Banking Sector Competition, Financial Dependence and Export

Volatility: Theory and Evidence - Online Appendix∗

Chenghao (Matt) HuUniversity of California, Davis

Department of Economics1 Shields Ave, Davis, CA 95616

E-mail: chhu@ucdavis.edu

February 1, 2018

Appendix A Mathematical Appendix

A.1 Solving Firm’s Problem under Binding Financial Constraint

From the model set up and simplified assumption, under binding financial constraints firm’s problem isgiven as

Maxϕ,l πf = (1− ϕ)ϕ(k + ul)− (1 + r)l(1− ϕ)− kϕ− fe

s.t. l = λθk

Plug in the expression of l into above profit function and take first order derivative with respect to ϕ weget

∂πf

∂ϕ = (1− 2ϕ)(k + uλθk) + (1 + r)λθk − k = 0ϕ = (1+u+r)λθ

2(1+uλθ)

Firms optimal export sales equals to

x = (1− ϕ)ϕ(k + uλθk)x = [ (1+u+r)λθ

2 − (1+u+r)2(λθ)2

4(1+uλθ) ]k

Comparative statistics for bank loan l = λθk is straightforward. Using Assumption 3 which implied that(1− λθ − λθr) < 0, comparative statics with respect risky technology parameter are:

∂ϕ∂r = λθ

2(1+uλθ) > 0∂ϕ∂u = 2λθ(1−λθ−λθr)

(2+2uλθ)2 < 0∂ϕ∂θ = 2λ(1+u+r)

(2+2uλθ)2 > 0∂ϕ∂λ = 2θ(1+u+r)

(2+2uλθ)2 > 0

∗Not for publication.

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For second order derivative we have

∂2ϕ∂r∂u = − λ2θ2

2(1+uλθ)2 < 0∂2ϕ∂λ∂u = 4θ(2+2uλθ)(1−λuθ−2λθ−2λθr)

(2+2uλθ)2 < 0∂2ϕ∂λ∂r = 2θ

(2+2uλθ)2 > 0

Finally, for export sales under a binding financial constraint we have

∂x∂r = λθ(1−λθ−λθr)

2(1+uλθ) k < 0∂x∂u = λθ(1−λθ−λθr)

2(1+uλθ) k(−1) λθ(1+uλθ)2 > 0

∂x∂θ = k[2λ(1+uλθ)(1+u+r)(1−λθ−λθr)+(λθ)2(1+u+r)2uλ

4(1+uλθ)2 ] > 0

Where the last equality follows the firm’s problem of the non-binding state as we will show below. In asimilar way to ∂x

∂θ , we can show ∂x∂λ > 0. For second order derivative we have

∂2x∂r∂u = −λ2θ2(1−λθ−λθr)

2(1+uλθ)2 k > 0∂2x∂u∂λ = θ2k

2 [ (θ+θr)( 1λ

+uθ)2+ 2λ2 (λθ+λθr−1)( 1

λ+uθ)

( 1λ

+uθ)4 ] > 0∂2x∂r∂λ = k[ θ(1−2λθ−2λθr)(2+uλθ)−θ2uλ

4(1+uλθ)2 ] < 0

A.2 Solving Firm’s Problem under Non-binding Financial Constraint

From the model set up and simplified assumption, under non-binding financial constraints firm’s problemis given as

Maxϕ,l πf = (1− ϕ)ϕ(k + ul)− (1 + r)l(1− ϕ)− kϕ− fe

s.t. l < λθk

Take first order derivative with respect to l we get

∂πf

∂l = (1− ϕ)ϕu− (1 + r)(1− ϕ) = 0ϕ = 1+r

u < 1

Where the last equality follows the implicit assumption that firms will never adopt extremely riskytechnology and thus fail the technology innovation with 100%. Similarly, the first order condition withrespect to ϕ is

∂πf

∂ϕ = (1− 2ϕ)(k + ul) + (1 + r)l = k

l = 2ϕk(1−2ϕ)u+1+r = 2(1+r)k

u(u−1−r)

