Export Experience and the Choice of Invoice Currency: Evidence
from Questionnaire Survey for Japanese SMEs†
Mizuki GOTO

Kazunobu HAYAKAWA

Graduate School, Hitotsubashi University, Japan

Development Studies Center, Institute of Developing
Economies, Japan

Satoshi KOIBUCHI

Taiyo YOSHIMI*

Faculty of Commerce, Chuo University, Japan

Faculty of Economics, Chuo University, Japan

Abstract: This study examines determinants of the invoice currency with a focus on the effect of export
experience based on the questionnaire survey for Japanese small and medium enterprises (SMEs) in the
manufacturing industry. We find that exporters who have a long experience in exporting tend to switch
the invoice currency from Japanese yen to foreign currencies. The interpretation is that export
experience provides firms with the know-how to deal with the exchange rate risk and enables firms touse foreign currencies in their exports. This effect survives even if firms intermittently export from theirfirst exports. We also find that the yen is more likely to be chosen in the first export when the age of theexporter is higher, the sales value of the exporter is smaller, the exporter has an initiative in thedetermination of the invoice currency and the exporter started exporting before the global financial
crisis in 2007.
Keywords: Invoice currency; Export dynamics; Questionnaire survey; SMEs; JapanJEL Classification: F1; F3†

We would like to thank Dai Mi, Jianwei Xu and Shajuan Zhang. We also thank seminar participants at Kobe
University, Japan Society of Monetary Economics and Research Institute of Economy, Trade and Industry (RIETI)
for helpful comments and suggestions. We acknowledge financial support from Chuo University Joint Research
Grant and the JSPS KAKENHI Grant (JP20H01518). All remaining errors are ours.
*
Corresponding author. Faculty of Economics, Chuo University, 742-1 Higashinakano, Hachioji-shi, Tokyo,
192-0393, Japan. Email: yoshimi@tamacc.chuo-u.ac.jp.
11. Introduction
The currency used in invoicing of trade defines who takes the short-run exchange
rate risk in international trade. The type of the invoice currency is generally classified into
three types: producers’ currency pricing (PCP) where the exporter’s currency is used in
invoicing, local currency pricing (LCP) where the importer’s currency is used, and vehicle
currency pricing (VCP) where the internationalized third currency is used. As known, theUS dollar is mostly used in the case of VCP and many researchers call this phenomenon
the dominant currency paradigm (DCP) in international trade.1 To reveal how and whatcurrencies are employed in international trade, many researchers investigate thedeterminants of invoice currency. Our study is related to at least two pieces of literature.
One is the literature on the choice of invoice currency. For instance, as a seminal
theoretical analysis, Engel (2006) investigates how the invoice currency is determinedbased on the profit-maximizing motivation of exporters. Gopinath et al. (2010) introduce
the dynamic perspective into the framework of Engel (2006) and conduct a detailed
empirical analysis of the choice of invoice currency with the firm-level data of the US. In
addition, Chung (2016) considers how exporters’ dependence on imported inputs affects
their choice of the invoice currency using the firm-level data for the United Kingdom.
Devereux et al. (2017) investigate the role of firms’ market share in the choice of theinvoice currency using Canadian data.
Among many potential determinants, we focus on the effect of firms’ export
experience on the choice of the invoice currency. This aspect has rarely been examined inthis literature. To the best of our knowledge, the only research on this aspect is Hayakawa
et al. (2019) which theoretically and empirically investigate the relationship between thelength (years) of Thai firms’ export experience and changes in the invoice currency fromthe first export to the current export. In this study, we conducted a questionnaire survey
for Japanese small and medium enterprises (SMEs) to examine the effect of firms’ export
experience on the choice of the invoice currency. The reason why we focused on SMEs isthat learning effects through export experience on firms’ activities may be clearer in
relatively small companies. In large companies, we expect, other elements arecomplicatedly related to each other and it may be difficult to identify the learning effect. In
this survey, we sent the questionnaire to 2,100 SMEs and accepted responses from 9
November 2019, to 31 January 2020. The respondence rate is 14.1%. By using the uniquedataset constructed using these responses, we examine how the length of firms’ export
experience affects a change in the invoice currency from the year when firms started
exporting to the present (the year of 2019).
Our expectation for the effect of the export experience on the choice of the invoicecurrency is as follows. In the literature of export dynamics, it is pointed out that export
starters tend to begin with a small number/value of exports, then continue to export if they1

