its sensitivity to external financial shockstransmitted via the banking system?

I Do shocks similar to the global financial crisis affect countries withmore/less financial participation differently?

Financial inclusion: access to financial services = financialparticipation = financial development = credit-to-GDP ratio =deposits-to-GDP ratio


What we do in this paper

1 Two-country RBC modelI banking frictions (Gertler and Kiyotaki, 2010) and AE banks lend to

EME banks (Cuadra and Nuguer, 2018)I financially integrated and excluded firmsI endogenous firm entry– extensive marginI quality of capital shocks in the AE transmitted to the EME through

cross-border bank flows

2 Empirical evidenceI impact of U.S. banks’ net charge-offs on cross-border bank flows of

EMEsI relevance of the degree of EME domestic financial participationI impact of U.S. increase in interest rate (working on it)

Higher firm financial participation in EMEs shows smaller domesticpropagation of adverse financial shocks in AEs (in the model and inthe data!)


IntuitionA negative quality of K shock emanating from AE, ↓ Ψt ,

I ↓ cross-border bank flows to EME, EME ↓ banks’ net worth, ↓ EMEdomestic credit –financial mechanism

I EME currency depreciates and consumption ↓ –RER mechanism

I financially-included firms face ↓ credit, ↓ n. firms, ↓ output (bothincluded and excluded firms)

What is the different impact across EMEs of a financial AE shockgiven 6= credit-to-GDP ratio? (higher credit-to-GDP)

I without endog. firm entry: ↓↓ in cross-border bank flows and a largerreaction of the RER mechanism

I with endog. firm entry: ↓ cost of entry, households adjust better to theshock and react less, smoother reaction of the economy (firms areconsidered assets), cross-border bank flows react less, fall in credit islower

Under endogenous firm entry: higher firm financial participation inEMEs is negatively associated with the size of the domesticpropagation of adverse foreign financial shocks emanating from AEs


Related Literature

Effects of foreign shocks in EMEs via the banking system – Aoki,Benigno and Kiyotaki (2015); Cuadra and Nuguer (2018)

Financial inclusion and monetary policy or labor markets – Galı,Lopez-Salido and Valles (2004); Mehrotra and Yetman (2014);Yetman (2017); Alberola and Urrutia (2019); Epstein and FinkelsteinShapiro (2019)

Firm entry and macro fluctuations – seminal work of Bilbiie, Ghironiand Melitz (2012), applied to two-country model Ghironi and Melitz(2005), Cacciatore, Ghironi and Sebunovs (2015), Cacciatore, Fioriand Ghironi (2016)

Our contribution: mix of these three elements comparing differentdegrees of domestic firm financial participation


Layout of the presentation

1 Why do we say firm financial participation or domestic financialdevelopment (scarcity of data to measure financial participation)?

2 Theoretical Model: banking frictions, fin. included and excluded firmsand endogenous firm entry

3 Why endogenous firm entry is relevant: matching the model with thedata

4 Robustness checks

5 Summary


Data Firm Financial Part. and Dom. Financial Dev.

10 20 30 40 50 60 70 800

5

10

15

20

25

30

35

40

45

BRA-2009

CHL-2006

CHL-2010

COL-2017

CZE-2009

HUN-2009

HUN-2013

IND-2014

POL-2009

POL-2013

RUS-2009

RUS-2012

ZAF-2007

TUR-2008

TUR-2013

ARG-2010

COL-2010

MEX-2010

=0.5116

ARG-2006ARG-2017

COL-2006

CZE-2013

IDN-2009

IDN-2015

MEX-2006

UGA-2006UGA-2013

Source: Authors’ calculations using data from the BIS and the WBES.


Data Firm Financial Part. and Dom. Financial Dev.

0 10 20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

=0.3171

Source: Authors’ calculations using data from the Global Financial DevelopmentDatabase and the WBES.


