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The Private Credit


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The Private Credit Insurance Effect on Trade

Koen van der Veer *

* Views expressed are those of the author and do not necessarily reflec

of De Nederlandsche Bank.


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The Private Credit Insurance Eect on T

Koen J.M. van der Veery

October, 2010

Abstract

International trade relies on trade nance (credit or insurance) by tutions. Data limitations, however, have made it dicult to quantify

changes in the supply of trade nance on trade. This paper is the rsa causal link between the supply of private credit insurance and exportendogeneity issues by using a unique bilateral data set which covers the a1992 to 2006 of one of the worlds leading private credit insurers. Thisables me to use the insurers claim ratio a primary determinant of tcredit insurance as an instrument for insured exports. Subsequently,method of instrumental variables and a variety of trade models, I consipositive and statistically signicant eect of private credit insurance onestimates are economically relevant and suggest that, depending on the

supply of private credit insurance during the 2008-09 international tradereduction in private insurance exposure explains about 5 to 9 percent oworld exports and 10 to 20 percent of the drop in European exports.

JEL codes: F10, F14, G01, G20, G22.

Keywords: trade nance, private credit insurance, international trade, trade c


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1 Motivation

Financial institutions play an important role in facilitating international tra

90 percent of world trade relies on some form of credit, insurance or guara

or other nancial institution (Auboin, 2007). However, direct evidence on

nance and trade is still missing, because detailed data on trade nance is h

result, it is unclear to what extent changes in the supply of trade nance hav

A number of authors have studied the trade nance channel, but use inade

credit provided by banks or nancial institutions. These studies examine wh

trade credit or dollar-denominated short-term credit aect exports (Ronci, 200

2009; Iacovone and Zavacka, 2009; Levchenko, Lewis, and Tesar, 2010). Shor

can be used for reasons other than trade nancing and does not cover all trade

standard proxies for trade credit usage by rms accounts receivable and pa

extended between rms instead of a nancial institution and a rm, and incl

purchases. More fundamentally, the link between trade credit provided by a

trade credit usage by rms is ambiguous, since institutional nance and trad

tutes (Petersen and Rajan, 1997). Amiti and Weinstein (2009) overcome t

endogeneity issues by relating rms export performance to the health of the

trade nance. Their results show convincingly that nancial shocks are tran

exporters, but provide only indirect evidence on the trade nance channel.

This study exploits a unique bilateral data set on the worldwide activit

credit insurer to examine the eect of private credit insurance on exports.

on private export credit insurance and to establish a causal link between t

trade nance product and exports. Importantly, the data enable me to dea

and other potential endogeneity issues by using the private insurers claim

over premium income as an instrument for insured exports. Past and cur


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enforce payment, the provision of credit insurance could foster trade. The

of credit insurance is described in a formal model by Funatsu (1986), who

cover of trade credits will result in a higher output level as compared to the c

Empirically, the evidence of a trade-promoting eect of credit insurance i

public guarantees. Two important contributions are Egger and Url (2006) and

Wedow (2008) who nd that Austrian and German public export credit guar

in the long run.

For a number of reasons, however, the private credit insurance eect on tr

dier from the impact of public guarantees. First, changes in the exposure of p

are likely to aect exports immediately, whereas the short run impact of pub

to be very small (see Egger and Url, 2006; and Moser, Nestmann and Wedow

follows from the varying maturities of private versus public credit insurance.

usually cover short-term credits with a tenor of 60 to 120 days and medium- o

play a minor role (Swiss Re, 2006). Public guarantees, on the other hand,

projects with a duration between two and ve years. So the actual shipme

follows a few years after the public provision of insurance cover. This di

especially clear in Europe, where ocial export credit agencies have been re

guarantees covering export risks to OECD core members with a maturity of

Second, relative changes in the supply of private credit insurance are

impact on total exports than changes in the supply of public guarantees. O

countries where the value of privately insured exports exceeds the value of

example, private insurers covered an estimated 16.7 percent of Dutch export0.9 percent of exports insured by the Dutch State.3 Aside from a bigger impa

private credit insurance market, the greater eect on overall exports also st

inuence of private credit insurers on the export decision of non-insured


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insured exports from 25 countries to 183 destination countries covering th

2006. The data cover the insurance provided and claims and premiums rece

Three" private credit insurers.4 Importantly, the data enable me to identify

link between the claims received by the insurer and the supply of insuranc

endogeneity issues. Also, I show results using more than one strategy to

resistance" to trade; the average barrier of two countries to trade with all th

Finally, I shed some light on the role of private credit insurance during the

2008-09. Anecdotal evidence suggests that private credit insurers reduced their

in reaction to the increased risk environment. I extrapolate the estimates o

elasticity of exports and calculate the contribution of the decline in the su

insurance to the world trade collapse. Conditional on the actual decline in

credit insurance, the estimates suggest that the reduction in private insuranc

third quarter of 2008 and 2009 explains about 5 to 9 percent of the drop in w

20 percent of the drop in European exports.

In what follows, I describe the rise of private credit insurance since the e

briey review the literature (Section 3), and examine empirically the private

on trade (Section 4). In Section 5, I test the sensitivity of the benchmark resu

endogeneity issues, the availability of public export credit guarantees, and p

related to measuring "multilateral resistance" to trade. Section 6 examines th

insurance in the 2008-09 world trade collapse. Section 7 concludes.

2 The Rise of Private Credit Insurance

Since the early 1990s, private trade credit insurance has registered strong grow

the short term market.5 In 1999, more than 95 percent of the short term bus

underwritten by the private sector (Swiss Re, 2006). Private credit insuran


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Euler Hermes (36%), Atradius (31%) and Coface (20%). These private insu

term commercial and political risk. Commercial risk refers primarily to the

the importer due to default or insolvency, whereas political risk relates to n

of action by an importers government.6 More recently, the "Big Three" hav

longer maturities, but ocial export credit agencies are still the primary play

The rise of private credit insurance followed a number of actions by OECD

debt crisis in the 1980s. The international debt crisis of the 1980s and 1990s

on how countries viewed their export credit agencies (Stephens, 1999). The cr

claims for ocial export credit agencies that became a drain on government b

credit agencies experienced a net cash ow decit during the period from 19

2005). These losses led governments to rethink their role in the provision of e

competition and overlap with private sector insurers.

At the national level, OECD governments started to privatize their sho

privatization trend began by the decision of the United Kingdom in 1991

business of its export credit agency (Stephens, 1999). The United States gove

1992, and Coface (one of the "Big Three" private insurers) of France was priv

of sales or transfers removing the export credit agencies short-term business t

the privatization has taken place more silently (Wang et al. 2005). For exam

Atradius (formerly NCM), acting as an agent of the government, has insured

of business on its own accounts.

At the international level, the European Union dened the concept of "mar

what type of business should be left to private insurers. As a result, since 199agencies have been restricted from providing guarantees covering export risks t

with a maturity of less than two years. Public guarantees, therefore, generall

credit period longer than two years.


