Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Export Dynamics and Invoicing Currency
Kazunobu Hayakawa, Nuttawut LAKSANAPANYAKUL,
Toshiyuki Matsuura, Taiyo Yoshimi
3 March, 2024
DP2024-005
https://ies.keio.ac.jp/en/publications/23518/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
3 March, 2024

Export Dynamics and Invoicing Currency
Kazunobu Hayakawa, Nuttawut LAKSANAPANYAKUL, Toshiyuki Matsuura, Taiyo Yoshimi
Keio-IES DP2024-005
3 March, 2024
JEL Classification: F14, F31, F39
Keywords: Invoicing currency, Export dynamics, Inertia, Learning effect, Customs data
Abstract
This study explores the evolution of invoicing currency choice, focusing on inertia in invoicing
currency and the role of export experience. We theorize that inertia in the producer’s currency
pricing (PCP) weakens with lower forex risk management costs, whereas inertia in foreign
currency pricing is more pronounced under similar conditions. For the export experience,
exporters tend to adopt PCP when they start exporting if the costs are significant. Empiricalanalysis using firm-level export data in Thailand from 2007 to 2014 supports these predictions.
Specifically, we show that the inertia in PCP diminishes with access to forward exchange
contracts or when the importer’s currency has a higher forex turnover than the Thai baht. We
also show that the tendency to adopt PCP in first exports diminishes under these conditions.
Our findings imply that exporters initially prefer invoicing in their own currency, but thispreference decreases as export experience accumulates or if there are financial tools or
favorable currency turnover conditions.
Kazunobu Hayakawa
Institute of Developing Economies
3-2-2, Wakaba, Mihama-ku, Chiba-shi, Chiba
kazunobu_hayakawa@ide-gsm.org
Nuttawut LAKSANAPANYAKUL
Thailand Development Research Institute
565 Soi Ramkhamhaeng 39, Phlabphla, Wang Thonglang, Bangkok 10310 Thailand
nuttawut@tdri.or.th
Toshiyuki Matsuura
Keio Economic Observatory, Keio University
2-15-45, Mita, Minato-ku, Tokyo, 108-8345, Japanmatsuura@sanken.keio.ac.jp
Taiyo Yoshimi
Faculty of Economics, Chuo University742-1, HIgashi Nakano, Hachioji-shi, Tokyo
yoshimi@tamacc.chuo-u.ac.jp
Acknowledgement: This research was conducted as part of a project of the Economic ResearchInstitute for ASEAN and East Asia “Export Dynamics and Export Industry Development.” Wewould like to thank Kiyoshi Matsubara, Ju Hyun Pyun, Etsuro Shioji, Kenya Takaku, Yushi
Yoshida, and the seminar participants at the Research Institute of Economy, Trade andIndustry (RIETI), Japan Society for International Economics, Japan Society of Monetary
Economics, University of Niigata Prefecture, and Nanzan University. We acknowledge financial
support from the JSPS under KAKENHI Grant Numbers JP2638011 and JP22H00063 (toMatsuura), JP15H05393, JP20H01518, and JP20KK0289 (to Yoshimi). All remaining errors areours.

Export Dynamics and Invoicing Currency*
Kazunobu HAYAKAWA

Nuttawut LAKSANAPANYAKUL

Development Studies Center, Institute of Developing

Science and Technology Development Program,

Economies, Japan

Thailand Development Research Institute, Thailand

Toshiyuki MATSUURA

Taiyo YOSHIMI#

Keio Economic Observatory, Keio University, Japan

Faculty of Economics, Chuo University, Japan

This research was conducted as part of a project of the Economic Research Institute for ASEAN andEast Asia “Export Dynamics and Export Industry Development.” We would like to thank Kiyoshi
Matsubara, Ju Hyun Pyun, Etsuro Shioji, Kenya Takaku, Yushi Yoshida, and the seminar participants at
the Research Institute of Economy, Trade and Industry (RIETI), Japan Society for International
Economics, Japan Society of Monetary Economics, University of Niigata Prefecture, and Nanzan
University. We acknowledge financial support from the JSPS under KAKENHI Grant Numbers
JP2638011 and JP22H00063 (to Matsuura), JP15H05393, JP20H01518, and JP20KK0289 (to Yoshimi). Allremaining errors are ours.
# Corresponding author: Taiyo Yoshimi; Address: 742-1 Higashinakano, Hachioji-shi, Tokyo, 192-0393,
Japan; E-mail: yoshimi@tamacc.chuo-u.ac.jp.
*

1Abstract: This study explores the evolution of invoicing currency choice, focusing on inertia ininvoicing currency and the role of export experience. We theorize that inertia in the producer’s currency
pricing (PCP) weakens with lower forex risk management costs, whereas inertia in foreign currency
pricing is more pronounced under similar conditions. For the export experience, exporters tend to
adopt PCP when they start exporting if the costs are significant. Empirical analysis using firm-level
export data in Thailand from 2007 to 2014 supports these predictions. Specifically, we show that theinertia in PCP diminishes with access to forward exchange contracts or when the importer’s currency
has a higher forex turnover than the Thai baht. We also show that the tendency to adopt PCP in firstexports diminishes under these conditions. Our findings imply that exporters initially prefer invoicing
in their own currency, but this preference decreases as export experience accumulates or if there arefinancial tools or favorable currency turnover conditions.
Keywords: Invoicing currency; Export dynamics; Inertia; Learning effect; Customs data
JEL Classification: F1; F3

2

1. Introduction
The exchange rate exposure of trade prices depends on the invoicing currency,
which is the currency in which the prices and total amounts are stated on an invoice. The
invoicing currency determines who takes the forex risk in international transactions. Trade
pricing can usually be classified into three types depending on the invoicing currency:
producer currency pricing (PCP), local currency pricing (LCP), and third vehicle currency
pricing (VCP). For VCP, the United States dollar (USD) is mostly used. Recent studies,including Gopinath et al. (2020), Amiti et al. (2022), and Boz et al. (2022), emphasize thatmost international transactions are invoiced in a limited number of international
currencies, mainly USD. This phenomenon is called the “dominant currency paradigm.”
As Gopinath et al. (2010) discuss, exchange rate changes are rarely passed throughinto the price denominated in the currency used for export invoicing, at least in the shortrun. In other words, exporters suffer forex risk when their currency is not used in
invoicing. The effect of exchange rate movements on exporters’ profits depends cruciallyon the invoicing currency, and exporters prefer PCP if they are averse to forex risk.
Conversely, if exporters choose PCP and impose the forex risk on importers, importers
may decrease their demand to avoid assuming a considerable forex risk. This risk-averse
behavior by importers has not been discussed much in the literature, although it isnaturally predicted from studies such as Wolak and Kolstad (1991) and Coppejans et al.
(2007).1 Therefore, an exporter faces a trade-off: although PCP frees the exporter from theforex risk, imposing that risk on an importer may reduce the importer’s demand for theexporter’s goods and export profit.
Considering this trade-off, we theoretically and empirically investigate the choice ofinvoicing currency and how it evolves over time. Specifically, we examine which currency
tends to be chosen when firms start exporting and whether these firms change thecurrency once they have accumulated some export experience. We study the relationshipbetween firms’ export experience and their choice of invoicing currency. Export starters
tend to begin with small sales to determine whether they are profitable in the destination
market. They often suspend exporting if they expect their overseas business will not be
successful based the first export results (Albornoz et al., 2016, 2023). Furthermore,
Esteve-Pérez (2021) reveals that export experience positively impacts export survival. The
novel insight behind this argument is that firms learn from their initial experiences and,given the knowledge they developed, decide on their next course of action. In this context,
we focus on how firms’ export experience affects their choice of invoicing currency.
Recently, how firms expand their foreign sales has attracted the attention of researchers
Wolak and Kolstad (1991) and Coppejans et al. (2007) theoretically demonstrate that risk-averse
agents decrease the demand for products whose prices are uncertain in advance.
13

because increasing the number of exporters (i.e., extensive margin) is a key policy agenda
in both developed and developing countries. Exploring how the choice of invoicing
currency evolves over time provides insight into the monetary aspects of export dynamics,
which have not been sufficiently analyzed.2
Theoretically, we model a small open economy in which the exporter dynamicallychooses the invoicing currency and guides the empirical analysis. Our model introduces
the cost of managing forex risk through a forward exchange contract with a bank. This costappears when exporters use foreign currencies in invoicing and represents the effort ofdealing with the risk of exchange rate fluctuations. Costs include hiring staff to collect theforex market information or finding an appropriate bank for a forex contract. It may also
include the cost of assessing the forex risk and deciding on the proper number of foreign
sales to be hedged. Exporters must cover these costs when they invoice in foreign
currencies. We assume that cost is mitigated by overseas business experience andknowledge. In other words, we introduce a learning effect for forex risk management into
exporters’ choice of invoicing currency.
Using the developed model, we provide three testable propositions for two issues:
the inertia in invoicing currency and the choice of the invoicing currency in the first export.
For the former, the model shows that an exporter is likelier to keep adopting PCP in thesecond period once the exporter has chosen PCP in the first period when the cost to use
the forward exchange contract is larger (Proposition 1). This proposition provides apotential determinant of inertia in the invoicing currency discussed in studies such as
Ogawa and Sasaki (1998) and Cohen (2011). Furthermore, it is shown that the inertia in theinvoicing currency becomes more significant for foreign currency pricing (FCP) comparedwith PCP if the cost for the forward exchange contract is more significant (Proposition 2).
For the latter, the model predicts that exporters will likely choose PCP in their first exports
when the cost of a foreign exchange contract is high (Proposition 3). This proposition
reveals how exporters change their invoicing currency from the early stage of overseas
business to the following stages.
We empirically test these propositions using firm-level export data in Thailand from2007 to 2014. The data were obtained from the Customs Office of the Kingdom of Thailand
and covered all commodity exports during this period. Our dataset contains customs
clearing date, the HS eight-digit code, export destination country, firm identification code,
To the best of our knowledge, Goto et al. (2023) is the only paper to examine the impact of export
experience on the choice of invoicing currency. They conducted their analyses on the basis of aquestionnaire survey of Japanese SMEs in the manufacturing industry. They focus on SMEs because weshould consider other elements, such as product value chains and financial ties with trade partners forlarge companies, which makes it challenging to identify the effect of export experience on firms’
activities. The clear advantage of this study is that we use comprehensive Customs data, including
various companies and industries. Goto et al. (2023) can be regarded as a complement to this study.
24Thai baht (THB) export values, and invoicing currency. As shown in Section 3.1, the shareof exports in Thailand under the producer’s currency (i.e., THB) is, on average, 17.8% interms of the number of country–product pairs and 8.4% in terms of values during thesample period (see Table 1). Despite THB not being an international currency, theproducer’s currency plays a certain role in exporting, implying that Thai firms choose
invoicing currency among the producer’s and others. Therefore, we examine determining
the invoicing currency by exporters who choose between the producer’s and othercurrencies. In addition, the cost of the forward exchange contract (i.e., the cost to manage
the forex risk) between the producer’s currency and other currencies may be significant
because THB is still a minor currency in the forex market. Thus, Thailand is a good sample
country to examine the trade-offs involved in using the producer’s currency mentioned
above.
Empirically, to test Proposition 1, we employ a dummy variable that takes the valueof one if the invoicing currency is the producer’s currency as the dependent variable. We
use a 1-year lagged-dependent variable for the explanatory variable at the firm–country–
product level. The variable’s coefficient is positively estimated, which supports thepresence of inertia in the invoicing currency in the case of PCP. The coefficient is larger forimporting countries for whose currencies Thai local banks provide forward exchange rates
with THB. When Thai firms export to such countries, managing the forex risk through theforward exchange contract is relatively easy. We find similar results for importing
countries whose currencies have a higher turnover in the forex market than THB. Higher
turnover indicates lower transaction costs, indicating lower exchange rate risk
management costs. These results support Proposition 1. In the empirical test of
Proposition 2, we also investigate the case of the local currency dummy as a dependent
variable and its one-period lag as an explanatory variable. The degree of inertia is morevital for the local currency than the producer’s currency, especially for importing countries
where the forward exchange contract is available or their currencies have higher turnoverthan THB. These findings support Proposition 2.
Proposition 3 is related to the choice of the invoicing currency in the first export. To
test the proposition, we use a dummy variable that takes the value one if a concerned
export transaction is the first one for firms as a main explanatory variable. The PCP
dummy variable is used as the dependent variable in the same way as in the above tests
for Proposition 1. This explanatory variable’s coefficient is estimated to be positive,
indicating that PCP is likelier to be chosen in the first export than in the following exports.
In our baseline estimation, the probability of selecting PCP in the first export is 5.8 percent
points higher than in the export of the second and subsequent products/destinations. The
coefficient becomes lower for importing countries whose currencies Thai local banks
provide forward exchange rates with THB or if the turnover of the importing country’s
currency is higher than that of THB. These results imply that the tendency for PCP to be5