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Firms’ optimal export sale in this case equals to

x = (1− ϕ)ϕ(k + ul)x = (1− 1+r

u )(1+ru )[k + u 2(1+r)k

u(u−1−r) ]x = [1+r

u + (1+ru )2]k

Comparative statistics with bank loans can be shown as

∂l∂r = 2k

(u−1−r)2 > 0∂l∂u = −2(1+r)k(2u−1−r)

u2(u−1−r)2 < 0∂l∂θ = 0; ∂l∂λ = 0; ∂2l

∂λ∂u = 0; ∂2l∂r∂λ = 0

∂2l∂r∂u = −4k

(u−1−r)3 < 0

With respect to risky technology parameter, it is easy to show that

∂ϕ∂r = 1

u > 0; ∂ϕ∂u = −1+ru2 < 0; ∂2ϕ

∂u∂r = − 1u2 < 0

For export sales we have∂x∂r = [ 1

u + 2(1+r)u2 ]k > 0

∂x∂u = −[1+r

u2 + 2(1+r)2

u3 ]k < 0∂2x∂r∂u = −[ 1

u2 + 4(1+r)u3 ]k < 0

A.3 Proof of Lemma1

Proof. From bank’s problem we have

πb = p(ϕb)(1 + rb)lb + [1− p(ϕb)](slb + k

n)− (1 + rd)[1− εω

ε− εφ+ ω(1 + χ)]lb − ϑlb − fb

Taking first order derivative with respect to rb we have

∂πb

∂rb= [∂p(ϕb)∂rb

+ ∂p(ϕb)∂rb

rb + p(ϕb)]lb + [p(ϕb)(1 + rb)− (1 + rd)(1−εωε−εφ + ω + ωχ)− ϑ)] ∂lb∂rb

−∂p(ϕb)∂rb

kn + [ ∂lb∂rb −

∂p(ϕb)∂rb

lb − p(ϕb) ∂lb∂rb ]s = 0

Where from the text we have

∂p(ϕb)∂rb

= [− (1−γ)λθ2(1+uλθ) −

γu ]( 1

n + ρ) < 0∂lb∂rb

= 2γkn(u−1−r)2 ( 1

n + ρ)− η(1− ρ) < 0p(ϕb) = 1− (1− γ)[ (1+r+u)λθ

2(1+uλθ) ]− γ(1+ru ) > 0

lb= (1−γ)λθkn + 2γ(1+r)k

nu(u−1−r) > 0

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Through some algebra, we can show that

(∂p(ϕb)∂rb

lb + p(ϕb)∂lb∂rb

)︸ ︷︷ ︸A<0

rb = (1− εωε− εφ

+ ω + ωχ) ∂lb∂rb︸ ︷︷ ︸

B<0

rd

+ (1− εωε− εφ

+ ω + ωχ+ ϑ− p(ϕb)− s)︸ ︷︷ ︸>0

∂lb∂rb︸︷︷︸<0

+ ∂p(ϕb)∂rb︸ ︷︷ ︸<0

(kn− lb)︸ ︷︷ ︸>0

− p(ϕb)lb︸ ︷︷ ︸>0

+ [∂p(ϕb)∂rb

lb+p(ϕb)∂lb∂rb

]︸ ︷︷ ︸<0

s

︸ ︷︷ ︸C<0

DenoteA = ∂p(ϕb)

∂rblb + p(ϕb) ∂lb∂rb < 0

B = [1−εωε−εφ + ω(1 + χ)] ∂lb∂rb < 0

C = B + [ϑ− p(ϕb)− s] ∂lb∂rb + ∂p(ϕb)∂rb

(k/n− lb)− p(ϕb)lb +As < 0

Then we have bank loan interest

rb = BAr

d + CA

Use above expression and take derivative of rb with respect to exogenous parameters ω, χ, ϑ, s, η, ε, φand ρ yield

∂rb∂χ

=ω ∂lb∂rb

Ard︸ ︷︷ ︸

>0

+ω ∂lb∂rb

A︸ ︷︷ ︸>0

> 0; ∂rb∂ϑ

=∂lb∂rb

A> 0;

