what do we know about the relationship between access to
TRANSCRIPT
Research Division Federal Reserve Bank of St. Louis Working Paper Series
What Do We Know about the Relationship between Access to
Finance and International Trade?
Silvio Contessi
and Francesca de Nicola
Working Paper 2012-054A http://research.stlouisfed.org/wp/2012/2012-054.pdf
October 2012
FEDERAL RESERVE BANK OF ST. LOUIS
Research Division P.O. Box 442
St. Louis, MO 63166
______________________________________________________________________________________
The views expressed are those of the individual authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors.
Federal Reserve Bank of St. Louis Working Papers are preliminary materials circulated to stimulate discussion and critical comment. References in publications to Federal Reserve Bank of St. Louis Working Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the author or authors.
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What Do We Know about the Relationship between
Access to Finance and International Trade?*
Silvio Contessi
Federal Reserve Bank
of St. Louis
Francesca de Nicola
International Food Policy
Research Institute
October 2012
Abstract In part as a response to the recent financial crisis, the relationship between access to finance and international trade has received much attention in the recent years. This article reviews trade finance, its role and functioning. It discusses the relevance of the more general concept of access to credit for firms engaging in international trade both in normal times and during times credit may be scarcer because of a banking and financial crisis. Part of the paper focuses on the evidence from the recent episode of the Great Trade Collapse, and argues that the mixed empirical evidence is at least partially explained by the heterogeneous measurements of access to finance used in the empirical literature. JEL Classification: D92, F12, F36. Keywords: International Trade, Export Margins, Credit Constraints
* The views expressed are those of the authors and do not represent official positions of the International Food Policy Research Institute, the Federal Reserve Bank of St. Louis, the Board of Governors, or the Federal Reserve System. Silvio Contessi: Federal Reserve Bank of St. Louis, Research Division, P.O. Box 442, St. Louis, MO 63166-0442. Email: [email protected] Francesca de Nicola: 2033 K Street NW, Washington D.C. 20006. Email: [email protected]
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1. Introduction
Along with the spread of the financial crisis that began in 2007, the World experienced
the largest recession since the Great Depression. According to the International
Monetary Fund (IMF), World GDP fell by 0.6 percent in 2009, while advanced
economies experienced a contraction of 3.6 percent, the largest decline in the past 50
years.
The global recession was associated with a collapse of international trade, now known as
the Great Trade Collapse. The volume of exports of goods and services from the
advanced economies fell by a staggering 11.5 percent, almost four times as much as the
drop in GDP, and more than in emerging and developing countries (see Figure 1.
Growth of Export and GDP in Emerging and Developing Countries and Advanced
EconomiesFigure 1). Such magnitude promptly triggered analyses and research to
explain the collapse. In the aftermath of the global financial crisis, the immediate
conjecture was that the credit crunch may have caused the large decline in world trade
by reducing exporters’ access to finance. A few years later, there is some consensus that
demand for intermediates and durable goods - whose purchases are easier to postpone
until households and firms’ economic situation improve - played a large role. Recent
research by (Eaton, Kortum, Neiman, & Romalis, 2011) attributes more than 80 percent
of the decline in trade/GDP during the Great Recession to the large drop in demand
and particularly to the collapse of expenditure on durable goods. Further evidence based
on micro data supports this view (Behrens, Corcos, & Mion, Forthcoming). However,
these estimates leave room to other factors to explain the remaining 20-30 percent of the
trade collapse. Indeed, there is some evidence that at least part of the trade collapse
may have been caused by the contraction trade finance during the crisis (Chor &
Manova, 2012) and (Amiti & Weinstein, 2011).
The paper tackles three issues. First, it provides some background information on the
role of trade finance in international trade; second, it discusses the relevant theoretical
work that explains it, and finally it surveys the existing empirical evidence on the
subject. The final objective is to evaluate the current knowledge on the relationship
between finance and trade and provide a roadmap of the literature and its challenges to
researchers. The paper is organized as follows: Section 2 explains the basic elements of
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trade finance; Section 3 discusses recent models linking trade and finance; Section 4
reports the various definitions of credit constraints used in the literature; Section 5
presents the related empirical evidence; Section 6 addressed the relative econometric
issues; and Section 7 concludes.
2. What is Trade Finance? And what does Access to Finance Mean?
Why do exporters need credit in a way that differs from domestically oriented firms?
How does trade finance fulfill this need? Firms typically rely on external capital (as
opposed to own capital, internal cash flows, and reinvested earnings) to finance fixed
and variable costs. Examples of fixed costs are research and development, advertising,
fixed capital equipment; examples of variable costs are intermediate input purchases and
inventories, payments to workers, before sales and payments of their output take place.
Certain peculiar features of international trade may entail additional fixed and variable
costs compared to production for domestic markets. First, export activities entail extra
upfront expenditures that may force firms to rely on external finance; for example,
learning about the profitability of new export markets; making market-specific
investments in capacity, product customization and regulatory compliance; and setting
up and maintaining foreign distribution networks. Second, exporting generate additional
variable trade costs due to international shipping, duties and freight insurance, some of
which are incurred before export revenues are realized. In addition, cross-border delivery
can take longer times to complete than domestic orders, a fact that increases the need
for working capital requirements relative to those of firms that sell only domestically.
For example, ocean transit shipping times can be as long as several weeks, during which
the exporting firm typically would be waiting for payment.
Accordingly, governments, banks and other financial institutions have developed a wide
set of specialized instruments to provide so-called trade finance, i.e. financial
instruments that are used and sometimes tailored to satisfy exporters’ needs, normally
providing both liquidity and insurance. Most of these contracts require some form of
collateral, e.g. tangible assets such as inventories. The role of trade finance in
international trade appears anecdotally important but reliable estimates are difficult to
obtain because banks do not usually report export loans separated from other loans in
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their balance sheet. Some estimates suggest that up to 90 percent of world trade relies
on one or more trade finance instruments (Auboin, 2009), though, as pointed out by
(Love, 2011), the source of this figure is uncertain.
It is important to point to the distinction between trade credit and trade finance. Trade
credit is an agreement between two parties in which a customer can purchase goods on
account without paying cash immediately but rather paying the supplier at a later date.
