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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
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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
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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
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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
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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
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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%.
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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
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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
Currently Colony :06(:08)
: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
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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
Log Imp Population :31(:33)
: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(:
Currency Union :22(:04)
: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
Currently Colony :04(:04)
: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
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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 >
Log Insured Exports, Instrumented :01(:01)
: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
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
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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
Log Insured Exports, Instrumented :02(:01)
: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
Log Insured Exports, Instrumented :02(:01)
: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
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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
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.
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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)
Currency Union :42(:11)
:25(:10)
Common Language :07(:16)
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%.
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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)
Log Imp Population :76(:01)
: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)
Currency Union :29(:08)
:14(:11)
:44(:08)
:07(:05)
Common Language :30(:09)
: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
Currently Colony :37(:19)
: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
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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%
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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.
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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
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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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