See Gopinath et al. (2020) for instance.
2

expect from the first export that their overseas business will produce sufficiently positive
gains (Albornoz et al., 2016). The important insight of this argument is that firms learn
from their initial experiences and, given the know-how they gained from the experience,
they decide on their present behavior. Inspired by this insight, we explore the learning
effect of export experience on the choice of the invoice currency. In particular, we expectthat firms switch from PCP to foreign currency pricing (FCP, which includes LCP and
VCP) after sufficient export experiences. Experienced exporters have knowledge not onlyof the economic situations of destination countries but also of exchange rate risks.
Therefore, it might be a natural expectation that more experienced exporters are more
likely to use FCP. FCP brings the exchange rate risk to the exporter but can attract ademand by risk-averse importers. More clearly, if exporters choose PCP and impose theexchange rate risk on importers, importers may decrease their demand not to take a large
exchange rate risk. This behavior of risk-averse importers has not been treated sufficiently
although it is naturally predicted from studies such as Wolak and Kolstad (1991) and
Coppejans et al. (2007).2 Export experience may mitigate the disadvantage of FCP forexporters through the accumulation of the know-how to treat the exchange rate risk andincrease the relative advantage of FCP to attract demand. In sum, as our testable empirical
proposition, it is expected that firms switch PCP from FCP after accumulating export
experiences.
This study has three advantages to Hayakawa et al. (2019). The first advantage is that
we directly ask respondent firms the year of their first exports. Hayakawa et al. (2019)
regard the first year of exports as the year in which each firm first appears during theirsample period from 2007 to 2011. Thus, there remains a possibility that firms first exported
before 2007. We resolve this issue by asking about the first year of exports directly in thequestionnaire survey. 3 Also, in addition to the export experience, we define otherconventional determinants using the questionnaire results. For instance, we identify thetype of importer (trading companies, group companies, or nongroup companies) in thefirst and current exports, and investigate how changes in the type of the importer affect achange in the invoice currency. A change in the invoice currency can happen because theimporter changes. We can control for this element. Also, we define exporting SMEs’
initiative for the invoice currency by directly asking the major determiner (exporter orimporter) in the negotiation. Many researchers point out that the bargaining power is an
undoubted determinant of the invoice currency but it has been rarely examined because
defining this variable is difficult.4 Our initiative variable can be regarded as a modest
See Wolak and Kolstad (1991) and Coppejans et al. (2007) theoretically demonstrate that risk-averse agentsdecrease demand for products whose prices are uncertain in advance.
3
The clear advantage of Hayakawa et al. (2019) to our study is that they employ comprehensive Customs data ofThailand. As a result, the number of observations in their estimations reaches nearly 0.8 million. In contrast, ourquestionnaire is focused on Japanese SMEs in the manufacturing industry. Therefore, Hayakawa et al. (2019) andthis study are complements.
4
For example, Goldberg and Tille (2013) consider how bargaining between exporters and importers affects the
choice of invoice currency and export prices. Devereux et al. (2017) partially tackle this issue using market shares
3
2

proxy for the exporter’s bargaining power. The second advantage is that we deal with thesample selection bias in the choice of the invoice currency in the first export. Our main
dependent variable is the dummy variable that takes one if the invoice currency waschanged from one currency to another. Especially, we mainly investigate the impact ofyears of export on the likelihood that the exporter switched from PCP (yen pricing) to FCP,
expecting a positive impact. Therefore, basic Probit estimation leads to a sample selection
bias in the choice of the invoice currency in the first export. We tackle this issue using the
Heckman-Probit estimation and confirm the robustness of our empirical result. The third
advantage is that we use SMEs’ financial information provided by Teikoku Data Bank tosee how firms’ financial status is related to their invoice currency choice. For example, we
can identify “main banks” which have a primarily financial relationship with each SME. If
the main bank has international networks, firms may access the foreign exchange market
more easily. We examine this possibility. In addition, we identify the sales and the laborproductivity of each SME which may impact the choice of the invoice currency.
Our empirical analysis provides the following findings. It is revealed that exporters
who have a long experience in exporting tend to switch the invoice currency fromJapanese yen to foreign currencies. We interpret this result that firms accumulate theknow-how to deal with the exchange rate risk after export experience and switch theirmain invoice currencies to foreign currencies which enable exporters to accept a demand
from their local customers. This tendency survives even if exporters do not continuously
export from when they started exporting. It is also found that, in the selection of theinvoice currency in the first export, PCP is more likely to be chosen when the invoicecurrency is mostly chosen by the exporter (i.e. the exporter has the initiative in the choiceof the invoice currency). We also find that the yen is more likely to be chosen in the firstexport when the age of the exporter is higher, the sales value of the exporter is smaller, theexporter has an initiative in the determination of the invoice currency and the exporter
started exporting before the global financial crisis in 2007.
The rest of this paper is organized as follows. The next section presents a brief
overview of the questionnaire survey. Section 3 shows our baseline results. In Section 4,
we deal with the sample selection bias using the Heckman-Probit model. Section 5
performs some robustness checks and Section 6 concludes this paper.

2. Questionnaire Survey
We conducted a questionnaire survey for Japanese SMEs from 9 November 2019, to
31 January 2020. 5 The aim of the survey is to know how the invoice currency isdetermined from the exporter’s point of view. Especially, we explored how their choice ofof exporters and importers.
5
Also refer to Goto et al. (2021) for the detail of the questionnaire survey. In Goto et al. (2021), we display someother tables and investigate determinants of invoice currency with a focus on firms’ financial constraints.
4

invoice currency, the scheme of exchange rate risk management, and the financial statuschanged after they continued exporting for years. We sent the questionnaire to 2,100
companies and the respondence rate is 14.1%. Our questionnaire includes many questions
related to the choice of the invoice currency in the first and current exports. Table 1
provides some questionnaire results.
===