adopted in the first export is mitigated for importing countries with lower costs for forex
risk management, which is consistent with Proposition 3.
Our study is related to at least two strands of literature. The first is the choice ofinvoicing currency. Engel (2006) investigates the link between the choice of invoicing
currency and the decision on export prices. Gopinath et al. (2010) extended Engel’s (2006)
framework by introducing a dynamic perspective and conducted a detailed empiricalanalysis of the choice of invoicing currency. We also focus on the dynamic choice ofinvoicing currency, yet consider how firms’ experiences affect currency choice. Several
papers examine the firm-level choice of invoicing currency. Chung (2016) considers how
exporters’ dependence on imported inputs affects their choice of invoicing currency using
data for the UK, while Devereux et al. (2017) investigated how firms’ market share affects
the choice of invoicing currency using data for Canada.3 The primary contribution of thispaper lies in the analysis of the relationship between export experience and choice ofinvoicing currency. Additionally, it makes a secondary contribution by being the firststudy to utilize Thai customs data in researching invoicing currencies.
The other strand of literature is on export dynamics because we examine how
invoicing currency in firm-level exports evolves over time. Recent studies have empiricallyexamined how firms’ volume, duration, export destination countries, and export products
change. 4 For example, new exporters tend to start small and focus on a single, usuallyneighboring, country. Once they outlive their entry year, they tend to expand their sales abroad andreach a larger number of destinations (Albournoz et al., 2016). Albornoz et al. (2023) further
investigated firms’ export dynamics regarding product scope and destination. They foundthat new exporters are likelier to expand by introducing new products in their first export
market than by entering new markets with their first product. In contrast, our study
examines firms’ invoice currency choice over time. In particular, we show that newexporters tend to adopt PCP. However, in their second and subsequent exports, PCP isless likely to be chosen, perhaps because of the knowledge about forex risk management
developed through their overseas business experience.
The rest of this paper is organized as follows. The next section presents a theoreticalmodel demonstrating the relationship between export experience and currency invoicing.
Goldberg and Tille (2013) consider how bargaining between exporters and importers affects the
choice of invoicing currency and export prices. Although our dataset does not enable us to identify
importers’ information at a firm level and investigate the bargaining aspect precisely, we attempt tocontrol importers’ characteristics using a variety of fixed effects.
4 Early work on export dynamics includes Baldwin (1988) and Baldwin and Krugman (1989). The long
list of firm-level studies includes Aeberhardt et al. (2014), Albornoz et al. (2012), Albornoz et al. (2016),
Araujo et al. (2016), Bekes and Murakozy (2012), Berman et al. (2015), Berthou and Vincent (2015), Blumet al. (2013), Buono and Fadinger (2012), Defever et al. (2015), Fernandes and Tang (2014), Lawless(2009), and Vannoorenberghe et al. (2016). Alessandria et al. (2021) present a review of recent studies onexport dynamics.
3

6

Section 3 empirically investigates the relationship between firms’ export experience andthe choice of invoicing currency. Finally, Section 4 concludes this paper.

2. Theoretical Underpinnings
This section provides theoretical guidance for conducting our empiricalinvestigations. We examine how an exporter selects the first-period invoicing currency forexporting and whether that exporter uses the same type of invoicing currency given theexport results in the first period.

2.1. Setup
The literature has observed that less capable exporters prefer PCP in their exports toavoid forex risks. For instance, Fabling and Sanderson (2015) found that exporters withlower performances are likelier to adopt PCP based on New Zealand’s firm-level export
data, which is, in turn, associated with greater exchange rate pass-through into importprices. In addition, Goto et al. (2021) concluded that the share of PCP is higher for small
and medium enterprises (SMEs) compared to the large listed companies based on aquestionnaire survey of Japanese SMEs. They also revealed that firms with deteriorated
capital ratios depend more on PCP. These studies suggest there is a tendency that lesscapable exporters tend to prefer invoicing in their home currencies.
Based on these arguments, a straightforward prediction for dynamic choice ofinvoicing currency is that firms may choose PCP in the early export stage since they do nothave sufficient financial knowledge to hedge the forex risk. Then, they may consider whatcurrency to use in the following period, given the knowledge and resources that theyobtained in past experiences. In other words, we expect that exporters’ capability will be
improved by their export experiences, affecting their choice of invoicing currencies
onward. To determine the theoretical accuracy of this prediction and provide specifictestable propositions for the empirical analysis, we build a simple model of the dynamic
choice of invoicing currency. Our model consists of two periods of time (𝑡 = 1, 2).
Between the home and foreign countries, the home country is so small that home country
exporters do not affect the variables of the foreign country. The home and foreigncountries have their own currencies.5 At 𝑡 = 1, a home country firm starts exporting to
We focus on the case where exporters choose between two currencies (producer’s currency and one
foreign currency) to obtain simple testable propositions. Regarding the ternary choice, Chung (2016)
investigates the choice of invoicing currency when the third vehicle currency is present in addition to
the producer’s and importer’s currency and obtains similar results in the VCP and LCP cases. We did
not show the case with multiple destinations and products while we investigated those cases in the
57

the foreign country, which we call the first export. The firm continues to export at 𝑡 = 2.
Each firm produces and exports one homogeneous product.
The price denominated in the foreign country’s currency, which we call the customer
price, is exogenous to each exporter in the home country because each exporter is so smallthat it does not impact the foreign country’s market. There are two-time points in eachperiod: the beginning of the period when the export contract is signed and the end when
the payment is settled. Figure 1 illustrates the model’s time flow. The spot exchange rate at
the beginning and end of period 𝑡 is represented by 𝑒 and 𝑒 , respectively. At the
beginning of each period, exporters simultaneously determine the invoicing currency andexport quantity to maximize expected profit by taking the customer price as given.
Exporters receive payment at the end of each period. Thus, exporters suffer the risk of
exchange rate changes between the beginning and end of each period if they employ FCP.
To hedge this forex risk, exporters offer forward exchange contracts to banks. In this case,
exporters use the prefixed forward exchange rate, paying a positive fixed cost.6 This fixed
cost captures the cost of forex risk management and comprises various costs that do notdepend on the export value. For example, the fixed cost may capture the cost of hiring
staff who deal with forex risk in the exporting company and the cost of preparing
documentation for preliminary review by a bank. The cost also consists of collecting
information and knowledge about the forex market. Under PCP, exporters determine theinvoicing currency and export quantity to maximize the expected profit calculated basedon the expected exchange rate.
===

Figure 1===

2.2. Importer Risk Aversion and Customer Price
We assume that importers are risk averse. Studies such as Wolak and Kolstad (1991)
and Coppejans et al. (2007) have argued that risk-averse agents decrease the demand for
products with uncertain prices. If an exporter selects PCP at the beginning of each period,
the customer price that an importer pays at the end is uncertain. As a result, demand forexport products becomes smaller under PCP than FCP. To incorporate this mechanism
most simply, we employ the following form of the demand function:𝑞(𝑝) = 𝜇 − 1{

} 𝜎 − 𝑝,where 𝜇 is the exogenous demand component, and 𝑝 is the customer price. 1{}

represents the indicator function, which is one under PCP and zero under FCP. The
earlier version of this paper (see Hayakwa et al., 2019)
6 Variable costs for forex risk management are interpreted to be imputed on the forward exchange rate.
8positive parameter 𝜎 captures the degree of the importers’ risk aversion, implying thatthe gap between demand under PCP and FCP is larger if the importer is more risk averse.
Destination markets are competitive, and importers are risk averse. Thus, no
importers purchase products invoiced in the exporter’s currency if the customer price isthe same for PCP and FCP cases. In other words, the customer price must be lower under
PCP than FCP so that an exporter gains a positive demand under PCP. Specifically, an
exporter gains the same level of demand under PCP as in the case of FCP (𝑞 = 𝑞 ) when
the PCP price is lower than the FCP price in the following manner:7𝑝(1)= 𝑝 − 𝜎.

𝑝 and 𝑝 are customer prices under PCP and FCP, respectively. This equation indicatesthat the customer price must be discounted under PCP compared with FCP to gain thesame demand level. The gap between 𝑝 and 𝑝 is present because the importer is risk
averse.
Under PCP, the exporter receives the unit price 𝑒 𝑝 at the end of period 𝑡. The
foreign country’s market is competitive, so the customer price 𝑝 is given to the exportereven under PCP. Nevertheless, the exporter can fix the unit value that it receives at the endof the period by converting the exogenous customer price 𝑝 into the price denominatedin the producer’s currency using the spot exchange rate at the beginning of the period. In
other words, 𝑒 𝑝 is the contract price under PCP. From the importer’s perspective, thepayment in terms of the importer’s currency becomes larger or smaller if the exchange ratechanges from the beginning to the end of the period (i.e., 𝑝 ≠ 𝑒 𝑝 ⁄𝑒 if 𝑒 ≠ 𝑒 ).
Alternatively, the importer may utilize the forward exchange contract paying an
additional cost to avoid the forex risk, as the exporter in our model does. As compensation
for that forex risk or the use cost of the forward exchange contract, the importer offers alower customer price under PCP than the FCP case, as shown in equation (1).8 Meanwhile,
under FCP, the contract customer price is 𝑝 . The exporter receives 𝑝 units of theforeign currency at the end of the period and converts it into the producer’s currency
using the forward exchange rate 𝑓 .