Usually when the goods are delivered, a trade credit is given for a specific amount of
days (30, 60 or 90 days) and it is recorded in the accounts receivable section of the
firm’s balance sheet. Trade credit is a relatively expensive form of financing as implicit
interest rates can be over 40 per cent if the borrower does not take advantage of early-
payment discounts. 1 Several firms record trade credit but are not engaged in
international trade. Trade finance generally refers to formal borrowing by firms from
banks or other financial institutions to facilitate international trade activities, as we
describe in the next section. How does trade finance work? Banks and financial
institutions essentially provide trade finance for two purposes. First, it serves as a
source of working capital for individual traders and international companies in need of
liquid assets. Second, it provides insurance against the risks involved in international
and domestic trade, such as price or currency fluctuations. Each of these two functions
is fulfilled by a certain set of credit instruments. (Chauffour & Malouche, 2011) contains
an extensive description of these instruments: open accounts in inter-firm or supply
chain financing, traditional bank financing (for investment capital, working capital, and
pre-export finance), as well as more direct payment mechanisms such as letters of credit,
suppliers credit linked with bank financing, countertrade, factoring and forfeiting,
instruments of risk management (such as advance payment guarantees, performance
bonds, refund guarantees, hedging) and finally export credit insurance and guarantees.
By far, among the various instruments that are used to provide liquidity, the most
widely used is the commercial letter of credit, a form of documentary credit.2 A second
instrument is credit to buyer or supplier. Credit counters the off balance sheets
financing provided by documentary credit, and represents the more traditional form of
1 One explanation for such high rates is that the illiquidity of the goods reduces the risk of moral hazard, providing suppliers with trade credit when bank credit would not be extended. 2 With this instrument, the issuing bank state its commitment to pay the beneficiary (seller) a given amount of money on the behalf of the buyer as long as the seller comply with the terms and conditions specified by the sale contract. On the one hand this allows the importer to use his cash flow for alternative purposes than paying the exporter, and on the other hand the letter of credit ensures that the exporter will be paid in a timely manner. This instrument is particularly suitable for international contracts that
difficult to enforce and riskier. A similar purpose is achieved by bills avalisation whereby the buyer’s bank guarantees payment to the seller in case the buyer will not pay. Other examples of documentary credit are advance payment guarantees, custom bonds which allow to postpone tax payments until after the goods are sold, and custom bonds for temporary transit that waive paying for custom duties if goods are imported with the intent of being exported.
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lending. It may happen in the form of providing working capital, or overdraft or term
loan facilities. Counter trade arrangements are used in situations and countries in which
a shortage of foreign exchange reserves or liquid assets may prevent exchange of goods
for money. Under such arrangements, buyer and seller agree that goods will be traded at
a fixed value without involving the use of cash or credit terms, but rather barter-
exchange, counter-purchase or buy-back promises. For example, countertrade emerged
as an important instrument after the break-up of the USSR. A fourth instrument is
called factoring (if trade is domestic) or forfeiting (if trade is international). The seller
(exporter) remits guaranteed debt, from a sale on credit, to a third party (financial
firm) that pays him in cash upfront the face value of debt minus a discount. The seller
is then no longer liable for default of the buyer (importer) when debt comes to
maturity. Finally, part of trade finance provides instruments that carry out an
insurance function against the risks involved in international and domestic trade, chiefly
as price or currency fluctuations. Trade finance instruments that combine an insurance
component and a credit component are often offered by government agencies involved in
export promotion.3
To better grasp the importance of trade finance for potential exporters relative to other
obstacles to trade, we present evidence based on a quite unique module of the 2006
World Bank Enterprise Survey conducted in Jordan. The survey directly asks: During
the last fiscal year did this establishment export or is the management considering
entering the export market? Out of 352 valid respondents 52% responded positively and
48% responded negatively. Among the exporting firms 94% and 10% report having
exported directly or indirectly in the previous year. The survey then asked respondents
to identify the three most important business services that most help increase exports or
facilitate entering a foreign market. Figure 1Figure 2 reports the distribution of the first,
second and third most important reason. Though export finance ranks relatively low
among the most important obstacles to business, it plays a relatively important role
overall.
3 Though the importance of export promotion agencies is sometimes questioned, in certain countries their direct insurance activity is as important as the extension of loans. (Ilias, Hanrahan, & Villarreal, 2012) for example, report that in 2011 the Im-Ex Bank of the U.S. approved 3,751 transactions of credit and insurance support, which amounted to about $33 billion in authorizations. Among these authorizations about 25.7 million were directed to loans and loan guarantees (corresponding to 802 authorizations) but 7 million were insurance (to 2,949 authorizations).
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3. Theoretical underpinnings
In this section we focus only on international transactions and trade finance because an
excellent discussion of the different theories explaining the existence of trade credit is
already provided by (Love, 2011). To summarize briefly she focuses in particular on the
following: (i) theories of comparative advantage in information acquisition by suppliers
on the financial health of the buyers, (ii) comparative advantage in liquidating
repossessed goods in the event of non-payments, (iii) the use of trade credit as a
warranty for product quality to allow the customer sufficient time to test the product,
(iv) price discrimination by suppliers between cash and credit customers or in an
oligopolistic supplier market, (v) sunk costs and customized products generated by the
repeated interaction between pairs of suppliers and customers, and finally (vi) theories
of moral hazard positing that suppliers may be less susceptible to the risk of strategic
default than banks because inputs are less liquid and thus less easily diverted than cash.
From aggregate perspective, the relationship between financial development and
international trade at country level was recognized long before the recent crisis and
certainly before the recent theoretical contributions in the new trade theory literature.
An early study by (Kletzer & Bardhan, 1987) showed that even in a world in which
countries have identical technology or endowments, comparative advantage may differ
in the presence of credit market imperfections, modeled as both moral hazard in
international credit market under sovereign risk and also as cross-country differences in
credit contract enforcement under incomplete information. (Matsuyama, 2005) and (Qiu
L. D., 1999) make a similar point though in different framework and using different
types of frictions but focusing on a cross-country perspective with representative firms.
Though these studies related a country’s level of financial development to international
trade, they do not consider specifically the role of financing for exporters and importers.
In fact, formal explanations of why trade finance may be more important in an open
economy than in closed economy are recent. One element of differentiation is that
international and domestic trade finance loans carry different levels of risk and
international trade may require stronger credit protection. (Ahn, 2011) develops a model
in which letters of credit emerge as payment tools only in international trade. The
model explains why the riskiness of international transactions increases relative to
domestic transactions during economic downturns, and why international trade finance
is more sensitive to adverse loan supply shocks than domestic trade finance. Banks
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optimal screening decisions in the presence of counterparty default risks explain why
banks maintain a higher precision screening test for domestic firms and a lower precision
screening test for foreign forms, which justifies the more widespread use of letters of
credit.
