1. food and drink
2. food
3. baked goods

Crisis-Proof Services:
Why Trade in Services Did Not Suffer
During the 2008-2009 Crisis∗
Andrea Ariu†
October 17, 2013
Abstract
During the 2008-2009 crisis trade in goods experienced the deepest decline ever
recorded. Surprisingly, trade in services lived through the crisis unharmed and
some service categories carelessly stuck to their growth paths. In this paper,
we investigate the reasons behind the different reaction of services and goods
trade using firm-country-product exports for Belgium. First, we decompose the
change in exports during the crisis into the different margins, then we investigate
econometrically the possible reasons behind the different behavior of the two
types of trade. Our analysis shows that both goods and services trade reacted
qualitatively similar during the crisis: both the 3% drop in exports of services
and the 27% fall in exports of goods were mostly driven by changes in the average
quantities exported per market and product, while we do not observe within-firm
reallocations in terms of number of destination countries and number of products.
The main reason for the different quantitative reaction of services exports is that
they are, unlike exports of goods, immune to negative income shocks. We observe
that to a one percent drop in GDP growth, it is associated an increase in exports
of services equal to 0.05% of the decrease of exports of goods.
Keywords: Trade Collapse, Belgium, Services and Goods Trade.
JEL Classification: F10, F14, L80.
∗
Financial help under the Globalisation Investment and Trade in Services (GIST) project, funded
by the EU 7th Framework Programme (ITN-2008-211429), is gratefully acknowledged. This work was
carried out while I was an intern at the National Bank of Belgium, I thank for the hospitality and the
support provided. All views expressed in this paper, as well as the errors, are my own solely and do
not necessarily reflect the views of the National Bank of Belgium. This paper has greatly benefited
from the discussions with Anca Cristea, Chiara Farronato, Martina Lawless, Kalina Manova, Giordano
Mion, Mathieu Parenti, Alberto Russo and Ilke Van Beveren as well as from the suggestions received
from participants at the International Trade Workshop at Stanford University, GIST Conference in
Alghero, ETSG at the University of Leuven, EDP Jamboree at the European University Institute,
RIEF conference at Universit´e Sorbonne, the Belgian Trade Day at the University of Antwerp, LETC
conference at the University of Ljubljana, ITSG conference at the University of Ancona, Doctoral
Workshop at University of Namur, CompNet Conference at the European Central Bank.
†
FNRS and IRES, Universit´e catholique de Louvain, Belgium e-mail: [email protected]
1
1
Introduction
Between the third quarter of 2008 and the second quarter of 2009, trade in goods experienced the steepest decline ever recorded, with both exports and imports unexpectedly
falling four times more than income (Freund, 2009). The fall was very severe, highly
synchronized across countries and mostly concentrated in the category of durable goods
(Baldwin, 2009). In this period of economic turmoil, trade in services barely reacted to
the crisis. The most important services markedly continued growing without hesitation
and only the category of transport services registered negative figures (Borchert and
Mattoo, 2009; Francois and Woerz, 2009). This peculiar resilience is also unpredicted,
since most of the studies analyzing trade in services at micro level1 suggest that trade in
services shares the same characteristics of trade in goods. Despite this intriguing incongruity, while a large amount of research has attempted to understand the causes of the
“Great Trade Collapse” (Baldwin, 2009) for trade in goods,2 the distinctive resilience
of trade in services did not get the attention of the international trade literature.
In this paper, we analyze the peculiar response of trade in services during the trade
crisis of 2008-2009 to understand why trade in services reacted differently with respect
to trade in goods. We benefit from a unique micro-level dataset with firm-countryproduct3 exports of goods and services for Belgium and we are able to provide evidence
on the reaction of firms to the crisis. The analysis is divided into three sections. In the
first, we perform a descriptive comparison of goods and services trade by decomposing
changes in Belgian exports into changes in the extensive and the intensive margins,
where the former refers to changes in the average number of destination countries per
firm and the average number of products exported per firm-country and the latter to
the average exports per firm, country and product. Keeping a descriptive spirit, in the
second section we use a diff-in-diff approach similar to Behrens et al. (2011) in which
we use the first semesters of 2007 and 2008 as the pre-treatment period and the first
semesters of 2008 and 2009 as post-treatment period and we explore the differential
post-treatment effect of firm, country and product covariates separately for goods and
services trade.4 In the third part of the analysis, we perform a triple-difference analysis
1
Breinlich and Criscuolo (2011) for the UK, Kelle and Kleinert (2010) for Germany, Walter and
Dell’mour (2010) for Austria, Gaulier et al. (2011) for France, Federico and Tosti (2012) for Italy and
Ariu (2012) for Belgium.
2
See Baldwin (2009) for a review.
3
For the sake of expositional clarity, we use the expression “product” also when we refer to a service.
4
This analysis is made separately for trade in goods and trade in services. For both types of trade
we run a different regression in which the change in exports between the first semesters of 2007 and
2008 represent the “normal” period and first semesters of 2008 and 2009 the “treatment” period.
2
in which we compare changes in exports of services in the pre and post period with
changes in exports of goods in the pre and post period by using only firms that export
both goods and services. The goal is to understand the magnitude and statistical
significance of the different role played by demand and financial factors for services
and goods exports during the crisis. Moreover, we check the robustness of the results
by applying the triple-difference strategy to matched “mono-exporters” of goods and
services.
Our results reveal that, besides the difference in the magnitude of the response to
the crisis, there are some qualitative similarities. Both the 3% drop in exports of services and the 27% fall in exports of goods were mostly driven by changes in the average
quantities exported per market and product, while we do not find evidence of withinfirm reallocations in terms of number of destination countries and number of products.
This means that, both for goods and services, firms did not drop destination countries
or products and they only adjusted the values shipped per destination and product.
We do not observe relevant heterogeneity in destination countries but an important
differential response across different products and firms. In particular, the small decline in services was mostly driven by transport services, while business, financial and
telecommunication services continued growing along their trends. For trade in goods,
all the product categories dropped, but the fall was mostly concentrated on the durable
and investment goods. In terms of firm heterogeneity, we observe that for exporters
of services the non-multinational, non foreign-owned, smaller and more financially exposed exporters of services suffered relatively more from the crisis, while for exporters
of goods we do not find any relevant difference.
Focusing on the determinants of the crisis, we show that the different behavior of
goods and services trade is mainly due to a different elasticity to income. On the
one hand, the evolution over time of trade in goods is heavily related to changes in
GDP of partner countries and demand shocks magnify the reaction. On the other
hand, changes over time in trade in services are not related to changes in GDP in
partner countries, and dramatic negative shocks as the 2008-2009 crisis do not interfere
with their growth paths. By applying the triple-difference strategy explained above,
we find that the reaction of trade in services during the “Great Trade Collapse” was
statistically different from that of trade in goods: a one percent decrease in GDP growth
in partner countries is associated to an increase in trade in services which is 0.05% of
the magnitude of the decrease of trade in goods. This means that, if we suppose the
Belgian drop in exports of goods (26,681 million euros) being entirely driven by demand,
3
we should observe an increase of 133 millions euros in exports of services, which would
mean an increase of about 0.6%. The same result is confirmed both qualitatively and
quantitatively if we perform propensity score matching and compare exports of firms
exporting only services and firms exporting only goods. This feature of services is more
important, with respect to the export of durable goods than to consumables and is
mostly accounted by the exports of business services. These results can be explained
by the fact that, from a demand point of view, services represent essential inputs for
the production process, they are not storable and they are not directly related to the
production size. Therefore, their flow must be continuous, it cannot be stored and it
cannot easily be modified following changes in the size of the production process. These
three characteristics make services look like a fixed cost that firms have to pay in order
to ensure the continuity of the production process. Therefore, shocks at the demand
level have little impact on the international flows of services.
