No Guarantees, No Trade: How Banks Affect Export Patterns Friederike Niepmann and Tim Schmidt-Eisenlohr Federal Reserve Bank of New York and University of Illinois, Urbana-Champaign
The views expressed in this presentation are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System.
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Banks in international trade finance Growing interest in trade finance: World trade relative to world GDP collapsed by 20 percent in 2008/2009; debate about whether trade finance played a role Perceived trade finance gap; large programs by multinational development banks, e.g. IFC about $5 billion p.a. Basel III rules, risk weights on off-balance sheet items Recently securitization of trade guarantees (ABS) Importance of finance for trade: international trade takes longer (working capital channel) and is riskier (risk channel) Does trade finance matter? YES, risk channel matters: a shock to the supply of letters of credit (LCs) has significant effects on exports due to high concentration, reduction in the supply of LCs by single bank can have effects in the aggregate Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Our data source Foreign Exposure Report (FFIEC 009) collected by U.S. regulators Trade finance claims tfbct of bank b in country c at time t Data reflect mostly letters of credit and similar bank guarantees in support of U.S. exports Go to figure Example: U.S. bank confirms a letter of credit that is issued by a bank in Brazil and guarantees the payment X of a Brazilian importer ⇒ Trade finance claims of U.S. bank vis-a-vis Brazil increase by X All U.S. banks with more that $30 million of foreign assets must report Total trade finance claims account for roughly 20 percent of U.S. exports in 2012 From 1997 until present at a quarterly frequency
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Evolution of trade finance claims and exports over time Trade Finance Claims
U.S. Goods Exports
(Billions of USD)
(Billions of USD)
200
450
180
400
160
350
140
300
120 100
Goods Exports
80 60 40
250 200 150
Trade Finance Claims
100
20
50
0
0
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Empirical strategy In line with Greenstone and Mas (2012) and Amiti and Weinstein (2013): Identification of idiosyncratic bank shocks based on observed changes in banks’ trade finance claims Aggregation of shocks by weighing each shock with the lagged market share of bank b in country c to obtain country-level supply shock Compared to existing studies: Identification of risk channel: I I I
Paravisini et al. (2011): working capital channel Amiti and Weinstein (2011): both channels Ahn (2013): also risk channel, does not look at effect on total trade
Exploring heterogeneity of effects across destination countries and over time
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Results A negative country-level supply shock of one standard deviation decreases exports by 1.5 percentage points, on average To get the same trade effect, the USD would have to appreciate by 6 percent Effect of shocks is larger: I
I
for export to smaller and poorer countries and those that are served by a smaller number of banks during financial crisis (3.1 percentage points)
Because banks specialize in certain markets, the same bank-level shock has asymmetric effects across destination countries ⇒ Banks affect trade patterns Shock to a single bank can have significant effects in the aggregate (Gabaix (2011)) ⇒ Banks play important role in trade finance and disruptions in the business are likely to have contributed to the Great Trade Collapse Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Literature Financial shocks and trade: I