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exports. In turn, they compute an average multiplier of 2.8, implying that eve

guarantees creates 2.8 euro worth of exports. Moser, Nestmann and Wedow (

analysis for German guarantees, but account for possible endogeneity issues

turn, they nd a somewhat lower multiplier of 1.7.

The theoretical explanation for this trade-promoting eect of export cred

to Funatsu (1986). He shows that a government can aggressively promot

public guarantee against default by the importer and demanding a "more-than

rate. By using a credit guarantee, a rm can reduce its prot uncertainty

thereby increasing the rms optimal output level. The reduction in risk incre

where the rm would not sell otherwise. Abraham and Dewit (2000) demon

guarantees can stimulate rms to export even without subsidisation by ch

Thus, the rationale for the trade-promoting eect of export credit insurance

to private insurers, who are unlikely to subsidize their clients.

These models, however, cannot explain the multiplier eect; the ndin

export value is greater than the value of insured exports. The rationale for

follows from the presence of sunk costs (Dixit, 1989). When rms face substa

export experience increases the probability of exporting by as much as 60 perc

and Tybout, 1997). By providing insurance cover, public and private cre

costs of insecurity and information related to the entry in foreign markets

to learn about the creditworthiness of their trade partners (buyers). Subse

transactions, the client may decide to export without costly export credit ins

A multiplier eect of private credit insurance could, however, also follow fforeign markets and rms that private insurers provide to non-insured rms.

insurers policy stance vis--vis a particular rm (buyer) or country could ha

inuencing the export decision of non-insured rms. Indeed, the news of a


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premia and their maximum level of exposure. As a result, a rm can have a

still have a low or zero credit limit when the rm is situated in a weakly rated

p. 518). Finally, the "Big Three" insurers all oer some kind of information se

get access to the insurers detailed rm-level information on key customers, p

even without buying insurance cover.8 In short, private credit insurance cou

a reduction in export risk and information costs.

4 The Private Credit Insurance Eect on Exports4.1 Specication and Data

To estimate the private credit insurance eect on exports, I rely on the stand

bilateral trade. The gravity model explains trade between a pair of countries

their economic "masses". I augment the basic specication with a number of

that might also aect bilateral trade, such as currency unions (Glick and

agreements (Rose, 2004). I employ the following specication:

ln(Xijt) = 0 + 1 ln(Dij) + 2 ln(P opit) + 3 ln(P opjt) + 4 ln(GDPpcit) +

+ 7(Langij) + 8(RT Aijt) + 9(Borderij) + 10(Islandsij) + 11 ln

+ 13(Colonyijt) + 14(EverColij) + 15(SameCtryijt) + 1 ln(InsE

where i denotes the exporting country, j denotes the importer, t denotes

natural logarithm operator, and the variables are dened as:

Xijt denotes real FOB exports from i to j, measured in euro,

D is the distance between i and j,

P op is population,

GDPpc is annual real GDP per capita,

CU is a binary dummy variable which is unity if i and j use the same c


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ComCol is a binary variable which is unity if i and j were both colonize

Colony is a binary variable which is unity if i colonizes j at time t (or

EverCol is a binary variable which is unity if i ever colonized j (or vic

SameCtry is a binary variable which is unity if i is part of the same co

versa),

InsExp denotes real privately insured exports from i to j, measured in

" represents the omitted other inuences on bilateral exports, assumed

The parameter of interest is 1. This represents the private credit insu

holding other export determinants constant through the gravity model. I esti

OLS, using a robust covariance estimator (clustered by country-pair dyads) t

ticity, adding year-specic xed eects. I also adjust this specication in two

I add a comprehensive set of dyadic-specic xed eects (i.e., a mutually ex

haustive set of {ij} intercepts) to absorb any time invariant characteristic

pair of countries. Second, I add comprehensive sets of exporter and importe

of {i} and {j}) to take account of any time invariant country-specic fact

that the key results are insensitive to the use of other estimation strategies.

The sources of the bilateral data set are described in more detail in Append

set includes annual observations between 1992 and 2006 (though with many m

some 183 territories and localities (I refer to these as "countries" below). Th

are tabulated in Table A2. A correlation matrix for the variables used in th

presented in Table A3.

4.1.1 Data on Private Credit Insurance

The data on privately insured exports is the novel part of the data set and me

exports insured (InsExpijt) by one of the "Big Three" private credit insurer


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varies considerably (Table 1, Column 2). This reects i) the entrance of the p

markets (countries) over the years and ii) dierences in the number of destin

exporter.

In addition, a special feature of the data is the variability in the private in

to total exports, varying from zero percent up to one hundred percent. 10 A

share of insured exports for all exporters is 6.6 (1.5), but this gure varies b

Poland to 20.4 in Denmark.

Finally, the insurance data suer from some measurement issues. Possib

arise because i) clients of the insurer declare their turnover (value of insure

frequencies; monthly, quarterly or yearly, ii) the amounts are allocated to p

invoiced by the insurer which does not always coincide with the period wh

place, and iii) data is migrated from systems used by acquired companies. Pa

errors is reduced by the yearly frequency of the data. More importantly,

instrumental variables which was pioneered to overcome measurement error p

variables (Angrist and Krueger, 2001; Hausman, 2001).11

4.2 Benchmark Results

The results of estimating the default specication are presented in Table 2. T

with three dierent sets of xed eects (none, dyad, and exporter/importer

private credit insurance eect on trade, I briey discuss the other determinan

The model ts the data well. I obtain a high R-squared which is typical for

coecient estimates are sensible. For instance, exports between a pair of cou

and increase when countries share a currency, language, trade agreement o

addition, countries with a higher GDP per capita import more. The sign o

importers population and exporters real GDP per capita changes, howeve

eects Thus larger and richer countries trade more (cross sectional variatio


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.10 per cent.

Two issues regarding these rst regression results come up immediately

specication does not control for other insurers, either public or private. B

shows what happens to a countrys exports when the value of exports insured b

increases, while the trading countries GDP per capita, population size, tra

trade costs related to various institutional settings, do not change. I show be

robust to the inclusion of public credit insurance in a sample with Dutch exp

control for the activities of other private insurers.

Consequently, one could argue that an increase of coverage could simply r

insurers share of the credit insurance market, making it unclear why this wou

however, unlikely that substitution of credit insurance towards the private in

For one thing, the credit insurance penetration rate (measured as premium

risen steadily since 1990 in most of the large European markets, and credit ins

Europe have grown even faster (Swiss Re, 2006). In addition, I show below

for various reasonable changes in the sample. These robustness checks mak

that market share increases of the private insurer explain the ndings. Ano

could be that I overestimate the credit insurance eect on trade because I

the private insurer is only a small player. In the sensitivity analysis below, I

various subsamples related to the share of insured to total exports covered

Excluding the markets in which the private insurer is likely to have a small sh

estimate of the private credit insurance eect on trade. On the contrary, the p

eect increases with the share of insured to total exports.