Table 1===

The top panel of Table 1 shows that, for exports to Asian countries (China, Thailand,and other Asian countries), 387 respondents among 510 (about 76%) answer that they use
the Japanese yen the most. According to the public data reported by the Japanese Ministryof Finance (MOF), the share of yen-invoicing in Japan’s exports to Asian countries isaround 50%. The MOF data includes not only SMEs but also large listed companies.
Therefore, it is expected that the share of yen-invoicing is higher in exports by SMEs thanexports by large companies which are included in the MOF data. The interpretation is that
SMEs are not enough capable of the risk of exchange rate fluctuations and prefer PCP to
FCP.
The middle panel of Table 1 presents how the respondence companies changed theirmain invoice currency from the time when they start exporting (i.e. first exports) to thecurrent export. The panel shows that approximately 90.8% of respondents did not change
the main invoice currency. This result is consistent with the finding of some existingstudies such as Ogawa and Sasaki (1998) that there is inertia in the use of currency. It isnotable that the remaining 9.2% changed their main invoice currency.
The bottom panel shows how the invoice currency is usually determined. We used
the answer to this question to define the degree of the exporter’s initiative in thedetermination of the invoice currency. Surprisingly, 74.9% of respondents answered thatthe currency that they (respondents) prefer is usually chosen. In contrast, the share of
SMEs who answer that importers have a primary role to determine the invoice currency is
only 3.9%.
Other than these answers, we obtained much additional information. For instance,
we have information on the type of major trading partner, major export product, andmajor invoice currency both in the first and current exports for each destination country.
We also know the year of the first export for each destination. Therefore, we can calculate
the length (years) of export experience of each SME for each destination.

3. Baseline Analysis
3.1.

Empirical Framework
Based on the questionnaire survey, we define our empirical variables. Table 2
5

provides descriptive statistics of the variables, and Figures 1 and 2 present the distributionof our experience variables.
===

Table 2 and Figures 1 and 2===

Our main dependent variables is the dummy variable 𝑆𝑊𝐼𝑇𝐶𝐻𝑓𝑑 , which takes 1 ifexporter 𝑓 switched from PCP in the first export to FCP in the current export fordestination country 𝑑 , and otherwise 0. Note that each observation has thefirm-destination dimension and our dataset is cross-sectional. We firstly investigate theeffect of export experience on 𝑆𝑊𝐼𝑇𝐶𝐻𝑓𝑑 focusing on observations where PCP is chosen
in the first export. The following is the baseline equation in our empirical investigation:
𝑆𝑊𝐼𝑇𝐶𝐻𝑓𝑑 = 𝑎 + 𝑏ln⁡(𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒)𝑓 + 𝑐𝑣𝑓𝑑 + 𝐹𝐸𝑠 + 𝜖𝑓𝑑 .

(1)

We estimate this equation using the Probit model. ln⁡(𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒)𝑓 is (the log of) the
length (years) of export experience. This variable represents the number of years from thefirm’s first-export year to the present year (i.e., 2019). This variable is defined at the firmlevel and this definition is the same as Hayakawa et al. (2019). To see the robustness, we
also use ln⁡(𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒2)𝑓𝑑 which is defined as (the log of) the length of the firm’s export
to each destination country. Thus, ln⁡(𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒2)𝑓𝑑 is defined at the firm-destinationlevel. Exporters might switch from PCP to FCP after they accumulate sufficient export
experience. Thus, we expect a positive sign for 𝑏. 𝑣𝑓𝑑 is the vector of other control
variables and 𝑐 is its coefficient. For fixed effects (FEs), we employ industry and region
Fes in the baseline estimation.6 We do not employ a firm FE in the baseline estimation
because our main explanatory variable ln⁡(𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒)𝑓 is defined at the firm level.
Nevertheless, we try a firm FE using ln⁡(𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒2)𝑓𝑑 which is defined at thefirm-destination level. In addition, we do not employ destination country FE in thebaseline estimation because we face crucial decrease of the number of samples in Probit
estimations if we use a country FE. In fact, using a firm FE also leads to a significant
decrease of the number of samples. Therefore, we estimate the ordinary least square (OLS)
model using firm and country FEs to see the robustness of our results. 𝜖𝑓𝑑 is thedisturbance.

3.2.
6

Baseline Results

For industry, we follow the definition by TDB. Regions are Africa (Egypt and the Republic of South Africa),

America (Brazil, Canada, Chile, Mexico, Panama, Peru, Puerto Rico and Uruguay), Asia (Indonesia, Korea, Sri
Lanka, Malaysia, the Philippines, Singapore, Thailand, Taiwan, Vietnam), China, EU (Switzerland, Czech
Republic, Denmark, Luxembourg, Poland, Sweden, Turkey and the UK), Euro Area (Belgium, Germany, Spain,
Finland, France, Ireland, Italy, Netherland and Portugal), Pacific (Australia and New Zealand) and the US.
6

Table 3 shows estimation results for equation (1). Columns (I) to (VI) employ the
Probit model, and (VII) and (VIII) employ the OLS model. In all columns, we focus on
observations where exporters started exporting under PCP. Column (I) includes
ln⁡(𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒)𝑓 , 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝐼𝑚𝑝𝑓𝑑 , and 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝑃𝑟𝑜𝑑𝑓𝑑 with industry and region FEs.
𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝐼𝑚𝑝𝑓𝑑 is the dummy variable which takes 1 if the type of importer is changed
from the first export to the current export and otherwise 0. It is reasonable to assume thatthe importer is changed when the type of the importer is changed. Thus, we can regard
that the importer is changed when this dummy variable takes the value 1.
𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝑃𝑟𝑜𝑑𝑓𝑑 is the dummy variable which takes 1 if the type of product is changed
from the first export to the current export and otherwise 0. The coefficients for thesedummies can take either sign. We check these signs to find a fact for Japanese SMEs.⁡
Column (I) shows that ln⁡(𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒)𝑓 has a significant positive impact on theprobability that firms switch from PCP to FCP. Also, 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝐼𝑚𝑝𝑓𝑑 has a positivecoefficient implying that the currency tends to be switched from the PC to the FC when
the type of the importer changes. We will show that this tendency is observed also forcases where the currency is switched from the FC to the PC. Therefore, the positive sign ofthe coefficient observed here only indicates that changes in the type of importer may leadto changes in the invoice currency. 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝑃𝑟𝑜𝑑𝑓𝑑 does not have any significant
impact.
===Table 3===