I
Working capital channel: Amiti and Weinstein (2011), Ahn et al. (2011), Paravisini et al. (2011), Del Prete and Federico (2012) Risk channel: Amiti and Weinstein (2011), Ahn (2013), Van der Veer (forthcoming), Hale et al. (2013)
Financial conditions and trade: Chor and Manova (2012), Schmidt-Eisenlohr (2013) Financial shocks and real effects: Greenstone and Mas (2012), Khwaja and Mian (2008), Amiti and Weinstein (2012), Chodorow-Reich (2012), Peak and Rosengren (2000) Payment contract choice: Schmidt-Eisenlohr (2013), Antras and Foley (2011), Glady and Potin (2011), Hoefele et al. (2012), Niepmann and Schmidt-Eisenlohr (2013) Global banks and shocks: Cetorelli and Goldberg (2011), Kalemli-Ozcan et al. (2011), ... Relationship lending: Greenstone and Mas (2012), Jiminez et al. (2012), ... Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Background on Trade Finance
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Background on letters of credit
Guarantee business is highly concentrated: I I
Top 5 banks in the U.S. take 92 percent of the business Fixed cost are a barrier to entry: the larger the market is, the more banks are active there
→ Difficult for firms to switch to other bank Use of letters of credit I I
increases with distance of destination from the U.S. and shipping times is hump-shaped in country risk
→ See Niepmann and Schmidt-Eisenlohr (2013)
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Empirical Strategy
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Empirical Strategy: Step 1 Obtaining bank shocks: ∆tfbct =
tfbct − tfbct−1 = αbt + βct + bct tfbct−1
(1)
βct : absorbs destination country demand shocks αbt : captures trade finance growth shock to bank b in period t ⇒ estimated for each country i separately by excluding country i information Actually estimate αibt , dropping information on country i when estimating banks shocks for i Drop one set of country-time fixed effects (otherwise over-identified) ⇒ then for each i, run regression of αibt on time fixed effects to obtain predicted α ˆ ibt 1st and 99th percentile of growth rate distribution dropped Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Step 1: Estimation of bank shocks
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Empirical Strategy: Step 2
Aggregation of shocks by summing over all banks and weighing each shock by the market share of bank b in country i at time t − 2: ∆tfit =
B X b
Niepmann (New York Fed)
tfbit−2 φibt−2 αibt , where φibt−2 = PB b tfbit−2
No Guarantees, No Trade
(2)
The views expressed in this p
Step 2: Obtaining country-level shocks
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Empirical Strategy: Step 3
Estimating the effect of supply shocks on exports: ∆Xit =
Xit − Xit−1 = γ∆tfit + δt + δi + ηit Xit−1
(3)
where ∆Xit is the growth rate of U.S. exports to destination i at time t
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
When does the strategy work?
shockit =
B X
φibt−2 αibt
b 1
There is variation in banks’ market shares across countries X
2
Banks’ market shares are persistent X Identification assumption: COV (∆tfit , ηit ) = 0:
3
I
αibt captures idiosyncratic reductions in supply (uncorrelated with changes in exports)
I
Banks’ market shares are not endogenous to changes in exports
F
F
Banks are not specialized in different industries There is no sorting of banks into export markets
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Variation and persistence in banks’ market shares I Summary Statistics of φbit