A second, and related, issue is that the benchmark specication may su

problem. Instead of some exogenous factor leading the insurer to extend mo

marketing of products, improvements in risk management practices reducing


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5 Sensitivity Analysis

5.1 Robustness of the Private Credit Insurance Eect

I start the sensitivity analysis with a battery of robustness checks based on

the sample. The purpose of this exercise is to show that the main results a

small subset of the sample. The results are presented in Table 3. Each o

corresponds to a dierent sensitivity check, while the columns correspond to

estimated with three dierent sets of xed eects, and also report the numbersubsample.

I check the sensitivity of the results by selectively dropping dierent sets

am interested in exporter eects, I begin by dropping dierent sets of impor

I drop all observations for importers that are industrial. I then successiv

for developing countries from Latin America or the Caribbean, the Middle E

(formerly) centrally managed economies.12 These robustness checks leave the b

The same goes when dropping small importers (dened as a country with

people) or poor importers (those with real GDP per capita of less than 10

then check the sensitivity of the results for some sets of exporter observatio

non-European exporters and exporters not in the sample before 1995. Again

to the sample undermine the ndings. Further, I check the sensitivity of t

to time. I separately drop the observations before and after 1999 respectiv

resilient. Finally, I successively delete observations in which the share of i

private insurer) to total exports is smaller than 1, 2, 5 and 10 percent. Aga

a positive and statistically signicant eect of insured exports on total ex

estimates, however, increases with the share of insured exports.

I conclude that the nding of a positive and statistically signicant eect

ance on trade is not due to some subset of the sample and is robust to reas


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reverse causality, a third possibly omitted variable could arguably inuence

insured exports. The risk environment is a case in point. An increase in

exporters might decrease total exports and increase demand for insurance.

causality argument and the omitted variable bias story would result in opp

direction of the bias in the benchmark results, if any, is unclear beforehand.1

I address the issue of endogeneity by using an instrumental variable for ins

the two-stage least squares xed eects estimator. The instrument is the

ratio (by exporter-importer-year), dened as claims over premium income.

determinant of the supply of credit insurance.

The link between claims and insured exports runs through two channels.

claim ratios are important ingredients in the formula to calculate premia (Be

a shock, i.e. a credit crisis or sovereign default, claims increase. The claim rat

private insurer can only raise the premia of new contracts. 15 The bulk of the

one year during which the premium charge cannot change, and about 25 perce

a duration of 2 or 3 years. A rise in the claim ratio reduces the prot of the pr

an increase of the premium charged in new insurance contracts, thereby lo

insurance and hence the total value of insured exports.

The second channel linking claims and insured exports is more direct, and i

right to reduce or remove the credit limit of a specic buyer at any given time

2010).16 While premium rates on contracts are xed, credit insurers can man

"cover limit") to mitigate claims. This way, credit insurers can react to pro

foreign buyers credit quality even before they worsen. Thus, the mere expe

can immediately aect insured exports via a reduction in the maximum expo

The results for the First Stage regression on insured exports are presented

The F-statistic for the excluded instruments exceeds the rule of thumb value of


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the claim ratio has no direct eect on total exports.17 Thus, the eect of t

exports runs only via the insured exports.

The results for the Second Stage regression on exports are presented in c

4. I estimate four specications to check the sensitivity of the estimates to a

The rst uses the contemporaneous, rst and second lag of the claim ratio as i

exports. The second up to fourth estimations use either the contemporaneou

the claim ratio as instrument. All instruments are valid according to variou

of the models is under- or weakly identied and the rst specication with th

overidentied.18 The point estimate for the instrumented insured exports r

.09, a slightly smaller range compared to the benchmark results.

Since I use the log of the claim ratio I lose all observations with zero clai

the sample. To see whether the results are sensitive to the sample size, I est

the claim ratio in levels. The results are presented in the nal column of Tab

point estimate of .06 for the instrumented insured exports is equal to the e

sample in column 2, but the coecient is not signicant. Notice, however,

the excluded instruments is only 6.89, well below the threshold value of 10

claim ratio in levels is less t as an instrument for the log insured exports.

Next, I examine whether the instrumental variable estimates are sensitive

related to the share of insured to total exports. The results are presented in

system using the contemporaneous log claim ratio as instrument for the log

way, I maximize the number of observations and the F-statistic for the exclu

being conservative on the size of the estimated private credit insurance e

2 to 5 of Table 4). Again, I nd that the size of the estimate for insured

successively dropping observations with a share of insured to total exports be

The size of the eect ranges between .02 for the full sample (Table 5, Colu


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variable bias is still a concern in the instrumental variable estimates by i

importer country risk (see also Moser, Nestmann and Wedow, 2008). This c

is a combined measure of a countrys political, economic and nancial risk. T

reecting corruption, bureaucracy quality, and law and order among other

percent of the composite rating, while the nancial and economic risk rati

percent (see the International Country Risk Guide for details). The origin

0 (very high risk) to 100 (very low risk). I inverted the index to make th

coecient more intuitive. Hence, a higher risk indicator implies higher risk a

correlation with exports. The results are presented in Table 6. As expecte

countries with a higher risk environment. Importantly, the results are rob

importer country risk. The size of the private credit insurance eect ranges

somewhat larger range compared to previous results. Controlling for countr

a slight negative bias in the estimates of the private credit insurance eec

observations with a relatively high share of insured exports.

Further, all the results presented are based on static specications of the

models allow only for contemporaneous eects of regressors on trade. Pas

however, aect current trade ows in the presence of sunk costs (Dixit, 1989

1997). Therefore, some authors propose to extend the standard gravity m

(Eichengreen and Irwin, 1998; Bun and Klaassen, 2002). I examine whether

a dynamic specication of the instrumental variables model by including on

variable.19 The results presented in Table 7 conrm that past exports aec

the main result is robust to this inclusion of trade dynamics. Insured exports

The range of the private credit insurance eect is again somewhat larger, ran

So far, I examined whether private credit insurance stimulates trade b

insured exports to the value of total exports. A possible concern of this


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for all subsamples, although only at the 10 percent level for the subsamples

exports above 5 or 10 percent. Again, the range of the private credit insura

larger compared to the results of the preferred approach, ranging from .02 to

Thus far, I have been largely concerned with the statistical signicance

results, but I have given no attention to the economic signicance. The instru

estimate a private credit insurance eect on exports ranging between .01 a

These coecients can be interpreted as the elasticity of exports to insured e

1 per cent increase in insured exports leads to an increase in exports in the

cent, depending on the threshold taken for the minimum share of insured exp

share of private short term insured exports to total exports of 6.1 percent in

I calculate the median share of insured exports for each of the subsamples

observations with a share of insured exports above 1 percent to resemble th

term insured exports. The average estimate of the elasticity for this subsamp

7). Likewise, for the Euro area countries, I calculate a share of private short t

total exports of 12.3 percent, and subsequently nd an elasticity of .29. 21 Us

of insured and total exports, I compute an average multiplier of private cred

This result is important for a number of reasons. First, it shows that p

stimulates exports. Indeed, the short run impact of private credit insurance

run multiplier of public guarantees found in Moser, Nestmann and Wedow (

insurance allows rms to learn about the creditworthiness of their trading part

business. The recurring trade transactions help a trading partner to build up

reducing the need for the exporter to use costly insurance. Also, the impressi

suggests that private credit insurers provide information on foreign market

the export decision of non-insured rms. Finally, it demonstrates that cred

exports even without subsidisation, assuming private insurers charge a fair p


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65672 observations of which 5082 correspond to zero exports and 49515 to ze

I examine the sensitivity of the results when correcting for sample selecti

exports and insured exports. I follow Wooldridge (1995) by applying a samp

is suitable for panel data with xed eects.25 Accordingly, for each year, I e

where the dependent variable equals one if exports are positive. I derive t

this model for each year and calculate the inverse Mills ratio (IMR). Finally,

regressor in the instrumental variable model estimated with dyadic xed eect

for sample selection due to zero insured exports and estimate a probit mode

variable equals one if insured exports are positive. Notice that the second mo

selection with respect to zero exports and zero insured exports simultaneou

zero exports imply zero insured exports.