∂rb∂ω

=(1 + χ) ∂lb∂rb

Ard︸ ︷︷ ︸

>0

+(1 + χ) ∂lb∂rb

A︸ ︷︷ ︸>0

> 0; ∂rb∂s

= 1−∂lb∂rb

A< 0

∂rb∂η

= ∂rb

∂ ∂lb∂rb

∂ ∂lb∂rb∂η

= [(1−εωε−εφ + ω + ωχ)(1 + rd) + ϑ

A− p(ϕb)(1 + rb) + (1− p(ϕb))s

A]︸ ︷︷ ︸

<0

(1− ρ)︸ ︷︷ ︸>0

< 0

∂rb∂ε

=− 1ε2(1−φ)

∂lb∂rb

Ard︸ ︷︷ ︸

<0

+− 1ε2(1−φ)

∂lb∂rb

A︸ ︷︷ ︸<0

< 0; ∂rb∂φ

=1−εωε(1−φ)2

∂lb∂rb

Ard︸ ︷︷ ︸

>0

+1−εωε(1−φ)2

∂lb∂rb

A︸ ︷︷ ︸>0

> 0

∂rb∂ρ≈ −[(1− εω

ε− εφ+ ω + ωχ)(rd + 1) + ϑ− p(ϕb)− s](

n+ n2

1+ρn )︸ ︷︷ ︸<0

− p(ϕb)lb( 1n + ρ)2︸ ︷︷ ︸>0

< 0

Above equations are Lemma 1 that is shown in text.

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A.4 Proof of Proposition 1

Proof. Firms export standard deviation equals to

Std(x) =√γ(1− γ)[E(x|θ = θ)− E(x|θ = θ)]

Std(x) = k√γ(1− γ)[1+r

u + (1+ru )2 − (1+u+r)λθ

2 + (1+u+r)2(λθ)2

4(1+uλθ) ]

Denote V = Std(x)k√γ(1−γ)

, using r = rdM and taking derivative of V with respect to bank interest ratemarkup M yield

∂V∂M = ∂V

∂r∂r∂M = [ 1

u + 2(1+r)u2 + λθ[λθ(1+r)−1]

2(1+uλθ) ]rd > 0

Taking second order derivative with respect to u it is easy to prove that ∂2V∂M∂u < 0. When financial

dependence increases (u decreases), the effect of bank competition on export volatility is stronger.1

A.5 Proof of Proposition 2

Proof. Assume bank interest rate markup M is a function of banking characteristics c with M = M(c).Then, taking derivative of ∂V

∂M with respect to banking characteristics c yield

∂2V

∂M∂c=∂ ∂V∂M∂r

∂r

∂M

∂M

∂c= [ 2

u2 + (λθ)2

2(1 + uλθ) ]︸ ︷︷ ︸>0

(rd)2︸ ︷︷ ︸>0

∂M

∂c

When ∂M∂c > 0, ∂2V

∂M∂c > 0 and when ∂M∂c < 0, ∂2V

∂M∂c < 0. That is any banking characteristics that cancontribute to a higher markup (M) should strengthen the effect of low banking sector competition onexport volatility. On the other hand if a banking sector characteristic can contribute to a lower markup(M) it should weaken the effect of low banking sector competition on export volatility. Combined withLemma 1, Proposition 2 is proved.

A.6 Aggregate Volatility and Its Decomposition

Denote variance (numerator of the aggregate volatility expression) of an industry’s export sales as

V AR(Y ) = V AR(∑mf=1 xf ) = E((

∑mf=1 xf )2)− [E(

∑mf=1 xf )]2 = E(

∑mf=1

m∑g=1

xfxg)− [∑mf=1E(xf )]2

V AR(Y ) =∑mf=1

m∑g=1

E(xfxg)−∑mf=1

m∑g=1

E(xf )E(xg) =∑mf=1

m∑g=1

[E(xfxg)− E(xf )E(xg)]

V AR(Y ) =∑mf=1

m∑g=1

Cov(xf,xg) =∑mf=1 V AR(xf ) + 2

∑mf<g Cov(xf , xg)

1 Similarity, through some algebra, we can show ∂V∂λ

< 0, ∂V∂u

< 0, ∂2V∂M∂λ

< 0 and ∂2V∂u∂λ

< 0.