Columns (II) and (III) break down the different importer type dummy. In particular,
columns (II) includes the dummy variable which takes one if the type of importer changes
from a trading company (called “Shosha” in Japanese) to other types (𝑇𝑜𝑁𝑜𝑛𝑆ℎ𝑜𝑠ℎ𝑎𝑓𝑑 ). It
is well known that transactions with trading companies are usually invoiced in thedomestic currency because these transactions are domestically conducted. Consistent withthis evidence, 𝑇𝑜𝑁𝑜𝑛𝑆ℎ𝑜𝑠ℎ𝑎𝑓𝑑 has a positive coefficient. In columns (III), we employ thedummy variable which takes one if the type of importer changes from non-groupcompanies which do not have capital ties with the exporter to a group company. Thisvariable does not have any significant impact.
In columns (IV) and (V), we control for the rate of changes in sales and productivity,
respectively. 𝑑𝑙𝑛(𝑆𝑎𝑙𝑒𝑠)𝑓 employed in column (IV) is the difference in the log of sales
from the start year to 2019. As smaller firms do not have a capacity to accept the exchange
rate risk, these firms may prefer PCP to FCP, implying a negative coefficient of thisvariable.7 Nevertheless, the coefficient is not significant hence the sign is negative as

Hayakawa et al. (2019) present evidence that export prices are lower under PCP in Thai exports. This evidence
implies that there are no other choices for exporters but to put lower export prices to accept sufficient demandwhen they employ PCP. In other words, PCP is the invoicing scheme that negatively affects the demand fromrisk-averse importers.
7
7

expected. Column (IV) adds the difference in the logged sales, 𝑑𝑙𝑛(𝑆𝑎𝑙𝑒𝑠)𝑓 , to column (I).
We find that the coefficient for this additional variable is not significantly estimated,indicating that a change in sales does not let exporters who started with PCP reconsider
the invoice currency. This insignificance might stem from the fact that changes in sales are
not sufficiently large in our sample. In fact, the mean of this explanatory variable is only
0.25 as shown in Table 2. In column (V), the difference in the logged productivity is
employed. Here we define the productivity by the ratio of the sales to the number of
employees. This variable does not have any significant impact maybe because, as shown in
Table 2, the rate of growth in the productivity is only 15 percent on average in our sample.
Column (VI) replaces the firm level experience ( 𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒𝑓 ) with thefirm-destination level experience (𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒2𝑓𝑑 ), and show that the firm-destinationlevel experience does not have a significant impact. This result implies that theaccumulation of the know-how in the management of exchange rate risks might start fromwhen firms start exporting regardless of destinations. Given this result, we will mainly use
the firm level experience in the following robustness checks.
Columns (VII) and (VIII) employ OLS models so that we avoid decreasing thenumber of samples with inclusion of more detailed FEs. Column (VII) includes region andfirm FEs. Major results do not differ from column (I): 𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒𝑓 has a positive impact,
𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝐼𝑚𝑝𝑓𝑑 has a positive impact, and 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝑃𝑟𝑜𝑑𝑓𝑑 does not have a significant
impact. Column (VIII) includes destination country and firm FEs jointly with thefirm-destination level experience 𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒2𝑓𝑑 . Results do not change quantitatively
from column (VI). In sum, our main findings do not significantly depend on the choice ofFEs.

4. Dealing with the Sample Selection Bias
4.1.

Empirical Strategy
Estimation results in the previous section might suffer from the sample selection bias

because we focused on observations where firms started exporting under PCP. To
overcome this sample selection bias, we employ the Heckman-Probit model in which theselection of invoice currency in the first export is explicitly examined. Our selection
equation describes firms’ decision on whether to start exporting with PCP or not (i.e.,
FCP):
𝑦𝑃𝐶𝑃 = 𝜋𝑃𝐶𝑃 − 𝜋𝐹𝐶𝑃 = 𝑥𝛽 + 𝑢𝑃𝐶𝑃
where 𝑦𝑃𝐶𝑃 = {

1 𝑖𝑓
0 𝑖𝑓

𝑦𝑃𝐶𝑃 > 0.
𝑦𝑃𝐶𝑃 ≤ 0

These equations indicate that firms choose to export under PCP if the gross profit from8

exporting under PCP (𝜋𝑃𝐶𝑃 ) dominates that under FCP (𝜋𝐹𝐶𝑃 ). Vector 𝑥 includes various
elements that affect the gap between these profits. Specifically, 𝑥 contains 𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒𝑓 ,𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝐼𝑚𝑝𝑓𝑑 , and 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝑃𝑟𝑜𝑑𝑓𝑑 that have significant impacts in the baseline
estimations shown in Table 3. Vector 𝛽 indicates coefficients to be estimated. 𝑢𝑃𝐶𝑃 is theerror term.
Next, the outcome equation describes the firms’ decision on whether to switch from
PCP to FCP given that they started exporting under PCP:′′𝑦𝑆𝑊𝐼𝑇𝐶𝐻 = 𝜋𝐹𝐶𝑃
− 𝜋𝑃𝐶𝑃= 𝑧𝛾 + 𝑢𝑆𝑊𝐼𝑇𝐶𝐻 ⁡⁡1
where 𝑦𝑆𝑊𝐼𝑇𝐶𝐻 = {
0