φibt
nit
date 2000 q1 2006 q1 2012 q1
N 758 453 484
mean .1517 .2561 .2769
std. .2497 .3144 .3239
min .0003 .0003 .0001
max 1 1 1
2000 q1 2006 q1 2012 q1
115 116 134
6.591 3.905 3.612
6.569 2.871 2.810
1 1 1
34 14 13
Large variation in banks’ market shares Declining number of reporting banks Increasing number of countries Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Variation and persistence in banks’ market shares II dep. var φibt φibt−1 φibt−2
(1) 0.913*** (0.00331)
(2)
0.880*** (0.00399)
(3) 0.704*** (0.0132) 0.236*** (0.0132)
Observations 32,896 29,538 28,196 R-squared 0.836 0.773 0.854 Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Banks’ market shares are highly persistent The 2-quarter lagged market shares explain more than 77 percent of the variation in present market shares
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Bank shocks αibt 107 different banks Number of banks decreasing over time: from 54 in 1997 q1 down to 18 in 2012 q2 325,389 bank shocks α ˆ ibt
0
.5
Density
1
1.5
Distribution of bank supply shocks
-2
Niepmann (New York Fed)
0
2 Bank shock
No Guarantees, No Trade
4
6
The views expressed in this p
Validating the estimated bank shocks I Check in how far balance sheet information predicts bank-level shocks P Dependent variable α ¯ bt = N1 N i αibt Loan growth together with deposit growth, growth in real-estate charge-offs and bank CDS spreads predict shock dep. var α ¯ bt deposit growthbt
(1) 0.454 (0.354)
loan growthbt
(2)
(3)
0.492 (0.305)
charge-offs growthbt
(4) 0.216 (0.245) 0.385* (0.210)
-6.58e-05*** (2.27e-05)
(5) 0.231 (0.347) 0.779 (0.574) -6.02e-05** (2.40e-05)
CDS spreadbt
Time FE Bank FE Observations R-squared Niepmann (New York Fed)
(6)
-0.0124* (0.00672) yes
yes
yes
yes
yes
no
no 1,887 0.012
no 1,887 0.016
no 1,169 0.021
no 1,887 0.017
no 1,169 0.046
yes 270 0.124
No Guarantees, No Trade
The views expressed in this p
Validating the estimated bank shocks II Regression of observed values ∆tfibt on estimated bank shocks αibt Standard errors clustered at bank-time level Bank-level shocks have high predictive power for observed trade finance growth rates dep. var ∆tfibt α ˆ ibt
(1) 0.305*** (0.0521)
(2) 0.307*** (0.0531)
(3) 0.326*** (0.0680)
Country FE
no
yes
no
Time FE
no
yes
no
Time×County FE
no
no
yes
32,025 0.002
32,025 0.009
32,025 0.142
Observations R-squared Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Summary Statistics
variable trade finance growth ∆tfbct
(1) N 32,256
(2) mean 0.225
(3) sd 1.049
(4) min -.904
(5) max 9.8
bank shock α ˆ ibt
325,389
0
0.500
-2.316
5.806
country-level shockit
6,751
0.030
0.171
-0.97
2.202
country-level shockit in sample with controls
4,904
0.032
0.174
-0.971
1.813
export growth ∆Xit in sample with controls
4,904
0.057
0.309
-.667
2.06
Regressions: Offshore centers, 1st and 99th percentile of export growth rates excluded Bootstrapped standard errors; very close to clustering by country
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Results
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Baseline results: ∆Xit = γ shockit + δt + δi + ηit dep. var. ∆Xit shockit
(1)
(2)
(3)
0.0875*** (0.0331)
0.0878** (0.0350)
0.0888** (0.0351)
shockit < p50
(4)
(6) 1997-2004
(7) 2005-2012
0.0943* (0.0531) 0.0858** (0.0418) -2.298*** (0.721) -0.0857 (0.0696) -0.252*** (0.0726) 0.361*** (0.0504) yes yes 4,904 0.102
0.158** (0.0760) -0.0250 (0.101) -2.282 (1.716) -0.0321 (0.128) -0.164* (0.0978) 0.333*** (0.0699) yes yes 2,105 0.112
-0.00318 (0.0669) 0.109* (0.0580) -2.625*** (0.838) -0.142 (0.103) -0.459*** (0.163) 0.386*** (0.0740) yes yes 2,799 0.113