Using a broader concept of finance, a small theoretical literature has developed to
explain the role of access to credit in export. While in a closed economy setting a large
literature has studied extensively the distortionary impact that credit frictions can have
on firms’ decisions and dynamics (See for example, (Bernanke & Gertler, 1990) and
(Clementi & Hopenhayn, 2006)), its open economy developments are less mature. The
general approach of these studies is to develop models that can provide testable
implications using firm- or plant-level micro-data, and therefore tend to develop from
(Melitz, 2003), (Chaney, 2005), (Manova, 2008), (Manova, forthcoming) and (Caggese &
Cunat, 2011) that provided the theoretical foundations for a rich empirical literature.4
Two key elements of these models shape the related empirical analysis and testable
implications. First, the existence of sizeable fixed costs for entering a foreign market
that must be paid up front, a cash-in-advance constraint. Part of the empirical trade
literature has spent a great deal of energy estimating the size of such sizeable fixed costs
(for example, (Das, Roberts, & Tybout, 2007)) but the issue of how they are financed
has been traditionally disregarded assuming perfect capital markets. Second, firms’
inability to fully pledge the returns of foreign sales to financiers is the product of
information frictions and monitoring problems. Lender may be reluctant to provide
finance for export ventures because information about foreign markets and their
profitability are difficult or costly to obtain.5 Such reluctance is aggravated by the fact
that the enforcement of contacts in an international setting is potentially partial. Both
Manova and Chaney embed credit frictions in Melitz model with fixed cost and firms’
heterogeneity deriving the implication that larger, more productive firms, are less likely
to be credit-constrained and therefore more likely to export. However, while Manova
assumes that firms must borrow to finance export Chaney conjectures that firms must
finance the costs for entering foreign markets using cash flows from domestic sales.
Higher productivity generates larger profits in both model but in the Manova model it
4 (Jiao & Wen, 2012) provide a dynamic general equilibrium version of these models focused on the trade collapse, while (Buch, Kesternich, Lipponer, & Schnitzer, 2010) and (Manova, Wei, & Zhiwei, Firm Exports and Multinational Activity under Credit Constraints, 2011) provide extensions that include foreign direct investment. 5 A similar point is raised in (Hale, 2012) in her discussion of information flows and the relationship between banking and trade
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increases the probability of repaying the debt while in the Chaney model it increases
that of reinvesting earnings. In both cases, however there’s a positive link between
productivity, the ability to finance the fixed cost, and the probability of assuming export
status (extensive margin).
The implications of the two models however differ with respect to the intensive margin, i.e.
the magnitude of firms’ exports conditional on the export status. In Manova’s model credit
affects the extensive margin because variable production costs need to be financed with
external capital as well, while in Chaney’s once a firm has enough liquidity to pay the fixed
cost of exporting it will be able to finance the variable costs of expanding the scale of
production with internal funds or even borrow externally.
Interestingly Manova’s model weakens the sharp prediction of Melitz’s model that the
likelihood of exporting increases with own productivity. In a range of intermediate
productivity levels firms may have an incentive to shrink their exports below the
unconstrained first-best, a situation in which they may not be able to obtain sufficient
funding to repay financiers. With lower export, the need for external finance is also lower the
level of outside finance required and manage to satisfy the participation constraint of
financiers.
(Caggese & Cunat, 2011) provide a more explicitly dynamic model in the spirit of (Manova,
forthcoming) that takes into account specifically the probability of bankruptcy. Friction to
financing affect export along two dimensions: directly, by hindering from the payment of
export fixed cost; indirectly, altering the selection into entry in the home market and the
riskiness of operating firms. Considered jointly, these effects determine the joint endogenous
distribution of firms across productivity, volatility and financial wealth.
In the models just discussed the role of the banking institutions is very limited and lacks the
nuances caused by banks monitoring problems; one major issue of international trade
transactions is that banks do not observe firms’ productivity levels and find evaluating
potential export profits difficult due to informational problems, an issue that motivated many
papers focused on trade credit. (Feenstra, Li, & Yu, 2009) model explicitly the monitoring
process of banks in such an environment. To maintain incentive-compatibility, banks lend
below the amount needed for first-best production. The longer time needed for export
shipments induces a tighter credit constraint on exporters than on purely domestic firms,
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even in the exporters’ home market. Greater risk faced by exporters also affects the credit
extended by banks. Extra fixed costs reduce exports on the extensive margin, but can be
offset by collateral held by exporting firms.
4. Measurement of access to credit
Simply put good representative measures of trade finance are difficult to obtain. This is
due to three reasons. First, banks do not report export trade finance separately in the
assets side of their balance sheet, and are reluctant to release sensitive data related to
the identity of their clients that engage trade finance. Second, firms do not report
export financing independently on the liability side. Third, it is technically difficult and
cost-ineffective for statistical agencies to track trade finance in Balance of Payment
statistics.
As discussed in (Auboin, 2009) until 2004, a series of trade finance statistics was derived
from balance of payments and BIS banking statistics, thanks to inter-agency efforts by
the IMF, World Bank, BIS and OECD, but were then discontinued. Currently, the only
available and reliable source of statistics concerning trade finance comes from the Berne
Union database. It provides data on the amount of business of export credit agencies,
mainly trade credit insurance. Absent official country-level statistics on trade finance,
survey-based data on banks activities provide some information on developments in
trade finance, a tool that has been relied upon during the recent financial crisis (See
(Asmundson, Dorsey, Saito, Niculcea, & Kachatryan, 2011)).
Here we are concerned with the ways access to finance is measured in empirical work.
Roughly speaking the literature has focused on (i) indirect measurement through
industry-level indicators measures of external vulnerability, (ii) subjective measures, and
(iii) a wide variety of objective measures.
Measures of external financial vulnerability exploit cross-industry variation in financial
dependence. They primarily based on three measures of financial vulnerability,
generally constructed from firm-level commercial data such as Compustat North
America. thus considering only publicly traded firms. Financial vulnerability is captured
by first measures of dependence on external finance like the fraction of total capital
expenditure not financed by internal cash flows from operations, as in the seminal work
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of (Rajan & Zingales, 1998). Second, access to buyer-supplier trade credit is measured as
the ratio of the change in accounts payable to the change in total assets and it reflects
how much credit firms receive in lieu of having to make upfront or spot payments (see
the seminal work of Fisman and Love, 2003). Third, a measures of asset tangibility
similar to (Claessens and Laeven, 2003) is the inverse share of net plant, property and
equipment in total book-value assets and reflect firms’ ability to pledge collateral in
securing external finance.