To date, Borchert and Mattoo (2009) is the only paper analyzing trade in services
during the crisis. Using aggregate US trade data, they are the first to show that
services did not collapse during the crisis of 2008-2009. Then, using the descriptive
evidence of Indian exporters in the IT sector, they argue that services did not suffer
from the 2008-2009 crisis because their demand is less cyclical and they rely less on
external capital. The main contribution of this paper is to go beyond the descriptive
evidence available and provide a micro-econometric analysis of the determinants of the
different response of trade in goods and trade in services trade during the “Great Trade
Collapse”. With our unique data, we are able to test their hypotheses based on the
descriptive evidence drawn from a particular set of Indian firms. Secondly, this is the
first paper to describe the behavior of trade in services during the crisis at micro level by
using a unique dataset on firm-country-product exports from Belgium during the 20082009 crisis. As highlighted by Behrens et al. (2011) and Bricongne et al. (2012) in the
analysis of Belgian and French exporters of goods during the crisis, the big advantage
of using data at the firm level is that we are able to disentangle the effects of the crisis
by looking at within-firm reallocations in terms of changes in the number of products,
destination countries and average exports per country and product.
Thirdly, this paper builds on the large literature analyzing the effect of macroeconomic shocks on trade. Bernard et al. (2009) for the Asian crisis dissect the fall in
exports of goods of US firms into the different margins, concluding, as in our case, that
most of the action was on the intensive margin. Amiti and Weinstein (2011) and Iacovone and Zavacka (2009) show that exporters of goods relying more on external capital
4
are those that suffer the most in times of financial crisis. With respect to the recent
“Great Trade Collapse”, the emerging consensus points to both demand and supply
shocks as the main drivers behind the sudden fall. From the demand side, Behrens
et al. (2011), Bricongne et al. (2012) and Eaton et al. (2011) provide evidence of a
disproportionate fall in demand for “postponable” goods, such as consumer durables
and investment goods. As pointed out by Alessandria et al. (2011), this pushed firms to
intensively use inventories and to stop the provision of intermediates, thus reinforcing
the negative effect on trade. Moreover, since “postponable” goods constitute a small
part of countries’ GDP, but a large share of international trade, this demand shock
had dramatic consequences for trade in goods, but relatively little impact on GDP
(Francois and Woerz, 2009; Levchenko et al., 2010). From the supply side, Chor and
Manova (2012) and Auboin (2009) argue that the financial sector difficulties led to a
severe credit crunch that prevented firms from getting enough funds to continue operating in the export markets for goods. As highlighted by Bems et al. (2011), Levchenko
et al. (2010) and Altomonte et al. (2012), the interruption of a link in an international
production chain can cause the destruction of the entire chain, thus having magnified
effects on trade flows. Finally, Evenett (2009) and Jacks et al. (2011) argue that protectionism measures played a further negative role in the collapse. Our paper offers to
this literature a new perspective, by adding the service dimension to the analysis of the
collapse and by showing that services are immune to sudden negative income shocks.
Moreover, it shows that that the different reaction was not due to firm-level differences,
since the same divergence is also observed for firms that export both services and goods.
Finally, this paper has important policy implications. Our analysis demonstrates the
relative stability of services exports during major crises, therefore, countries specializing
in the international commerce of services can more easily overcome difficult economic
periods and enjoy relatively higher stability. Moreover, it provides further motives for
service trade liberalization in the current Doha negotiations.
The paper is organized as follows: in section 2 we describe the data; in section 3
we show the descriptive statistics of the crisis; in section 4 we present our diff-in-diff
analysis; in section 5 we develop the triple-diff approach and section 6 outlines some
concluding remarks and future research directions.
5
2
Data Description
The bulk of the dataset used in this paper is composed of three different datasets provided by the National Bank of Belgium (NBB henceforth) concerning trade in services,
trade in goods and firm-level accounts.
Data on trade in services come from the NBB Trade in Services dataset used to
compile the balance of payments and covers the period from 2006 to 2010. The dataset
is formed using different surveys conducted by the NBB5 and contains information about
trade in services at the firm-destination-product level, so for any Belgian firm present
in the dataset we have, depending on the survey, monthly or quarterly information on
export values per type of product and destination country. Service products are listed
in Table 2 and countries are classified using ISO 2-digit codes. We exclude from the
analysis “services to affiliates” (code H7000) because this does not contain information
on which specific service is traded and “goods included in the construction services”
(code E0002) because this category does not strictly represent trade in services. The
dataset captures more or less 60% of total exports of services by Belgium and about
40% of Belgian exporters. The survey nature of the dataset rules out any analysis of
entry and exit patterns in foreign markets.6 Therefore, the analysis of this paper will be
focused only to the firms that we observe continuously during the period of analysis.7
This means that we are not able to make any analysis on across-firms adjustments, but
we can still explore the service and product margins, and thus within-firm adjustments
during the crisis. This limitation is not too serious, since entry and exit account for
less then 2% of total exports (Ariu, 2012) for both goods and services and since entry
and exit has been shown to be a marginal channel of adjustment for firms during the
crisis (Behrens et al., 2011; Bricongne et al., 2012).8 Moreover, despite this constraint,
this is the only dataset available that can allow the analysis of trade in services at the
micro level during the 2008-2009 crisis.
Information on trade in goods is taken from the NBB Trade in Goods Dataset,
which contains exports and imports of goods performed by Belgian firms at the firm5
For more information on the surveys see Table 1.
The main problem is the fact that when a firm enters in the dataset, it is kept for some years
even if after a few years it does not meet the thresholds to get into it. Moreover, even by excluding
those firms by checking the fulfillment of the criteria, it would provide an idea of the entry and exit
into/from the survey but it is questionable whether this would also be representative of entry and exit
into/from export markets.
7
These continuing firms account for about 96% of exports and imports present in the surveys, so
we can be confident that the data covers the bulk of Belgian trade
8
Bernard et al. (2009) show that during the Asian crisis too the extensive margin was a minor
adjustment channel and all the action was concentrated on the intensive margin.
6
6
destination-product level. The data is collected monthly and comes from the Intrastat
(Intra-European) and the Extrastat (Extra-European) declarations. Firms are identified thanks to the VAT number, countries are classified using the ISO 2-digit codes and
products are classified using the CN nomenclature at 8-digit level. Data on firm-level
accounts come from the Business registry covering the population of firms required to
file their (unconsolidated) accounts with the NBB. From this dataset we take information on full-time equivalent employment, turnover, operating profits, equities, liabilities,
stocks and purchases of intermediates for the year 2007. Unfortunately, turnover figures comprise both goods and services together and there is no information available to
distinguish between the two. This prevents us from analyzing the dynamics of goods
and services in the domestic market. Multinational and foreign ownership status of
firms are taken from the NBB Survey of Foreign Direct Investments. Finally, we take
information on GDP growth in destination countries from the IMF World Economic
Outlook database (2012 version)9 and information on daily exchange rates on the 1st
of April of each year considered from the Statistical Data Warehouse of the European
Central Bank.10
3
The Crisis in Figures
As previously mentioned, the crisis hit goods more severely than services. Looking at
monthly exports of goods and services in Figure 1, we can see that after September
2008 there is a clear rupture and exports of goods fell by about 30%. For services
instead, there is no definite sign of discontinuity and they kept about the same pace.
This phenomenon was not only confined to Belgium, but worldwide and significant in
terms of magnitude. In Figure 2, we plot for OECD countries the ratio of quarterly
exports of services over exports of goods, with the first quarter of 2006 normalized to
one.11 By looking at the average for the whole OECD, represented by the thick red
line, it is evident how as from the third quarter of 2008 the ratio of services over goods
increased rapidly and significantly, meaning that, while trade in goods collapsed, trade
in services held up relatively well on average in all OECD countries.