The results are presented in Tables 9 and 10. The estimates for the inve

that there is signicant selection into the sample for some subsamples. Howev

on the point estimates of the parameters of interest. The size of the private

ranges between .02 and .35, similar to the results in Table 5.

5.4 Changes on the Extensive Margin

I have examined the eect of private credit insurance on exports conditiona

positive. These results can be interpreted as an increase in exports on the in

section, I attempt to examine if private credit insurance also aects the exten

that is, does the availability of private credit insurance increase the likeliho

pair of countries.

I follow the approach taken by Head, Mayer and Ries (2010) and estima

model (LPM) where the dependent variable equals one if exports are positiv

model, the LPM allows for estimation with dyadic xed eects.26 To evaluate t

insurance on the extensive margin I cannot use the value of insured export


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of importer country risk. Hereto, I group countries according to the ve categ

as identied by the International Country Risk Guide.

The results are reported in Table 11. I only nd evidence of a positive

insurance on the extensive margin for the very high risk group of destination

results seem to suggest that private credit insurance stimulates exports prim

margin. Nevertheless, while this might be true at the country-level, it is not u

of private credit insurance on the extensive margin is much more prominent

5.5 Public Credit Insurance

Next, I briey examine whether the positive and signicant eect of private e

holds up when accounting for the public alternative. A priori, there is not

the results to change, since private and public credit insurance are due to

generally complements instead of substitutes.

I examine the public and private insurance eect simultaneously by addi

insurance premium income to the benchmark model.28 Since I only have data

the Netherlands, the sample reduces to Dutch exports in the period 1992-200

in Table 12. I do not nd public insurance to stimulate exports, at least

More importantly, the private insurance eect remains positive and statisti

coecient of .17 (no xed eects) or .05 (dyadic xed eects), larger even than

5.6 Methodological Issues

In this section, I test the sensitivity of the results to two dierent specication

Both specications deal with the possibility of misspecication in the bench

"monadic" problems. These refer to omitted factors that are specic to a s

vary over time, such a those associated with "multilateral resistance" to tra

Van Wincoop (2003)].


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gravity equation that can be estimated using OLS. In practice, this invol

countrys multilateral resistance to trade with other countries based on the si

average of the indicators of trade barriers with all countries (such as distan

and Bergstrand (2009) provide more details.

I estimate the benchmark model twice; rst including the transformed tra

simple averages, and then using GDP weights. The results are presented in T

4. I run both models for the full sample and the subsample of observations

exports above 10 percent. All estimations conrm the positive eect of insured

point estimates are statistically signicant, but with a range of .13 to .91 and

simple and GDP-weighted average), much larger than any of the previous est

5.6.2 Tetradic Estimates

Another way to deal with the presence of multilateral resistance is the "method

by Head, Mayer and Ries (2010) (see also Rose and Spiegel, 2010). Under t

estimates can be attained in the presence of multilateral resistance by compar

to exports for a pair of base countries for the same year (the technique is tetra

trade ows for four countries). See Head, Mayer and Ries (2010) for more de

The method presents two special issues. First, one needs to select a base

to do the tetradic calculations. To check the sensitivity of the results I us

countries: a) United Kingdom and The Netherlands; and b) France and G

observations are likely to be dependent as the error terms in the tetrads ap

observations. I therefore use multi-way clustering to correct the standard

Head, Mayer and Ries (2010).

The results are presented in Table 13, Columns 5 to 8. Again, I obtain a p

signicant eect of private credit insurance on exports, regardless of the base

taken The estimates range from 15 to 36 and 12 to 18 for the two respectiv


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the "smoking gun" (Levchenko, Lewis and Tesar, 2010). In this section, I

issue by examining the role of private credit insurance.

Some observers suggested that a shrinking supply of trade nance contribu

(see Auboin, 2009; Dorsey, 2009; and OECD, 2009). But the lack of detaile

lending, insurance or guarantees issued by nancial intermediaries precludes

on the role of trade nance. Schmidt-Eisenlohr (2009) develops a theory of trad

the co-existence of dierent trade nance products depending on enforcemen

Numerical experiments of his model show that limiting the choice between t

can reduce trade by up to 60 percent. Also, a few inventive studies do esta

shock to the nancial sector and exports. For example, Amiti and Weinstein (

the Japanese nancial crises in the 1990s, a rms export performance was r

the rms main bank. Their results suggest that trade nance accounted for

decline in Japanese exports. Chor and Manova (2010) nd some evidence tha

interbank rates exported less to the United States during the recent crisis. N

however, uses data on the actual supply of a trade nance product (credit, in

by a nancial institution. Thus, they do not identify a direct link between tra

The results in this paper are evidence of a direct link between the supply of

and exports, but the data do not cover the 2008-09 global nancial crisis. In

the role of private credit insurance in the world trade collapse, I need to kno

private credit insurers reduced their supply of insurance during the crisis.

evidence shows that private credit insurers reacted to the deteriorating econo

end of 2008 by reducing their exposure. For example, the 2008 annual repo

the "Big Three" private insurers shows that claims were rising fast in the s

suggest that measures were taken to reduce exposure substantially:

"The net claims ratio for the second half of 2008 was 134 2% compar


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"The initial action was a review of our risk portfolio, protecting ou

the risk of default of buyers that carried unacceptable risks. This resulte

or cancellation of those credit limits that showed imbalanced risks. The

executed in a timeframe of two months and resulted in a substantial

exposure." 30

In order to get a sense of the size of this "substantial" reduction of priv

during the crisis, I use Berne Union data. As Figure 1 illustrates, world pricredit insurance exposure declined by roughly 23 percent between the four

third quarter of 2009. Public insurance exposure declined by less than 7 p

credit agencies have generally increased their insurance supply during the cr

the impact of the trade nance crisis.31

The Berne Union gures give a rst idea of the possible change in the s

insurance, but there are two important caveats. First, the gures report

quarter-end instead of actual insured exports during a quarter. Insurance exp

a quarterly basis, while only the yearly value of short term insured exports is

Union. For the period considered, however, the change in insurance expos

approximation for the change in insured exports. Indeed, the Berne Union

that short term insurance exposure and new business insured were both d

2009. Second, demand factors are likely to have contributed to the report

exposure, thus leading to an overestimation of the reduction when interpr

gures strictly in terms of the supply of insurance. Since public export credit

to have reduced their supply, one could use the dierence between the decline

insurance exposure (-16 percent) as a rough indication of the decline in the s

insurance.