2.3. Cost Structure
Each exporter produces one homogeneous product, incurring the unit production
To support this outcome, Table B1 in the Appendix shows that unit prices are lower for PCP than FCPeven after controlling for various fixed effects using the same dataset used in our empirical sections.
8 The importer may not sign a contract with the exporter under PCP if the importer is extremely
vulnerable to forex risk. In the same way, vulnerable exporters may not be able to accept the forex risk
under the FCP. From this perspective, we focus on the case where both exporter and importer have acertain degree of capability to accept the exchange rate risk or the forex risk is not extremely large.
7

9

cost 𝑐̂ . Hatted variables are denominated in the producer’s currency. We also assumed
that exporters pay a fixed cost when they employ FCP using a foreign exchange contract
with a bank. We let 𝐹 represent this first-period fixed cost. Under PCP, exporters do not
pay this fixed cost. In the second period, exporters pay the same type of fixed cost if they
employ FCP. We assume that the level of fixed cost in the second period depends on the
choice of first-period invoicing currency. Specifically, the second-period fixed cost is 𝐹 if
the exporter has employed PCP during the first period, while it is 𝛽𝐹 if the exporter has
chosen FCP during the first period. 𝛽 is the positive constant and is lower than one (0 <𝛽 < 1). This setup reflects the learning effect, which reduces the second-period fixed costthrough the first-period results of proceeding with the forward exchange contract with abank. For instance, the exporter’s burden to proceed with this type of contract, such as
documentation preparation, may be lighter if the exporter has experienced the sameprocess in the previous period.

2.4. Determining the Invoicing Currency
We solve for firm decisions using backward induction to provide testable
implications. In other words, we derive the subgame perfect equilibrium. 𝑡 = 2
exporters determine export quantities and the invoicing currency given the export
experience at 𝑡 = 1. At 𝑡 = 1, exporters determine the export quantity and invoicing
currency considering the expected profits at 𝑡 = 2.

2.4.1. Inertia in Invoicing Currency: Currency Choice in the Second Period
Export profit in the second period depends on the first-period invoicing currency
selected, as the second-period fixed cost for the forward exchange contract depends on thefirst-period currency choice. Suppose that the exporter has chosen PCP at 𝑡 = 1. Then,the export profits at 𝑡 = 2 when the exporter exports 𝑞 unit of the product under PCP
and FCP are given by𝑐̂
𝜋 | = (𝑒 𝑝 − 𝑐̂ )𝑞 = 𝑒 𝜇 − 𝜎 − 𝑞 −𝑞 for PCP,𝑒𝜋 |= (𝑓 𝑝 − 𝑐̂ )𝑞 − 𝐹 = 𝑓 𝜇 − 𝑞 −𝑐̂
𝑞 − 𝐹 for FCP.
𝑓

We abstract from the usual trade costs, such as tariff and transportation costs, without lossof generality of our statements. Profit-maximizing export quantities under respective
invoicing currencies are derived as follows:
1
𝑐̂
𝑞| =
𝜇−𝜎−
for PCP,
(2)
2
𝑒
10

𝑞|

=

1
𝑐̂
𝜇−
2
𝑓

for FCP.

Inserting these quantities into the profits, we obtain the maximized export profits asfollows:
1
𝑐̂
𝜇−𝜎−
2
𝑒

𝜋 |

= 𝑒

𝜋 |

1
𝑐̂
= 𝑓
𝜇−
2
𝑓

for PCP,(3)− 𝐹 for FCP.

The exporter chooses PCP during the second period if 𝜋 |< 𝜋 | . For the exchange rate

evolution, we assume three alternative scenarios: depreciation ( 𝑒 = (1 + 𝜀)𝑒 ),appreciation ( 𝑒 = (1 − 𝜀)𝑒 ) and stable ( 𝑒 = 𝑒 ) scenarios, where 𝜀 is a positive
constant (0 < 𝜀 < 1). Each scenario is realized with an equal probability of one-third. As
a result, the unconditional expectation for the exchange rate at the end of each period
equals the rate at the beginning of each period (𝐸 [𝑒 ] = 𝑒 ). In addition, we assume thatthe forward exchange rate fully reflects the market expectation for the spot exchange rate
at the end of each period (𝑓 = 𝐸 [𝑒 ]). Therefore, the future exchange rate equals thecurrent exchange rate (i.e., 𝑓 = 𝑒 ).9 As a result, the condition that the exporter selects
PCP, given that it has chosen PCP in the first export (𝜋 | < 𝜋 | ), can be rewritten as𝐹 >𝜎(𝑒 [2𝜇 − 𝜎] − 2𝑐̂ ).
4(4)Note that the right-hand side of this condition is always positive, indicating that PCP
inertia (i.e., an exporter adopts PCP at both the first and second periods) occurs when the
fixed cost for the forward exchange contract (𝐹 ) is significantly large. This is because anexporter cannot gain enough profit to cover the fixed cost if it is large, given the exporter’s
production cost (𝑐̂ ). In summary, we state the following testable proposition:
Proposition 1. Fixed cost for the forward exchange contract and PCP inertia: Given that an
exporter has chosen PCP during the first period, the exporter is likelier to choose PCP during thesecond period when the forward exchange contract fixed cost (𝐹 ) is larger.
From a different angle, we can interpret condition (4) as indicating that an exporter who
has adopted PCP during the first period may switch to FCP if the exporter is cost-efficient(i.e., low 𝑐̂ ) upon realizing that FCP is more profitable during the second period.
These setups pin down the forward exchange rate and dramatically simplify our model. Relaxing
these assumptions may alter or complicate the consequences of the model. Nevertheless, we stick to thecurrent tractable setup so that we focus on the impact of export experience on the choice of invoicing
currency and obtain testable propositions.
911In Appendix A1, we obtain the condition for FCP inertia analogous to the case of
PCP shown in condition (4). Specifically, the condition that the exporter employs FCP in
the second period, given that it has chosen FCP in the first period, is derived as follows:𝜎𝛽𝐹 < (𝑒 [2𝜇 − 𝜎] − 2𝑐̂ ).
(5)
4
Notably, the left-hand side of condition (5) is smaller than that of condition (4), whereas
the right-hand side is the same. This is because the learning effect for the fixed cost of theforward exchange contract is the only essential difference between FCP and PCP in ourmodel. The learning effect works only when an exporter has chosen FCP during the first
period. Conditions (4) and (5) jointly imply that inertia may occur regardless of anexporter’s first-period choice of invoicing currency if the right-hand side of thoseequations is between 𝛽𝐹 and 𝐹 . In other words, the learning effect of export experience
under FCP enables the model to replicate the observation that the inertia of PCP and FCP
coexists for the same exporter, product, and destination country. Notably, conditions (4)and (5) become stricter and looser when the forward exchange contract fixed cost (𝐹 ) islower. In other words, FCP inertia is likelier to occur compared with that of PCP when thecost is less significant. As a result, we state the following proposition:
Proposition 2. Comparing PCP and FCP inertia: Inertia in the invoicing currency is likelier tooccur for FCP than for PCP if the forward exchange contract fixed cost between home and foreign
currencies is lower.

2.4.2. Invoicing Currency in the First Export
The expected profits in the first period when the exporter exports 𝑞 units of its
product employing PCP and FCP are, respectively, given by𝜋𝜋= (𝑒 𝑝 − 𝑐̂ )𝑞 + 𝐸 𝜋 |= 𝑒= (𝑓 𝑝 − 𝑐̂ )𝑞 − 𝐹 + 𝐸 𝜋 |

1
𝑐̂
𝜇−𝜎−
2
𝑒
= 𝑒

1
𝑐̂
𝜇−
2
𝑒

+ 𝐸 𝜋 |

for PCP,(6)−𝐹 + 𝐸 𝜋 |for FCP.

𝐼 is the first-period invoicing currency selected by the exporter (𝐼 = 𝑃, 𝐹), and 𝐸 𝜋 |represents the expectation of the second-period profit in the first period based on having
selected invoicing currency 𝐼. We ignored the discount for future profit for simplicity. In
the second equality of these equations, we used the profit-maximizing export quantity thatcan be obtained analogously to equation (2), given that 𝐸 𝜋 | is not affected by the
first-period export quantity. The exporter selects PCP (FCP) if 𝜋 > 𝜋 (𝜋 < 𝜋 ) at thefirst period.
𝐸 𝜋 | is derived as the weighted average of second-period profits with each type12

of invoicing currency and is expressed as
𝐸 𝜋 |= 𝑃𝑟𝑜𝑏 𝜋 | > 𝜋 | 𝐸 𝜋 |+ 1 − 𝑃𝑟𝑜𝑏 𝜋 | > 𝜋 |𝐸 𝜋 | .

(7)

The weights are the probabilities that each invoicing currency is chosen during the second
period. The second-period profits ( 𝜋 | and 𝜋 | for 𝐼 = 𝑃, 𝐿 ) depend on the spot
exchange rate at the beginning of the second period, as shown in Equations (3) and (A1) inAppendix A. Therefore, we need an additional setup for the exchange rate evolution
between the beginning of 𝑡 = 1 to the beginning of 𝑡 = 2 to derive 𝐸 𝜋 | . Again,
we assume three alternative scenarios for exchange rate changes between these two-time
points: depreciation (𝑒 = (1 + 𝛿)𝑒 ), appreciation (𝑒 = (1 − 𝛿)𝑒 ), and stable (𝑒 = 𝑒 )scenarios, where 𝛿 is a positive constant 0 < 𝛿 < 1. Similar to the case between eachperiod’s beginning and end, we assume that each scenario is realized with an equal
one-third probability. As a result, 𝐸 𝜋 | and 𝐸 𝜋 | are, respectively, given by10𝑒𝑐̂
1 − 𝛿 ⁄3 𝑐̂(𝜇 − 𝜎) − 2(𝜇 − 𝜎)
+
4
𝑒
1−𝛿
𝑒

𝐸 𝜋 |

=

𝐸 𝜋 |

𝑒
𝑐̂
1 − 𝛿 ⁄3 𝑐̂=
𝜇 − 2𝜇+4𝑒1−𝛿𝑒Note that 𝐹 |= 𝐹 and 𝐹 |,(8)−𝐹 | .

= 𝛽𝐹.