In the rest of the paper the analysis will be focused only on the first semester of
each year (as also in Behrens et al. (2011)). This choice is made in order to i) reduce
seasonality issues evident from the monthly data; ii) include the maximum amount
9
Available at http://www.imf.org/external/pubs/ft/weo/2012/01/weodata/index.aspx
Available at: http://sdw.ecb.europa.eu/
11
Data come from the OECD database available at http://stats.oecd.org
10
7
of firms (it should be noted that some firms declare exports only quarterly and most
of the firms do not export every month so making an analysis on continuing firms
at monthly level would reduce the number of firms dramatically); iii) do a clear prepost comparison avoiding the shock present in the statistics of the third and fourth
quarters of 2008. The first step to understanding the composition of the changes in
Belgium’s exports is to decompose total Belgian exports at time t (where, in this case
t = {S12008, S12009}), of trade type y (where y = {Services, Goods}), Xty , into the
number of firms ft , the average number of served markets per firm c¯t , the average
number of exported products per market-firm p¯t and the average exports per firmmarket-product (service) x¯t : Xty = f y ∗ c¯yt ∗ p¯yt ∗ x¯yt . By taking the ratio between
the first semester of 2008 and the first semester of 2009, we can thus break down
the change in total exports, ∆X y =
y
X2009
,
y
X2008
into the change in the extensive margins
(firms-services-markets) and the change in the intensive margin (the average exports
per firm-market-service):
∆X y = ∆f y ∗ ∆c¯y ∗ ∆p¯y ∗ ∆x¯y
(1)
Since we focus only on continuing firms, the change in the number of firms, ∆f , is equal
to one.12 Looking at Table 3, we can appreciate that the change in Belgian exports
between the first two semesters of 2008 and 2009 is -26.81% for goods and only -3.13% for
services. Even if these falls differ dramatically in quantitative terms, qualitatively they
are both generated almost entirely by a reduction in the quantities exported per market
and product. This means that Belgian firms, both for goods and services trade, did
not significantly leave destination markets or reduce the number of products provided
for each market, but they only adjusted the values exported per market and product.
Therefore, this huge difference in the reaction of the average quantities exported per
market and product suggests that the intensive margin is the key to understanding the
different reaction of services and goods trade.
By dividing Belgian exports into the different product categories, we can appreciate
in Table 4 a wide heterogeneity across products, both for services and goods. Services related to transport experienced a drop commensurate to that of goods. On the
other hand, business and telecommunication services continued their sustained growth.
Therefore, besides transport services, the other services did not suffer from the crisis
and they continued their normal growth paths. If we consider also that financial, insurance and business services represent more than 50% of Belgian exports, this is a quite
12
Which means that it does not contribute to explaining the growth of exports.
8
important result that can have relevant policy implications. For exports of goods, all
product categories experienced a decline, yet the bulk of the collapse is accounted for
the intermediates and durable goods. By decomposing Belgian exports into EU and
non-EU and to OECD and non-OECD in Table 5, we see a mixed country pattern.
Intra-EU and extra-OECD exports of services experienced a more important drop than
non-EU and OECD. This is because most of the extra-OECD trade is represented by
transport of services, which we have seen is the only service category that collapsed.
For exports of goods instead, the fall is similar in all the country categories.
To discern differences across firms, in Table 6 we divide exports following the multinational and foreign ownership status, size and financial situation of the exporter. The
numbers say that, for services, the non-multinational and non foreign-owned firms were
hit by the crisis, while multinational and foreign owned registered positive figures. However, these declines are much smaller than those for goods, for which we do not observe
any heterogeneity following the multinational and foreign ownership status. Finally,
by defining a firm as big if it has the full-time equivalent employment higher than the
median exporter in the same industry and as financially exposed if it has higher external financial dependency than the median exporter in the same industry, we can see
from Table 7 that there is no heterogeneity for firms exporting goods, while small firms
exporting services suffered more from the crisis than big ones.
Summing up the descriptive evidence on the crisis in Belgium, it looks like exports
of services did not suffer as much as goods exports. Both service and goods exporters
kept the same number of destinations and products per destination, adjusting only at
the intensive margin, although they did so with very different magnitudes. We find
that there is no particular pattern looking at the partner countries for both goods and
services, but there is great heterogeneity looking at the different product types. In
particular, transport services dropped similarly to trade in goods, while professional,
financial and telecommunication services continued growing at a very high pace. For
trade in goods, we find that the decrease is mostly due to a reduction in the intermediates and durable goods, while other types of goods declined more smoothly. Finally, we
observe an important heterogeneous response of firms, based on ownership and multinational status, size and financial situation for exports of services, but not for exports of
goods. Firm, country and product dimensions provide together important information
on the nature of the crisis, therefore we are going to take them into consideration in
our empirical strategy.
9
4
Diff-in-Diff of the Crisis
To understand which factors led to a different response of exports of services with
respect to exports of goods, we use a type of diff-in-diff approach similar to Behrens
et al. (2011), in which the change in the logged exports to a particular market c, of a
particular product p, by a Belgian firm f between S12007 and S12008, and S12008 and
S12009, ∆Xfy,tcp = logXfy,t+1
− logXfy,tcp , is regressed, separately for goods and services
cp
(remember that y = {Services, Goods}), against the treatment dummy T t a vector
containing firm, country and product characteristics, Zfy,tcp , and the interaction of this
vector with the treatment dummy, Zfy,tcp ∗ T t .
∆Xfy,tcp = α + β00 T t + β10 Zfy,tcp + β20t Zfy,tcp ∗ T t + tf cp
(2)
In this specification, β00 represents the treatment specific effect, β10 the contribution of
the firm, country and service characteristics in normal times and β202009 the contribution of these same variables during the crisis in 2009. Since we do not have services
characteristics, Zfy,tcp contains service or product dummies13 to capture heterogeneity
across goods and across services. Given that our variables of interest vary along three
dimensions (firm, product and country), we use the multi-level clustering procedure
developed by Cameron et al. (2011) to correct standard errors. Finally, to alleviate
endogeneity issues of our firm-level variables, we use the balance sheet data from 2007
only and trade data from 2006 for computing export and import to turnover ratios.
In the first specification, we use dummy variables for all our firm-level variables indicating whether a firm is above or below the median among all exporters in the same
industry. In this way, the interaction between the firm-level variable and the crisis
dummy would tell what happened in terms of export growth, for example during 2009,
to a firm that in 2007 was among the most productive. In a second specification, we
also make use of our firm-level covariates in levels. Our independent variables aim to
capture the heterogeneity observed in the descriptive statistics, the supply and demand
features assumed by Borchert and Mattoo (2009) as the cause of the resilience of trade
in services and the different determinants of the fall of trade in goods. We use the size,
productivity and multinational and foreign ownership status of the firm to control for
heterogeneity across firms, different variables that measure their exposure to external
finance14 , variables capturing the involvement in global value chains, the importance of
13
Please note that in order to have the same level of disaggregation between services and goods, we
use the CN classification at the 2-digit level.
14
In line with the previous literature measuring credit constraints (see for example Manova and Yu
10
stocks and typical demand determinants as GDP growth, exchange rates and dummies
for OECD (but not EU) destinations and non-OECD (and non-EU) destinations. The
complete list of variables, their description and their source is presented in Table 8.