Either way the actual decline in the supply of private credit insurance du


23/47

exports in 2007 were covered by private short term export credit insurance

reduction in the supply of private insurance during the crisis can explain a d

of 1.4 to 2.9 percent, or 5 to 9 percent of the total drop in world exports

Rows 4 to 6). In the Euro area, where an estimated 12.3 percent of export

insurers, the reduction in the supply of private export credit insurance durin

a decline of exports of 2.9 to 5.8 percent, or 10 to 20 percent of the total dr

Euro area countries (Table 14, Column 4, Rows 7 to 9). Thus, while macroe

an important role in the world trade collapse, these calculations suggest th

credit insurance on exports identied in this paper can account for part of th

7 Conclusion

The main contribution of this paper is to estimate the private credit insuran

a unique data set on the insurance provided and claims received by one of th

insurers. The matched insurance-claims data enable me to identify the link b

supply of export credit insurance, thus overcoming endogeneity issues. I n

multiplier of private credit insurance of 2.3, implying that every euro of ins

2.3 euro of total exports. This multiplier is impressive, especially considerin

nd a long run multiplier of public guarantees of smaller size.

The paper is unique in its focus on the role of private export credit insur

establish a causal link between the supply of a trade nance product and ex

a number of arguments explaining why private export credit insurance is i

particular, credit insurance stimulates exports to markets where rms wo

allowing trade partners to build up reputation, thereby reducing the need for

insurance. Moreover, private insurers are likely to inuence the export decisi

via the "signalling eect" of their policy stance vis--vis individual rms, t


24/47

the reduction in private insurance exposure during the 2008-09 world trade

5 to 9 percent of the drop in world exports and 10 to 20 percent of the drop


25/47

Figure 1. World Exports of Goods and Short Term (ST) Export Credit Insurance E

Source: World exports in goods from the World-Trade Monitor, CPB Netherlands Bureau f

Insured export credit exposure from the Berne Union, through the BIS-IMF-OECD-WB Join

in private and public short term expos ure in the period from 2008Q4 to 2009Q3 is calculatedECAs increased from 25% to 30% (see ICC, 2010 p. 47) [25] at a constant rate. The figures a

quarter-ultimodollar/euro exchange rate from the ECB. I use u ltimos since the Berne Union

converting the Euro values into US dollars. Short term exposure is comprised of short term

amounts insured under all current policy limits for which premium has been paid or invoice

payment (arrears) until claims have been paid or rejected, and including uninsured percenta

World exports

65

70

75

80

85

90

95

100

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q

2007 2008 20

Index 2008Q3=100, nominal values in Euro


26/47

Table 1: Summary Statistics for Insured Exports and Share of Ins

Insured Exports (millions)

First year

in sample Obs: M ean S td:Dev: M in: M ax: All Exporters 17596 69 309 0 6220

By Exporter

United Kingdom 1992 2713 131 428 0 6220 The Netherlands 1994 1898 107 454 0 5760 France 1992 1471 20 67 0 553 Australia 1993 1387 16 61 0 793 Germany 1994 1216 195 623 0 6030

Belgium 1997 1041 70 261 0 2220 Denmark 1999 995 74 255 0 2220 United States 1997 897 41 156 0 1890 Sweden 1998 824 73 260 0 3650 Spain 1994 748 11 52 0 762 Italy 1998 728 20 70 0 922 Norway 1994 721 41 112 0 1150 Mexico 1993 706 23 142 0 1990

Ireland 1997 439 13 82 0 1410 Luxembourg 1997 423 15 41 0 405 Finland 1999 382 24 67 0 569 Switzerland 2003 278 54 203 0 2010 New Zealand 2004 241 8 28 0 270 Austria 2003 171 23 64 0 473 Czech Republic 2004 68 41 138 0 894 Poland 2005 64 1 3 0 24

Hungary 2005 59 3 6 0 24 Greece 2004 58 20 29 0 108 Slovak Republic 2004 49 9 27 0 135 Hong Kong 2006 19 9 17 0 70 aUnit of analysis: exporter-importer-year. Data on insured exports from one of the "Big

di i


27/47

Table 2: Eect of Private Credit Insurance on Exports in Gravi

Fixed Eects: None Dyadic Exporter, I

Log Insured Exports :10

(:01)

:02

(:00)

:08

(:01)

Log Distance :97(:03)

1:36(:05)

Log Exp Population :81(:02)

2:00(:58)

1:31(:68)

Log Imp Population :84(:02)

1:03(:23)

1:39(:24)

Log Exp Real GDP p/c 1:05(:09)

:96(:28)

:38(:31)

Log Imp Real GDP p/c 1:13

(:03)

:48

(:08)

:42

(:08)Currency Union :18

(:08):17(:04)

:21(:07)

Common Language :45(:07)

:39(:06)

RTA :03(:06)

:15(:04)

:13(:07)

Common Border :06(:10)

:38(:10)

No. Islands :27

(:06)

9:30

(3:09)Log Product Area :05

(:01)2:16(:72)

Common Colonizer 1:59(:17)

1:62(:46)

Currently Colony :48(:13)

:03(:03)

:24(:22)

Ever Colony :51(:10)

:74(:08)

Common Country 1:50(:11)

:78(:41)

R2 :85 :98 :93RMSE :97 :35 :68

Data set includes 14,389 bilateral annual observations covering 183 countries, 1992 - 20

errors (clustered by country-pairs) in parentheses. Year eects included but not re

***1%, **5%, *10%.


28/47

Table 3: Sensitivity Analysis of Private Credit Insurance Eec

Fixed Eects: None Dyadic Exporte

Drop Industrial Importers :09(:01)

:03(:00)

:0

Drop Latin America, Caribbean Importers :11(:01)

:02(:00)

:0

Drop Middle Eastern Importers :10(:01)

:02(:00)

:0

Drop Asian Importers :09(:01)

:02(:00)

:0

Drop African Importers :10(:01)

:02(:00)

:0

Drop (Formerly) Centrally Managed Importers :09(:01)

:02(:00)

:0

Drop Small Importers (Population


29/47

Table 4: Instrumental Variables, The "Insurance Supply Elasti

First StageDependent Variable

Log Insured Exports

SDependen

Log Claim R

t; t 1; t 2 t Log Claim Ratiot :11

(:02)

Log Claim Ratiot1 :06

(:01)

Log Claim Ratiot2 :04

(:01)

Log Insured Exports, Instrumented :06(:02)

:02(:01)

Log Distance

Log Exp Population 16:55(3:38)

:40(:89)

1:45(:58)

Log Imp Population 3:03(:80)

:70(:26)

:53(:30)

Log Exp Real GDP p/c 7:14(1:97)

2:59(:48)

1:71(:32)

Log Imp Real GDP p/c :95(:28)