0.159** (0.0721) 0.0602 (0.0401)
shockit ≥ p50 shock smallit shock top 5 it pop. growthit GDP growthit USD xrate gr.it non-U.S. imp. gr.it Country FE Time FE Observations R-squared
(5)
no yes 5,357 0.049
-1.527** (0.607) -0.0418 (0.0748) -0.199*** (0.0701) 0.370*** (0.0519) no yes 4,904 0.068
-2.299*** (0.703) -0.0859 (0.0699) -0.252*** (0.0783) 0.361*** (0.0536) yes yes 4,904 0.102
-2.289*** (0.656) -0.0838 (0.0796) -0.252*** (0.0863) 0.361*** (0.0519) yes yes 4,904 0.102
A shock of one standard deviation decreases exports by 1.5 perc. points Shocks below the median have larger effects than shocks above the median Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Heterogenous effects across countries and over time I
dep. var. ∆Xit shockit
(1) crisis 0.183*** (0.0668)
(2) no crisis 0.0611 (0.0409)
(3) all 0.0587 (0.0461) 0.123* (0.0744)
(4) small cntry 0.189*** (0.0620)
(5) large cntry -0.00196 (0.0262)
(6) small & crisis 0.332** (0.133)
-3.785 (2.664) -0.0675 (0.278) -0.547 (0.375) 0.503*** (0.182) 701 0.212
-1.915** (0.767) -0.114 (0.0803) -0.212*** (0.0798) 0.332*** (0.0515) 4,203 0.099
-2.292*** (0.690) -0.0823 (0.0774) -0.253*** (0.0799) 0.359*** (0.0487) 4,904 0.103
-2.925*** (0.812) -0.198 (0.129) -0.413** (0.188) 0.381*** (0.0759) 2,203 0.082
0.169 (0.838) -0.0443 (0.0745) -0.165** (0.0734) 0.377*** (0.0478) 2,701 0.228
-4.139 (4.437) -0.137 (0.424) -1.017 (0.670) 0.507 (0.343) 320 0.175
shockit × crisist shockit × sm. ctryi pop. growthit GDP growthit USD xrate growthit non-U.S. imp. grit Observations R-squared
(7) all 0.00411 (0.0425) 0.139** (0.0681) 0.0906 (0.0567) -2.320*** (0.699) -0.0842 (0.0775) -0.252*** (0.0764) 0.360*** (0.0466) 4,904 0.103
Effect more than two times larger in crisis times Effect mostly for smaller countries (lower value of U.S. exports) Evidence for effect also for non-crisis period Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Heterogenous effects across countries and over time II dep. var. ∆Xit shockit
(1) small # banks 0.186*** (0.0558)
(2) large # banks -0.0346 (0.0314)
-2.702*** (0.833) -0.115 (0.138) -0.333* (0.201) 0.414*** (0.0792) 2,390 0.088
-0.708 (0.982) -0.0736 (0.0631) -0.226*** (0.0644) 0.324*** (0.0449) 2,514 0.234
shockit × crisis dummyt shockit × large # banks dummyit large # banks dummyit pop. growthit GDP growthit USD xrate growthit non-U.S. import growthit Observations R-squared
(3) all 0.0915* (0.0477) 0.129* (0.0752) -0.0927** (0.0464) 0.00649 (0.0152) -2.321*** (0.700) -0.0872 (0.0793) -0.254*** (0.0835) 0.359*** (0.0489) 4,904 0.103
Effect is larger when fewer banks are active in destination country Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Identification
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Identification issues and robustness
Placebo regressions Evidence against specialization of banks in certain industries Excluding regions, small countries, nearest neighbors Evidence against sorting
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Robustness I: Placebo regressions and other checks dep. var. ∆Xit shockit shockit−4 pop. growthit non-U.S. import growthit GDP growthit USD xrate growthit Observations R-squared
(1) lagged shock
-0.00155 (0.0374) -2.413*** (0.666) 0.355*** (0.0533) -0.0781 (0.0843) -0.218** (0.0899) 4,440 0.112
(2) EU15 export growth 0.00189 (0.0290)
(3) cntry time trend 0.0858** (0.0372)
-0.502 (0.575)
-2.325*** (0.610) 0.359*** (0.0424) -0.0963 (0.0864) -0.255** (0.103) 4,904 0.114
-0.101** (0.0402) -0.356*** (0.0486) 4,916 0.168