These objective indicators are complemented by subjective measures that are simply
collected through questionnaires to entrepreneurs. The most comprehensive subjective
measures are reported the World Bank Enterprise Surveys. In these -- largely
representative -- surveys firms’ managers are simply asked whether they consider access
to credit, either in general or in terms of quantity and prices, a problem in their
operations. Answers are then ranked on a 1 to 4 (or to 5 in some surveys) scale where 1
corresponds to the absence or irrelevance of such constraint.
Objective measures are either derived from balance sheet information or reconstructed
using combinations of other responses to questionnaires. In Section 5, we will look in
more detail into the choices made by different authors and their implications for the
estimation strategy. For now it is important to notice that as perceptions measures may
be biased by individual respondents opinions and generally imprecisely quantified,
efforts have been made to verify the correspondence between objective and subjective
measures. For example, (Hallward-Driemeier & Aterrido, 2009) use the WBES to
compare the subjective perception of respondents to balance sheet based measures of
constraints for a large number of obstacles to business and report that the two are
generally positively and significantly correlated; such correlation would suggest that
researchers should feel at ease when using subjective measures. To show how the two
measures correlated we select 27 WBES from the Business Environment and Enterprise
Performance Survey (BEEPS). Similar to other WBES, it includes a section where firms
can identify the main constraints to their business, such as access to financing and cost
of financing.6 Using these subjective measures may help better capturing the business
6 The other constraints are telecommunications, electricity, transportation, access to land, tax rates, tax administration, customs and trade regulations, labor regulations, skills and education of available workers, business licensing and operating permits, economic and regulatory policy uncertainty, macroeconomic instability (inflation, exchange rate), corruption, crime, theft and disorder, anti-competitive or informal practices, and legal system or conflict resolution.
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environment minimizing omitted variable bias, these qualitative indicators do not
perfectly match their quantitative counterparts.
We use an approach developed in (Kuntchev, Ramalho, Rodriguez-Meza, & Yang, 2012)
to study access to credit for small size enterprises that consists in exploiting other
answers to the survey on loan applications, rejections, and so on (see Figure 1 in
(Kuntchev, Ramalho, Rodriguez-Meza, & Yang, 2012). In Figure we compare the
subjective measures reported in by each firm in the BEEPs with the objective measure
derived by other questions. In both cases the measures go from “non credit constrained”
to “fully credit constrained”. Figure suggests a positive correlation between the two
measures. Interestingly, these measures correlate with country-wide measures of the
amount of credit to private sector as a percentage of GDP, a variable collected by the
World Bank in the World Development Indicator. Figure 6 shows a positive correlation
between country-wide credit and share of firms classified as either being unconstrained
or maybe being constrained but a negative correlation with the per-country share of
firms reporting full or partial credit constraints.
5. Trade and Finance: Evidence
The empirical literature has been scant until the second part of the 2000s when studies
on trade and finance started mushrooming. We list the studies we are aware of in Table
1 and discuss some of them in detail. The existing analyses use aggregate data, industry-
level data, and firm-level data with various approaches and results. We will discuss later
how this wealth of approaches may be possibly confounding meta researcher due to their
heterogeneity.
5.1. Aggregate views
The classic analysis by (Kletzer & Bardhan, 1987) is further developed by (Beck T. ,
2002) who shows that in two-sector small economies with a better-developed financial
sector have a comparative advantage in sectors with high scale economies and, all else
equal, are net exporters of the goods they produce. Estimation results from a 30-year
panel with 65 countries give support to the predictions of the model in the sense that
countries with a higher level of financial development have higher shares of
manufactured exports in GDP and in total merchandise exports and have a higher trade
balance in manufactured goods. In a similar fashion but a model-free estimation
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strategy, the first step by (Ronci, 2004) to analyze the relationship between trade credit
and international trade focuses on financial crises episodes (more in Section 5.4).
A more detailed analysis making use of industry level data for a large number of
countries is (Manova, 2008) that studies the empirical connection between shocks to the
availability of external finance and the export behavior in a large panel of countries.
The result shows that equity market liberalizations increase exports disproportionately
more in financially vulnerable sectors that require more outside finance or employ fewer
collateralizable assets, are more pronounced in economies with initially less active stock
markets (indicating that foreign equity flows may substitute for an underdeveloped
domestic financial system), and in the presence of higher trade costs caused by
restrictive trade policies.
Taken altogether, these papers support the view of a positive link between credit and
trade at country-level data though this relationship may just hiding the common
determinant that richer countries simply tend to be more open and have more
developed credit sectors. Moreover, it should be noted, however, that already in this
small group of papers the heterogeneity of the measures of access to credit renders the
comparison quite arduous. Such heterogeneity also permeates firm-level studies that we
discuss in the next Section.
5.2. Firm-level Views
A handful of recent papers has focused on the trade/finance nexus exploiting rich micro
data. The focus has been on determining the effect of measures of access to finance on
the extensive and intensive margin, with a recent push to calibrate quantitative models
of firm dynamics. As in some theoretical models, the extensive margin has been
interpreted in at least three ways: (i) export status (exporter vs. non-exporter); (ii) the
number of destination markets served by an individual firm; and (iii) the number of
products exported by an individual firm; as well as the combination of (i) and (ii), the
combination of (i) and (iii), and the combination of (i), (ii), and (iii). The intensive
margin normally refers to the magnitude of foreign sales, expressed either in monetary
values or as share of total sales. A large part of the firm-level literature has developed
from the (Chaney, 2005) and (Manova, forthcoming) theoretical model. This last paper
contains a rich empirical section in which the author tests the heterogeneous firms’
model.
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In order to guide our analysis we constructed Table 1 that lists the authors and year of
the study, the most important details of the sample, the focus of the analysis and the
results.
Most papers take a single-country perspective and focus on developed countries for
which census data are available. The most used dataset is the Belgian Business Registry
covering the population of firms (census) required to file their accounts to the National
Bank of Belgian firms; an excellent description of these data is contained in (Behrens,
Corcos, & Mion, Forthcoming). (Muûls, 2008) develops a two-countries Chaney-Manova
style model with heterogeneous firms and focuses on export status, destinations, total
exports and products. She finds that credit constraints have a positive effect on the
extensive margin in terms of destination but the effect on both the export status and
the intensive margin (the volume of exports) are not statistically significant.