:77(:14)

:73(:10)

Currency Union :12(:19)

:12(:04)

:14(:03)

Common LanguageRTA :16

(:12):13(:06)

:13(:04)

Common Border

No. Islands

Log Product Area

Common Colonizer


:06(:04)

:03(:04)

Ever ColonyCommon Country

F-statistic for excluded instruments 23:95 23:95 230:98 RMSE :64 :18 :21Observations 2974 2974 5210


30/47

Table 5: IV Estimates of the "Insurance Supply Elasticity of Exports" for Vari

Second StagInstrument: Log Claim

Share of Insured to Total Exports > 1 > 2 > 3 > 4 > 5 >Log Insured Exports, Instrumented :16

(:05):20(:07)

:24(:08)

:29(:10)

:25(:10)

:23(:0

Log Distance

Log Exp Population 1:47(:76)

2:20(:82)

1:58(:79)

:82(:81)

:82(:84)

:91(:87


:39(:27)

:56(:27)

:54(:26)

:51(:26)

:(:2

Log Exp Real GDP p/c 1:94(:50)

2:08(:54)

2:25(:58)

2:21(:63)

2:24(:69)

2

Log Imp Real GDP p/c :51(:12)

:52(:12)

:49(:13)

:51(:13)

:53(:14)

:61(:


:22(:04)

:23(:04)

:26(:04)

:30(:04)

:29(:0

Common Language

RTA :13(:04)

:13(:04)

:15(:04)

:16(:04)

:16(:04)

:19(:0

Common Border

No. Islands

Log Product AreaCommon Colonizer


:07(:03)

:10(:03)

:12(:04)

:10(:03)

:(

Ever Colony

Common Country

F-statistic for excluded instruments 63:56 49:40 33:65 20:76 17:47 23RMSE :20 :20 :20 :20 :19 :19

Observations 3924 3383 3112 2821 2485 21All models include dyadic and year xed eects. Robust standard errors (clustered by c

parentheses. Signicance: ***1%, **5%, *10%.

Table 6: Importer Country Risk and IV Estimates of the "Insurance Sup

Second StInstrument: Log Clai

Share of Insured to Total Exports All >1 >2 >3 >4 >Log Insured Exports, Instrumented :02

(:01):13(:05)

:16(:07)

:21(:08)

:25(:11)

:27(:1

Importer Country Risk :01(:00)

:01(:00)

:01(:00)

:01(:00)

:01(:00)

:(:0

F-statistic for excluded instruments 218:11 54:39 41:23 27:03 16:51 13RMSE 20 19 19 19 18 18


31/47

Table 7: Trade Dynamics and IV Estimates of the "Insurance Supply

Second StInstrument: Log Clai

Share of Insured to Total Exports All >1 >2 >3 >4 >


:14(:04)

:18(:05)

:23(:07)

:31(:10)

:35(:

Importer Country Risk :01(:00)

:01(:00)

:01(:00)

:01(:00)

:01(:00)

:(:0

Log Exportst1 :59

(:02) :50

(:04) :47

(:04) :45

(:05) :39

(:06) :38(:0F-statistic for excluded instruments 215:19 60:29 44:07 29:89 18:13 14RMSE :16 :16 :16 :16 :17 :17Observations 4881 3627 3104 2846 2575 22

All models include dyadic and year xed eects. Robust standard errors (clustered by c

parentheses. Signicance: ***1%, **5%, *10%. Regressors included but not recorded: Lo

Exporter Population; Log Importer Population; Log Exporter Real GDP p/c; Log Imp

p/c; Currency Union dummy; Common Language dummy; Regional Trade Agreement du

Border dummy; # Islands; Log Product Area; Common Colonizer dummy; Currently

Ever Colony dummy; and Common country dummy.

Table 8: Share of Insured to Total Exports and IV Estimates of the "Insurance

Second Instrument: Log C

Share of Insured to Total Exports All >1 >2 >3 >4 >

Log Share of Insured Exports, Instrumented :02

(:01)

:20

(:08)

:25

(:11)

:32

(:14)

:41

(:21)

:3

(F-statistic for excluded instruments 215:51 50:69 36:55 23:73 12:64 1RMSE :22 :24 :25 :26 :28 :2Observations 5210 3924 3383 3112 2821 2


parentheses Signicance: ***1% **5% *10% Regressors included but not recorded: Lo


32/47

Table 9: Zero Exports, Endogenous Sample Selection and the "Insurance Su

Second StaInstrument: Log Claim

Share of Insured to Total Exports All >1 >2 >3 >4 >5


:16(:05)

:20(:07)

:24(:08)

:29(:10)

:24(:10)

Inverse Mills Ratio 15:35(6:91)

13:54(8:00)

13:06(7:32)

4:45(6:51)

4:12(6:91)

9:2(6:69

F-statistic for excluded instruments 230:26 62:58 49:26 33:99 20:94 17:2

RMSE :21 :20 :20 :20 :20 :19Observations 5210 3924 3383 3112 2821 2485

The inverse Mills ratio is calculated from the linear prediction of a probit model on P(Xijt>

each year, following Wooldridge (1995). All models include dyadic and year xed eects. R

errors (clustered by country-pairs) in parentheses. Signicance: ***1%, **5%, *10%. Reg

but not recorded: Log Distance; Log Exporter Population; Log Importer Population; Lo

GDP p/c; Log Importer Real GDP p/c; Currency Union dummy; Common Language du

Trade Agreement dummy; Common Border dummy; # Islands; Log Product Area; Com

dummy; Currently Colony dummy; Ever Colony dummy; and Common country dummy.

Table 10: Zero Insured Exports, Endogenous Sample Selection and the "Insuran

Second StaInstrument: Log Claim

Share of Insured to Total Exports All >1 >2 >3 >4 >5


:16(:05)

:20(:07)

:24(:08)

:29(:10)

:25(:10

Inverse Mills Ratio :21(:05)

:14(:06)

:13(:07)

:11(:08)

:07(:09)

:1(:09)

F-statistic for excluded instruments 220:60 67:75 54:03 37:14 22:69 19:3RMSE :21 :20 :20 :20 :20 :19Observations 5210 3924 3383 3112 2821 2485

The inverse Mills ratio is calculated from the linear prediction of a probit model on P(Ins

mated for each year, following Wooldridge (1995). All models include dyadic and year xe


33/47

Table 11: Linear Probability Model Estimates of the Private Credit Insurance

Dependent Variable: Dum

(1) (2) (3)

Sum Insured Exportsit (billion) :0004(:0001):0004(:0001)

:0004(:0001)Sum Insured Exportsit * Very High Riskjt :0001

(:0000)

Sum Insured Exportsit * High Riskjt :0000(:0000)

Sum Insured Exportsit * Moderate Riskjt :0000

(:0000)

Sum Insured Exportsit * Low Riskjt

Sum Insured Exportsit * Very Low Riskjt

R2 :27 :27 :27RMSE :07 :07 :07Observations 41600 41600 41600


parentheses. Signicance: ***1%, **5%, *10%. Regressors included but not recorded: Lo

Exporter Population; Log Importer Population; Log Exporter Real GDP p/c; Log Imp

p/c; Currency Union dummy; Common Language dummy; Regional Trade Agreement du

Border dummy; # Islands; Log Product Area; Common Colonizer dummy; Currently

Ever Colony dummy; and Common country dummy.