Results cannot be due to time trend in shocks and exports Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Robustness II: Specialization
Specialization of banks into firms or industries Difficult to see how specialization could generate effect that depends on country size Bank shocks correlated with balance sheet variables, in particular real-estate chargeoffs Small number of banks (5!), large number of firms Largest firms rely less on letters of credit (intra-firm trade and reputation effects) Banks have an incentive to diversify No evidence that banks specialize from industry share regressions
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Robustness II: Testing for specialization First, we test for a correlation between banks’ market shares and industry export shares φbit = σ k industry sharekit + δi + ψit This regression is estimated for each bank b and each industry k Second, we obtain industry shocks αkt by running the following regression: ∆Xkit = αkt + βit + kit where ∆Xkit reflects the growth in U.S. exports to destination i in industry k at time t. As with the bank-level shocks, we regress the estimated industry shocks αkt on time fixed effects and work with the residuals α ˆ kt . Then we regress the average bank shocks α ¯ bt , bank by bank, on the different industry shocks: α ¯ tb = θk α ˆ tk + ξt Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Robustness II: Bank-by-bank regressions (1) (2) Bank A Shares Shocks σk θk
Industries
Chemicals & Allied Industries Food Stuff Footwear & Headgear Machinery & Electrical Metals Mineral Products Miscellaneous Other Plastics & Rubbers Raw Hides, Skins, Leather, & Furs Stone & Glass Textiles Transportation Wood & Wood Products
-0.0551 (0.201) 1.167 (0.836) 0.651 (2.643) 0.398*** (0.109) -0.827*** (0.253) -0.261** (0.113) 0.804*** (0.270) 0.180 (0.125) -0.288 (0.619) 1.155 (1.458) -0.540*** (0.176) 0.0387 (0.255) -0.206** (0.0870) 1.844** (0.805)
-0.531 (0.358) -0.361 (0.300) 0.0928 (0.280) -0.302 (0.329) 0.0392 (0.267) 0.143 (0.198) -0.0955 (0.410) -0.302 (0.193) 0.0105 (0.192) 0.0731 (0.178) 0.366 (0.220) 0.0944 (0.455) 0.218 (0.247) -0.341 (0.400)
(3) (4) Bank B Shares Shocks σk θk -0.164 (0.127) 0.0419 (0.405) 1.943 (9.510) -0.0959 (0.115) -0.222 (0.355) 0.238** (0.104) 0.285** (0.137) 0.0269 (0.185) -1.092** (0.426) 2.127 (1.529) -0.0383 (0.162) -0.525* (0.306) -0.0478 (0.0621) -0.358 (1.163)
-0.0998 (0.237) 0.508** (0.217) 0.108 (0.290) 0.316 (0.685) -0.0827 (0.287) -0.220 (0.154) 0.466 (0.431) 0.203 (0.294) -0.475 (0.312) 0.0223 (0.356) 0.0329 (0.289) -0.120 (0.394) 0.114 (0.577) 0.0522 (0.315)
(5) (6) Bank C Shares Shocks σk θk -0.0784 (0.0998) -0.339 (0.250) 0.815 (1.129) 0.112 (0.0817) -0.140 (0.195) 0.127 (0.0947) 0.206 (0.223) -0.0662 (0.0671) -0.134 (0.276) 0.763 (2.490) 0.213 (0.274) -0.571* (0.317) -0.0663 (0.0547) -0.347 (0.745)
0.129 (0.271) -0.117 (0.207) -0.332 (0.272) -0.322 (0.338) -0.0124 (0.232) 0.232 (0.139) -0.272 (0.289) -0.215 (0.259) 0.248 (0.198) 0.212 (0.293) -0.0663 (0.175) 0.171 (0.325) 0.00502 (0.219) -0.680** (0.307)
(7) (8) Bank D Shares Shocks σk θk -0.306*** (0.110) 0.108 (0.392) -4.837 (3.150) -0.173 (0.105) 0.204 (0.306) 0.298*** (0.105) -0.344 (0.278) 0.0941 (0.104) -0.502 (0.463) -0.488 (1.969) 0.0998 (0.168) -0.353** (0.174) 0.0230 (0.0661) -0.370 (0.805)
0.268 (0.552) -0.338 (0.382) -0.647 (0.533) 0.558 (0.412) 0.412 (0.438) 0.00886 (0.235) 0.268 (0.505) -0.561* (0.288) 0.0750 (0.351) 0.274 (0.513) -0.118 (0.384) 0.449 (0.519) -0.101 (0.364) -0.204 (0.718)