𝑖𝑓
𝑖𝑓

𝑦𝑆𝑊𝐼𝑇𝐶𝐻 > 0
𝑦𝑆𝑊𝐼𝑇𝐶𝐻 ≤ 0

𝑎𝑛𝑑
𝑎𝑛𝑑

𝑦𝑃𝐶𝑃 > 0.
𝑦𝑃𝐶𝑃 > 0

The equation indicates that firms switch to FCP if the gross profits from exporting under′′
FCP (𝜋𝐹𝐶𝑃) are greater than those under PCP in future periods (𝜋𝑃𝐶𝑃). Vector 𝑧 includes
various elements that affect the gap between these profits. Vector 𝛾 indicates coefficientsto be estimated. 𝑢𝑆𝑊𝐼𝑇𝐶𝐻 is the error term. For error terms, we assume
𝑢𝑃𝐶𝑃 ~𝑁(0,1),

𝑢𝑆𝑊𝐼𝑇𝐶𝐻 ~𝑁(0,1).

𝑦𝑆𝑊𝐼𝑇𝐶𝐻 can be defined if 𝑦𝑃𝐶𝑃 is unity. Therefore, this model is effectively a probit modelwith sample selection (Heckman-Probit) discussed in Van de Ven and Van Pragg (1981).
𝑧 contains six variables. First, we employ the log of the age of the exporter when it
started exporting (ln(𝐴𝑔𝑒)𝑓 ). Second, we employ the log of sales when the firm started
exporting, ln(𝑆𝑎𝑙𝑒𝑠0)𝑓 , to examine the size effect. Larger firms may be more capable toexchange rate risks, thus the coefficient is expected to become negative. To check therobustness, we also used the log of the number of employees, and the log of laborproductivity which is defined as the ratio of sales to the number of employees although
we do not report as the results do not change much. Third, we employ the dummy
variable 𝐼𝑛𝑖𝑡𝑖𝑎𝑡𝑖𝑣𝑒𝑓 which which takes 1 if the SME chooses “The currency your company
prefers is chosen” to the question presented in the bottom panel of Table 1 and 0 for othertwo options. If the exporter has a significant initiative, it is more likely that the invoicecurrency is PC so that the exporter can avoid the exchange rate risk. Thus, we expect a
positive impact of this variable. Fourth, we employ the dummy variable 𝑆ℎ𝑜𝑠ℎ𝑎𝑓𝑑 whichtakes 1 if the type of importer is a trading company. As we discussed above, it is known
that Japanese yen tend to be used in exports through trading companies because thecontract between trading companies and Japanese exporters is domestically signed. Thus,
we expect a positive impact. Fifth, we introduce the dummy variable 𝐶𝑖𝑡𝑦𝐵𝑎𝑛𝑘𝑓 whichtakes 1 for firms whose main banks are city banks (Mizuho, Mitsubishi UFJ, Sumitomo
Mitsui, Resona or Saitama Resona) and otherwise 0.8 These city banks are supposed to
Uchida et al. (2008) point out that large companies tend to have borrowing relationship with city banks,
indicating the multicollinearity between 𝐶𝑖𝑡𝑦𝐵𝑎𝑛𝑘𝑓 and ln(𝑆𝑎𝑙𝑒𝑠0)𝑓 . Nevertheless, major results do not change
98

provide better options to avoid exchange rate risks (such as forward exchange contract) for
their customers, thus we expect a negative impact of this variable. Sixth, we introduce thedummy variable 𝐴𝑓𝑡𝑒𝑟𝐺𝐹𝐶𝑓 which takes 1 if the exporter started exporting on and after
2008. After the GFC occurred in 2007, it is expected that the degree of risk aversion
increased and firms prefer using international currencies such as the USD. Thus, we
expect a negative impact of this variable.

4.2.

Results

Column (I) of Table 4 shows the results of the Heckman-Probit estimation without
FEs. Same as the baseline estimation, in the outcome equation, 𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒𝑓 and𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝐼𝑚𝑝𝑓𝑑 have positive impacts. 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝑃𝑟𝑜𝑑𝑓𝑑 does not have any significant
impact. The Chi-squared statistics show that the likelihood of the Heckman-Probit model
is significantly larger than the basic Probit model, indicating the presence of sample
selection bias in our baseline estimations. The signs of coefficients for all explanatory
variables in the selection equation are consistent with our expectation although the impact
of 𝐶𝑖𝑡𝑦𝐵𝑎𝑛𝑘𝑓 is not significant. In column (II), the region FE is included both in selection
and outcome equations.9 Major results do not differ from column (I).
===