Using a similar approach (Forlani, 2010) and (Minetti & Zhu, 2011) use a confidential
Italian dataset, the CAPITALIA survey of small and medium sized firms (smaller than
500 employees) that also contains detailed balance sheet information but only for
certain years and for a limited number of firms. (Minetti & Zhu, 2011) focus on the
2001 survey and analyze the extensive margin in terms of both pure exporting status
and number of destination (that reach at most 8 regions in the survey), and the
intensive margin in terms of total foreign sales. Their paper is particularly interesting
for two reasons, First, they exploit a peculiar feature of the Italian banking system
(restriction to inter-province entry) to control for endogeneity, something we will discuss
in the next section. Second, they use (binary) subjective measures of credit constraints,
thanks to the fact that in their dataset, firms are asked directly whether they feel credit
constrained or not. As this measure does not allow them to gauge the severity of the
constraint (for example, some firms could be denied a larger amount of bank credit than
others or have easier access than others to forms of financing alternative to bank loans),
they also exploit information on firms’ characteristics and on the industries in which
firms operate, showing that credit constraints especially hinder export by firms with
short relationships with creditors and by firms with few creditors. Finally, their analysis
also reveals that liquidity constraints depress firms’ export especially in industries with
high external financial dependence (as defined in Section 4). With the same data,
(Forlani, 2010) uses balance sheet information to construct various measure of credit
constraint (equity ratio and quick ratio) and finds consistent results with (Minetti &
Zhu, 2011).
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This dataset is also used in (Caggese & Cunat, 2011) where the capital structure and
the financial constraints faced by the firms are determined endogenously, given the
investment decisions of the firms and their idiosyncratic demand shocks. Financially
constrained firms, which would become exporters in an unconstrained model, may
postpone the decision to export in foreign markets because the fixed costs associated to
export may increase their bankruptcy risk. Their main measure of financing constraints
is reconstructed from three questions in the survey, that ask (i) whether a firm had a
loan application turned down recently; (ii) whether it desires more credit at the market
interest rate, (iii) whether it would be willing to pay an higher interest rate than the
market rate in order to obtain credit. A positive answer to any of these questions
identifies a credit constrained firm, and 14% of the firms in the sample appear credit
constrained. They find evidence consistent with the idea that financing constraints are
relevant to explain export status, but less relevant to explain the intensive margin,
because the goods can be used as collateral and international trade credit is generally
available.
On the contrary, using four different measures of credit constraints and a large dataset
of Chinese firms, (Egger & Kesina, 2010), find significant impact of firms’ financial
constraints on both extensive margin and intensive margin. They estimate the impact of
a one-standard deviation increase in financial constraints on the extensive margin is at
least half as strong as a same decrease in firms’ productivity. Meanwhile, such increase
in financial constraints reduces the intensive margin (measured by export-to-sales ratio)
by near 10 percent. While the studies posted before 2011 failed to obtain consistent
results, more recent studies seem to agree among each other.
Finally, a firm-level study in a cross-country context by (Berman & Hericourt, 2010)
finds that lower financial constraints have a positive effect on the extensive margin
using the WBES for 9 countries. They also examine the interaction effect between firms’
credit constraints and productivity and conclude productivity has a greater impact on
export participation in countries that are more financially developed. But they also find
no role of financial constraints on the extensive margin in terms of destinations and the
intensive margin.
15
These few papers highlight clearly two elements of this firm-level literature. First,
there’s a large variety of approaches in measuring access to credit. Second, results may
appear contradictory though they really are the outcome of different models and
measures, a point we will return in detail in the next Section.
5.3. Trade Insurance and Government Support
In the last part of this section, we focus on two specific topics that are part of this
literature.
There is scarce evidence on the role of public guarantees offered by import-export
government in promoting trade. (Egger & Url, 2006) and (Moser, Nestmann, & Wedo,
2008)both find a small positive impact of Austrian and German public export credit
guarantees on trade in the long run. 7 The role of private guarantees is even less
explored. While government guarantees are quantitatively limited, target specific
destination markets and industries, and have generally long duration, private credit
insurance is likely to be quantitatively more relevant and more related to trade
patterns, being much more short-term (typically 60 to 120 days). We discussed that
part of the trade finance instruments contain an insurance component (Figure 4).
These differences also imply that variations in private credit insurance supply are more
likely to impact export than variations in public credit insurance supply. In a rare
study of trade insurance, (van der Veer, 2010) reports that private insurers covered an
estimated 16.7 percent of Dutch exports in 2006, compared to 0.9 percent of exports
insured by the Dutch State. If a similar ratio characterized U.S. export a back-of-the-
envelope calculation would suggest that about 50 percent of U.S. exports would be
guaranteed by either private or public insurance. This paper quantifies the impact of
changes in the supply of private credit insurance and exports using a unique bilateral
data set which covers the activities from 1992 to 2006 of one of the world’s leading
private credit insurers. This piece consistently finds a positive and statistically
significant effect of private credit insurance on exports. Based on these estimates, the
reduction in private insurance exposure during the 2008-09 international trade collapse
explains about 5 to 9 percent of the drop in world exports.
7 These studies report an average multiplier between 1.7 and 2.8, implying that every euro spent on public guarantees creates between 1.7 and 2.8
euro worth of exports.
16
5.4. Financial and Banking Crises
Given that banking and financial crises result in large shocks to the supply of credit,
these episodes represent powerful environments to study the relationship we are
interested in (Ronci, 2004) studies a panel of 10 economies that experienced financial
and balance of payment crises using the change in outstanding short-term credit in U.S.
Dollars reported in the World Bank Global Development Finance as proxy for trade
credit; this measure includes both short-term credit for trade (as reported by the
OECD) and international banks’ short-term claims (as reported by the Bank of
International Settlements). Measures of total import and export volume are regressed on
various macroeconomic variables and the proxy for trade financing showing that the
latter affects both export and import volumes positively and significantly but with small
estimated elasticity measures. A fall of 20 percent of trade finance explains only a
decline of 0.6 percent in exports and 1.6 percent in imports. An important problem with
this approach is that it is known that a portion of exports is financed outside the
banking system (for example, within the boundaries of multinational firms) which may
explain why export volumes may not be very sensitive to changes in bank-financed
trade credit. Similar conclusions are reached also by (Iacovone & Zavacka, 2009) that
find that exports in sectors more heavily dependent on external finance suffer
significantly more during a crisis as shown by using 23 banking crises between 1980 and
2000 and exploiting industry-level differences in external financial dependence.