Focusing attention on the left hand side of Table 9, we replicate in a slightly different
setting15 the results of Behrens et al. (2011) for trade in goods. Our estimates point
to a weak influence of supply factors in explaining the reaction of goods during the
crisis, while demand determinants played a very important role. In particular, GDP
growth coefficient is positive and significant, both before and during the crisis, which
means that exports of goods follow the economic cycle, and important negative shocks
magnify the reaction. As pointed out by Behrens et al. (2011), the drop in demand is
the most important factor in explaining the “Great Trade Collapse” and it accounts
for about two-thirds of the fall. With respect to the global value chain, we observe
that firms having higher exports of goods over intermediates suffered more from the
crisis, while those more involved in imports of goods were less affected. Finally, our
variables capturing the financial situation of the firm point to a weak incidence of
credit constraints in the explanation of the fall. Switching to the right-hand part of
Table 9, we clearly see that demand and all the other factors that characterized the
huge drop for trade in goods did not play any role both before and during the crisis
for trade in services. This means that the growth of trade in services does not depend
significantly on demand and supply determinants, and it proves to be immune to shocks
at the supply and demand level. By performing the same regressions using continuous
firm-level variables (Table 10), we get the same qualitative results both for goods and
services exports.
What is the reason behind the different reaction of services to the supply and demand
determinants? From the demand point of view, services represent essential inputs for
the production process: a company without its call center, janitorial, accounting and
financial services can hardly continue producing; the work of construction companies is
seriously hampered by the lack of architectural and engineering services. At the same
time, services are intangible: this means that they cannot be stored and their flow must
be continuous, thus forcing firms to buy services to keep the production process going.
Finally, services are often not directly related to the size of the firm’s production: the
value of audit, advisory and call center services purchased by a firm do not necessarily
(2012), Whited (1992), Fazzari and Petersen (1993), Greenaway et al. (2007) and Knight et al. (2010))
we control for the dependence on external finance, the structure of the debts (time horizon and debt
types) and the debt burden.
15
The main differences are based on the fact that we use product dummies instead of product
characteristics to capture heterogeneity across products.
11
change following a reduction in the sales of a firm. Therefore, services flows cannot
be easily modified following changes in the size of the production process. These three
characteristics: essentiality, non-storability and size independency of services, make
them look like a fixed cost that firms have to pay to ensure the continuity of the
production process. Therefore, sudden income shocks do not have direct effects on the
international flows of services. From the supply side, our results suggest that services
seem to be less affected than goods by financial constraints. Borchert and Mattoo
(2009) show that many services are traded over the internet and that they can more
easily rely on advance payments. Therefore, they do not need big investments to be
produced and traded internationally and in general these characteristics lower their need
for external capital. At the same time, it must be noted that services can hardly be
used as collateral: first, they are intangible and it is hard to objectively value something
that is immaterial; second, they are usually very customized and they have little value
outside the specific contract between two parties. Therefore, service exporters might
not even be able to ask for external trade capital and thus be less sensitive to credit
shocks.
5
Diff-in-Diff-in-Diff of the Crisis
The previous section highlighted the different role that supply and demand factors
had on services and goods during the crisis. In this section, we delve deeper into the
analysis and we test the significance and magnitude of the differences across goods
and services by focusing on the income and financial determinants highlighted in the
previous section. A possible issue to test the differences in the role of GDP growth and
external finance across exports of goods and services can be represented by the fact that
firms exporting services might be different from those exporting goods. Our strategy to
tackle this problem is to consider only firms that export both services and goods, the
“bi-exporters”, and compare changes in exports of services before and during the crisis
with changes in exports of goods before and during the crisis for the same firm. In this
way, we rule out any difference across goods and services related to both observable and
unobservable components of supply. In a certain sense, it is like performing a “perfect
matching” since we compare the same firm when exporting services and when exporting
goods. Then, to check whether these “bi-exporters” represent a particular set of firms
and control the robustness of our results, we focus solely on the “mono-exporters” and
for every service exporter we find the closest goods exporter by applying propensity
12
score matching.16 Using these matched “mono-exporters”, we can make the same type
of comparison, but we are able to match exporters of services with exporters of goods
only on observables.
With respect to the previous analysis, we have to drop the product dimension, since
we cannot say which good should be matched with a particular service and vice-versa.
Therefore, the unit of analysis is represented by the change in exports type y of a firm
f in a country c at time t, (∆Xfyct ). The interaction between a dummy indicating the
service flow Sf , the treatment dummy for the crisis T t and the GDP growth, GDPct , will
provide evidence on the differential impact of GDP on exports of services with respect
to exports of goods during the crisis. This triple-difference strategy can be expressed
analytically as:
∆Xf ct = α+β00 T t +β10 GDPct +β20 GDPct ∗T t +γ00 Sf +γ10 Sf ∗T t +γ20 Sf ∗GDPct +γ30t Sf ∗GDPct ∗T t +tf c (3)
Where β00 , β10 and β20 have the same interpretation as the diff-in-diff strategy we used
before. γ00 is the specific treatment effect controlling for differences across goods and
services. γ10 captures the different response across goods and services during the crisis.
γ20 controls for specific differences in the effect of GDP growth across goods and services.
Our variable of interest, γ30t captures the differential effect of GDP growth on export
of services (with respect to exports of goods) during (for 2009) and after (for 2010)
the crisis. Since the GDP growth is at the country level, we cluster standard errors
accordingly. Moreover, to control for firm-level determinants, we use firm-year dummies.
Looking at the estimated coefficient of γ302009 in the first column of table 12 (panel a),
we can see that the GDP growth had a negative and significant effect on exports of
services with respect to exports of goods during the crisis of 2009. This means that the
reaction of services’ exports to the income shock was significantly different from that
of goods’ exports. Our estimates say that a one percent decrease in income growth
is associated with an increase of exports of services equal to 0.05% of the reduction
of trade in goods. By using the “matched” mono-exporters, and performing the same
analysis, we can see from the first column of Table 12 (panel b) that the results are the
same both qualitatively and very similar also in quantitative terms. Therefore, it looks
like the different reaction of services with respect to goods is not influenced by the fact
that bi-exporters represent a particular category of exporters.
As pointed out in the descriptive part of the paper, most of the decline in the exports
16
We present results using Mahalanobis Matching (with replacement) where firms are matched in
terms of: size, productivity, capital intensity, average wage, multinational and foreign ownership status.
Using other matching functions like Nearest Neighbor or Kernel Matching does not change the results.
Table 11 reports the statistics and differences for the control and the treatment group.
13
of goods was accounted by durable goods. At the same time, we outlined the fact that
different services reacted differently to the crisis. In order to refine our results, we divide
exports of goods and services into the different product categories and we apply the
same type of analysis for each of them. We divide exports of goods into the exports of
durable (or postponable) goods and exports of non-durable (or consumables). Results
in columns 2 and 3 of Table 12 indicate that the resilience of services is more important
with respect to durable goods than to consumables. This is not an unexpected result,
since most of the decline in goods exports was accounted by durable goods. In columns
4-10 of Table 12, we present the results for exports of the different service categories.
As can be seen from the magnitude and significance of the coefficients, most of the
findings are accounted by the Business Services. This can be explained by the fact that
during the crisis firms might have needed more external support than in usual times.
Therefore the demand for services like consultancies, legal, audit, remained high besides
the economic downturn.
Having found evidence of a significant differential effect of the GDP growth on export
of services with respect to exports of goods, we can apply the same type of analysis to
check if there is a differential effect of financial constraints on the export of services with
respect to goods. In this case, the interaction between a dummy indicating the service
flow Sf , the treatment dummy for the crisis T t and the external financial exposure
variable17 F INft will provide evidence on the role of credit constraints. Analytically
the equation to be estimated is very similar to (3):
∆Xf ct = α+β00 T t +β10 F INct +β20 F INct ∗T t +γ00 Sf +γ10 Sf ∗T t +γ20 Sf ∗F INct +γ30t Sf ∗F INct ∗T t +tf c (4)
The only differences are that we cluster standard errors at the firm level and we use
country-year dummies to control for demand determinants. The results in Table 13 do
not provide as solid results as for the GDP growth. In most of the specifications the
coefficient is not significant, so we do not find strong evidence supporting a different role
of credit constraints for exports of services relative to exports of goods during the crisis.