Table 4===

5. Robustness Checks
In this section, we perform variety of robustness checks. First, there may be a
discussion that experienced firms switch the invoice currency just because there weremany chances (many years) to reconsider the invoice currency. To answer this caveat, we
examine the opposite switch of the invoice currency, that is, FCP to PCP. We define thedummy variable 𝑆𝑊𝐼𝑇𝐶𝐻2𝑓𝑑 which takes 1 if the invoice currency is switched from FCP
to PCP and 0 otherwise. Then, we estimate the Heckman-Probit model where the selection
equation investigates the determinants of the probability that the FCP is chosen in the firstexport. We employ same explanatory variables that are used in the previous section.
Columns (III) and (IV) of Table 4 show the estimation results. We confirm that theexperience does not have a significant impact in both columns. Therefore, firms’ export
experience enhances themselves to switch from PCP to FCP but not for opposite directionof switch. Intuitively, the impacts of most explanatory variables in the selection equation
are opposite to those in columns (I) and (II).
Table 5 shows the results of other robustness checks. Industry and region FEs areused in all columns. In column (I), we include the interaction term between the log ofmuch if we exclude ln(𝑆𝑎𝑙𝑒𝑠0)𝑓 to avoid the multicollinearity.
9
The convergence is not obtained if we introduce more detailed FEs.
10

experience and the dummy variable which takes one for observations where firms started
exporting after the revision of Foreign Exchange and Foreign Trade Act in 1998. In thisrevision, restrictions on foreign exchange transactions were removed and agents otherthan banks were allowed to deal with foreign exchange transactions. The interaction term
does not have any significant impact, indicating that the effect of the export experience on
the probability of currency switch is not affected by the revision of this law.
===

Table 5===

In columns (II) and (III), we dropped outliers. Specifically, in column (II), we drop
observations with the upper and lower one percentile of the log of experience. As a result,estimation samples are limited to those with 4 to 64 years of export experience. In column(III), we remove samples with the top quartile of the length of export experience to avoid
the estimation bias generated by exporters with long export experience. By doing this,samples are limited to those with less than 30 years of export experience. As shown major
findings do not change.
It can be expected that exact years of export experience do not affect the likelihood ofswitch from the PC to the FC while exporters with relatively long experience tend toswitch the currency. To see this possibility, we employ rougher measure of the export
experience in column (IV). In particular, we employ the dummy variable which takes one
if the log of experience is larger than its mean (20.8 years). Also in this case, the results do
not change much.
In column (V), we exclude observations where the destination country is the U.S. as
the USD has a distinguish position as a dominant currency in international trade as statedby the literature of DCP and the USD is the local currency in the U.S. Thus, we supposethat we should differentiate the U.S. from other destination countries. In fact, the numberof samples is not reduced much because Asian countries such as China and Thailand are
the destination country for the majority of observations. The results do not change muchalthough the coefficient for the log of export experience slightly decreases.
In column (VI), we introduce the interaction term of the dummy variable
𝐷𝑖𝑠𝑐𝑜𝑛𝑡𝑖𝑛𝑢𝑒𝑓 and the export experience. 𝐷𝑖𝑠𝑐𝑜𝑛𝑡𝑖𝑛𝑢𝑒𝑓 takes 1 if the firm answer that it
does not continuously exported from its first year of export to the current year. The
coefficient for this interaction term is not significant indicating that the effect of export
experience survives if firms intermittently export from their first exports.
In the questionnaire survey, we asked the import-side information. We utilize thisinformation in columns (VII) and (VIII). Specifically, (VII) includes the dummy variablewhich takes one if the firm not only exports but also imports (𝐼𝑚𝑝𝑜𝑟𝑡𝑒𝑟𝑓 ), and (VIII)
includes the dummy variable which takes one if the firm is an importer and it uses FCs in
importing (𝐼𝑚𝑝𝑜𝑟𝑡𝐹𝐶𝑓 ). As shown in the table, only 𝐼𝑚𝑝𝑜𝑟𝑡𝐹𝐶𝑓 has significant positive
impact implying that it is more likely that this firm switches from the PC to the FC if a firm
11

uses the FC in imports. We interpret this result that firms try to marry the exchange raterisk in export and import sides.
Columns (IX) focuses on observations of firms’ first exports. More concretely, we
dropped observations where the destination country is second and subsequent destination
for respective firm. Also in this case, the positive impact of export experience survives
although the coefficient becomes somewhat lower than other columns.

6. Concluding Remarks
This study examines the determinants of the probability that the invoice currency ischanged from first export to current export using a unique dataset based on thequestionnaire study for Japanese SMEs. We find that exporters who have longer
experience of exporting tend to switch from PCP to FCP. It is also found that PCP is more
likely to be chosen in the first export when the age of the exporter is higher, the sales issmaller, the exporter has a significant initiative, exports are conducted through trading
companies, and firms started exporting before the GFC.
This study focuses on positive investigations of the learning effect in the choice of theinvoice currency, which has been rarely considered. Nevertheless, there are some policy
implications. Internationalization of a currency in trade should be conducted consideringfirms’ profit maximization incentives. Our empirical results present the fact thatwell-experienced exporters prefer FCP to PCP. This indicates that there is a potential
benefit to employ FCP (maybe internationalized currencies such as the USD and euro) not
only for importers but also for exporters that has not been sufficiently discussed in theliterature. Internationalization of national currency brings a significant seigniorage to thegovernment. However, promoting the use of national currency with the ignorance of thispotential benefit of FCP may not be successful. From the result obtained in this study,
enforcing exporters’ initiative leads to a promotion of the use of national currency. Thus,
government’s support for exporting firms to differentiate their products may help theinternationalization of national currency in trade.