Unfortunately, evidence on ban king crisis is scarce other than for the recent financial
crisis with the notable exception of a study on Japanese Lost decade by (Amiti &
Weinstein, 2011). This piece provided an important methodological contribution because
it linked bank-level condition with firm-level export performance
As a response to the Great Collapse, at the outset of the recovery, a vivid debate
emerged on the explanation for the large trade collapse discusses in Section 1. What
happened to trade finance during times of crisis and specifically during the global
financial crisis and how that connects to the dynamics of trade? A survey jointly
administered by the IMF and the BAFT-IFSA (Bankers Association for Finance and
Trade -BAFT- merged with International Financial Services Association –IFSA-)
provides some insight on the magnitude of trade finance collapse. The survey shows that
changes in trade finance conditions were particularly pronounced among large banks
that suffered more the financial crisis. Consequently they were in greater need to
quickly deleverage and responded by increasing the pricing margins. As a result, the
17
letter of credit and the terms of credit of trade-related lending worsened particularly
among large banks.
Figure 7 Changes in Trade Finance: By Groups of Countries (percent growth)shows
that the drop in trade was larger than the contraction in trade finance but the latter
was significant nonetheless. At the onset of the crisis (2007:Q4-2008:Q4), trade finance
actually increased and even during the peak of the crisis (2008:Q1-2009:Q1) it only fell
only by one third relative to the collapse in goods export, with much geographic
variation but the largest drop in Central Asia and Southeastern Europe. The situation
remained negative but stable in the second quarter of 2009 and started to recover by
the end of 2009 when Maghreb and Middle Eastern countries (Emerging Asia)
experienced the largest increase in goods exports (trade finance) worldwide. When
interviewed about the perceived causes of the contraction of trade finance, the surveyed
banks returned answers surprisingly similar to the consensus emerging among
economists. Respondents identified the fall in the demand for trade activities as the
major source of decline in the value of trade finance but attributed about 30 percent of
the fall to the reduced credit availability at either their own institutions or counterparty
bank.
While economists agree that it lead to tighten credit conditions that may have affected
trade, the relative importance of credit relative to other factors like demand shocks and
restrictions is an open research question. Initial evidence based on monthly U,S,
imports studied in (Chor & Manova, 2012) is consistent with (Iacovone & Zavacka,
2009) and suggests that countries with tighter credit availability during the crisis
exported less to the US. However, a number of recent studies have focused on firm-level
performance. For example, the reduction in loans from poorly-performing banks has
been shown to have reduced exports significantly in Peru, as documented by
(Paravisini, Rappoport, Schnabl, & Wolfenzon, 2011). This piece builds on the (Amiti
& Weinstein, 2011) approach by matching customs and firm-level bank credit data: the
authors estimate that a 10% contraction in credit supply translates into a 2.3% (3.6%)
fall in the exports intensive (extensive) margin.
(Bricongne, Fontagn, Gaulier, Taglioni, & Vicard, 2012) use monthly French firm-level
data through April 2009 at the product and destination level for about 100,000
individual French exporters and show that the drop in French exports is mainly due to
the intensive margin of large exporters but small and large exporters are evenly affected
when sectoral and geographical specialization is controlled for. Consistent with the
18
literature, exporters of all sizes in sectors structurally more dependent on external
finance are the most affected by the crisis.
To summarize, the current status of the literature on the Great Trade Collapse is quite
concordant to suggest a large role for demand shocks, a minor but non-negligible role for
credit supply shocks, and a quantitatively dominant source of export adjustment coming
from the intensive margin.
6. Econometric issues
The analysis of the impact of financial frictions on international trade poses three main
econometric challenges: (i) the estimates of financial constraints may be inconsistent
because of measurement error; (ii) they may be biased because of endogeneity bias, or
(iii) because of sample selection implying external validity concerns. In this section we
review these econometric challenges and the empirical strategies to address concerns
about the reliability of the estimates.
6.1. Measurement Error
First we consider the presence of measurement error in the variables of interest and the
possible solutions adopted in the literature to minimize it. The problem may be reduced
by using administrative rather than survey data, since the former tend to be of better
quality. However, when this is not possible and there are concerns about the reliability
of the data, an instrumental variable approach should be followed for the variable of
interest, for instance trade finance, liquidity constraints and the like. The researcher
would estimate a 2SLS model where in the first stage the financial variable is
approximated by a valid instrument. A crucial assumption behind this approach is that
measurement error is classical, i.e. it is uncorrelated with the true value of the
instrumented variable.
6.2. Endogeneity Bias
The second econometric challenge regards the presence of endogeneity bias arising from
two distinct sources. First omitted variables may generate endogeneity bias. For
example, the firm productivity may be better observed by lenders than by the
econometrician working with only a subsample of the information available about each
19
firm. Lenders may also be better informed than the econometrician regarding possible
firm’s internal agency problem that would affect the solvency of the exporters. For
instance firms are more likely to be financially constrained if they would enter foreign
markets mainly for prestige considerations using external finance.
In order to correct these otherwise biased estimates, researchers need to find a suitable
instrumental variable for the financial constraints. Such instrument should be correlated
with the ability of the firm to finance its activities, but should not be correlated with
the ability to export. Exogenous shocks to the firm’s cash flow represent good examples
of such instrument. They have been measured in the literature in several ways. Overdue
payments to suppliers, the share of payments settled by debt swaps or offsets and
exchange of goods for goods, or the amount of sales lost because of events that are
outside the control of the firm are popular choices since these measures are available for
a wide variety of countries and years. Yet they may be an imprecise proxy for liquidity
constraints, as they may also reflect low demand for the firm’s products or low
productivity. Studies based on more detailed data from a specific country typically
exploit better instrumental variable given the richer information set available. For
example, as an instrument for credit rationing (Minetti & Zhu, 2011) use the changes in
the regulations of the Italian banking system, specifically the number of number of bank
branches locally available to firms. While they capture credit restrictions likely to affect
the firms’ ability to borrow, they are unlikely to have an impact on firms’ exports.