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Next, we decompose the first part of the aggregate export sales variance into various components. Denotethe mean of export sales for representative as u, then under symmetric case with θ ∈ (0,+∞) (firms withidentical size) we have

m∑f=1

V AR(xf ) = m ∫∞0 (x− u)2f(θ)dθ = m[∫ θ00 (x1 − u)2f(θ)dθ + ∫∞θ0 (x2 − u)2f(θ)dθ]

Where θ0 is the intersection point of export sales curve before and after a decrease of bank competition.Denote V1 and V2 as variance for a single firm before and after a decrease of bank competition, we have

V1 = ∫∞0 (x− u)2f(θ)dθ = [∫ θ00 (x1 − u)2f(θ)dθ + ∫∞θ0 (x2 − u)2f(θ)dθ]

V2 = ∫∞0 (xnew − unew)2f(θ)dθ = [∫ θ00 (x1 −∆x1︸ ︷︷ ︸

x1new

−u)2f(θ)dθ + ∫∞θ0 (x2 + ∆x2︸ ︷︷ ︸x2new

−u)2f(θ)dθ]

Where ∆x1 and ∆x2 are a positive change of export sales in two intervals of cash flow shock distribution.Through some algebra, we can show

V2 = ∫ θ00 (x1 − u− [∫ θ0

0 ∆x1f(θ)dθ + ∫∞θ0 ∆x2f(θ)dθ]︸ ︷︷ ︸B>0

)2f(θ)dθ+

∫∞θ0 (x2 − u+ [∫ θ00 ∆x1f(θ)dθ + ∫∞θ0 ∆x2f(θ)dθ]︸ ︷︷ ︸

B>0

)2f(θ)dθ

Thus, we haveV2 = V1 +B2 + ∫∞θ0 2B(x2 − u)f(θ)dθ − ∫ θ0

0 2B(x1 − u)f(θ)dθV2 = V1 +B2 + 2B [∫∞θ0 (x2 − u)f(θ)dθ − ∫ θ0

0 (x1 − u)f(θ)dθ]︸ ︷︷ ︸A>0

V2 = V1 +B2 + 2BA > V1

Where A is a constant shape parameter describing the distribution of firm export sales across all firmstates (cash flow shocks) before a change of banking sector competition. Denote change of aggregate salesvariance under high and low bank competition regime as ∆V AR, we have

∆V AR =m∑f=1

[(Bf +Af )2 −A2f ]

As shown in Figure 6 in text, B can be further decomposed into five parts

B = ∫ θnew¯θold∆x1f(θ)dθ︸ ︷︷ ︸C1>0

+ ∫ θoldθnew

∆x2f(θ)dθ︸ ︷︷ ︸C1>0

+ ∫ θ0θold

∆x3f(θ)dθ︸ ︷︷ ︸C1>0

+ ∫ θnewθ0∆x4f(θ)dθ︸ ︷︷ ︸C1>0

+ ∫∞θnew

∆x5f(θ)dθ︸ ︷︷ ︸C1>0

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Where C1, C2, C3, C4 and C5 are what we described in text, each representing a different source of volatilitychange. Given a small change of bank competition (xf low ≈ xf high = xf ) it is easy to show:

∆V ol(Y ) = V ol(Ylow)− V ol(Yhigh) ≈ 1(mxf )2

m∑f=1

[(Bf +Af )2 −A2f ] > 0

A.7 Proof that Export Cutoff ¯θ is Decreasing in Bank Competition

Proof. Profit for a firm under financially binding state is

πf = (1− ϕ)ϕ(k + uθkλ)− (1 + r)θkλ(1− ϕ)− kϕ− fe

Through some algebra, we can show zero profit cut-off cash flow shock ¯θ for above equation satisfies

k(u− 1− r)2λ ¯θ − 4fλ ¯θ

= 4fu+ 4k(1 + r)

Then, it is easy to find out that

∂ ¯θ∂λ

< 0; ∂¯θ

∂u< 0; ∂

¯θ∂r

> 0; ∂ ¯θ∂fe

> 0; ∂¯θ

∂k> 0

That is, the cutoff cash flow value of zero profit export (¯θ) is increasing in fixed cost of export (fe), capitalendowment (k) and financial dependence (u) and decreasing in financial development (λ) and degree ofbanking sector competition (r).