The Model Overview

Two-country RBC model

Banking frictions (Gertler and Kiyotaki, 2010)

AE banks lend to EME banks through cross-border bank flows

(Cuadra and Nuguer, 2018)

Firm financial participation: financially-included and -excluded firms

Endogenous firm entry (Bilbliie, Ghironi and Melitz, 2012) and

endogenous measure of both included and excluded firms


The Model Agents

AE (H) and EME (∗, F) – share of financial participation and bank

credit-GDP ratio differ, and AE banks lend to EME banks

Households

Domestic perfectly-competitive intermediate-goods firms (produce

using labor and capital – financially included and excluded firms)

Domestic monopolistically-competitive wholesale-goods firms (with

endogenous entry)

Final good firms (domestic and imported)

Capital producers

Banks – cross border bank flows


The Model Households and Firm Creation

A household chooses consumption, ct , labor supply, Le,t and Li ,t , realdomestic deposits, Bd ,t , the desired number of wholesalefinancially-included and -excluded firms, Ni ,t+1 and Ne,t+1, andmeasure of new wholesale firms needed to hit those targets, N i

E ,t andNeE ,t , max E0 ∑∞

t=0 βtu(ct , Le,t , Li ,t) subject to

ct + ψeNeE ,t + ψiN

iE ,t + Bd ,t = we,tLe,t + wi ,tLi ,t

+ Rt−1Bd ,t−1 + de,tNe,t + di ,tNi ,t + Πe,t + Πi ,t + Πk,t + Πb,t ,

and the evolution of each category of wholesale firms j ∈ e, i

Nj ,t+1 = (1− δ)(Nj ,t +N j

E ,t

)First-order conditions are standard for ct , Le,t , Lie,t , and Bd ,t

Firm creation conditions for each domestic wholesale firm category

ψj = (1− δ)EtΞt+1|t (dj ,t+1 + ψj )


The Model Domestic Production Structure

Financially included (i) and excluded (e) firms

1 perfectly-competitive intermediate-goods firms –measure 1

2 monopolistically-competitive wholesale firms –with endogenous entry

and exit, and endogenous measure

Intermediate-goods e firms use internal resources to purchase capital

and they are intensive in labor

Intermediate-goods i firms borrow funds from banks to purchase

capital from capital producers


The Model Intermediate-Goods Firms e vs. i

Financially-excluded firms

maxLe,t ,ke,t+1

E0

∞

∑t=0

Ξt|0mce,tze,t(ke,t)

αe (Le,t)1−αe − we,tLe,t

−Qe,t [ke,t+1 − (1− δ)ke,t ]

Financially-included firms (si ,t = ki ,t+1/Ψt+1), αi > αe

maxLi ,t ,ki ,t+1,si ,t

E0

∞

∑t=0

Ξt|0

mci ,tzi ,t (ki ,t)

αi (Li ,t)1−αi − wi ,tLi ,t

−Qi ,t [ki ,t+1 − (1− δ) ki ,t ] +Qi ,tsi ,t − Rki ,tQi ,t−1si ,t−1

Combining the FOCs for physical capital demand and bank-credit

demand

Rki ,t+1 = Ψt+1

[αimci ,tzi ,t(ki ,t)αi−1(Li ,t)1−αi +Qi ,t+1 (1− δ)

]Qi ,t


The Model Monopolistically-Competitive Wholesale Firms

Firm-creation decision is part of the household’s problem

Ωj denotes the potential mass of firms in firm category j

Each incumbent firm produces a single differentiated good ωj (good

produced and firm) j ∈ e, i

Total output for each domestic wholesale firm category

Yj ,t =

[∫ωj∈Ωj

yj ,t(ωj )ε−1

ε dωj

] εε−1

Each incumbent firm purchases inputs from their intermediate-goods

counterparts at price mcj ,t , with the real price of their output given

by ρj ,t(ωj ) = pj ,t(ωj )/Pt

The optimal pricing condition is ρj ,t(ωj ) = [ε/(ε− 1)]mcj ,t


The Model Total Domestic Production

Total output from financially-excluded and financially-included

wholesale firms, Ye,t and Yi ,t

Firms maximize profits

YP,tPP,t − Yi ,tPi ,t − Ye,tPe,t

We assume a perfectly-competitive domestic output aggregator, with

a CES production function

YP,t =

[(1− αy )

1φy Y

φy−1φy

i ,t + (αy )1

φy Yφy−1

φye,t

] φyφy−1


The Model Capital Producers

Choose investment in both sector, i and e, to maximize profits

Et

∞

∑s=t

Ξt|s Qe,s ie,s − ie,s [1 + Φ (ie,s/ie,s−1)] +Qi ,s ii ,s−

ii ,s [1 + Φ (ii ,s/ii ,s−1)]

subject to ie,s = ke,s − (1− δ)ke,s−1

and ii ,s = si ,s − (1− δ)ki ,s−1

The first order conditions yield the price of capital goods Qj ,t for eachfirm category j ∈ e, i:

Qj ,t = 1 + Φ(

ij ,tij ,t−1

)ij ,t + ij ,t−1Φ′

(ij ,t

ij ,t−1

)−EtΞt+1|t

(ij ,t+1

ij ,t

)2

Φ(ij ,t+1

ij ,t

)


The Model AE BanksGertler and Kiyotaki (2010) with cross-border bank flows

raise deposits from AE households, bd ,t

lend

I to AE non-financial firms, si ,tI to EME banks, bt

Assets Liabilities

Qi ,tsi ,t bd ,t

Qbtbt nwt

Incentive compatibility constraint

V (si ,t , bt , bd ,t) ≥ θ (Qi ,tsi ,t +Qb,tbt) .

Aggregate net worth of AE banks

NWt = (σ + ξ) (Rki ,tQi ,t−1Si ,t−1 + Rb,tQb,t−1Bt−1)− σRt−1Bd ,t−1.

At the end of the period t − 1 the value of the banks satisfies

V (st−1, bt−1, bd ,t−1) = EtΞt|t−1

(1− σ)nwt + σ

[max

st ,bt ,bd ,t

V (st , bt , bd ,t)

]


The Model EME Banks

raise funds from

I EME households, b∗d ,t

I AE banks, b∗t

make loans to EME non-financial firms, s∗i ,t

Assets Liabilities

Q∗i ,ts∗i ,t b∗d ,t

Q∗btb∗t

nw∗t

Incentive compatibility constraint

V ∗(s∗i ,t , b

∗t , b∗d ,t

)≥ θ∗

(Q∗i ,ts

∗i ,t −Q∗b,tb

∗t

)Aggregate net worth of EME banks

NW ∗t = (σ∗ + ξ∗)R∗ki ,tQ

∗i ,t−1S

∗i ,t−1 − σ∗R∗b,tQ

∗b,t−1B

∗t−1 − σ∗R∗t−1B

∗d ,t−1


The Model Banks Summary

From the problem of AE banks

EtΞt+1|tΛt+1Rki ,t+1 = EtΞt+1|tΛt+1Rb,t+1 + Φ [exp(Bt − B)− 1]

H banks are indifferent between providing funds to intermediate goodsdomestic firms and to F banks because the expected return on both assetsis equalized in equilibrium.From the problem of EME banks

EtΞ∗t+1|tΛ∗t+1R

∗ki ,t+1 = EtΞ∗t+1|tΛ

∗t+1R

∗b,t+1

In this framework, the cross-border bank flows’ return transmits a shock ineconomy H economy to economy F through the impact on the return ondomestic assets. Additionally, the expected discounted rate of return onthe cross-border bank asset is equal to the one on loans tointermediate-goods i firms in H: Et(Rb,t+1) = Et(R∗b,t+1)

RERt+1

RERt


The Model Final Goods Production

Total domestically-produced output and imported output from the

EME

Yt =

[α

1φaa Y

φa−1φa

H,t + (1− αa)1

φa Yφa−1

φa

F ,t

] φaφa−1

Standard first-order conditions

YH,t = αa (ρH,t)−φa Yt

and

YF ,t = (1− αa) (ρF ,t)−φa Yt ,

where ρj ,t = Pj ,t/Pt with j ∈ (H,F )


The Model Market Clearing

Total demand for output produced in H must be equal to what is

produced, so that

YH,t +

(1−m

m

)Y ∗H,t = YP,t

Market clearing in each domestic firm category implies that

Ne,tye,t = ze,t(ke,t)1−αe (Le,t)

αe and Ni ,tyi ,t = zi ,t(ki ,t)1−αi (Li ,t)

αi

The resource constraint in H is given by

Yt = ct + ii ,t + ie,t + ψiNiE ,t + ψeN

eE ,t

The current account

RERtQb,tBt − RERtRb,tQb,t−1Bt−1 =

(1−m

m

)Y ∗H,t

PH,t

Pt− YF ,tToTt

PH,t

Pt


Quantitative Analysis

Calibration

Steady state comparison of high and low financial participation

Role of endogenous firm entry

Empirical validation of main results


Quan. Analysis CalibrationParameter Value Source or Target

Preferences and technology parametersβ Subjective discount factor 0.985 DSGE literatureσc Coefficient of relative risk aversion 2 DSGE literatureξ Inverse of Frisch elastisticity of labor supply 1 DSGE literatureκ AE labor weight on the ut. function 16.215 Match ave. hours workedκ∗ EME labor weight on the ut. function 9.809 Match ave. hours workedψi AE cost of creating an i firm 0.031 1% of GDP per capitaψ∗i EME cost of creating an i firm 0.253 10% of GDP per capitaψe AE cost of creating an e firm 0.001 Ni /N = 0.8ψ∗e EME cost of creating an e firm 0.032 N∗i /N∗ = 0.2αy AE Share of dom. produced goods 0.644 PiYi /PY = 0.80α∗y EME Share of dom. produced goods 0.433 P∗i Y