The second source of endogeneity bias is reverse causality, as pointed out by
(Greenaway, Guariglia, & Kneller, 2007). On one hand, firms with better financial
standings may be more likely to participate to international markets. On the other
hand, firms trading internationally may improve their financial health and relax their
credit constraints by diversifying the sources of financing and the relative risks. To shed
light on this issue, (Greenaway, Guariglia, & Kneller, 2007) analyze the evolution of
firms’ financial health over time, before and after entering foreign markets. They find
that continuous exporters enjoy better average financial health than starter exporters,
and conclude that through exports firms improve their financial status. Using similar
empirical strategy, but working with a sample of French instead of UK firms, (Bellone,
Musso, Nesta, & Schiavo, 2010) reach opposite conclusions. (Iacovone & Zavacka, 2009)
instead test if being financially constrained during the recent crisis negatively correlated
with being a larger exporter before the crisis and is not significantly correlated with
being a small exporter before the crisis. As an additional robustness check of reverse
20
causality, the authors repeat the estimation on a subsample of observations where the
financial turmoil originated in a neighboring country one or two years before spreading
onto the country where the exporting firms analyzed are located. Their empirical
evidence points in the opposite direction, suggesting that reverse causality concern can
be dismissed. To gain further insights on the link between the health of banks supplying
external finance and export growth, (Amiti & Weinstein, 2011) construct a unique
dataset matching Japanese firms’ level information to banking data and use as an
instrument of (bank) financial health the residuals from the regression of the changes in
bank market-to-book value on the industry-time dummies and firm’s share price
changes. They conclude that while the bank health influences firm’s exports which is
unaffected by the firm’s financial health.
6.3. Selection Bias
Finally, the last econometric challenge that researchers face regards sample selection
bias that arises because, by definition, positive foreign sales are observables only for
exporting firms. To deal with such issue, researchers estimate a Heckman selection
model augmented with the inverse Mills ratio (IMR). The IMR is estimated from a
probability model where the dependent variable is a dummy for being an export and
among the independent variable there is one firm characteristic that is then omitted,
since not relevant, in the subsequent estimation steps.
6.4. The Role of Demand
Thus far we have discussed the link between trade finance and firm’s export decisions.
One important aspect that has not been addressed yet in this discussion is the economic
environment where firms operate and in particular the role of demand. Positive demand
shocks may stimulate exports and may even be sufficiently important to overturn the
role of credit rationing.
The literature on exports and trade finance initially focused on financial constraints,
dismissing the relevance of output variations. (Chor & Manova, 2012) account for the
effect of aggregate production on trade flow by controlling for industry dummies and
their interactions with the monthly log industrial production index in each sending
21
country. The interaction with industry fixed effects allows the demand effect to vary
across sectors. However it also imposed that changes in output are proportional at the
aggregate and sectorial level.
(Eaton, Kortum, Neiman, & Romalis, 2011) take a step further and propose an holistic
analysis of the potential causes for the collapse in world trade. Specifically, they
evaluate the relative importance of shocks to demand, trade deficit, productivity, and
trade frictions by estimating structurally a general equilibrium model with data from 23
countries.8 This comprehensive exercise underscores the role of the decline in demand for
(durable) manufactures as the main driver for the decline in trade. The decline in
demand for manufactures (durables) accounts for 80% (65%) of the fall in the global
trade/GDP ratio. Trade frictions played a significant role only in China and Japan,
while they had almost no effects in the rest of the world.
The conclusions from the structural estimation of the multi-country general equilibrium
model are echoed by the empirical study of (Behrens, Corcos, & Mion, Forthcoming)
that once again exploits the richness of the Belgian firm-level data. The forcefully
summarize their findings, musing that “It is not a trade crisis -just a trade collapse:”:
the fall in international trade is mainly explained by a contraction along the intensive
margin, a decline in demand and unit price driven by contraction of GDP growth in the
destination countries. (Bems, Johnson, & Yi, 2010) contribute to this literature by
estimating that changes in the composition of GDP account for about 70% of the trade
collapse. In fact, their studies have investigated the demand channel in further details.
For example (Levchenko, Lewis, & Tesar, 2010) attribute the fall in trade to a product
composition effect and are skeptical of the role attribute to financial factors alone:
sectors that depend more on imported intermediate inputs suffered larger contractions
because their supply chains were more likely to be disrupted. Similarly (Alessandria,
Kaboski, & Midrigan, 2010) identify the inventory adjustments (fall in stocks) as the
responsible for the decline in imports.
We anticipate that future research will try to account for both demand and financial
factors in determine changes at the intensive and extensive margin.9
8 Specifically they estimate a gravity model of trade nested in a production model for 3 sectors (durable manufacturing, non-durable manufacturing and non-manufacturing). 9For a contribution in this direction see (Contessi & De Nicola, 2012).
22
7. Conclusions The fall in trade observed worldwide in the aftermath of the financial crisis has
attracted the attention of several researchers that both investigated its theoretical
underpinnings and tested the empirical validity of a variety of possible explanations.
When summarizing the existing evidence, we point out that the, at times, conflicting
findings could be explained simply by the fact that researchers used different definitions
of the variables of interest because of data limitations.
23
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A. Figure and Tables
Figure 1. Growth of Export and GDP in Emerging and Developing Countries and Advanced Economies
Source: IMF World Economic Output (2011).
-6
-4
-2
0
2
4
6
8
10
1980 1985 1990 1995 2000 2005 2010
GD
P c
ost
ant
pri
ce, %
Change
Advanced economies
Emerging and developing economies
-15
-10
-5
0
5
10
15
20
1980 1985 1990 1995 2000 2005 2010
Volu
me
of ex
port
s of goods
and
serv
ices
, %
change
Advanced economies
Emerging and developing economies
28
Figure 2. Top 3 Business Services that Respondents Suggest may Help Increase or Facilitate a Foreign Market in Jordan
Source: WBES for Jordan 2006.
29
Figure 3. Changes in Merchandise Exports and Trade Finance: By Groups of Countries (percent growth)
Source: Authors calculations based on (Asmundson, Dorsey, Saito, Niculcea, & Kachatryan, 2011)
-60 -50 -40 -30 -20 -10 0
IndustrialCountries
Emerging Europe
Latin America
Middle East,Maghreb
Sub-SaharanAfrica
Southeast Europeand Central Asia
Emerging Asia incl.China and India
Developing Asia
Trade Finance
Goods Exports
30
Figure 4. Trade Finance Arrangements in 2009, by Market Share
Source: (Chauffour & Malouche, 2011).