Appendix B Construction of Banking Sector Competition Measures

This appendix introduces how we construct three indices of bank competition that is used in this paper.First, the Lerner index for a single bank i is defined as the difference between banking output prices andmarginal costs (relative to prices).

Lerneri = pi −mcipi

Where pi is the price of outputs and is calculated as the total gross revenue of the bank divided by thetotal assets; mci is the marginal cost of bank i estimated using a flexible (allow flexible cost structure)

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semi-parametric approach.2 The second index is the efficiency adjusted Lerner index, which is given as

Adj Lerneri = πi + tci −mci ∗ qiπi + tci

The third index is the Profit Elasticity which is given as

Profit Elasticityi = mci ∗ qiqi ∗mci − tci (1−AdjLerneri)

.

Estimation of above three indices requires us to accurately measure each of the components inthose formulas. In particular, πi (bank profit) is measured by total profit of bank i before taxes; pi(aggregate output price) is captured by the ratio of total income of bank i over its total earning assets(Beck et al. 2013); qi (bank output) is measured by real total earning assets of bank i, which include loans,securities, and other earning assets (such as investments and insurance assets)3; finally, tci (bank totalcost) is measured by real total expenses of bank i.

In above three formulas, the marginal cost mci is usually not directly available. Thus, as a keyprocedure before we construct those competition indices we need to estimate this marginal cost. Inparticular, we compute the marginal cost by taking the derivative of the total cost tci with respect tobank output qi . We assume that the total cost function takes a general form as

tcit = f (qit, wl,it, wk,it, wd,it)

To identify bank inputs and output for cost function, we use intermediation approach assumingthat labor, capital and deposits are three inputs used in the production process to produce bank outputs.Specifically, wl (price of labor) is proxied by ratio of personnel expenses to total assets; wk (price of capital)is proxied by ratio of capital expenditures to fixed assets; and wd (price of deposit) is proxied by totalinterest expenses over total customer deposit. Starting from a standard translog production function andimpose linear homogeneity restriction in input prices (normalize total cost and input prices by the price ofdeposits before taking logs) we obtain

ln tcitc = β1 + β2 lnwl,itc + β3 lnwk,itc + α1 ln qitc

Then we apply semi-parametric partial linear smooth coefficient (PLSC) model estimation (see2To obtain marginal cost estimates previous work has to either estimate a cost function (banking literature e.g. Clerides et al.(2015) or impose equilibrium conditions (IO literature e.g. Bresnahan (1989) derived from theoretical model. Here, we followthe first strand of the literature.

3 Those activities tend to reflect traditional banking activities. Therefore, indices constructed here reflect mainly competitionin traditional activities.

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Delis et al. 2014 for similar application). Specifically, using the local polynomial fitting regression and theGaussian kernel function we can obtain estimates of α1 for each bank i at time t in country c. Rewriteabove equation in econometrics from we have

ln tcitc = β1 + α1 (zitc) ln qitc + β2itc lnwl,itc + β3itc lnwk,itc + eitc

Where zitc is the smoothing variable required by the PLSC semi-parametric approach (we treat α1 as afunction of zitc) which we defined/chosen it as zitc = lnwl,itc + lnwk,itc. Based on those specifications,marginal costs for each bank i can be obtained as

∂tcitc/∂qitc = α1 (zitc) tcitc/qitc

Once the marginal cost is estimated, one can use three formulas specified above to estimate allthree bank competition measures. Finally, as a convention, country level measures are computed as theweighted mean of individual measures using market shares as appropriate weights.