From conditions (4) and (5), the probability that PCP is chosen in the second period,
given that the exporter has chosen invoicing currency 𝐼 in the first period, is given by𝜎
𝑃𝑟𝑜𝑏 𝜋 | > 𝜋 | = 𝑃𝑟𝑜𝑏 𝐹 | > (𝑒 [2𝜇 − 𝜎] − 2𝑐̂ ) .
4
Notably, PCP is likelier to be chosen when the value of the exporter’s currency is higher(i.e., 𝑒 is lower), regardless of the first-period invoicing currency . The interpretation isthat the exporter is willing to receive the high-value home currency. Specifically, PCP ischosen when the second-period exchange rate is lower than the following thresholds
according to the first-period currency:𝑒̅ |=𝜎𝐹 ⁄4 + 2𝑐̂
if PCP has been chosen at 𝑡 = 1,
2𝜇 − 𝜎𝑒̅ |=𝛽𝜎𝐹 ⁄4 + 2𝑐̂
if FCP has been chosen at 𝑡 = 1.
2𝜇 − 𝜎PCP is chosen at the beginning of the second period when 𝑒̅ | > 𝑒 given that theexporter has chosen invoicing currency 𝐼 at the first period. Note that 𝑒̅ | is higher than𝑒̅ | due to the learning effect for the fixed cost under FCP. For empirical consistency that
there are always positive shares of PCP and FCP in the data, we assume that depreciation
10

The derivation of these equations is given in Appendix A2.
13

(appreciation) of the exporter’s currency in the depreciation (appreciation) scenario issignificant so that FCP (PCP) can be chosen in the second period. Specifically, we assume
that (1 + 𝛿)𝑒 > 𝑒̅ | and 𝑒̅ | > (1 − 𝛿)𝑒 for 𝐼 = 𝑃, 𝐹.
For the stable scenario (𝑒 = 𝑒 ), the exporter would choose the same invoicing
currency as in the first period because macroeconomic conditions that affect the currency
choice do not change. When the exporter selects FCP in the first period, the second-period
fixed cost for the forward exchange contract is reduced through the learning effect.
Therefore, the inertia must be more significant when the exporter chooses FCP during thefirst period. As a result, the choice probabilities are given by
𝑃𝑟𝑜𝑏 𝜋 |

> 𝜋 |

=

2
3

and

𝑃𝑟𝑜𝑏 𝜋 |

> 𝜋 |

=

1
.
3

(9)

Combining Equations (7), (8), and (9), the second-period expected profit is derived asfollows:
𝐸 𝜋 |

=

𝑒
𝑐̂
2 (𝜇 − 𝜎) − 2(𝜇 − 𝜎)12𝑒+ 𝜇 − 2𝜇

𝑐̂
𝑒

+

3−𝛿
1−𝛿

𝑐̂
𝑒

𝐸 𝜋 |

=

𝑒
𝑐̂
(𝜇 − 𝜎) − 2(𝜇 − 𝜎)12𝑒

+ 2 𝜇 − 2𝜇

𝑐̂
𝑒

+

3−𝛿
1−𝛿

𝑐̂
𝑒

1
− 𝐹,
3
(10)

2
− 𝛽𝐹 .
3
As a result, by combining Equations (6) and (10), the condition that PCP is chosen in thefirst export (𝜋 > 𝜋 ) is rearranged as𝜋1> 𝜋 → 𝑒 𝜇𝜎 − 𝑒 𝜎 − 𝑐̂ 𝜎 < (1 + 𝛽)𝐹 .
2(11)This condition indicates that PCP is likelier to be chosen in the first period when the fixed
cost for the forward exchange contract (𝐹 ) is small. The exporter hesitates to employ FCP if
the fixed cost is large. As a result, we state the following proposition based on condition(11)
Proposition 3. Invoicing Currency in First Exports: Exporters will likely choose PCP in firstexports when the fixed cost for the forward exchange contract (𝐹 ) is large.

3. Empirical Analysis
This section empirically investigates the implications derived in the previous section.
We first overview the data. Second, we investigate the inertia in the invoicing currency inthe current year, which will likely be chosen again next year. Third, we examine the14relationship between export experience and the choice of invoicing currency by comparing
the first and subsequent exports.

3.1. Data Overview
This section provides an overview of our dataset. As mentioned in the introductory
section, we employ transaction-level export data in Thailand during the 2007–2014 period,
obtained from the Customs Office of the Kingdom of Thailand. Our data cover allcommodity exports during this period, including customs clearing date, the HS eight-digit
code, export destination country, firm identification code, export values, and invoicing
currency.
Table 1 presents shares of each invoicing currency in exports from Thailand in termsof number and value. Panel (A) shows the results for a full sample. Panel (B) presents thoseshares focusing on exporters first observed in the dataset (i.e., export starters) because thetheoretical framework of this paper focuses on firms that have started exporting. Panel (B)does not show the share in 2007 as we define the firm that did not export in 2007 but did
export after 2007 as a new exporter in our baseline estimation. In Panel A1, which shows
the share based on the number of transactions, the share of VCP ranges between 66.8 %and 68.9% and is highest among PCP, LCP, and VCP. VCP is the currency other thanexporters’ and importers’ currencies, and USD is the majority currency in VCP. During thesample period, the average share of export transactions under PCP (i.e., THB pricing) was
17.8%. Thus, a considerable share of exports is conducted under PCP, implying thatexporters in Thailand face the choice between PCP and FCP. Therefore, Thailand is a fairsample country to examine the trade-off in the use of PCP discussed in the theoreticalsection. As shown in Panel A2, the average PCP share decreases in terms of values(compared with the number of transactions). This is 8.3%, indicating that THB will likely
be used in transactions with small values. This observation is consistent with Goldberg
and Tille (2016), which revealed that LCP is used more in transactions of higher value
using Canadian import data. Panels A3 and A4 compare currency shares in the size
quartiles of transaction value to examine the relationship between transaction value andcurrency choice. PCP has the lowest number and value of transactions in the fourth
quartile. The PCP’s share is highest in the first quartile for transaction value (Panel A4).
For the number of transactions, PCP’s share is the second highest in the first quartile(Panel A3). Therefore, PCP is likelier to be chosen in transactions with small values.
===

Table 1===

We next focus on new exporters. As shown in Panel B1, the PCP share in terms of thenumber of transactions is much higher among new exporters than among all exporters (i.e.,
15Panel A1). This implies that firms are likelier to adopt PCP when they first export thanwhen they export again. However, the PCP share in terms of value is not so different
between new exporters and all exporters, as shown in Panel B2. In many sample years, theshare of PCP for new exporters was even lower than that for all exporters because thevalue of new exports invoiced in THB usually is considerably smaller than that of newexports invoiced under VCP. As a result, the PCP share in value does not differ much
across new and all exporters, whereas the VCP share is significantly higher for newexporters. Panels B3 and B4 confirm the relationship between transaction value size andcurrency share for export starters. Notably, the share of PCP in the first quartile oftransaction value is approximately 30%, which is higher than that in Panels A3 and A4.
This suggests that the relationship between currency choice and transaction size is greaterfor export starters.
Figure 2 shows the number of firms appearing for the first time in the dataset inrespective years. Most export firms appear from the beginning of our sample period, i.e.,
2007. Nevertheless, we can see a non-negligible number of new exporters afterward. In thebaseline estimations of the empirical part of this paper, we define a new exporter as a firmthat did not export in 2007 but did export after 2007. One may use alternative definitionsof new exporters. For instance, firms that did not export during 2007–2009 but did export
after 2009 (i.e., three-year window) are defined as new exporters. This definition is moreconservative than our 1-year definition. We employ definitions based on a two- to six-year
window and confirm that our empirical results are qualitatively unchanged. However, wemust consider the possibility that such a new exporter may have experience in exporting
before 2007.
===

Figure 2===

This study focuses on exporters’ decisions about invoicing currency. From theexporters’ perspective, PCP differs from other currencies because it frees them from forex
risks. Because a critical difference exists between PCP and FCP for exporting firms, we firstclassify invoicing currency into two types, PCP and FCP, the latter of which includes LCP
and VCP as a baseline. We will differentiate between LCP and VCP in the later sections.
Our theoretical propositions are related to the time-series change of invoicing
currency, so we briefly examine that aspect of our data. Table 2 shows how the invoicing
currency within a firm–country–product pair changes over the sample period. For eachfirm–product–country pair, we identify the first and last years with positive exports
during our sample period and then examine whether the invoicing currency differs
between the two years. In this analysis, we drop the observations where multiple invoicing
currencies can be detected at the firm–product–country–year level. Table 2 shows how
many percentages of firm–product–country pairs use different invoicing currencies
16between the first and last years . The first appearance year is shown in the first row of thetable, and the last appearance year is shown in the first column. The table shows many
firm–country–product pairs change invoicing currency. At the maximum, the rate ofchange is 18% from 2008 to 2013 for firms that started exporting in 2008. In our dataset,
information on trading partner firms is unavailable. We cannot identify the change in
trading partner firms over time Within a firm–country–product pair. Accordingly, in Table
2, the invoicing currency change may occur due to the shift in trading partners within afirm–product–country pair. Therefore, it should be noted that the inertia in the invoicing
currency can be undervalued in Table 2.
===

Table 2===3.2. Invoicing Currency Inertia
In this subsection, we empirically investigate the inertia in invoicing currency withina firm–product–country pair. We examine whether exporters tend to adopt PCP in thenext two years, then investigate the case for LCP and VCP.

3.2.1. Baseline Results
To empirically investigate the choice of invoicing currency, we estimate thefollowing lagged-dependent variable model:
𝑃𝐶𝑃= 𝛼𝑃𝐶𝑃+ 𝛽 ln 𝑉𝑎𝑙𝑢𝑒+ 𝑢 + 𝑢 + 𝑢 + 𝜖
The dependent variable is a dummy variable that takes the value of one if firm f adopts
PCP in the export transaction of product p to country i in year t and zero otherwise. The
positive coefficient for the lagged variable (lagged THB dummy) indicates that inertia ispresent for PCP. We utilized all firm-export destination-HS eight-digit level observations
during 2007–2014 while we dropped the observations where multiple invoicing currencies
can be detected in any year within a firm–product–country pair. In the previous
subsection, we observed that PCP is likelier to be used in small transactions. To control forthis “size effect” on invoicing currency choice, we introduce the natural log of the export
value of a concerned transaction (ln 𝑉𝑎𝑙𝑢𝑒) in our estimation.
We also introduce export firm–year fixed effects, which control for time-variant
firm-specific characteristics such as productivity. We also control for import country–year
and HS six-digit code (denoted by s)-year fixed effects. For example, the former fixed effectcontrols for time-variant country pair-specific elements such as exchange rates. Similarly,
the latter fixed effect captures the effects of time-variant sector-specific elements in theexport country, e.g., production cost. Due to the introduction of several fixed effects, we

17estimate this model using the ordinary least square (OLS) method (i.e., a linear-probability
model) to avoid the incidental parameter problem.
The baseline estimation results are presented in column (I) in Panel (A) of Table 3.
The lagged-dependent variable’s coefficient is estimated to be significantly positive. Its
magnitude is approximately 0.84, which is economically high. We can see the strong PCP
inertia. Note that we cannot exclude the case of the invoicing currency change due to thechange of trading partners within a firm–product–country pair. Thus, the degree of inertia
may even be undervalued in our estimation. The coefficient for a log of Value issignificantly negative, indicating that PCP is less likely to be chosen when the export value
is larger, consistent with the observations in Table 1.
===