∗i /P∗Y ∗ = 0.70

αi Share of capital in the i prd. function 0.32 DSGE literatureαe Share of capital in the e prd. function 0.20 Endog. FE literatureδ Depreciation rate 0.025 DSGE literatureε Elasticity of substitution of wholesale firms 6 Endog. FE literatureφy Elasticity of substitution for total domestic output 5 Baseline assumptionφa Elasticity of substitution of imported goods 1.557 CN (2018)m Size of the AE 0.900 CN (2018)

Banking parametersσ Survival rate 0.972 GK (2010)ξ Start-up fraction 0.002 GK (2010)θ fraction of divertable assets 0.5042 Rki − R = 110θ∗ fraction of divertable assets 0.8952 Rki − R = 110Φ Country-specific int rate premium 0.01 SGU (2003)

ShockΨ AE quality of capital shock -0.05 GK (2010)


Quan. Analysis High and low financial participation SS

↑ financial participation, ↓ sunk-entry cost, ↓ Mg.C. of creating ifirms, ↑ ki ,t , Li ,t↑ Y ∗, ↑ c∗, ↑ S∗, ↑↑ B∗d , and ↑ B∗

Variable Baseline 50 Percent FFP 80 Percent FFP

Output Y ∗ 1.69 2.292 2.836Consumption-to-Output ratio c∗/y ∗ 0.640 0.628 0.625Capital i-to-Output ratio k∗i /y ∗ 4.331 5.681 6.079Foreign claims-to-Output ratio B∗/y ∗ 0.039 0.051 0.055Deposits B∗d/y ∗ 0.259 0.340 0.364Included f. labor-to-capital ratio L∗i /k∗i 0.028 0.025 0.022Excluded f. labor-to-capital ratio L∗e/k∗e 0.050 0.043 0.039Net worth-to-Output ratio NW ∗/y ∗ 0.917 1.203 1.288Included to total firms ratio N∗i /(N∗i +N∗e ) 0.167 0.336 0.410Labor Prod∗i 5.733 6.575 7.269Labor Prod∗e 4.873 5.589 6.179

ψ∗i /Y ∗ 0.150 0.0218 0.007


Quan. Analysis Low financial participation IRFs

0 5 10 15 20 25-5

-4.5

-4

0 5 10 15 20 25-14

-12

-10

-8

-6

0 5 10 15 20 25

-1.5

-1

-0.5

0 5 10 15 20 25

-4

-3

-2

-1

0 5 10 15 20 25

-0.5

0

0.5

1

1.5

0 5 10 15 20 25-1.5

-1

-0.5

0 5 10 15 20 25

-2

-1.5

-1

-0.5

0

0 5 10 15 20 25

-1

-0.5

0

0 5 10 15 20 25

-3

-2

-1

0 5 10 15 20 25-1.4

-1.3

-1.2

-1.1

-1

0 5 10 15 20 25

-1

-0.5

0

0 5 10 15 20 25

0

0.2

0.4

Conditional on a financial shock in the AE, greater domestic financialparticipation by EME firms limits the adverse effect of the shock in theEME


Quan. Analysis High and low financial participation IRFs

0 5 10 15 20 25-5

-4.5

-4

0 5 10 15 20 25-14

-12

-10

-8

-6

0 5 10 15 20 25

-1.5

-1

-0.5

0 5 10 15 20 25

-4

-3

-2

-1

0 5 10 15 20 25

-0.5

0

0.5

1

1.5

0 5 10 15 20 25-1.5

-1

-0.5

0 5 10 15 20 25

-2

-1.5

-1

-0.5

0

0 5 10 15 20 25

-1

-0.5

0

0 5 10 15 20 25

-3

-2

-1

0 5 10 15 20 25-1.4

-1.3

-1.2

-1.1

-1

0 5 10 15 20 25

-1

-0.5

0

0 5 10 15 20 25

0

0.2

0.4

0.6

Conditional on a financial shock in the AE, greater domestic financialparticipation by EME firms limits the adverse effect of the shock in theEME