This result holds both for “bi-exporters” and “mono-exporters”, and differentiating
goods and services into their different categories. Moreover, it holds across different
variables controlling for the financial situation of the firm.18
17
Measured as investments minus operating profits over investment.
As previously mentioned, the results in the tables refer to investments minus operating profits over
investment, but they are also available upon request when using the share of financial debts and the
share of short-term debts.
18
14
6
Conclusions
This paper shows that exports of services did not suffer from the 2008-2009 crisis because
they are more immune to income shocks than trade in goods. This difference proves
to be statistically significant: a one percent decrease in GDP growth is associated with
an increase of trade in services equal to 0.05% of the reduction in exports of goods.
This means that had the crisis been totally driven by an income shock, we should have
observed an increase of trade in services of 133 million euros, which means a rise of 0.6%.
This peculiar resilience of trade in services is more pronounced with respect to durable
goods than to consumables and is mostly accounted by the business services category.
This means that despite the crisis, demand for services like consultancies, audit and
legal remained high. In terms of theoretical models, the resilience of trade in services
with respect to goods can be coherent with differences in demand and supply. On the
one hand, trade elasticities with respect to income might be higher for goods than for
services. On the other hand, trade costs may differ as well. In particular, higher fixed
costs for services would result in more productive and resilient services exporters.19
Finally, these results provide strong policy implications: countries specializing their
exports towards services can benefit from a lower sensitivity to demand shocks, thus
alleviating the consequences of an economic turmoil thanks to the special nature of
services. Moreover, they provide further motives for service trade liberalization to
Doha trade negotiations round.
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7
Appendix
In this appendix we discuss how the differences between services and goods trade can
be thought within existing theoretical frameworks. We do not intend to present a new
framework or a complete and fully-fledged model, but rather to depict how the interaction between services and goods trade highlighted in this paper can be rationalized
into existing models.
Suppose consumers can choose to consume a continuum of two products: goods G,
denoted by jG ∈ [0, 1] and services S, denoted by jS ∈ [0, 1]. They can choose the
quantities of goods jG , q(jG ) and services jS , q(jG ) to maximize the following constant
relative income elasticity (CRIE) utility function:
αS
σS
σS − 1
Z
1
q(jS )
σS −1
σS
djS
+ αG
0
σG
σG − 1
Z
1
q(jG )
σG −1
σG
djG
(5)
0
These non-homothetic preferences were introduced by Hanoch (1975) and Chao et al.
(1982) and they have been recently used for trade models by Fieler (2011) and Caron
et al. (2012). αS > 0 and αG > 0 represent the weights of, respectively, services and
goods (with αS + αG = 1) and σS and σG can be interpreted as the income elasticity of
demand of goods and services.20 To understand the reaction of services and goods to
income changes, it is useful to take the ratio between the total expenditure of services
and goods:
xS
= λσG −σS
xG
αS (PS )1−σS
αG (PG )1−σG
As explained in Fieler (2011), the term in parentheses sets the level of
captures how
xS
xG
xS
xG
xS
xG
(6)
xS
,
xG
while λσG −σS
varies with income changes. In particular, if σS > σG , the ratio
is decreasing in λ and hence increasing in income. If instead σS < σG the ratio
is increasing in λ and hence decreasing in income. This second case is indeed
representative of the results we get from the empirical analysis. Changes in income have
a strong effect on goods, while they have very little influence on services. Therefore, a
negative income shock leads to increase the ratio
xS
,
xG
which is exactly in line with our
empirical evidence.
From the supply side, let us suppose an environment similar to Melitz (2003) in
which firms are heterogeneous in their productivity ϕ and pay a fixed costs to produce
and export. Assume that the fixed cost of exporting services, fS is higher than the fixed
cost of exporting goods fG .21 At the same time, assume that trade is subject to iceberg
20
See Fieler (2011) for a complete discussion of this. Please note that if σ = σS = σG we are back
to the usual CES environment.
21
This choice would motivated by the evidence that introducing services in foreign countries is
19
costs that are higher for goods than for services τG > τS .22 Given this setting, it is
possible to map the productivity of the firm and the export choice. In Figure 3, we plot
the profits, π and the productivity, ϕ, of the firms choosing different export strategies.
We can see that the least productive firms (those below ϕD ) will stay in the domestic
market and produce services or goods; those in the middle (between ϕD and ϕG ) export
goods and the most productive ones (those above ϕS ) export services. Bearing this
framework in mind, the consequences of a decrease in income are straightforward: as
σS < σG the demand for services decreases less than the demand for goods and firms
exporting goods are more seriously hit by the income shock; at the same time, firms
exporting services are more productive and thus more resilient to shocks. The same
mechanisms can also be extended for firms exporting both services and goods (and thus
for the analysis of within-firm adjustments).23
subject to higher costs than for goods and fewer (but more productive) firms are able to enter foreign
markets for services (Breinlich and Criscuolo, 2011; Ariu, 2012).
22
Services are immaterial and they do not physically travel around the world (for example, many
services are traded over the internet, thus enjoying negligible transportation costs). Moreover, services
are not subject to tariffs, so once the fixed cost of exporting is paid, firms can freely trade their desired
amounts. Therefore, it is plausible to think variable trade costs being lower than for goods, which
instead are subject to physical transportation and the payment of tariffs.
23
Suppose that firms can invest a fixed sum fB to add to its exports also the other type. This
investment represents the cost of adapting the production process to the other type of product and it
leads to increase the productivity of the firm to γϕ > ϕ. This means that by exporting both goods
and services firms can more efficiently use their internal resources and decrease their marginal costs.
Using this simple strategy, inspired by the works of Bustos (2011) and Bas (2012), the most productive
firms export both goods and services and an income shock hurts more goods than services even within
the same firm.
20
Figure 1: Belgian Monthly Exports 2006-2010
Table 1: Overview of the NBB Surveys on Trade in Services
Survey Name
F01DGS
F01CDC
F01MER
F03MER
F03CMS
Frequency
M
M
M/Q
M/Q
M/Q/A
Mandatory declaration criteria
e10M annual Intrastat or e5M monthly Intrastat or e1M annual Extrastat
List of firms in Law n 187 of 30.12.1982
e10M annual Intrastat or e5M monthly Intrastat or e1M annual Extrastat
All firms declaring e10M Intrastat
e1000 annual Intrastat or e5000 monthly Intrastat or e1000 annual Extrastat
F13CON
F23CON
F03TRP
F03AVS
F02BRO
F02CCI
F02TRA
M/Q
M/Q
M/Q/A
Q
M/Q
M
M/Q
All firms
All firms
All firms
All firms
All firms with more than 10 employees
All payments with credit cards not included in the other surveys
Travel Agencies with more than 10M annual turnover
Services Targed
All
All
Merchanting
Merchanting
All except Transport, Merchanting, Telecommunications,
Insurance, Construction, Energy
Construction
Energy, Recycling, Real Estate
Transport
Telecommunications
Insurance
All
Travel
Note: This table presents the different surveys that constitute the NBB Trade in Services dataset from 2006 onwards, their frequency (M stands for monthly, Q for quarterly and A for annual), the criteria
to make the declaration mandatory and the services targeted by the each survey. All values are in Euros.