Table 3===

Proposition 1 states that PCP inertia is more significant when the cost of forward
exchange contracts is large. To test this proposition, we use two potential measures forthat cost: the availability of the forward exchange rate of the importer’s currency against
THB and the turnover of the importer’s currency in the forex market. Accessibility to theforward exchange rate vis-à-vis the importer’s currency may significantly reduce the costof managing forex risk. In addition, the transaction cost in the forex market for theimporter’s currency must be low if that currency’s turnover is large. In columns (II) and(III) of Panel (A) of Table 3, we restrict sample export destination countries based on theavailability of forward exchange rates for their official currency in Thailand. 11 Thesecolumns show that the lagged PCP dummy coefficient is lower for importing countries
with forward rates (column III) than for countries without forward rates (column II). In
other words, PCP inertia is weaker in exports to countries with accessible forward
exchange rates. Columns (IV) and (V) show the estimation results for countries that havecurrencies with lower and higher turnovers than THB, respectively. The turnover data
were obtained from the Triennial Central Bank Survey by the Bank for International
Settlements. The lagged PCP dummy coefficient is slightly smaller for the latter countrygroup. These results support Proposition 1, indicating that exporters are likelier to switch
to a foreign currency if they can use the forward exchange rate between THB and theimporter’s currency or if the importer’s currency is more prevalent in the forex market.
To test Proposition 2, we also investigate LCP inertia. The results are shown in Panel(B) of Table 3. As shown in column (I) of the panel, inertia is also significant for LCP. The
impact of the export value is estimated to be negative for LCP, although the impact
Through a telephone survey of the headquarters of major Thai city banks, we confirmed that mostbanks provide forward exchange contracts between THB and the following currencies: EUR, GBP, JPY,
AUD, CHF, HKD, SGD, CAD, DKK, NOK, SEK, NZD, CNY, and USD.
1118

becomes insignificant in other columns. These results imply that VCP is used more inlarge-value transactions than PCP and LCP. Comparing the coefficients in column (II) of
Panels (A) and (B), the degree of inertia is higher in PCP than in LCP for countries without
access to forward exchange rates. As shown in column (III), the inertia is lower in PCP for
countries without forward rates. We observe the same tendency for turnover: inertia ismore and less prevalent for PCP than LCP for countries without and with forward rates,
respectively, as shown in columns (IV) and (V). These results are consistent with thestatement in Proposition 2 that inertia would be more significant for FCP than PCP when
the cost to use the forward exchange rate is low.
Table 4 uses the instrumental variable (IV) method. Since the transaction value andthe invoicing currency are simultaneously determined in practice, unobservable shocks to
the transaction value also influence the choice of invoicing currency. The import country’s
total imports of a concerned product from the world except for Thailand are used toaddress this endogeneity issue.12 This instrument captures the demand size in importcountries and is, thus, related to the transaction size in exporting from Thailand. We
identify the demand-driven component of transactions. Thus, our instrument is notdirectly related to firms’ choice of invoicing currency. Indeed, the results show that our
instrument is not weak as indicated by Kleibergen–Paap rk LM statistics and Cragg–
Donald Wald F statistics. Compared with our baseline estimation shown in Panel (A) of
Table 3, the results in the lagged-dependent variable are qualitatively and quantitatively
unchanged, although the coefficients for transaction values are insignificant.
===Table 4===3.2.2. Robustness Checks
Below, we conduct various robustness checks. Due to space limitations, we briefly
introduce these results in this section, while the tables for robustness checks are presented
in Appendix B. To determine the differences across industries, we estimate our equation
using the one-digit Standard International Trade Classification (SITC) code, presented inTable B2. All industries show positively significant coefficients for the lagged-dependent
variable. Manufactured goods classified chiefly by material (category 6) have a relatively
small coefficient, whereas a relatively large coefficient is found in food and live animals(category 0 & 1). We also estimate trade in parts and finished products separately in Panel(A) of Table B3. Both products have significantly positive coefficients for the
lagged-dependent variable, although its coefficient is somewhat smaller for finishedproducts. This can happen if intrafirm trade is more relevant for parts, and inertia becomes
12

Data on these imports were obtained from the UN Comtrade.
19stronger for parts because there may be a rigid rule for determining the invoicing currency.
Finally, we estimate for the high- and low-income import countries separately, and theresults are shown in Panel (B). Both cases show significantly positive coefficients for the
lagged-dependent variable, although the coefficient is slightly larger in low-income importcountries. Low-income Asian countries, typically Cambodia and Lao PDR, are likelier toaccept THB-invoiced imports because of their close economic relationship with Thailand.
This may enhance the stable use of THB and result in greater inertia for Thai exports to
low-income countries.
Because estimating a discrete choice model with a linear model faces problems suchas heteroskedasticity, Table B4 uses random effect estimation and instrumental variableprobit.13 We find a significantly positive coefficient for the lagged PCP dummy in all
estimations. The coefficient becomes larger for countries without forward rates or
low-turnover currencies, consistent with Proposition 2.
Thus far, we have estimated binary choice models, i.e., “PCP or non-PCP” or “LCP
or non-LCP.” In Table B5, we use a multinomial logit model because firms in practicechoose between PCP, LCP, and VCP as their invoice currencies. Table 5B contains fivepanels: estimates for the full sample (Panel A), countries with forward rates (Panel B),
countries without forward rates (Panel C), countries with high-turnover currencies (PanelD), and countries without high-turnover currencies (Panel E). In all panels,
lagged-dependent variables had a significantly positive impact, indicating inertia for all
types of invoicing currency. Comparing Panels B and C, we find that PCP inertia is lesssignificant, whereas the LCP and VCP inertia are more significant for countries withforward rates. Similarly, by comparing Panels D and E, we find a less significant PCP
inertia for countries with high-turnover currencies. These results are consistent with
Proposition 2.
As in our theoretical analysis, we narrow down the sample to new exporters in Table
B6. Panel (A) displays the results of the OLS analysis and Panel (B) shows those of the IV
analysis. In all results, the lagged-dependent variable coefficient is positive and significant,indicating that even in analyses restricted to new exporters, a significant and robust PCP
inertia is observed. Furthermore, regardless of whether it is OLS or IV, the PCP inertia
weakens when there is a forward exchange rate for the ’importer’s currency (see columns
II and III) or when the turnover of the ’importer’s currency is larger than THB (seecolumns IV and V). These results are consistent with the baseline analysis.

To estimate lagged-dependent variable models in panel data with nonlinear models such as probit orlogit, we need to address the so-called initial conditions problem. Following Wooldridge (2005), we
introduce the initial values of the dependent and independent variables as additional regression
variables.
1320

3.3. Export Experience
This subsection examines the relationship between export experience and the choiceof invoicing currency. Specifically, we investigate whether the choice differs between thefirst and subsequent exports. After introducing our empirical specification, we show ourbaseline estimation result and the results of various robustness checks.

3.3.1. Baseline Results
To empirically investigate the choice of invoicing currency, we estimate thefollowing reduced-form equation:
𝑃𝐶𝑃= 𝛼𝐹𝑖𝑟𝑠𝑡+ 𝛽 ln 𝑉𝑎𝑙𝑢𝑒+ 𝑢 + 𝑢 + 𝜖
In this analysis, our primary independent variable is a dummy variable “First,” whichtakes the value of one if an observation is the first export of a firm and zero otherwise.
Coefficient 𝛼 takes a positive value if new and less-experienced exporters tend to adopt
PCP. The firm-export destination-HS eight-digit level observations are restricted to thosethat appear after 2007 in our dataset. Furthermore, we include only those that appear in
the first year of the dataset. 14 Thus, each firm-export destination-HS eight-digit
observation appears in our dataset for estimation only once. 15 We also drop theobservations where multiple invoicing currencies can be detected in any year within afirm–product–country pair. As a result, we exclude firm–year fixed effects because of themore minor variation in observations. We estimate this model using the OLS method.
The baseline estimation results are reported in column (I) of Table 5. The coefficientfor First is estimated to be significantly positive, indicating that PCP is likelier to be chosenwhen firms export for the first time than when they export subsequent products or tosubsequent destinations. This may indicate that adopting FCP becomes less costly forexporters because of their experience in overseas business, and, thus, experienced
exporters tend to use foreign currencies.16 The log of the Value coefficient is negatively
significant, which is consistent with the results in the last subsection on inertia.
===

Table 5===

Therefore, our sample also includes observations that appeared in only 1 year.
Thus, unlike the case in Section 3.2, our sample includes the firm-export destination-HS eight-digit
pairs that appear after 2007 in. the dataset even if the firm has an export record in other export
destination-HS eight-digit pairs in 2007.
16 Another possibility is that by starting exporting, firms may improve their productivity or cost
efficiency and then be able to choose foreign currencies. In our framework, we cannot introduce thefirm–year fixed effect because it has a perfect correlation with our main explanatory variable. First, we
control for this factor. In addition, we cannot match firm- or plant-level data in Thailand with ourdataset. Thus, we cannot control firm characteristics such as productivity.
14
15

21

Columns (II) and (III) of the table show the results for countries without and withforward rates, respectively. The first export dummy coefficient is larger in column (II) thanin column (III), indicating that new exporters are likelier to adopt PCP when there is noaccessibility to the forward exchange rate of the importer’s currency. In addition, as shownin columns (IV) and (V), the coefficient is larger for countries with low-turnover currencies.
Therefore, the transaction cost of the forward exchange rate may matter for the choice ofinvoicing currency in first exports. These results are consistent with Proposition 3.
Table 6 uses the IV method, whose instruments are the same as those in the previous
subsection. Columns (I)–(V) of the panel show the results for the IV method. The firstexport dummy has a positive impact on all cases. The coefficient becomes slightly larger
for countries without forward rates than those with forward rates. Furthermore, thecoefficient is larger for countries with low-turnover currencies. These results are consistent
with the baseline case shown in Table 5. These estimations show that the coefficient for alog of Value is estimated to be significantly negative.
===