References
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Chung, W., 2016, Imported Inputs and Invoicing Currency Choice: Theory and Evidence from UK
Transaction Data, Journal of International Economics, 99, 237–250.
Coppejans, M., Gilleskie, D., Sieg, H., and Strumpf, K., 2007, Consumer Demand under Price
Uncertainty: Empirical Evidence from the Market for Cigarettes, Review of Economics and12Statistics, 89, 510–521.
Devereux, M., Dong, W., and Tomlin, B., 2017, Importers and Exporters in Exchange Rate
Pass-through and Currency Invoicing, Journal of International Economics, 105: 187–204.
Engel, C.M., 2006, Equivalence Results for Optimal Pass-Through, Optimal Indexing to Exchange
Rates, and Optimal Choice of Currency for Export Pricing, Journal of the European
Economics Association, 4(6), 1249–1260.
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Goldberg, L.S., and Tille, C., 2013, A Bargaining Theory of Trade Invoicing and Pricing, NBER
Working Paper 18985.
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Pass-Through, American Economic Review, 100:1, 304–336.
Gopinath, G., Boz, E., Casas, C., Díez, F.J., Gourinchas, P.-O., and Plagborg-Møller, M., 2020,
Dominant Currency Paradigm, American Economic Review, 110:3, 677–719.
Goto, M., Hayakawa, K., Koibuchi, S., and Yoshimi, T., 2021, Invoice Currency Choice under
Financial Constraints and Bargaining: Evidence from Japanese SMEs, RIETI Discussion
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Hayakawa, K, Laksanapanyakul, N., Matsuura, T., and Yoshimi, T., 2019, Export Dynamics and theInvoicing Currency, ERIA Discussion Paper Series ERIA-DP-2018-14.
Heckman, J.J, 1974, Shadow Prices, Market Wages, and Labor Supply, Econometrica, 42, 679–694.
Ogawa, E., and Sasaki, Y. N., 1998, Inertia in the Key Currency, Japan and the World Economy,
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Journal of the Japanese and International Economics, 22, 242–267.
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American Economic Review, 81: 514–538.

13

Table 1. Some Questionnaire Results
What is the most frequently used currency in exporting?
Country

Number of valid responses

China

175

Thailand
Other Asian countries

Japanese yen

US dollar

Euro

Importer's currency

Other

126

35

0

13

1

115

95

17

0

3

0

209

166

38

0

4

1

Oceania

11

8

2

0

1

0

Total

510

395

92

0

21

2

How did your company changed the main invoice currency from when the company start exporting?
Number of valid responses
Main currency has not changed much

257

Main currency changed from Japanese yen to foreign currencies

15

Main currency changed from foreign currencies to Japanese yen

11

Total

283

How does your company usually determines the invoice currency in exporting?
Number of valid responses
The currency your company prefers is chosen212

The currency your counterpart (importer) prefers is chosen

56

Other

15

Total

283

Source: Authors’ computation.

14

Table 2. Descriptive Statistics
Variable

Obs

Mean

Std. Dev.

Min

Max

PCP0

676

0.70

0.46

0

1

LCP0

699

0.11

0.32

0

1

PCP1

699

0.66

0.48

0

1

LCP1

699

0.11

0.31

0

1

US

699

0.16

0.37

0

1

Switch

448

0.06

0.24

0

1

Switch2

177

0.12

0.32

0

1

ln(Experience)

699

2.83

0.67

0.69

4.16

ln(Experience2)

697

2.46

0.81

0

4.16

DifferentImp

627

0.16

0.36

0

1

ToNonShosha

627

0.05

0.21

0

1

ToGroup

627

0.02

0.15

0

1

DifferentProd

629

0.05

0.22

0

1

d ln(Sales)

667

0.25

0.54

-1.22

2.23

d ln(Productivity)

667

0.13

0.46

-1.20

2.86

ln(Age)

688

3.47

0.70

0

4.67

ln(Sales0)

667

7.70

1.32

4.64

11.16

Initiative

696

0.74

0.44

0

1

Shosha

699

0.27

0.45

0

1

CityBank

699

0.45

0.50

0

1

AfterGFC

699

0.52

0.50

0

1

After1998

699

0.77

0.42

0

1

ExperienceLong

901

0.53

0.50

0

1

Discontinue

683

0.09

0.28

0

1

Importer

901

0.94

0.23

0

1

ImportFC

901

0.46

0.50

0

1

Source: Authors’ computation.

15

Table 3. Determinants of the probability that the invoice currency has been changed fromPC (in first exports) to FC (in current exports) (Dependent variable: SWITCH)ln(Experience)

(I)

(II)

(III)

(IV)

(V)

(VI)
Probit(VIII)OLS

OLS

ProbitProbitProbitProbitProbit0.03***

0.03*

0.05**

0.02**

0.02**

0.03*(0.01)(0.01)(0.02)(0.01)(0.01)(0.02)ln(Experience2)
DifferentImp

(VII)

0.01

-0.03

(0.01)(0.02)

0.20***

0.24***

0.24***

0.22***

0.20***

0.20***(0.06)(0.07)(0.07)(0.07)(0.05)(0.06)ToNonShosha

0.24***(0.11)

ToGroup

0.11(0.12)

DifferentProd

-0.02

-0.01

-0.00

-0.02*-0.02**-0.02*-0.09-0.26**(0.01)(0.03)(0.04)(0.01)(0.01)(0.01)(0.06)(0.10)YESNO

d ln(Sales)-0.00(0.01)d ln(Productivity)-0.00(0.01)

Industry FE

YES

YES

YES

YES

YES

YES

Region FE

YES

YES

YES

YES

YES

YES

NO

NO

Country FE

NO

NO

NO

NO

NO

NO

YES

YES

Firm FE

NO

NO

NO

NO

NO

NO

NO

YES

No. Obs.