In this paper we use bank-level data from Bankscope to calculate the above competition indices.Bankscope is a database compiled by Fitch/Bureau Van Dijk that contains information on banks aroundthe globe, based on publically available data sources. Our interested periods covers from year 2001 to2011 (2011 is the most recent date that the trade data is available when this paper first starts, while wedrop years earlier than 2001 due to concerns on data coverage and accounting issues). Before applyingdata to the model, we clean the raw data required to estimate above three indices considering variousissues such as double-counting stemming from merger and acquisition, ownership issues, inflexible featuresof the Bankscope database, etc. For example, if banks report information on the consolidated level wedelete the unconsolidated entries of the group from the sample to avoid the double counting. We alsoexclude countries for which we have information for less than 3 bank-year observations and considerbanks classified as commercial, cooperative, savings banks only. That means we exclude real-estate andmortgage banks, investment banks, other non-banking credit institutions (mainly in Germany), specializedgovernmental credit institutions, bank-holding and other holding companies. Those excluded banks areless dependent on the traditional intermediation function and have a different financing structure compareto the banks we pick. Then, within each country, we omit outlying observations that are in the top andbottom first percentile of the distribution for each variable we use. We also clean sample by droppingunreasonable negative values of total assets and total expenses and drop top and bottom 1% of the inputprice distribution.

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Appendix C Data Source

Bank competition data: BankScope:https://www.bvdinfo.com/en-us/our-products/company-information/international-products/bankscope

Banking sector characteristics: Bank Regulation and Supervision Database (Barth et al. 2013)http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20345037˜pagePK:64214825˜piPK:64214943˜theSitePK:469382,00.html

Characteristics of banking sector globalization: Global Financial Development Database (GFDD)http://www.worldbank.org/en/publication/gfdr/data/global-financial-development-database

Country level control variables: Trade openness (Share as a percentage of GDP), GDP at markets prices constant (2005 US$) real GDP per capita and population are downloadedfrom World Bank’s World Development Indicators. Institutional quality is derived from World Governance Indictor Database where six indicators are available from1996 to 2013

Country level financial development:The amount of credit by banks and other financial intermediaries to the private sector as a share of GDP (private credit), which is obtained from Financial Developmentand Structure Dataset (updated Nov. 2013) (see Beck et al. 2000, Beck et al. 2010 and Cihak et al. 2013)http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20696167˜pagePK:64214825˜piPK:64214943˜theSitePK:469382,00.html

http://www.worldbank.org/en/publication/gfdr/data/global-financial-development-database

Cross border capital flows: IFS Balance of Payment - BOPhttp://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:23407813˜pagePK:64214825˜piPK:64214943˜theSitePK:469382,00.html

Financial market development: stock market capitalizationhttp://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20696167˜pagePK:64214825˜piPK:64214943˜theSitePK:469382,00.html

Investor protection index or creditor right data: (Djankov et al. 2007)http://scholar.harvard.edu/shleifer/publications?page=9

Large commodity exporter : If the main export for a given country is mineral or metal according to CIA book.

Legal origin: (Porta et al. 1998) and the CIA Factbook (2003).

Oil exporters: If a country satisfies two criteria on average during the sample period: (i) rent from the oil sector constitutes more than 10% of its GDP (ii) it exports more than500 thousand barrel per day. http://www.worldstopexports.com/worlds-top-oil-exports-country/

Private credit bureau coverage:World Bank Doing Business database - percentage of the adult population covered by each registry bureau. www.doingbusiness.org

Sector level import and export data: NBER-United Nations trade dataset is provided by Robert Feenstra, which contains uni-directional export data at a SITC 4-digit level.Export flows are measured in constant 2005 U.S. dollar using the U.S. GDP deflator data obtained from the World Bank’s World Development Indicators.

Sector-level financial dependence data:Kroszner et al. (2007) and Manova et al. (2015), which is provided at ISIC 3-digit level. We use concordance provided by Prof.Marc-Andreas Muendler to match itwith sitc 4 digit trade flow data. For concordance see http://econweb.ucsd.edu/˜muendler/html/resource.html#sitc2isic

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Appendix D Summary Statistics and Additional Figures

Table D.1: Statistics: Pairwise Correlation Between All Bank Competition Measures

LernerIndex

AdjustedLerner

ProfitElasticity

FiveLargestAsset

FiveLargestDeposit

Lerner Index 1Adjusted Lerner 0.920*** 1Profit Elasticity 0.333*** 0.303*** 1Five Largest Asset 0.190** 0.238*** 0.069 1Five Largest Deposit 0.196* 0.259*** 0.049 0.584*** 1

Note: Significance level are denoted as *** 1%, ** 5% and * 10%.