Table 6===3.3.2. Robustness Checks
We conduct various robustness checks. Table B7 shows the analysis results for newexporters, as we did in Table B6. Panel (A) presents the outcomes of the OLS analysis,
while Panel (B) displays the results obtained using the IV method and probit models.
These results differ from those of the baseline analysis in the following aspects. In the OLS
analysis, the First dummy coefficient becomes insignificant when there is a forward rate
for the importer’s currency (as shown in column III of Panel A) and when the turnover ofthe importer’s currency is greater than that of THB (as indicated in column V of Panel (A).
Therefore, in these cases, it cannot be conclusively stated that export starters always
choose PCP for their first export. We interpret these findings that the currency choice for
new exports might exhibit greater flexibility than that of continuous exporters because of alower probability of being bound by inertia. Consequently, if the usage cost of theimporter’s currency is sufficiently low, selecting a currency other than PCP for the firstexport may be more likely. These results are robust in the analysis using the probit model,
as shown in columns (VI)–(VIII) of Panel B; however, they are not robust with the IV
method, as indicated in columns (II)–(V) of Panel B.
Table B8 shows the results based on the probit model because our dependent
variable is a binary variable. In this nonlinear estimation, we cannot introduce detailed
fixed effects. Therefore, we control for a log of GDP per capita in export destination
countries in addition to section and year fixed effects. Columns (I)–(III) show the22

estimation results with the probit model for all countries, those with forward rates andhigh turnover, respectively. Consistent with our baseline case, columns (II) and (III) show
a lower coefficient than column (I). Columns (IV)–(VI) show analogous results for newexporters. Notably, the impact of First becomes insignificant for countries with forward
rates or high-turnover currencies. These results support our propositions.
Next, we change the definition of new transactions. In the baseline estimation, we
restricted firm-export destination-HS eight-digit level observations to new transactions,defined as those that appeared in our dataset after 2007. However, those not traded only in2007 are also regarded as “new transactions” in this definition. In this robustness check,we adopt a more conservative definition by changing “2007” to “2008,” “2009,” “2010,”“2011,” or “2012.” For instance, we define a new transaction for “ > 2008” as theobservation that does not appear in both 2007 and 2008 but does appear after 2008. The
results are shown in Panel (A) of Table B9. The significance and sign of our variables areunchanged compared with the case in the previous tables. Thus, our result is somewhat
robust to the definition of new transactions. Furthermore, the First coefficient increases
with the threshold year. Panel (B) shows the results of the IV estimations. The First
coefficient becomes larger than that for the OLS case regardless of the definition of thestart year. As in the OLS case, the coefficient increases with the threshold year.
Table B10 considers that non-PCP includes VCP And LCP in our analysis. We
estimate the multinomial logit model on the choice of invoicing currency. The
categorical-dependent variables consider all pricing strategies, including PCP (the default
option), LCP, and VCP.17 As independent variables, we again include First and a log ofValue in addition to GDP per capita of the importing country. Table 1 shows the marginal
effects . Panel (A) presents the result for all samples. The First coefficient is positive withstatistical significance for both PCP and LCP, whereas it becomes negative for VCP. Thus,
VCP is less likely to be used in first exports than PCP and LCP. Notably, the coefficient
becomes significantly higher for PCP than LCP, indicating that exporters tend to adopt
PCP the most among the three options. Panels (B) and (C) show the results for countries
with and without forward rates, respectively. Panels (D) and (E) show the results forcountries with high and low turnover, respectively. The marginal effect of First is smaller
for PCP for countries with forward rates or high turnover than for those without forward
rates or low turnover, which is consistent with Proposition 3. The First coefficient issignificantly positive for LCP when forward exchange rates of local currencies areaccessible in Thailand, whereas the significance disappears when forward rates are notaccessible. This result implies that Thai exporters may choose the local currency forinvoicing in their first exports if they utilize the forward exchange rate between THB andthe local currency.
17

The observations under PCP, LCP, and VCP account for 28%, 15%, and 57%, respectively.
23

In Table B10, we consider the effect of the global financial crisis. In particular, it may
induce firms to choose non-USD because the risk of USD is expected to increase during the
global financial crisis (Ogawa and Muto, 2017). To observe its effects, we introduce theinteraction terms of the First dummy with year dummy variables. As shown in Table B11,
the coefficients for most interaction terms are significantly negative, indicating that PC is
likelier to be chosen in 2008 than in other years, implying that exporters tried to avoid
forex risks immediately after the global financial crisis by using their home currency.
We estimate our equation by the one-digit code of SITC to determine the differences
across industries. Table B12 shows that all industries show positively significant
coefficients for the First dummy. Food and live animals (category 0 & 1) have a relatively
small coefficient, whereas a larger coefficient is found in material (category 6). As shownin the left panel of Table B13, we estimate trade in parts and finished products separately.
Both products have significantly positive coefficients for the First dummy, although its
coefficient is slightly larger in finished products. Firms tend to start exporting to lessdeveloped countries, and PCP is likelier to be used for these countries because exporters
can force importers to accept the exporters’ currency (PCP). We estimate the high- andlow-income import countries separately to eliminate this possibility. The results are shownin the right panel of Table B13. Both cases show significantly positive coefficients for theFirst dummy, although its coefficient is slightly larger in low-income import countries.

4. Conclusion
International business activities affect firms’ behavior in many aspects. We focusedon the effect of firms’ export experience on the choice of invoicing currency. Employing
transaction-level export data in Thailand during the 2007–2014 period, we revealed asignificant degree of inertia in the invoicing currency for the same product/destination.
PCP inertia becomes more (less) significant than that of LCP when exporters cannot (can)access the forward exchange rate of the currency of the importing country or the turnoverof the currency of the importing country in the forex market is smaller (larger) than THB.
We also found that the probability of choosing PCP in the first export is significantly
higher than that in the second and subsequent products/destinations. This finding implies
that firms’ export experience enhances the use of foreign currency in internationaltransactions. We also found that the probability of PCP in the first export is higher whenexporters do not have access to the forward exchange rate of the currency of the importing
country or the turnover of the importer’s currency is smaller than THB.
Firms benefit from using their home currency because they can be free from forex
risk and do not need to make efforts to manage the risk of exchange rate fluctuations (e.g.,
Ito et al., 2016). If firms feel that those efforts are burdensome, internationalizing the homecurrency may benefit the country’s welfare and may become an obvious policy objective to24

pursue. In this study, we examined firms’ dynamic choice of invoicing currency in exports,
discussing the possibility that PCP decreases the demand of risk-averse importers.
Exporters should determine which currency to use in invoicing, considering the trade-off
between the benefit of avoiding forex risk and a potential negative impact on demand.
Our theoretical model provides insights into this trade-off mechanism in the dynamic
decision of invoicing currency and provides parsimonious guidance for empiricalinvestigations. The model reveals the potential impact of the cost of managing the forex
risk on the inertia in invoicing currency and the dynamic choice of the currency.
Our empirical findings indicated that the exporter’s currency tends not to be used ininternational transactions once firms accumulate export experience. This consequence isconsistent with conventional findings in the literature that more capable exporters tend touse foreign currencies for invoicing. Since firms make the decision about invoicing
currency, exporters feel some benefits from using foreign currencies in their exports to
attract their foreign customers. In particular, they are willing to use foreign currencies
once they acquire the knowledge to manage forex risks. Mitigating the cost of forex risk
management may enhance exporters’ choice of an appropriate invoicing currency to
attract their customers without shouldering the burden of dealing with forex risk. Thisissue is especially important in emerging countries, whose currencies are relatively minor
and whose financial markets are still under development.

25

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28

Table 1. Shares of Each Invoicing Currency in Exports from Thailand (%)(A) All exporters

A1. Currency shares by year based on the number of transactions
2007

2008

2009

2010

2011

2012

2013

2014

Total

PCP

16

16

17

18

18

19

20

20

18

LCP

16

16

15

15

14

14

13

13

14

VCP

69

69

68

68

67

67

67

67

68

A2. Currency shares by year based on the value of transactions
2007

2008

2009

2010

2011

2012

2013

2014

Total

PCP

8

7

7

8

8

9

10

10

8

LCP

24

21

21

20

18

20

19

19

20

VCP

68

71

72

72

74

71

71

71

71

A3. Currency shares by quartile based on the number of transactions
1st quartile

2nd quartile

3rd quartile

4th quartile

Total

PCP

21

21

17

13

18

LCP

15

14

14

15

14

VCP

64

65

69

72

68

A4. Currency shares by quartile based on the value of transactions
1st quartile

2nd quartile

3rd quartile

4th quartile

Total

PCP

22

20

16

8

17

LCP

14

14

14

20

15

VCP

64

66

70

72

68

(B) New exporters
B1. Currency shares by year based on the number of transactions
2008

2009

2010

2011

2012

2013

2014

Total

PCP

26

28

29

27

27

28

27

27

LCP

16

14

13

13

13

12

11

13

VCP

58

58

58

60

59

60

62

60

B2. Currency shares by year based on the value of transactions
2008

2009

2010

2011

2012

2013

2014

Total

PCP

14

10

7

5

7

7

6

8

LCP

16

11

8

10

9

11

8

10

VCP

70

79

86

85

84

83

85

82

29

B3. Currency shares by quartile based on the number of transactions
1st quartile

2nd quartile

3rd quartile

4th quartile

Total

PCP

30

32

27

19

27

LCP

14

12

13

12

13

VCP

56

56

60

69

60

B4. Currency shares by quartile based on the value of transactions
1st quartile

2nd quartile

3rd quartile

4th quartile

Total

PCP

33

32

26

6

24

LCP

13

12

13

9

12

VCP

55

56

61

84

64

Source: Customs in Kingdom of Thailand

30

Table 2. Time-series Change of Invoicing Currency within a Firm–country–product Pair
(%)
2008

2009

2010

2011

2012

2013

2009

10

2010

13

6

2011

14

9

6

2012

12

10

9

8

2013

18

14

15

8

8

2014

13

10

10

7

6

4

Total

13

9

9

7

7

4

Note: The table shows the share (percent) of firm–product–country pairs that use different invoicing
currencies between the first year (shown in columns) and the following years (shown in rows).

31

Table 3. Inertia(A) Inertia of PCP
(I)

(II)

(III)

(IV)

(V)

0.838***

0.838***

0.795***

0.842***

0.811***(0.002)(0.003)(0.005)(0.003)(0.004)−0.0004***−0.0005***−0.0003***−0.0005***−0.0003***(0.0001)(0.0001)(0.0001)(0.0001)(0.0001)

Forward rate

ALL

NO

YES

ALL

ALL

Higher turnover than THB

ALL

ALL

ALL

NO

YES

Number of observations

738,411361,101

359,590

281,049

441,150

R-squared

0.925

0.940

0.923

0.917

0.935

(I)

(II)

(III)

(IV)

(V)

0.882***

0.760***

0.867***

0.775***

0.874***(0.002)(0.020)(0.003)(0.022)(0.002)−0.00008*−0.00002−0.00003−0.00003−0.00007(0.00005)(0.00002)(0.00009)(0.00002)(0.00008)

Forward rate

ALL

NO

YES

ALL

NO

Higher turnover than THB

ALL

ALL

ALL

NO

YES

Method

OLS

OLS

OLS

OLS

OLS

Number of observations

738,411361,101

359,590441,150

68,113R-squared

0.945

0.765

0.945

0.946

0.620

Lagged PCP Dummyln Value(B) Inertia of LCP
Lagged LCP Dummyln ValueNotes: The dependent variables are dummy variables that take the value one if the invoicing currency is
PCP and LCP in panels (A) and (B), respectively. “Lagged PCP Dummy” and “Lagged LCP Dummy”
are 1-year lagged-dependent variables. ***, **, and * represent statistical significance at the 1%, 5%, and
10% levels, respectively. Parentheses contain the heteroscedasticity-consistent standard error. Standard
errors are clustered at country–product pairs. We employ OLS in all estimations. All specifications
include export firm–year, import country–year, and HS six-digit code–year fixed effects. In columns (II)and (III) of both panels, we restrict sample export destination countries according to the availability offorward exchange rates for their official currency in Thailand. In columns (IV) and (V), we restrict
sample export destination countries considering whether the turnover of the currency of the destination
country in the forex market is smaller than THB or not.