34/47

Table 12: Accounting for Public Export Credit Insurance, Sample with Dut

Fixed Eects: None Dyadic

Log Insured Exports :17

(:03) :05

(:02)

Log Publicly Insured Exports :03(:02)

:01(:01)

Log Distance :72(:09)

Log Exp PopulationLog Imp Population :63

(:09)1:16(:99)

Log Exp Real GDP p/c

Log Imp Real GDP p/c :97

(:13) :69

(:40)


:25(:10)


RTA :19(:14)

:25(:08)

Common Border :16(:18)

No. Islands

:27(:21)Log Product Area :06

(:07)

Common ColonizerCurrently Colony 1:33

(:27)

Ever Colony :44(:12)

Common Country

Observations 357 357R2 :92 :99RMSE :54 :21

Robust standard errors (clustered by country-pairs) in parentheses. Year eects included b

Signicance: ***1%, **5%, *10%.


35/47

Table 13: Bonus Vetus OLS and Tetradic Estimates of the Private Credit I

Transformation of trade costs variables

la Baier and Bergstrand (2009)

Using Using

Simple Averages GDP Weights

Base Exporter

Base Importer Share of Insured to Total Exports All > 10 All > 10 A

Log Insured Exports :13(:01)

:91(:02)

:26(:01)

:97(:01)

:

Log Distance 1:16(:06)

:36(:05)

:05(:01)

:02(:01)

Log Exp Population :71(:02)

:19(:02)

:70(:02)

:17(:03)


:15(:02)

:65(:02)

:11(:02)

Log Exp Real GDP p/c 0:23(:10) :31(:14) :51(:12) :16(:13)Log Imp Real GDP p/c 1:36

(:03):24(:03)

1:08(:03)

:15(:03)


:14(:11)

:44(:08)

:07(:05)


:07(:07)

:07(:02)

:04(:01)

:

RTA :38(:09)

:16(:07)

:23(:03)

:02(:02) (

Common Border

:15(:11)

:18

(:10) :28

(:07) :04(:05)

No. Islands 26:03(3:25)

1:58(2:50)

:17(:02)

:03(:01)

Log Product Area :38(:27)

:03(:21)

:01(:00)

:01(:00)

Common Colonizer 1:69(:58)

:25(1:14)

:08(:87)

:38(1:32)

2


:30(:08)

:42(:55)

:30(:22) (

Ever Colony :79

(:12) :19

(:07) :34

(:04) :00(:03) :Common Country 1:07

(:44):72(:09)

:39(:71)

:56(:22)

R2 :83 :96 :78 :96RMSE 1:03 :52 1:16 :53Observations 14389 2816 14389 2816 9


36/47

Table 14: The Supply of Private Credit Insurance and the World Trade Co

Share of privately insu

6 8 10

(World 6:1 in 2007) (EuropePercent decline in the

supply of privateexport credit insurance Estimated export decline in percent...

10 1:4 1:8 2:3 15 2:2 2:7 3:4 20 2:9 3:6 4:5

...percent of World export decline 2008Q4-2009

10 5 6 715 7 9 1120 9 12 15

...percent of European export decline 2008Q4-20

10 15 20

aThe nominal Euro value of World (Euro area countries) exports declined by 31% (28.4%


37/47

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Appendix

Table A.1: Data Sources

FOB exports in US dollars are taken from IFS Direction of Trade CD-Rare converted to euros at the average annual exchange rate. Pre-1999 exccalculated as the weighted bilateral dollar exchange rate of the 11 cou

ing at the start of the euro in 1999 (Source: FT/Reuters). All gurethe Harmonised Index of Consumer Prices (HICP), overall index, take2000=1. Population and real GDP per capita (rgdpl) taken from PWT Mark are unavailable, I use World Development Indicators. The gures are cat the average annual exchange rate. Country-specic data (on location, area, island-nation status, contiguionizer, and independence) taken from CIA World Factbook website.

Currency-union data taken from Glick-Rose (2002). Regional trade agreements taken from WTO websitehttp//www.wto.org/english/tratop_e/region_e/eif_e.xls The credit insurance data comes from one of the "Big Three" interprivate credit insurers; company details are condential.


42/47

Table A.2: Country List

Afghanistan, Albania, Algeria, Angola, Antigua & Barbuda, Argentintralia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados,

Belize, Benin, Bhutan, Bolivia, Bosnia & Herzegovina, Botswana, Brazil, Burkina Faso, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Cepublic, Chad, Chile, China, P.R.: Mainland, China, P.R.: Macao, CoCongo, Dem. Rep., Congo, Republic of, Costa Rica, Cote DIvoire, CroatCzech Republic, Denmark, Djibouti, Dominica, Dominican Republic, EcSalvador, Equatorial Guinea, Eritrea, Estonia, Ethiopia, Fiji, FinlandGambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, GuineGuyana, Haiti, Honduras, Hong Kong, Hungary, Iceland, India, Indones

land, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, KiribKuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libyaembourg, Macedonia, Madagascar, Malawi, Malaysia, Maldives, Mali,nia, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, MyNepal, Netherlands, Netherlands Antilles, New Zealand, Nicaragua, Nigway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, PPoland, Portugal, Qatar, Romania, Russian Federation, Rwanda, SamPrincipe, Saudi Arabia, Senegal, Seychelles, Sierra Leone, Singapore, Sl

Solomon Islands, Somalia, South Africa, Spain, Sri Lanka, St. Kitts & NeVincent & Grens., Sudan, Suriname, Swaziland, Sweden, Switzerland, Tanzania, Thailand, Togo, Tonga, Trinidad & Tobago, Tunisia, TurkeUganda, Ukraine, United Arab Emirates, United Kingdom, United StUruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yugoslavia, Zambi


43/47

Table A.3: Correlation Matrix

Xijt InsExp Dis P op1 P op2 GDPpc1 GDPpXijt 1:00InsExp :62 1:00Dis :45 :32 1:00P op1 :19 :06 :19 1:00P op2 :49 :19 :04 :05 1:00GDPpc1 :03 :04 :04 :36 :03 1:00GDPpc2 :48 :33 :28 :17 :13 :05 1:00

CU :26 :13 :35 :10 :04 :07 :23 Lang :04 :06 :14 :12 :14 :07 :11RT A :39 :25 :63 :18 :12 :06 :30Border :31 :16 :43 :01 :06 :04 :16Isl :28 :10 :37 :05 :33 :05 :06Area :27 :04 :26 :37 :62 :15 :19CCol :03 :02 :05 :02 :00 :03 :01Col :01 :02 :02 :00 :05 :01 :02