Figure D.1: Scatter Plot of Banking Sector Competition and Banking Sector Development

Note: Vertical axis denotes banking sector development measured by private credit to GDP ratio, while horizontalaxis denotes banking sector competition measured by the Lerner index. All variables are averaged over 2001-2011periods.

11

Table D.2: Summary Statistics of Key Variables Used in Final Sample

Variable Obs Mean Std. Min MaxExport Volatility 51908 0.759 0.540 0.000 2.000Lerner Index 51908 0.267 0.084 0.077 0.637Adj Lerner Index 51908 0.215 0.085 0.072 0.621Profit Elasticity 51908 -0.443 0.048 -0.604 -0.342Financial Dependence 51908 -0.085 0.481 -1.986 1.296Liquidity Needs 51908 0.207 0.880 -1.992 2.804Asset Intangibility 51908 0.001 0.737 -0.975 3.771R&D intensity 51908 -0.174 0.225 -0.386 0.548Trade Openness 51908 0.448 0.314 0.108 2.078Log GDP per capita 51908 25.080 1.933 20.301 30.196Institutional Quality 51908 0.258 0.900 -1.701 1.898GDP Volatility 51908 0.033 0.024 0.007 0.309Export Diversification 51908 0.410 0.248 0.027 1Trade Remoteness 51908 7.864 0.783 4.107 9.791Sector Size 51908 -0.018 0.304 -0.057 22.486Intra-Industry Trade Index 51908 0.642 0.272 0.013 1Real Exchange Rate Volatility 49798 0.079 0.045 0.000 0.323Destination GDP Volatility 51908 0.030 0.015 0.000 0.306Oil Exporter Dummy 51908 0.086 0.280 0 1Commodity Exporter Dummy 51908 0.043 0.203 0 1Banking Sector Development 78187 0.543 0.480 0.018 2.276Stock Market Development 55481 0.552 0.583 0.005 4.077Bond Market Development 27847 35.093 24.344 1.221 154.149Depth of Credit Information Sharing 71993 0.068 1.097 -0.469 6.412Depth of Credit Information Sharing 71993 0.150 1.065 -0.622 2.791Investor and Creditor Right Protection 63742 0.068 0.984 -1.577 1.918Capital Regulatory Variables 64362 -0.040 0.977 -2.538 0.913Deposit Insurance Scheme Variables 24292 -0.006 0.999 -1.223 5.847Official Supervisory Action Variables 50956 0.031 1.021 -2.753 2.161Private Monitoring Variables 57030 0.125 1.017 -2.523 2.134External Governance Variables 31185 0.089 0.919 -3.074 1.373Banking Sector Stability - Z-score 76753 15.151 9.572 -1.576 47.826Competition Regulatory Variables 69512 -0.030 1.078 -5.931 0.334Bank Activity Regulatory Variables 60002 -0.087 1.021 -2.900 2.124Financial Conglomerate Variables 60347 -0.007 1.009 -2.518 2.865Official Supervisory Action Variables 63569 0.030 1.007 -2.381 1.101Market Structure Indicators 50206 -0.111 0.931 -1.316 1.772External loans and deposits ratio 76999 -0.026 0.701 -0.137 10.597

12

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14

Table D.5: Frequency of Country-Industry-Export Spell 2001-2011

Frequencyof ExportGrowth

# ofCountry-IndustryPairs

Percentage(%)

CumulativePercentage(%)

2 3,806 4.66% 4.66%3 3,156 3.86% 8.53%4 2,992 3.66% 12.19%5 2,687 3.29% 15.48%6 3,522 4.31% 19.79%7 2,669 3.27% 23.06%8 4,678 5.73% 28.79%9 2,227 2.73% 31.52%10 55,927 68.48% 100.00%Total 81,664 100.00%

Table D.6: Sample Coverage: Final Data Sample

year Sample World Ratio2001 5.172 8.429 61.36%2002 5.408 8.713 62.07%2003 6.219 9.911 62.75%2004 7.495 11.701 64.06%2005 8.271 12.896 64.14%2006 9.472 14.396 65.79%2007 10.58 16.310 64.87%2008 11.66 18.220 64.00%2009 9.388 14.553 64.51%2010 11.29 17.162 65.78%2011 12.96 19.905 65.11%

Note: Unit is Trillion dollars in 2005 constantvalue. Total world trade data is derived fromWDI database.