325

325

325

307

307

325

417

353

R-squared

0.36

0.28

0.22

0.39

0.39

0.34

0.24

0.70

Source: Authors’ computation. Estimation results for equation (2).
Notes: Pseud R-squared and adjusted R-squared are reported for Probit and OLS, respectively. ***, **,and * indicate 1%, 5%, and 10% significance, respectively. Robust standard errors are shown inparentheses.

16

Table 4. Heckman-Probit estimation(I)
Dependent variable

Selection

OutcomeSelection

OutcomeSelection

Outcome

PCP0

SwitchPCP0

SwitchFCP0

Switch2

FCP0

Switch2

DifferentProd

Initiative
ShoshaCityBank
AfterGFC

-0.00

-0.02

(0.01)(0.00)(0.03)(0.03)

0.07***

0.01***

0.10*

0.10*

(0.02)(0.00)(0.05)(0.06)

-0.05

-0.00

-0.04

-0.030(0.03)(0.00)(0.07)(0.06)-0.06**-0.06**(0.03)(0.03)(0.03)(0.03)-0.05***-0.06***

0.03**

0.04**(0.02)(0.02)(0.01)(0.02)

0.42***

0.44***-0.34***-0.35***(0.04)(0.04)(0.04)(0.04)

0.20***

0.20***-0.15***-0.16***(0.05)(0.05)(0.05)(0.05)

-0.04

-0.06

0.07*

0.08**(0.04)(0.04)(0.04)(0.04)-0.14***-0.14***

0.14***

0.14***(0.04)(0.04)(0.04)(0.04)

Rho

1.00

Log pseudolikelihood

0.00**

0.07**NO

No. Obs.

0.02*

0.06*

Region FE

Chi-squared statistics(IV)

Outcome

DifferentImp

ln(Sales0)(III)

Selectionln(Experience)ln(Age)

(II)

NO

YES

YES

NO

NO

YES

1.00

0.32

0.34

(0.00)(0.00)(0.32)(0.31)

7.35***

976.03***

0.87

1.00

596

596

613

613

-376.02-348.63-345.38-318.08

Source: Authors’ computation.

17

YES

Table 5. Robustness checkln(Experience)ln(Experience)*After1998

(I)

(II)

(III)

(IV)

(V)

(VI)

(VII)

(VIII)(IX)

ProbitProbitProbitProbitProbitProbitProbitProbitProbit0.03***

0.03***

0.00**

0.02***

0.03***

0.02***

0.02**

0.00**(0.01)(0.01)(0.00)(0.01)(0.01)(0.01)(0.01)(0.00)

0.00(0.00)

ExperienceLong

0.03**(0.02)ln(Experience)*Discontinue-0.01(0.01)

DifferentImp
DifferentProd

0.20***

0.20***

0.04***

0.22***

0.25***

0.20***

0.20***

0.18***

0.012(0.06)(0.06)(0.03)(0.06)(0.07)(0.06)(0.06)(0.06)(0.01)

-0.02

-0.02

-0.00*-0.02-0.02*

-0.02

-0.02

-0.02

(0.01)(0.01)(0.00)(0.01)(0.01)(0.01)(0.01)(0.01)

Importer-0.01(0.02)

ImportFC

0.03**(0.02)

No. Obs.

325

323

204

325

280

321

325

325

125

R-squared

0.37

0.36

0.43

0.35

0.40

0.37

0.37

0.38

0.33

Source: Authors’ computation. Estimation results for equation (3).
Notes: Pseud R-squared is reported. Industry and region FEs are used in all columns. ***, **, and *indicate 1%, 5%, and 10% significance, respectively. Robust standard errors are shown in parentheses.
Each specification conducts the following robustness check: (I) includes the interaction term between
the log of experience and the dummy variable which takes one for observations where firms started
exporting after the revision of Foreign Exchange and Foreign Trade Act in 1998; (II) drops observations
with the upper and lower one percentile of the log of experience; (III) drops observations with the top
quartile of the log of experience; (IV) includes the dummy variable which takes one if the log ofexperience is larger than its mean; (V) drops exports to the U.S.; (VI) includes the interaction term
between the log of experience and the dummy variable which takes one if the firm intermittently
exported from its first export; (VII) includes the dummy variable which takes one if the firm is also an
importer; (VIII) includes the dummy variable which takes one if the firm is an importer and it uses FCs
in importing; (IX) focuses on observations of first exports.

18

Figure 1. Distribution of Experience

Source: Calculated by the authors.
Notes: The figure shows the distribution of Experience.

19

Figure 2. Distribution of Experience2

Source: Calculated by the authors.
Notes: The figure shows the distribution of Experience2.

20