Quan. Analysis The Role of Endogenous Firm Entry

0 5 10 15 20 25-5

-4.5

-4

0 5 10 15 20 25

-15

-10

-5

0 5 10 15 20 25

-1.5

-1

-0.5

0 5 10 15 20 25

-4

-3

-2

-1

0

0 5 10 15 20 25

-2

-1.5

-1

-0.5

0

0 5 10 15 20 25

-4

-3

-2

-1

0

0 5 10 15 20 25

-2

-1

0

0 5 10 15 20 25

-3

-2

-1

0

0 5 10 15 20 25

-5

-4

-3

0 5 10 15 20 25

-1.6

-1.4

-1.2

-1

-0.8

-0.6

Conditional on a financial shock in the AE, a model without firm entryprompts that higher EME financial participation leads to a larger drop inB∗ and consumption: goes against what we find in the data


Quan. Analysis Empirical Validation

Using a sample of EMEs, we want to characterize the link between

the response of these economies to external shocks and the average

degree of participation of firms in the domestic banking system

Strong link between share of firms with bank credit and the bank

credit to GDP ratio across economies

⇒ we use bank credit to GDP ratio as a rough proxy for firm

financial participation

Financial shocks in the AE are interpreted U.S. commercial bank’s net

charge-offs

and the transmission of the shocks works through the cross-border

bank flows


Quan. Analysis Data

U.S. commercial banks’ net charge-offs (St. Louis Fed)

I value of those loans that commercial banks assume will not be paid

I the shock is associated to a fall in the value of banks’ assets that

corresponds to a quality of capital shock in the AE

I Lambertini and Uysal (2013) and Cuadra and Nuguer (2018)

Cross-border bank flows (BIS)

I foreign claims of U.S. banks for specific economies

I financial shocks in the U.S. were transmitted to EMEs via cross-border

bank flows –Cuadra and Nuguer (2018)

Bank-credit-to-GDP ratio (BIS), real exchange rate (Stein,

Fernandez, Rosenow, and Zuluaga, 2018), consumption (IFS)

Data are logged and HP-filtered


Quan. Analysis Specification

Bit

GDPit= β0 + β1NCOt ×

¯(Cri

GDPi

)+ ε i + ιt + uit

BitGDPit

quarterly cross-border bank flows from U.S. to i

NCOt net charge-offsCri

GDPicredit-to-GDP ratio of i at certain t

ιt time-fixed effect

ε i firm-fixed effect

Cit = δ0 + δ1RERit−1 ×¯(

CriGDPi

)+ δ2RERit−1 + ε i + ιt + zit

Cit quarterly real consumption of country i

RERit real-exchange rate


Quan. Analysis Empirical Results EME

Dependent variable: Foreign claims to GDPBit

GDPitConsumption Cit

(1) (2) (3) (4) (5) (6)

US net charge-offs NCOt 0.102 0.102(0.070) (0.070)

Credit to GDPi ,2000:1−2018:3 0.001** 0.004***(0.001) (0.090)

Credit to GDPi ,2000:1−2018:3 × NCOt -0.149** -0.149** -0.149*(0.068) (0.068) (0.071)

Real Exchange Rate RERit−1 -0.230*** -0.192** -0.192**(0.074) (0.078) (0.077)

Credit to GDPi ,2000:1−2018:3 × RERit−1 0.198** 0.279** 0.279**(0.090) (0.122) (0.122)

Constant -0.001 -0.000 -0.006 -0.002 -0.005 0.002(0.001) (0.000) (0.014) (0.001) (0.007) (0.014)

Country FE No Yes Yes No Yes YesTime FE No No Yes No No Yes

Observations 1,022 1,022 1,022 873 873 873R-squared 0.012 0.013 0.176 0.283Number of countries 14 14 14 14 14 14

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1Notes: Authors’ calculations using data from the Bank for International Settlements (BIS) and the St. Louis FRED database

for the period 2000Q1-2018Q3. NCOt , RERit , Cit andBit

GDPitare real, logged and detrended using the Hodrick-Prescott filter.

Credit to GDP2000:1−2018:3 is the average bank credit to GDP for the period 2000:1-2018:3. Foreign claims to GDP correspondto foreign claims of each country on U.S. banks to GDP ratio. The sample of EMEs used is comprised of: Argentina, Brazil,Chile, Colombia, India, Indonesia, Israel, Korea, Malaysia, Mexico, Russia, South Africa, Thailand, and Turkey.