ECol :01 :17 :07 :17 :12 :05 :14SameC :00 :02 :01 :01 :04 :00 :01

RT A Border Isl Area CCol Col ECol

RT A 1:00Border :21 1:00Isl :26 :11 1:00Area :09 :01 :10 1:00CCol :02 :09 :01 :02 1:00Col :01 :01 :02 :06 :00 1:00ECol :13 :02 :04 :07 :01 :11 1:00SameC :03 :01 :01 :06 :00 :78 :08


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Notes

1 For example, knowledge on changes in the supply and price of trade loans, letters insurance, during the 2008-2009 global nancial crisis came only from bank surveys (see IMF

it is dicult to separate supply and demand in these surveys (Dorsey, 2009).2 See EU Council Directive 98/29/EC at http://eur-lex.europa.eu/JOIndex.do?ihmlang=14, 2010

3 The estimate of private insurance is based on the value of exports insured by one private idential data on its market share. The value of insured exports by the Dutch government is avaport of Atradius Dutch State Business at http://www.atradiusdutchstatebusiness.nl/publicalast accessed on October 14, 2010

4 The "Big Three" private credit insurers cover 87 percent of the world private credit insu(36 percent), Atradius (31 percent) and Coface (20 percent).

5 Short-term trade nance business is usually dened as business with a maximum credit l

in practice most short-term business involves 180 days or less (Stephens, 1999).6 Such action may include intervention to prevent the transfer of payments, cancellation

or civil war. Non-payment by sovereign buyers is also a political risk.7 Bernard and Jensen (2004) examine empirically whether public export promotion ex

participation by gathering information on foreign markets, but nd no signicant contesample of large plants.

8 For example, Atradius oers an information service called "Observa News" with currekey customers, prospects or competitors. The service is charged as a at annual fee of 24monitored and a reduced rate of 16 euro for Atradius insured customers (see www.atradivarious rating and business information services (see www.coface.com). Euler Hermes o

countries, see http://www.eulerhermes.com/en/products-solutions/eolis-online-service.htm14, 2010.

9 Company details are condential.10 The raw data includes 114 observations with a share of insured to total exports abov

observations seem to be randomly distributed over 15 dierent exporters and 61 destinatiovalue of insured exports in these 114 observations to equal the value of total exports. The to these adjustments.

11 Instrumental variables provide a consistent estimate even in the presence of measuremeuncorrelated with the measurement error and the equation error, but correlated with the c

12 I use country codes from the IMFs International Financial Statistics for these classic13 Hausman tests cannot reject the null that insured exports may be treated as exogenou14 In general, premia are calculated as the sum of the expected loss (due to claims), admin15 For example, the ICC Global Survey report (2010) reports that "Total claims paid t

Berne Union members more than doubled from 2008 to 2009 and reached USD2.4 billion. Aroughly the same at an estimated USD2.8 billion, the loss ratio jumped from 40 to 87 perceleading international organisation of public and private sector providers of export credit an

16 This ability to set and manage exposures distinguishes credit insurance from other kiother credit instruments (Swiss Re, 2006).

17 Results not recorded. Also, a test of the hypothesis that the conditional elasticity of t

to zero cannot be rejected by any reasonable signicance levels.18 I test for underidentication by applying Andersons canonical correlations test and uKleibergen-Paap (2006) rk statistic, weak identication using the Wald version of the Kleiberand the critical values calculated by Stock and Yogo (2005), and the Hansens J test of over

19 Including more lags of the dependent variable did not change the results. Moreover,statistically insignicant. Also, it is well known that xed eects regression including laggeyield biased estimates. I examined this potential bias by estimating the benchmark model (T


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2007 value of Euro area countries exports in goods is taken from the World Trade Monitorfor Economic Policy Analysis.

22 A 1 per cent increase in insured exports (2.62 million euro on average) leads to an incrto 5.96 million euro. The average multiplier increases somewhat with the share of insured e

and 3.2 (for the subsamples), with a mean (median) of 2.7 (2.8).23 The size of the multiplier is comparable to the long run multiplier of Austrian guaranteallthough they do not account for the endogeneity problem.

24 The benchmark gravity model regressions lose 1768 observations due to missing data o25 See also Egger and Nelson (2010). Cross-section procedures as in Helpman, Melitz and

applicable in this case, as pointed out by Wooldridge (1995).26 See Angrist and Pischke (2009, pp. 102-107, 197) for additional reasons for using LPM27 Insured exports would perfectly predict the probability of positive export ows.28 The measure for public insurance relates to premium income and thus diers from the me29 See "Atradius reports 2008 results", available at http://global.atradius.com/corporate/p

2008-results.html, last accessed on October 14, 2010.30 See Annual review 2008 Atradius N.V. available at http://global.atradius.com/corporate

last accessed on October 14, 201031 See Chauour and Farole (2009) for an overview of trade nance measures taken by g

impact of the trade nance crisis.

Previous DNB Working Papers in 2010


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No. 242 Leo de Haan and Jan Kakes, Momentum or Contrarian Investfrom Dutch institutional investors

No. 243 Ron Berndsen, Toward a Uniform Functional Model of

Settlement SystemsNo. 244 Koen van der Veer and Eelke de Jong, IMF-Supported ProgramSolvent Countries

No. 245 Anneke Kosse, The safety of cash and debit cards: a studbehaviour of Dutch consumers

No. 246 Kerstin Bernoth, Juergen von Hagen and Casper de Vries, Theand Latent Factors Day by Day

No. 247 Laura Spierdijk, Jacob Bikker and Pieter van den Hoek, Mean

Stock Markets: An Empirical Analysis of the 20

th

CenturyNo. 248 F.R. Liedorp, L. Medema, M. Koetter, R.H. Koning and I. van or cantagion? Interbank market exposure and bank risk

No. 249 Jan Willem van den End, Trading off monetary and financial framework

No. 250 M. Hashem Pesaran, Andreas Pick and Allan TimmermEstimation and Inference for Multi-period Forecasting Problems

No. 251 Wilko Bolt, Leo de Haan, Marco Hoeberichts, Maarten van OoProfitability during Recessions

No. 252 Carin van der Cruijsen, David-Jan Jansen and Jakob de Hapublic know about the ECBs monetary policy? Evidence households

No. 253 John Lewis, How has the financial crisis affected the EurozoCentral and Eastern Europe?

No. 254 Stefan Gerlach and John Lewis, The Zero Lower Bound, ECB InFinancial Crisis

No. 255 Ralph de Haas and Neeltje van Horen, The crisis as a wakescreening and monitoring during a financial crisis?

No. 256 Chen Zhou, Why the micro-prudential regulation fails? The imimposing a capital requirement

No. 257 Itai Agur, Capital Requirements and Credit RationingNo. 258 Jacob Bikker, Onno Steenbeek and Federico Torracchi , The im

and service quality on the administrative costs of pensioncomparison

No. 259 David-Jan Jansen and Jakob de Haan, An assessment of Communication using Wordscores

No. 260 Roel Beetsma, Massimo Giuliodori, Mark Walschot and PeFiscal Planning and Implementation in the Netherlands

No. 261 Jan Marc Berk, Beata Bierut and Ellen Meade, The Dynamic Vof EnglandsMPC


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