21
Figure 2: Ratio of Quarterly Exports of Services over Exports of Goods for OECD
Countries, Q1 2006=1
Source: OECD.Stat Extracts
Figure 3: Profits and Productivity
π
S
πexp
G
πexp
πD
ϕD
f
ϕG
ϕS
fG
fS
22
ϕ
Table 2: Service Categories
G0001
G0002
G1000
G5000
G6000
G7000
H0000
H1000
H1100
H1500
H2000
H3000
H4000
H5000
H5101
H5102
H6100
H6200
H6300
H6400
H6500
H7000*
H8000
H9000
H9100
H9200
BUSINESS SERVICES
IT services
IT services
Repair services for IT
Information services
Commission Fees
Franchising services
Patent services
Services for intangible investments
Other commissions
Technical and Business Services
Legal services
Accounting services
Consulting services
Marketing and opinion polling services
Research and development services
Architectural services
Waste treatment services
Agricultural and mining services
Local services
Secretarial services
Security services
Translation services
Photographic services
Janitorial services
Services to affiliates
Audio-visual services
Educational services
Health service
Other cultural and personal services
B1101
B1102
B1103
B1200
B2500
B3500
B4000
B4500
Passenger transport by different means of transportation
Freight transport by different means of transportation
Agency services for transport of passenger (different means of transportation)
Satellite launch services and freight transportation in the space
Passenger
Auxiliary services to transport
Auxiliary services to different transport modes
Any rental with crew
C0000
C0301
C0302
C0303
C0304
C0401
C0402
C1301
C2301
C9000
C9100
SERVICES TO NON RESIDENTS
Agency Services
Conference fees
Car rental services
Excursion fees
Cruises
Agency services for concerts
Agency services for sport and cultural events
Agency services for tourism
Car dealers services
Repair services
Teaching services
Healthcare services
D0001
D0002
D1000
B0001
B0002
B0003
B2001
B2002
B2003
B3000
B0101
B0102
B2101
B2102
B2103
B3100
B0201
B0202
B2201
B2202
B2203
B3200
B0301
B0302
B2301
B2302
B2303
B3300
B0401
B0402
B2401
B3400
B0500
B1000
TRANSPORT SERVICES
Sea Transport
Freight
Boat rental for freight transportation
Transport of floating devices
Passenger
Boat rental for passenger transportation
Agency services for transport of passengers
Auxiliary services to sea transport
Air Transport
Freight
Plane rental for freight transportation
Passenger
Plane rental for passenger transportation
Agency services for transport of passengers
Auxiliary services to air transport
Rail Transport
Freight
Train rental for freight transport
Passenger
Train rental for passenger transport
Agency services for transport of passengers
Auxiliary services to rail transport
Road Transport
Freight
Truck Rentals for freight transport
Passenger
Bus rentals for passenger transport
Agency services for transport of passengers
Auxiliary services to road transport
Internal Waters Transport
Freight
Boat rental for freight transport
Boat rental for passenger transport
Auxiliary services to internal waters transport
Other Transport
Freight
Freight transport by pipes and electricity transport
TELECOMMUNICATION SERVICES
Postal and Communication services
Postal services
Communication services
Telecommunication services
Telecommunication services
CONSTRUCTION SERVICES
E0001
E0002*
E0003
E0100
E0200
E0301
E0302
E5300
E5400
F1000
F1100
F2000
F2100
F3000
F3100
F4000
F5001
F5002
F6001
F6002
F6003
F6004
F6005
F6006
F6301
F6302
F6303
F6304
F6305
F6306
Construction services abroad for less than one year
Goods included in the construction services
Infrastructure reparation
Construction services abroad for less than one year (sub-tendered)
Construction services in Belgium for a foreign company (sub-tendered)
Construction services in Belgium for a sub-tendered foreign company (sub-tendered)
Purchase of construction services (only for imports)
Construction and installation services in Belgium (for a foreign company)
Construction and installation services abroad
FINANCIAL AND INSURANCE SERVICES
Insurance Services
Insurance premium against theft, damage and loss of freight
Freight insurance
Other insurances
Indemnities related to other insurance
Re-insurance premium
Indemnities for re-insurance
Fees, consultancies and other services related to insurance and re-insurance
Financial Services
Financial services from postal companies
Financial services
Financial services related to transport of passenger
Financial services related to transport of freight
Operational leasing
Other operational leasing
Real estate services to international organizations
Real estate services to non residents
Financial leasing for passenger transport
Financial leasing for freight transport
Financial leasing for IT
Other financial leasing
Financial leasing for real estate services to international institutions
Other financial leasing
Note: This table presents the different service categories present in the different surveys of the NBB. Codes marked with an asterisk (*) are excluded from the analysis.
23
Table 3: Change in the Margins of Belgian Exports (2008S1-2009S1)
Panel a: Exports
Period
2008S1
Total
21,757
Services
2009S1 (∆-1)%
2008S1
Goods
2009S1
(∆-1)%
21,075
-3.13%
99,534
72,853
-26.81%
2,107
11.37
1.55
-0.33%
2.00%
12,964
8.58
3.72
12,964
8.46
3.79
-1.41%
1.83%
0.57
-4.72%
0.24
0.18
-27.09%
Extensive Margins:
Firms
2,107
Countries
11.41
Products
1.52
Intensive Margin:
Average Sales
0.60
Note: This table presents the decomposition of the growth rate of Belgian exports between the first
semester of 2008 and the first semester 2009 into the extensive margin (average number of export
markets per firm and average number of product per market-firm) and the intensive margin (average
exports per firm, market and product).
Table 4: Change in the Margins of Belgian Exports (2008S1-2009S1) by Product and
Service Type
Total
% Change
Panel a: Services
Goods Transport
Passenger Transport
Auxiliary Services for Transport
Service to non-Residents
Telecommunication Services
Construction Services
Financial and Insurance Services
Business Services
Panel b: Goods
Intermediates
Capital Goods
Consumer Durables
Consumer non Durables
Energy
Other
Extensive Margins
Countries Services
Intensive
Margin
-22.25
-1.98
-10.62
-0.34
11.66
-0.79
21.49
4.90
-3.77
1.94
-4.21
-0.14
5.13
-2.77
1.59
-0.23
-0.33
1.80
1.99
1.29
-1.47
-0.82
0.26
2.11
-18.94
-5.55
-8.52
-1.47
7.80
2.87
19.27
2.97
-31.24
-23.64
-38.23
-7.74
-44.47
-25.51
-0.61
-1.62
-4.21
0.17
-3.94
-1.84
1.51
1.87
1.99
0.36
0.04
0.28
-31.85
-23.81
-36.00
-8.22
-42.22
-24.33
Note: This table presents the decomposition of the growth rate of Belgian exports between the
first semester of 2008 and the first semester 2009 for EU and non-EU countries, type of service
exported, multinational status of the firm and type of firm.
24
Table 5: Change in the Margins of Belgian Exports (2008S1-2009S1) by Country of
Destination
Total
% Change
Panel a: Services
EU
-4.30
non-EU
-1.14
OECD
-1.05
non-OECD
-14.85
Panel b: Goods
EU
-26.73
non-EU
-27.27
OECD
-26.64
non-OECD
-27.75
Extensive Margins
Countries Services
Intensive
Margin
-1.08
0.50
-0.84
-0.09
1.94
2.17
1.74
2.80
-5.09
-3.73
-1.92
-17.09
-3.15
-0.76
-1.92
-0.59
1.54
4.14
2.09
1.58
-25.50
-29.63
-26.74
-28.45
Note: This table presents the decomposition of the growth rate of Belgian exports between the first semester of 2008 and the first semester
2009 for EU, non-EU, OECD and non-OECD countries.