(9) (10) Bank E Shares Shocks σk θk 0.429** (0.187) -0.206 (0.579) -8.426 (5.785) -0.464*** (0.133) 0.334 (0.386) 0.139 (0.151) -0.476* (0.258) 0.143 (0.0880) -0.0113 (0.495) -1.403 (1.868) 0.384 (0.245) 0.249 (0.224) 0.0635 (0.105) 0.341 (1.214)
-1.123 (0.889) -0.0328 (0.361) 0.291 (0.917) 0.682 (0.550) -0.0514 (0.702) 0.383 (0.370) -0.150 (0.293) 0.104 (0.635) 0.00409 (0.369) 0.633 (0.617) -0.424 (0.322) -1.057 (0.930) 0.907 (0.764) -1.307 (1.239)
No indication that banks specialize in a few industries Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Robustness III: Alternative estimation of αbt
dep. var. ∆Xit shockit pop. growthit GDP growthit USD xrate gr.it non-U.S. imp. gr.it Observations R-squared
(1) all 0.0529** (0.0263) -1.487** (0.596) -0.0357 (0.0730) -0.197*** (0.0698) 0.370*** (0.0524) 4,902 0.068
(2) crisis 0.116** (0.0578) -3.808 (2.536) -0.0709 (0.274) -0.542 (0.330) 0.503** (0.199) 701 0.208
(3) no crisis 0.0330 (0.0288) -1.849** (0.791) -0.107 (0.0827) -0.209** (0.0836) 0.332*** (0.0532) 4,201 0.099
(4) large -0.00474 (0.0215) 0.404 (0.720) 0.0195 (0.0725) -0.0639 (0.0604) 0.394*** (0.0476) 2,782 0.202
(5) large & crisis 0.0141 (0.0464) -1.182 (1.585) 0.00253 (0.153) -0.0397 (0.198) 0.475*** (0.0789) 382 0.417
(6) small 0.0806* (0.0461) -2.880*** (0.931) -0.195 (0.123) -0.477*** (0.169) 0.327*** (0.0693) 2,514 0.077
(7) small & crisis 0.183 (0.122) -2.931 (3.790) -0.146 (0.415) -0.953* (0.495) 0.381 (0.268) 355 0.155
Columns (1) to (3): exclusion of region in which country i is located Columns (4) to (7): exclusion of all small countries
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Robustness III: Alternative estimation of αbt (cont.) dep. var. ∆Xit shockit pop. growthit GDP growthit USD xrate growthit non-U.S. import growthit Observations R-squared
(1) all 0.0677** (0.0307) -2.285*** (0.683) -0.0852 (0.0717) -0.250*** (0.0731) 0.362*** (0.0532) 4,903 0.102
(2) crisis 0.126** (0.0544) -3.724 (2.653) -0.0577 (0.253) -0.534* (0.279) 0.506*** (0.171) 701 0.209
(3) no crisis 0.0515 (0.0341) -1.909** (0.775) -0.115 (0.0839) -0.210** (0.0849) 0.332*** (0.0534) 4,202 0.099
Exclusion of 30 countries that are closest in terms of industry structure of U.S. imports to country i Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Robustness IV: Sorting into markets
Sorting of banks into markets: banks with above median shocks in period t could increase their market shares in t − 2 in destinations with deviations from trend in period t Negative serial correlation in bank shocks Lagging banks’ market shares by an alternative number of quarters does not change results ⇒ Sorting can be excluded
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Robustness IV: Serial correlation in bank shocks dep. var α ¯ bt α ¯ bt−1
(1) -0.0883*** (0.0337)
(2) -0.0909** (0.0363) 0.0184 (0.0292)
(3) -0.0889** (0.0420) -0.00572 (0.0294) -0.0143 (0.0276) -0.0214 (0.0298)
1,894 0.012
1,758 0.015
1,545 0.017
α ¯ bt−2 α ¯ bt−3 α ¯ bt−4
Observations R-squared
Quarterly bank shocks negatively serially correlated
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Robustness IV: Alternative specification of market shares
dep. var. ∆Xit shockit pop. growthit non-U.S. import grit GDP growthit USD xrate growthit Observations R-squared
(1) 1q lag 0.0785** (0.0317) -2.288*** (0.694) 0.360*** (0.0532) -0.0862 (0.0785) -0.254*** (0.0811) 4,904 0.102
(2) 3q lag 0.0655** (0.0321) -2.295*** (0.686) 0.360*** (0.0488) -0.0845 (0.0724) -0.251*** (0.0715) 4,904 0.101
(3) 4q lag 0.0709* (0.0406) -2.282*** (0.639) 0.360*** (0.0479) -0.0862 (0.0762) -0.251*** (0.0725) 4,904 0.101
(4) 4q rolling av. 0.0708* (0.0363) -2.267*** (0.720) 0.361*** (0.0553) -0.0868 (0.0715) -0.250*** (0.0802) 4,904 0.101