Table D.7: Correlation between Various Financial Dependence Measures

ExternalFinancial Dep(edp)

LiquidityNeeds(lqn)

AssetIntangibility(ain)

R&D ExpenseIntensity(rde)

External Financial Dep(edp) 1 -0.323 -0.309 0.568Liquidity Needs (lqn) – 1 0.077 0.218Asset Intangibility (ain) – – 1 .0.010R&D Expense Intensity (rde) – – – 1

Table D.8: Distribution of Financial Dependence Variable

Variables Percentile of Distribution10% 25% mean 75% 90%

External Financial Dep (edp) -0.716 -0.451 0 0.123 0.978Liquidity Needs (lqn) -1.31 -0.622 0 0.520 1.205Asset Intangibility (ain) -0.975 -0.699 0 0.294 1.012R&D Expense Intensity (rde) -0.386 -0.283 0 -0.179 0.548

Note: All four financial dependence variables are normalized. Statistics are based on the samplebefore merge with other data.

15

Table D.9: Correlation and Summary statistics of Three Measures of Information Sharing Variable

DCI PRC PBCDCI 1PRC 0.387 1PBC 0.763 0.016 1

Table D.10: Summary Statistics of Three Information Sharing Variable

Variable Obs Mean Std Min MaxDCI 172 0 1 -0.934 1.756PRC 172 0 1 -0.469 6.412PBC 172 0 1 -0.622 2.791

Note: Above statistics are based on cross country sample (before mergeinto the final regression sample). Data for information sharing are derivedfrom World Bank Doing Business database.

Table D.11: Frequency of Country-sector Observations

Group Freq. PercentCountry-sector obs do not have over 20 years of data 44,044 53.93Country sector obs who have over 20 years of data 37,620 46.07

Table D.12: Correlation between Different Volatility Measures

Log difference HPLog difference 1HP 0.938 1

Table D.13: Correlation Matrix of the Lerner Index and its Potential Instruments (IV)

LernerIndex

OverallFinancialConglomeratesRestrictiveness

Limitations onForeignBank Entry

Fraction ofEntryApplicationsDenied

Bank Competition (The Lerner index) 1Overall Financial Conglomerates Restrict 0.3623*** 1Limitations on Foreign Bank Entry -0.2588*** -0.1243*** 1Fraction of Entry Applications Denied 0.4261*** 0.1906*** -0.0527*** 1

Note: ***, ** and * denotes significance at 1%, 5% and 10% respectively.

16

Figure D.2: Effect of Banking Sector Competition: High vs. Low Financial Development

Above figure shows the effect of lower banking sector competition on export volatility of a representative firm ina discrete case model, where the red line represents regime with better financial development, blue line denotesregime with lower financial development. When banking sector competition is lower (r ↓), firm export sales undernon-binding sates will be the same across both regimes (reline overlaps blue line for θ = θ), while firm’s export salesunder binding state will be lower if level of financial development is lower (xθ2

< xθ1). Thus, given the same change

of banking sector competition, export sales will be more volatile if financial development is low. That is, low bankcompetition increases export volatility and the effect is even more pronounced if financial development is low, andvice versa.

17

Figure D.3: Effect of Financial Development: High vs. Low Financial Dependence

Above figure shows the effect of an increase of financial development on export volatility of a representative firm inthe discrete case, where the red line represents regime with high financial dependence, blue line denotes regime withlow financial dependence. When financial market is more developed (λ ↑), firm export sales under non-binding sateswill be the same across both regimes (reline overlaps blue line for θ = θ), while firm’s export sales under bindingstate will be higher if financial dependence is low (xθ2

> xθ1). Meanwhile, low financial dependence can also depress

export sales under financial non-binding state. Thus, given the same change of financial development, export saleswill be less volatile if financial dependence is low. That is, financial development can decrease export volatility andthe effect is even more pronounced if financial dependence is low, and vice versa.

18

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