Table 6: Change in the Margins of Belgian Exports (2008S1-2009S1) by Ownership
Status
Total
% Change
Panel a: Services
MNE
non-MNE
Foreign Owned
Non-Foreign Owned
Panel b: Goods
MNE
non-MNE
Foreign Owned
Non-Foreign Owned
Extensive Margins
Countries Services
Intensive
Margin
7.38
-8.54
3.17
-8.64
0.63
-0.60
0.86
-0.88
2.36
1.80
3.89
0.89
4.24
-8.54
-1.53
-8.64
-29.77
-25.04
-30.32
-22.98
-1.28
-1.44
-2.04
-1.27
2.44
1.65
4.53
0.99
-30.55
-25.19
-31.96
-22.75
Note: This table presents the decomposition of the growth rate of Belgian exports
between the first semester of 2008 and the first semester 2009 by ownership status
of the firms.
25
Table 7: Change in the Margins of Belgian Exports (2008S1-2009S1) by Firm Characteristics
Total
% Change
Panel a: Services
Big
Small
Financially exposed
Financially non-exposed
Panel b: Goods
Big
Small
Financially exposed
Financially non-exposed
Extensive Margins
Countries Services
Intensive
Margin
-0.27
-22.65
-1.32
-3.07
-0.38
-1.13
0.25
-0.74
2.60
-0.57
0.97
3.20
-2.42
-21.32
-2.51
-5.37
-27.08
-23.98
-29.68
-23.82
-1.85
-0.48
-1.94
-0.90
2.85
1.28
1.84
3.36
-27.76
-24.58
-29.58
-25.63
Note: This table presents the decomposition of the growth rate of Belgian exports
between the first semester of 2008 and the first semester 2009 by size and debt structure
of the firms (we define a firm as big if the number of full time equivalent employees is
above the median and as financially exposed if the share of financial debts of the firm is
above the median).
26
Table 8: Description of the Variables
27
Variable Name
Trade Variables:
Export of Services
Export of Goods
Firm-level variables:
Dsize
Dproductivity
Dintermediate share
Dshare exp sales
Dshare imp interm
Dvalue added chain
Dext f in dep
Dshare debts over liab
Dshare debts due af ter one year
Dshare f in debt
Dshare stock
F OR
MNE
Nace codes
Country-level variables:
OECD non − EU
non − OECD non − EU
Exchange rate change
GDP growth
Description
Source
2007-2010 monthly exports of services by firm, service, country
2007-2010 monthly exports of goods by firm, service, country
NBB Trade in Services Dataset
NBB Trade in Goods Dataset
Log of firm size, measured in terms of full-time equivalent employment
Log of Value added per worker
Share of intermediates over turnover
Share of exports over turnover
Share of imports over intermediates
Exports times imports over turnover
Investments minus operating profits over investments
Ratio of debts over total liabilities
Share of debts due after one year
Share of financial debt
Ratio of stock over turnover
Dummy indicating foreign ownership
Dummy indicating a multinational firm
NACE rev 1.1 2-digit industry
NBB
NBB
NBB
NBB
NBB
NBB
NBB
NBB
NBB
NBB
NBB
NBB
NBB
NBB
Dummy for countries belonging to the OECD (in 2008) but not to the EU
Dummy for countries belonging neither to the OECD nor to the EU
% change in the daily exchange rate with the euro between at the 1st
of april of each year
Average annual growth rate of the countrys GDP
OECD and European Commission
OECD and European Commission
European Central Bank
Business Registry
Business Registry
Business Registry
Business Registry
Business Registry
Business Registry
Business Registry
Business Registry
Business Registry
Business Registry
Business Registry
Survey of Foreign Direct Investments
Survey of Foreign Direct Investments
Crossroads Bank
IMF World Economic Outlook
Note: All firm characteristics prefixed with a D are dummy variables that take value one if the firm characteristic is above the NACE rev 1.1 2-digit industry median across all
trading firms (so both those trading services and those trading goods) and zero otherwise. All firm characteristics prefixed with a C indicate that we use the actual value of the
variable.
Table 9: DD Regression on continuing firm-country-service triplets, dummy variables
β1
Firm Characteristics
Dsize
Dproductivity
Dintermediate share
Dshare exp sales
Dshare imp interm
Dvalue added chain
Dext f in dep
Dshare debts over liab
Dshare debts due af ter one year
Dshare f in debt
Dshare stock
F OR
MNE
Country Caracteristics:
OECD non − EU
non − OECD non − EU
Exchange rate change
GDP growth
Constant
Service Dummies
Industry Dummies
Observations
R2
Goods
β22009
0.0395b
(0.017)
0.0105
(0.017)
0.0096
(0.016)
-0.0059
(0.016)
-0.0356b
(0.015)
-0.0005
(0.016)
-0.0477b
(0.023)
-0.0278
(0.019)
0.0197
(0.020)
0.0114
(0.021)
0.0098
(0.021)
0.0033
(0.025)
0.0160
(0.029)
-0.0287
(0.029)
-0.0108
(0.028)
-0.0285
(0.025)
-0.0520c
(0.028)
0.0558b
(0.026)
-0.0295
(0.028)
0.0351
(0.029)
-0.0053
(0.029)
0.0298
(0.024)
-0.0509c
(0.027)
0.0315
(0.029)
-0.0212
(0.043)
-0.0383
(0.038)
-0.1604a 0.2848a
(0.030)
(0.056)
-0.0773b 0.1088c
(0.034)
(0.060)
-0.2851a -0.1463
(0.085)
(0.134)
0.0101c
0.0139c
(0.005)
(0.008)
-0.1129
(0.176)
Yes
Yes
650,570
0.0147
Services
β1
β22009
-0.0584
(0.207)
0.0986
(0.097)
-0.0197
(0.125)
0.0481
(0.105)
-0.0998
(0.108)
0.0075
(0.136)
-0.0637
(0.080)
-0.0876
(0.097)
-0.1467
(0.091)
-0.0273
(0.103)
-0.0892
(0.095)
-0.0509
(0.126)
0.0381
(0.079)
0.5499
(0.409)
-0.0082
(0.157)
0.0575
(0.186)
0.0283
(0.147)
0.2219
(0.162)
-0.0661
(0.208)
0.2205
(0.135)
0.0374
(0.157)
0.2921b
(0.140)
0.0034
(0.189)
0.2339
(0.150)
0.0589
(0.172)
-0.1199
(0.140)
0.0486 -0.1359
(0.087) (0.155)
0.1760c -0.1961
(0.095) (0.138)
0.1552 -0.2725
(0.369) (0.526)
0.0142 -0.0075
(0.017) (0.027)
0.1076
(0.408)
Yes
Yes
23,249
0.0529
Note: This table presents the estimated coefficients for the variable of interest in this
study (the complete table is available upon request) β1 refers to the estimated effects
in normal time and β22009 refers to the estimated effect of the same variables during the
crisis in 2009. On the left side estimates for exports of goods and on the right for exports
of services. Multi-level clustered standard errors in parentheses (at the firm, product
and country level). a p<0.01, b p<0.05, c p<0.1.