32

Table 4. Inertia: Instrument Variable Method
(I)

(II)

(III)

(IV)

(V)

0.838***

0.838***

0.795***

0.842***

0.811***(0.002)(0.003)(0.005)(0.003)(0.004)−0.0002−0.0006−0.0005

0.0005−0.0005(0.0010)(0.0017)(0.0013)(0.0021)(0.0011)

Forward rate

ALL

NO

YES

ALL

ALL

Higher turnover than THB

ALL

ALL

ALL

NO

YES

Number of observations

738,411361,101

359,590

281,049

441,150

Kleibergen–Paap rk LM statistic

551.4

358.9

135.7

267.2

219.9

Cragg–Donald Wald F statistic

556.8

315.9

148.2

230.9

234.4

Centered R-squared

0.693

0.695

0.616

0.703

0.642

Lagged PCP Dummyln ValueNotes: The dependent variable is the dummy variable that takes the value one if the invoicing currency
is PCP. “Lagged PCP Dummy” is 1-year lagged-dependent variables. ***, **, and * represent
significance at the 1%, 5%, and 10% statistical levels, respectively. Parentheses contain theheteroscedasticity-consistent standard error. Standard errors are clustered at country–product pairs. We
employ IV estimation method in all estimations. All specifications include export firm–year, importcountry–year, and HS six-digit code–year fixed effects. In columns (II) and (III), we restrict sample
export destination countries according to the availability of forward exchange rates for their official
currency in Thailand. In columns (IV) and (V), we restrict sample export destination countries
considering whether the turnover of the currency of the destination country in the forex market issmaller than THB or not.

33

Table 5. Export Experience
(I)

(II)

(III)

(IV)

(V)

0.058***

0.062***

0.052***

0.071***

0.048***(0.002)(0.003)(0.003)(0.003)(0.003)−0.004***−0.003***−0.004***−0.003***−0.004***(0.000)(0.000)(0.000)(0.000)(0.000)Forward rate

ALL

NO

YES

ALL

ALL

Higher turnover than THB

ALL

ALL

ALL

NO

YES

Number of observations

894,997

465,443424,262

372,718

517,015

R-squared

0.179

0.246

0.123

0.258

0.120

First
ln ValueNotes: The firm-export destination-HS eight-digit level observations are restricted only to those thatappear after 2007 in our dataset. Furthermore, we include those in only the first year of appearance inthe dataset. The dependent variable is a dummy variable that takes the value one if invoicing currency
is THB and zero otherwise. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels,
respectively. Parentheses contain the heteroscedasticity-consistent standard error. Standard errors are
clustered at country–product pairs. We employ the OLS method. All specifications include importcountry–year and HS six-digit code–year fixed effects. In columns (II) and (III), we restrict sample
export destination countries according to the availability of forward exchange rates for their official
currency in Thailand. In columns (IV) and (V), we restrict sample export destination countries
considering whether the turnover of the currency of the destination country in the forex market issmaller than THB.

34

Table 6. Export Experience: Instrument Variable Method
(I)

(II)

(III)

(IV)

(V)

0.074***

0.077***

0.076***

0.084***

0.067***(0.003)(0.004)(0.005)(0.004)(0.004)−0.031***−0.030***−0.041***−0.035***−0.032***(0.003)(0.005)(0.007)(0.006)(0.005)

Forward rate

ALL

NO

YES

ALL

ALL

Higher turnover than THB

ALL

ALL

ALL

NO

YES

Number of observations

861,442435,166

421,054364,931491,329

Kleibergen–Paap rk LM statistic

1,245.0

759.2

234.8

618.0

349.8

Cragg–Donald Wald F statistic

1,403.0

775.9

262.9

616.9

399.4

Centered R-squared−0.036−0.031−0.074−0.041−0.042First
ln ValueNotes: The firm-export destination-HS eight-digit level observations are restricted only to those thatappear after 2007 in our dataset. Furthermore, we include those in only the first year of appearance inthe dataset. The dependent variable is a dummy variable that takes the value one if invoicing currency
is THB and zero otherwise. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels,
respectively. Parentheses contain the heteroscedasticity-consistent standard error. Standard errors are
clustered at country–product pairs. The IV method is employed in all columns. Our instrument in the
IV estimation is import country’s total imports of a concerned product from the world except for
Thailand. All columns include import country–year and HS six-digit code–year fixed effects. In
columns (II) and (III), we restrict sample export destination countries according to the availability offorward exchange rates for their official currency in Thailand. In columns (IV) and (V), we restrict
sample export destination countries considering whether the turnover of the currency of the destination
country in the forex market is smaller than THB or not.

35Figure 1. Time Flow

Figure 2. The First Appearance Year of Export Firms
90,00082,10980,00070,00060,00050,00040,00030,00020,000
9,586

10,000

6,383

4,861

4,292

3,393

3,100

1,935

2010

2011

2012

2013

2014

0
2007

2008

2009

Source: Authors’ compilation

36

Table B2. Inertia: Estimation by SITC Sectors
(I)

(II)

(III)

(IV)

(V)

(VI)

0.902***

0.867***

0.871***

0.884***

0.843***

0.860***(0.012)(0.015)(0.023)(0.005)(0.005)(0.004)−0.0008**

0.0000−0.0002−0.0002−0.0005***−0.0002*(0.0003)(0.0002)(0.0003)(0.0002)(0.0001)(0.0001)

SITC

0&1

2&3

4

5

6

7

Number of observations

18,54627,275

17,589104,856

168,746

159,218

R-squared

0.971

0.967

0.966

0.954

0.935

0.925

Lagged PCP Dummyln ValueNotes: The dependent variable is a dummy variable that takes the value of one for PCP and zerootherwise. “Lagged PCP Dummy” is a 1-year lagged-dependent variable. ***, **, and * represent
statistical significance at the 1%, 5%, and 10% levels, respectively. Parentheses contain theheteroscedasticity-consistent SE. We estimate using the OLS method. All specifications include export
firm–year, import country–year, and HS six-digit code–year fixed effects.

37

Table B3. Inertia: Production Stages and Income Levels of Importers(A) Production stage
Parts and(B) Income level

Finished

Low

High

0.861***

0.819***

0.838***

0.810***(0.003)(0.004)(0.003)(0.004)−0.0004***−0.0003***−0.0005***−0.0003***(0.0001)(0.0001)(0.0001)(0.0001)

Number of observations

342,838

384,413

298,034

423,009

R-squared

0.929

0.928

0.941

0.923

components
Lagged PCP Dummyln ValueNotes: The dependent variable is a dummy variable that takes the value of one for PCP and zerootherwise. “Lagged PCP Dummy” is a 1-year lagged-dependent variable. ***, **, and * represent
statistical significance at the 1%, 5%, and 10% levels, respectively. Parentheses contain theheteroscedasticity-consistent SE. We estimate using the OLS method. All specifications include export
firm–year, import country–year, and HS six-digit code–year fixed effects. In Panel (A), finishedproducts are defined as items categorized into 112, 122, 41, 51, 52, 61, 62, or 63 in. the BEC classification,
while the rest are parts. To differentiate between high- and low-income countries in Panel (B), we
follow the World Bank classification. Specifically, high-income countries include ABW, AND, ARE,
ATG, AUS, AUT, BEL, BHR, BHS, BMU, BRB, BRN, CAN, CHE, CYM, CYP, CZE, DEU, DNK, ESP, EST,
FIN, FRA, FRO, GBR, GNQ, GRC, GRL, HKG, HUN, IRL, ISL, ISR, ITA, JPN, KOR, KWT, LUX, MAC,
MLT, MNP, NCL, NLD, NOR, NZL, OMN, PRI, PRT, PYF, QAT, SAU, SGP, SMR, SVK, SVN, SWE,
TTO, TWN, and USA.

38

Table B4. Inertia: RE and IV Probit(A) RE Probit
(I)

(II)

(III)

(IV)

(V)

0.090***

0.097***

0.083***

0.105***

0.080***(0.001)(0.002)(0.002)(0.002)(0.002)−0.0013***−0.0012***−0.0015***−0.0011***−0.0015***(0.0001)(0.0001)(0.0001)(0.0001)(0.0001)

0.061***

0.059***

0.063***

0.060***

0.063***(0.002)(0.003)(0.003)(0.003)(0.003)−0.0010***−0.0012***−0.0002*−2.66E-11−5.88E-13(0.0001)(0.0001)(0.0001)(1.81e-11)(2.00e-12)

0.086***

0.094***

0.018

0.115***

0.0114(0.019)(0.023)(0.037)(0.026)(0.032)−0.087***−0.110***0.009−0.151***

0.015(0.019)(0.023)(0.036)(0.026)(0.032)

Forward rate

ALL

NO

YES

ALL

ALL

Higher Turnover

ALL

ALL

ALL

NO

YES

Number of observations

736,033

357,555

378,478

294,552

441,481Log-likelihood−48,292.3−24,586.2−23,522.3−21,338.6−26,873.0

(I)

(II)

(III)

(IV)

(V)

0.103***

0.107***

0.101***

0.113***

0.095***(0.001)(0.001)(0.002)(0.002)(0.001)−0.0016**−0.0051***

0.0033**−0.0093***−0.0025***(0.0007)(0.0010)(0.0015)(0.0007)(0.0006)

0.030***

0.031***

0.030***

0.032***

0.026***(0.001)(0.001)(0.001)(0.001)(0.001)−0.0007***−0.0006***−0.0008***

5.33e-11**

7.57E-11(0.0001)(0.0001)(0.0002)(2.71e-11)(3.88e-11)

0.082***

0.096***

0.023

0.104***

0.0227(0.017)(0.021)(0.032)(0.024)(0.027)−0.084***−0.115***0.010−0.142***−0.004(0.017)(0.021)(0.032)(0.024)(0.027)

Forward rate

ALL

NO

YES

ALL

ALL

Higher Turnover

ALL

ALL

ALL

NO

YES

Number of observations

736,033

357,555

378,478

294,552

441,481−1,904,281.5−914,269.5−987,547.5−745,415.4−1,146,872.6

Lagged PC Dummyln Value
Initial PC dummy
Initial ln Value
CapitaAverage Capita(B) IV Probit
Lagged PC Dummyln Value
Initial PC dummy
Initial ln Value
CapitaAverage Capita

Log-likelihood

39

Notes: The dependent variable is a dummy variable that takes the value of one for PCP and zerootherwise. “Lagged PCP Dummy” is a 1-year lagged-dependent variable. ***, **, and * represent
statistical significance at the 1%, 5%, and 10% levels, respectively. Marginal effects are reported.
Parentheses contain the heteroscedasticity-consistent standard error. Standard errors are clustered atcountry–product pairs. All specifications include export firm–year, import country–year, and HS
six-digit code–year fixed effects.