19%-22%($3.0 trillion-$3.5 trillion)
35%-40% ($5.5 trillion-$6.4 trillion)
38%-45% ($6.0 trillion-$7.2 trillion)
cash in advance bank trade finance open account
31
Figure 5. Comparison of Objective and Subjective Measures of Constraints in Access to Credit in the BEEPS 2009 Database
0% 50% 100%
Albania
Armenia
Azerbaija
Belarus
Bosnia and…Bulgaria
Croatia
Czech…Estonia
Georgia
Hungary
Kazakhstan
Kosovo
Kyrgyz…Latvia
Lithuania
Macedonia
Moldova
Mongolia
Montenegro
Poland
Romania
Russian…Serbia
Slovak…Slovenia
Tajikistan
Turkey
Ukraine
Uzbekistan
Not Maybe Partially Fully
0% 50% 100%
Albania
Armenia
Azerbaija
Belarus
Bosnia and…Bulgaria
Croatia
Czech…Estonia
Georgia
Hungary
Kazakhstan
Kosovo
Kyrgyz…Latvia
Lithuania
Macedonia
Moldova
Mongolia
Montenegro
Poland
Romania
Russian…Serbia
Slovak…Slovenia
Tajikistan
Turkey
Ukraine
Uzbekistan
No Weak Moderate Very
Subjective Measure of FirmsCredit Constraints
Objective Measure of Firms Credit Constraints
32
Figure 6. Share of Firms by Extent of Objective Measures of Credit Constraints and Private Credit to GDP ratio in BEEPS countries in 2007
0%
10%
20%
30%
40%
50%
60%
70%
80%
0% 20% 40% 60% 80% 100%
Share
of Fir
ms
Domestic Credit to Private Sector as a % of GDP
Partially CC
Fully CC
0%
10%
20%
30%
40%
50%
60%
70%
80%
0% 20% 40% 60% 80% 100%
Share
of Fir
ms
Domestic Credit to Private Sector as a % of GDP
Not CC
Maybe CC
33
Figure 7 Changes in Trade Finance: By Groups of Countries (percent growth)
Source: Authors calculations based on (Asmundson, Dorsey, Saito, Niculcea, & Kachatryan, 2011)
-15
-10
-5
0
5
10
Over
all
Indust
rial C
ountr
ies
Em
ergin
g E
uro
pe
Latin A
mer
ica
Mid
dle
East
and t
he
Maghre
b
Sub-S
ahara
n A
fric
a
South
east
Euro
pe
Cen
tral A
sia
Em
ergin
g A
sia incl
.C
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To trough (2007Q4-2008Q4)
Peak of the crisis (2008Q4-2009Q2)
Initial Recovery (2008Q4-2009Q4)
Table 1. Summary on the Literature Estimating the Effect of Access to Credit on International Trade
Study Sample (Country, Firms, Sectors)
Financial Measure The Margin of Trade Examined with Financial Constraint
Amiti & Weinstein, 2011
A cross-section of Japanese manufacturing firms (540-860),,1986 - 2010
Lagged log change in market-to-book Value of bank
Intensive (log firm-level exports)
Behrens, Corcos, & Mion, 2011
A cross-section of Belgium manufacturing firms (avg. 23600 firms/ year), 2006 - 2009
Debt ratio2, external finance dependence (firm)
Intensive
Berman & Hericourt, 2010
A cross-country (9 developing countries) of firms (5000) in main producing sectors, 1998 - 2004
Liquidity ratioi Leverage ratioii
Extensive (exporting decision probability) Intensive ( log of firm-level exports & the share of exports over total sales)
Bricongne, Fontagn, Gaulier, Taglioni, & Vicard, 2011
A cross-section of French exporters (100000) by source country (52), 2000-2009
Sectoral external finance dependence
Intensive (the difference between positive and negative growth rates)iii Extensive (the difference between entry and exit rates)
Chor & Manova, 2012
A cross-sector of monthly U.S. imports by source country, 2006-2009.
Interbank lending rate (exporting country) External finance dependenceiv Trade creditv
Intensive (log of industry exports to the U.S.)
Coulibaly, Sapriza, & Zlate, 2011
A cross-country (6 developing countriesvi) and cross-section of publicly traded non-financial firms (7200), 2008: Q3-2009:Q1
Leverage ratiovii Liquidity ratioviii External and internal financeix Trade creditx Asset tangibilityxi
Intensive (exports-to-sales ratio)
Egger & Kesina, 2010
A cross-section of Chinese firms (570000), 2001-2005 Debt ratio, financial cost ratio, profitability ratio, liquidity ratio
Extensive (export decision), intensive
Feenstra, Li, & Yu, 2009
A cross-section of Chinese manufacturing firms
(>160000), 2000-2008
Interest payment Tangible assets
Extensive (export decision probability) Intensive
Forlani, 2010 A cross-section of Italian manufacturing firms (2554), 1998-2003
Financial Independency Index Quick Ratio, Cash Stock
Extensive (export decision)
Greenaway, Guariglia, & Kneller, 2007
A cross-section of British manufacturing firms (9292), 1993-2003
Liquidity ratioxii Leverage ratioxiii Quiscorexiv
Extensive (export decision dummy and probability)
Minetti & Zhu, 2011
A cross-section of Italian manufacturing firms (4680), 2000
Survey variable Liquidity ratio Leverage ratio
Extensive (export decision) Intensive
Muls, 2008 A cross-section of Belgium manufacturing firms (9000), 1999-2005
Coface score
Extensive (export decision probability & export destination dummy) Intensive (log of number of destination and log of mean value per destination)
Paravisini, Rappoport, Schnabl, & Wolfenzon, 2011
A cross-section of Peruvian firms with at least one export registered (6169) , 2007-2009
Credit supply from banksxv
Intensive (log change of exports of product-destination market) Extensive (number of entries to product-destination market)
i Ratio of cash flow over total assets. ii Ratio of total debt over total assets. iii The growth rate is computed as mid-point growth rate: the monthly export flows by a French firm to a given destination of all XN8 products in a same HS2 sector. iv This is measured as the fraction of total capital expenditure not financed by internal cash flows from operations, and reflects firms’ requirements for outside capital. v This is calculated as the ratio of the changed in accounts payable over the change in total assets, and indicates how much credit firms receive in lieu of having to make upfront or spot payments. vi China, India, Indonesia, Malaysia, Taiwan and Thailand. vii Stock of short-term debt normalized by total assets. viii Quick ratio (the sum of cash, cash equivalents and net receivables divided by current liabilities) and working capital (the difference between current assets and current liabilities normalized by total assets). ix Total external finance and retained earnings in 2007 (each normalized by total assets). x An alternative source of financing: trade credit received from suppliers in 2007. xi This is constructed as the share of net plant, property and equipment in total book-value assets. xii Current assets less current liabilities. xiii Ratio of short-term debt to current assets. xiv A measure of a firm’s riskiness which is based on information about firms’ credit ratings and measures the likelihood of company failure in the 12 months following the date of calculation. xv This is measured by foreign financing, share of foreign liabilities in the bank’s balance sheet.