Quan. Analysis Empirical Results EME

Dependent variable: Foreign claims to GDPBit

GDPitConsumption Cit

(1) (2) (3) (4) (5) (6)

US net charge-offs NCOt 0.102 0.102(0.070) (0.070)

Credit to GDPi ,2000:1−2018:3 0.001** 0.004***(0.001) (0.090)

Credit to GDPi ,2000:1−2018:3 × NCOt -0.149** -0.149** -0.149*(0.068) (0.068) (0.071)

Real Exchange Rate RERit−1 -0.223*** -0.224*** -0.192**(0.034) (0.035) (0.047)

Credit to GDPi ,2000:1−2018:3 × RERit−1 0.167*** 0.168** 0.304**(0.046) (0.046) (0.092)

Constant -0.001 -0.000 -0.006 -0.002 0.001*** 0.002(0.001) (0.000) (0.014) (0.001) (0.000) (0.013)


Observations 1,022 1,022 1,022 871 871 871R-squared 0.012 0.013 0.176 0.087 0.088 0.279Number of countries 14 14 14 14 14 14




Credit to GDP2000:1−2018:3 is the average bank credit to GDP for the period 2000:1-2018:3. Foreign claims to GDP correspondto foreign claims of each country on U.S. banks to GDP ratio. The sample of EMEs used is comprised of: Argentina, Brazil,Chile, Colombia, India, Indonesia, Israel, Korea, Malaysia, Mexico, Russia, South Africa, Thailand, and Turkey.


Quan. Analysis Summary of results for EMEs

1 For countries with a higher credit-to-GDP ratio, a shock to U.S. NCO

affects their cross-border bank flows-to-GDP ratio less than for

countries with lower credit-to-GDP ratio

2 For countries with a higher credit-to-GDP ratio, a change in the RER

prompts a smaller effect on consumption than for countries with lower

credit-to-GDP ratio

Our model replicates these results


Quan. Analysis Robustness checks

Other measures of credit-to-GDP ratio

Deposits-to-GDP ratio

Lag of RER

AE and deposits-to-GDP ratio


Conclusion

We have presented empirical evidence of the implication of different

level of firm participation in the banking system in EMEs and the

consequences for the response to external financial shock, such as the

one of the global financial crisis

We build a two-country RBC model with banking frictions,

endogenous firm entry, and limited domestic financial participation by

firms

Higher financial participation in EMEs limits the effect of adverse

external financial shocks on EME financial and macro aggregates,

with endogenous firm entry playing a critical role in the

volatility-reducing effects of larger firm financial participation: results

are consistent with the data


Additional Tables Empirical Results AEDependent variable: Foreign claims to GDP

BitGDPit

Consumption Cit

(1) (2) (3) (4) (5) (6)

US net charge-offs NCOt 0.314*** 0.314***(0.095) (0.095)

Credit to GDPi ,2000:1−2018:3 0.001** -0.001(0.001) (0.001)

Credit to GDPi ,2000:1−2018:3 × NCOt -0.247*** -0.247** -0.249**(0.090) (0.090) (0.091)

Real Exchange Rate RERit -0.029 -0.016 -0.016(0.041) (0.039) (0.039)

Credit to GDPi ,2000:1−2018:3 × RERit 0.039 0.018 0.019(0.044) (0.040) (0.039)

Constant -0.003** -0.001*** -0.174*** 0.000 0.004 -0.012**(0.001) (0.000) (0.049) (0.001) (0.004) (0.006)


Observations 1,521 1,521 1,521 1,551 1,551 1,551R-squared 0.029 0.298 0.497Number of countries 21 21 21 21 21 21




Credit to GDP2000:1−2018:3 is the average bank credit to GDP for the period 2000:1-2018:3. Foreign claims to GDP correspondto foreign claims of each country on U.S. banks to GDP ratio. The sample of AE used is comprised of: Australia, Austria,Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Luxembourg, Netherlands, New Zealand,Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom.


Additional Figures

-1-.5

0.5

1H

P of

Log

U.S

. NC

O

1990q1 1992q1 1994q1 1996q1 1998q1 2000q1 2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1 2016q1 2018q1


Domestic Financial Participation and External

Documents

Transcript of Domestic Financial Participation and External