(5) last year’s av 0.0739** (0.0335) -2.289*** (0.728) 0.360*** (0.0534) -0.0861 (0.0805) -0.254*** (0.0755) 4,904 0.102
Results robust to exact specification of banks’ market shares
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Quantifications Assume that one large trade finance supplier has a shock in 2012 q2 corresponding to the tenth percentile of the shock distribution (-0.43), then aggregate U.S. export growth in that quarter would be 1.4 percentage points lower Effect on exports depends on where banks operate
Region East Asia and Pacific Europe and Central Asia South Asia Sub-Saharan Africa
Shock to Bank A all times (1) -0.469% -0.536% -0.411% -2.86%
Shock to Bank B all times (2) -1.257% -1.382% -1.861 % -0.375 %
Moderate shock to all banks crisis times (3) -3.64% -3.89% -3.74% -3.97%
Comparison to exchange rate shock: To get the same effect as an LC supply shock of one standard deviation, the exchange rate would have to depreciate by 6 percent
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
How to reconcile results in the literature? This paper: letter-of-credit channel, heterogeneous effects across export destinations (and over time) Amiti and Weinstein (2011) find that exports drop more than domestic sales: bank connections that capture working capital and risk channel Paravisini et al. (2010) find that effects are homogeneous across export destinations: information on loans, working capital channel Del Prete and Federico do not find a role for trade-specific financial instruments: no variation across export destinations Ahn (2013) finds reduction in LC-based trade ⇒ aggregate trade effect smaller because LCs only cover a part of trade and other payment forms can substitute (cash-in-advance, open account with or without trade-credit insurance)
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Conclusions
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Conclusions
Changes in the supply of bank guarantees have economically significant effects on exports International trade is more affected than domestic sales by bank shocks: on top of credit channel also letter of credit channel Suggests non-negligible role for finance in the Great Trade Collapse, especially in small and risky countries: I
I
Supply shock correlated with balance sheets items of banks, which deteriorated during the crisis Supply shocks have larger effects during crisis times
Banks affect export patterns and single banks are big enough to have sizable effect on aggregate exports
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
Thank you for your attention and comments!
Niepmann (New York Fed)
No Guarantees, No Trade
The views expressed in this p
19 97 -.5 0 19 10 98 1 0 19 10 99 1 0 20 10 00 1 0 20 10 01 1 0 20 10 02 1 0 20 10 03 1 0 20 10 04 1 0 20 10 05 1 0 20 10 06 1 0 20 10 07 1 0 20 10 08 1 0 20 10 09 1 0 20 10 10 1 0 20 10 11 1 0 20 10 12 1 01 01
0
.5
1
1.5
Bank supply shocks over time
median std Date
Niepmann (New York Fed) No Guarantees, No Trade
mean
The views expressed in this p
How a letter of credit works
Go back
Contract
Execution
1. Contract
5. Shipment
2. Apply for letter of credit.
4. Authenticate letter of credit.
6. Submit documents. 9. Payment
7. Send documents.
8. Payment
3. Send letter of credit .
Niepmann (New York Fed)
10. Payment 11. Release documents.
No Guarantees, No Trade
The views expressed in this p
Quantifications
Formula to calculate impact of the shock: PN ∆Xt =
Niepmann (New York Fed)
c=1 (γ(−0.535)φcbt−2 Xct−1 )
Xt−1
No Guarantees, No Trade
(4)
The views expressed in this p