28
Table 10: DD Regression on continuing firm-country-service triplets, continuous variables
β1
Firm Characteristics
Csize
Cproductivity
Cintermediate share
Cshare exp sales
Cshare imp interm
Cvalue added chain
Cext f in dep
Cshare debts over liab
Cshare debts due af ter one year
Cshare f in debt
Cshare stock
F OR
MNE
Country Caracteristics:
OECD non − EU
non − OECD non − EU
Exchange rate change
GDP growth
Constant
Service Dummies
Industry Dummies
Observations
R2
Goods
β22009
0.0260a
(0.008)
0.0333b
(0.015)
0.0055
(0.005)
-0.3424
(0.338)
-0.5168
(0.365)
0.0133
(0.009)
0.0000
(0.000)
-0.0195
(0.043)
0.0505
(0.043)
-0.0236
(0.038)
0.0013
(0.131)
-0.0286
(0.029)
-0.0146
(0.027)
β1
Services
β22009
-0.0187c
(0.011)
-0.0422b
(0.019)
-0.0039
(0.006)
-0.5167
(0.392)
-0.1397
(0.583)
0.0011
(0.014)
0.0001
(0.000)
-0.0429
(0.058)
-0.0474
(0.064)
-0.0029
(0.046)
0.0336
(0.140)
-0.0085
(0.043)
-0.0116
(0.041)
-0.0000
(0.000)
-0.0090
(0.039)
0.0171
(0.187)
-0.2919
(0.358)
0.3992
(0.376)
-0.0035
(0.002)
0.0000
(0.000)
-0.2010
(0.225)
0.1781
(0.216)
-0.2391
(0.181)
-0.0582
(0.423)
-0.0280
(0.117)
0.0496
(0.091)
-0.0000
(0.000)
-0.0017
(0.052)
-0.2233
(0.289)
0.5367
(0.541)
-1.5467b
(0.717)
0.0162a
(0.004)
-0.0000
(0.000)
0.2775
(0.353)
-0.1919
(0.379)
0.2993
(0.323)
0.1566
(0.647)
-0.0079
(0.159)
-0.1949
(0.161)
-0.1665a 0.2906a
(0.030)
(0.056)
-0.0824b 0.1124c
(0.034)
(0.062)
-0.2907a -0.1369
(0.089)
(0.141)
0.0095c
0.0141c
(0.005)
(0.008)
-0.0404
(0.145)
Yes
Yes
650,570
0.0143
0.0536
(0.087)
0.1864b
(0.091)
0.1650
(0.369)
0.0152
(0.017)
0.0655
(0.371)
Yes
Yes
23,249
0.0566
-0.1356
(0.156)
-0.2175
(0.138)
-0.2568
(0.534)
-0.0098
(0.026)
Note: This table presents the estimated coefficients for the variable of interest in this
study (the complete table is available upon request) β1 refers to the estimated effects
in normal time and β22009 refers to the estimated effect of the same variables during the
crisis in 2009. On the left side estimates for exports of goods and on the right for exports
of services. Multi-level clustered standard errors in parentheses (at the firm, service or
product and country level). a p<0.01, b p<0.05, c p<0.1.
29
Table 11: Treated and Control Group Characteristics
log Size
log Productivity
log Capital Intensity
log Average Wage
MNE
FOR
Control
2.983
(0.0445)
-2.302
(0.020)
-3.952
(0.043)
-2.867
(0.015)
0.052
(0.006)
0.130
(0.009)
Treated
3.091
(0.046)
-2.350
(0.021)
-4.020
(0.052)
-2.893
(0.013)
0.055
(0.006)
0.132
(0.009)
Diff
-0.107
(0.064)
0.048
(0.029)
0.068
(0.067)
0.026
(0.020)
-0.002
(0.008)
-0.002
(0.013)
Note: This table presents the characteristics and the differences
of Control and Treated groups, in terms of size, productivity,
capital intensity, average wage, multinational status and foreign
ownership. In the last column there is the t statistic of a t-test
where the null hypothesis is that the difference is equal to zero.
b indicates significance of the difference at the 5% level.
Table 12: DDD Regression on continuing firm-countries, GDP Growth
Panel a: Bi-Exporters
(1)
All
γ32009, GDP Growth
-0.0549b
(0.027)
Firm-Year Dummies
Yes
Observations
22,162
R2
0.0763
Panel b: Matched Mono Exporters
(1)
All
γ32009, GDP Growth
Firm-Year Dummies
Observations
R2
-0.0466a
(0.013)
Yes
40,272
0.0717
(2)
Durables
(3)
Non-Durables
-0.0802b
(0.038)
Yes
14,117
0.1093
-0.0427
(0.027)
Yes
16,129
0.1063
(2)
Durables
(3)
Non-Durables
-0.0560b
(0.022)
Yes
21,685
0.0945
-0.0334b
(0.017)
Yes
29,402
0.0857
(4)
Business
Services
-0.0943b
(0.040)
Yes
12,099
0.0798
(5)
Transport
Services
-0.0617a
(0.022)
Yes
10,671
0.0798
(6)
Services to
Non Residents
-0.2479
(0.153)
Yes
948
0.1562
(7)
Telecommunication
Services
0.0415
(0.051)
Yes
1,329
0.1208
(8)
Construction
Services
0.1391
(0.118)
Yes
1,243
0.1017
(9)
Financial
Services
0.0432
(0.150)
Yes
1,865
0.0874
(10)
Other
Services
-0.0360
(0.088)
Yes
2,860
0.0979
(4)
Business
Services
-0.1087c
(0.065)
Yes
11,908
0.0805
(5)
Transport
Services
-0.0480
(0.032)
Yes
18,931
0.0767
(6)
Services to
Non Residents
0.2993c
(0.172)
Yes
1,336
0.1013
(7)
Telecommunication
Services
-0.6340c
(0.347)
Yes
3,498
0.0588
(8)
Construction
Services
-0.0280
(0.044)
Yes
2,597
0.0879
(9)
Financial
Services
-0.0039
(0.025)
Yes
4,270
0.0506
(10)
Other
Services
-0.0534a
(0.019)
Yes
2,201
0.1128
Note: This table reports the estimated coefficients for γ3t, GDP Growth from equation 3 for the firms that trade both goods and services (Bi-Exporters) and for the matched firms exporting either goods
or services (Mono-Exporters). Clustered standard errors in parentheses (at the country level). a p<0.01, b p<0.05, c p<0.1.
Table 13: DDD Regression on continuing firm-countries, Credit Constraints
Panel a: Bi-Exporters
(1)
All
(2)
Durables
γ32009, F IN
0.4557
0.0938
(0.340)
(0.408)
Country-Year Dummies
Yes
Yes
Observations
18,790
12,127
R2
0.0313
0.0347
Panel b: Matched Mono Exporters
(1)
(2)
All
Durables
γ32009, F IN
Country-Year Dummies
Observations
R2
0.0275
(0.123)
Yes
26,317
0.0331
-0.0031
(0.128)
Yes
13,535
0.0180
(3)
Non-Durables
0.2772
(0.378)
Yes
13,817
0.0327
(3)
Non-Durables
0.1837
(0.183)
Yes
19,235
0.0413
(4)
(5)
(6)
Business Transport
Services to
Services
Services Non Residents
0.6000
0.1076
2.7639
(0.385)
(0.232)
(1.719)
Yes
Yes
Yes
10,360
9,006
699
0.0372
0.0597
0.2547
(7)
(8)
Telecommunication Construction
Services
Services
-0.6523
1.2524
(1.201)
(1.099)
Yes
Yes
1,073
1,201
0.1940
0.1788
(9)
(10)
Financial Other
Services Services
1.7447b
0.5249
(0.738)
(0.762)
Yes
Yes
1,708
2,536
0.1834
0.1169
(4)
(5)
(6)
Business Transport
Services to
Services
Services Non Residents
-0.0190
0.1138
-4.1627
(0.246)
(0.321)
(9.027)
Yes
Yes
Yes
7,182
12,503
1,043
0.0699
0.0644
0.3433
(7)
(8)
Telecommunication Construction
Services
Services
-0.0056
-0.7648
(0.280)
(2.102)
Yes
Yes
3,079
1,933
0.2400
0.2421
(9)
(10)
Financial Other
Services Services
-0.2652
0.9249
(1.637)
(2.144)
Yes
Yes
2,895
2,090
0.1614
0.1779
Note: This table reports the estimated coefficients for γ3t, F IN from equation 4 for the firms that trade both goods and services (Bi-Exporters) and for the matched firms exporting either goods or services (Mono-Exporters). Clustered
standard errors in parentheses (at the firm level). a p<0.01, b p<0.05, c p<0.1.
30