40

Table B5. Inertia: Multilateral Logit Model(A) Full sample
(I)

(II)

(III)

Dependent variable

PCP Dummy

LCP Dummy

VCP Dummy

Lagged PCP Dummy

0.445***−0.056***−0.390***(0.002)(0.003)(0.004)−0.024***0.405***−0.381***(0.004)(0.006)(0.007)−0.268***−0.225***0.493***(0.002)(0.005)(0.004)−0.0042***−0.0024***0.0066***(0.0001)(0.0002)(0.0002)

Lagged LCP Dummy
Lagged VCP Dummyln Value
Number of observations

2,020,199(B) Forward rate = YES
(I)

(II)

(III)

Dependent variable

PCP Dummy

LCP Dummy

VCP Dummy

Lagged PCP Dummy

0.398***−0.094***−0.304***(0.003)(0.006)(0.006)−0.068***0.571***−0.504***(0.003)(0.006)(0.007)−0.186***−0.390***0.576***(0.003)(0.007)(0.005)−0.0054***−0.0012***0.0066***(0.0001)(0.0003)(0.0003)

Lagged LCP Dummy
Lagged VCP Dummyln Value
Number of observations

998,419(C) Forward rate = NO
(I)

(II)

(III)

Dependent variable

PCP Dummy

LCP Dummy

VCP Dummy

Lagged PCP Dummy

0.486***−0.001*−0.485***(0.003)(0.001)(0.003)0.014

0.020***−0.034(0.023)(0.001)(0.023)−0.358***−0.005***0.363***(0.003)(0.000)(0.003)−0.0036***−0.0003***

0.0039***(0.0002)(0.0000)(0.0002)

Lagged LCP Dummy
Lagged VCP Dummyln Value
Number of observations

1,019,514

41

(D) Higher Turnover =YES
(I)

(II)

(III)

Dependent variable

PC Dummy

LC Dummy

VC Dummy

Lagged PC Dummy

0.382***−0.066***−0.316***(0.002)(0.005)(0.005)−0.039***0.532***−0.493***(0.003)(0.007)(0.007)−0.187***−0.352***0.539***(0.002)(0.006)(0.005)−0.0052***−0.0016***0.0068***(0.0001)(0.0003)(0.0003)

Lagged LC Dummy
Lagged VC Dummyln Value
Number of observations

1,208,492(E) Higher Turnover = NO
(I)

(II)

(III)

Dependent variable

PC Dummy

LC Dummy

VC Dummy

Lagged PC Dummy

0.523***−0.001−0.522***(0.004)(0.001)(0.004)0.040

0.020***−0.060**(0.026)(0.001)(0.027)−0.398***−0.004***0.402***(0.004)(0.000)(0.004)−0.0039***−0.0003***

0.0043***(0.0002)(0.0000)(0.0002)

Lagged LC Dummy
Lagged VC Dummyln Value
Number of observations

807,666

Notes: ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively.
Marginal effects are reported. Parentheses contain the heteroscedasticity-consistent SE. Standard errors
are clustered at country–product pairs. All specifications include section fixed effects and year fixed
effects. We used all samples in panel (A). Panels (B) and (C) narrow down the sample to destination
countries where the forward exchange rates between THB and the local currency of that country areavailable and unavailable, respectively. Panels (D) and (E) narrow down the sample to destination
countries where the turnover of the local currency of that country in the forex market is larger andsmaller than THB, respectively.

42

Table B6. Inertia: New Exporters(A) OLS
(I)

(II)

(III)

(IV)

(V)

0.735***

0.758***

0.632***

0.765***

0.650***(0.012)(0.017)(0.025)(0.018)(0.021)−0.0008***−0.0005−0.0010**−0.0006−0.0009***(0.0003)(0.0004)(0.0004)(0.0004)(0.0003)

Forward rate

ALL

NO

YES

ALL

ALL

Higher turnover than THB

ALL

ALL

ALL

NO

YES

Number of observations

48,093

24,844

19,17019,72724,449R-squared

0.958

0.971

0.954

0.973

0.950

(I)

(II)

(III)

(IV)

(V)

0.735***

0.760***

0.632***

0.768***

0.650***(0.012)(0.017)(0.024)(0.019)(0.021)0.00130.0170−0.00070.0218−0.0002(0.0055)(0.0163)(0.0046)(0.0184)(0.0057)

Forward rate

ALL

NO

YES

ALL

ALL

Higher turnover than THB

ALL

ALL

ALL

NO

YES

Number of observations

48,093

24,844

19,17019,72724,449R-squared

0.533

0.497

0.401

0.47

0.425

Kleibergen–Paap rk LM statistic

67.62

12.4

50.42

12.41

37.31

Cragg–Donald Wald F statistic

51.8

8.669

40.39

8.43

28.08

Centered R-squared

0.533

0.497

0.401

0.47

0.425

Lagged PCP Dummyln Value(B) IV
Lagged PCP Dummyln ValueNotes: The dependent variable is a dummy variable that takes the value of one for PCP and zerootherwise. “Lagged PCP Dummy” is a 1-year lagged-dependent variable. The sample is narrowed
down to new exporters. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels,
respectively. Parentheses contain the heteroscedasticity-consistent standard error. Standard errors are
clustered at country–product pairs. All specifications include export firm–year, import country–year,
and HS six-digit code–year fixed effects. We employ the OLS method in panel (A). In panel (B), we
estimate using the IV method by using as an instrument the import country’s total imports of aconcerned product from the world except for Thailand. In columns (II) and (III) of both panels, we
restrict sample export destination countries according to the availability of forward exchange rates fortheir official currency in Thailand. In columns (IV) and (V) of both panels, we restrict sample export
destination countries by considering whether the turnover of the currency of the destination country in
the forex market is smaller than THB or not.

43

Table B7. Export Experience: New Exporters(A) OLS
(I)

(II)

(III)

(IV)

(V)

0.006**

0.015***−0.0020.018***−0.001(0.003)(0.004)(0.004)(0.004)(0.004)−0.004***−0.004***−0.004***−0.005***−0.003***(0.000)(0.001)(0.001)(0.001)(0.001)

Forward rate

ALL

NO

YES

ALL

ALL

Higher turnover than THB

ALL

ALL

ALL

NO

YES

Number of observations

170,559

91,97973,25075,08590,148R-squared

0.317

0.4

0.258

0.41

0.254

(I)

(II)

(III)

(IV)

(V)

0.017***

0.027***

0.028***

0.031***

0.015**(0.004)(0.006)(0.009)(0.007)(0.006)−0.029***−0.036***−0.069***−0.042***−0.037***(0.008)(0.013)(0.016)(0.016)(0.011)

Forward rate

ALL

NO

YES

ALL

ALL

Higher turnover than THB

ALL

ALL

ALL

NO

YES

Number of observations

163,993

86,12872,66373,817

84,977

Kleibergen–Paap rk LM statistic−0.024−0.036−0.182−0.048−0.051

Cragg–Donald Wald F statistic

402.8

198.4

91.84

138.8

158.4

Log-likelihood

392

177

88

121

156

−0.0238−0.0363−0.182−0.0482−0.0508First
ln Value(B) IVFirst
ln Value

Centered R-squared

Notes: The firm-export destination-HS eight-digit level observations are restricted only to those thatappear after 2007 in. our dataset. Furthermore, we include only those in the first year of appearance inthe dataset. The dependent variable is a dummy variable that takes the value one if the invoicing
currency is THB and zero otherwise. The sample is narrowed down to new exporters. ***, **, and *represent statistical significance at the 1%, 5%, and 10% levels, respectively. Parentheses contain theheteroscedasticity-consistent standard error. Standard errors are clustered at country–product pairs. We
employ the OLS method in panel (A). The IV method is employed in columns (I)–(V) of panel (B). Our
instrument in the IV estimation is the import country’s total imports of a concerned product from theworld except for Thailand. In all columns, import country–year and HS six-digit code–year fixed effects
are included. In columns (II) and (III) of both panels, we restrict sample export destination countries
according to the availability of forward exchange rates for their official currency in Thailand. In
columns (IV) and (V) of both panels, we restrict sample export destination countries by considering

44

whether the turnover of the currency of the destination country in the forex market is smaller than THB
or not.

45

Table B8. Export Experience: ProbitFirst
ln Valueln GDP per capita
Sample

(I)

(II)

(III)

(IV)

(V)

(VI)

0.081***

0.055***

0.0531***0.027***−0.004−0.003(0.002)(0.003)(0.002)(0.003)(0.004)(0.003)−0.006***−0.007***−0.007***−0.010***−0.008***−0.008***(0.000)(0.000)(0.000)(0.000)(0.001)(0.001)−0.025***0.028***

0.017***−0.034***0.031***

0.019***(0.001)(0.002)(0.001)(0.001)(0.003)(0.002)

ALL

ALL

ALL

New

New

New

Exporter

Exporter

Exporter

Forward rate

ALL

YES

ALL

ALL

YES

ALL

Higher turnover than THB

ALL

ALL

YES

ALL

ALL

YES

Number of observations

868,415425,979

496,920

170,044

77,98290,515Log-likelihood−429,625.1 −191,407.9 −218,376.4−97,937−40,218−45,782

0.03470.04540.0468Centered R-squared

0.027

0.027

0.028

Notes: The firm-export destination-HS eight-digit level observations are restricted only to those thatappear after 2007 in. our dataset. Furthermore, we include only those in the first year of appearance inthe dataset. The dependent variable is a dummy variable that takes the value one if the invoicing
currency is THB and zero otherwise. ***, **, and * represent statistical significance at the 1%, 5%, and
10% levels, respectively. Parentheses contain the heteroscedasticity-consistent standard error. Standard
errors are clustered at country–product pairs. We employ the probit method in all columns, andmarginal effects are reported. Columns (I)–(III) show the results for all samples, while columns (IV)–(VI) show the results for new exporters. Section fixed effects and year fixed effects are included.
Columns (II) and (V) present the analysis results when the sample is narrowed down to export
destination countries where forward exchange rates between THB and the local currency of thatcountry are available. In columns (III) and (VI), we narrow down the sample to export destination
countries where the turnover of the local currency of that country in the forex market is larger than
THB.

46

Table B9. Export Experience: Different Definitions of New Transactions(A) OLS
(I)

(II)

(III)

(IV)

(V)

0.053***

0.055***

0.060***

0.0780***0.084***(0.002)(0.002)(0.003)(0.003)(0.004)−0.004***−0.003***−0.003***−0.003***−0.004***(0.000)(0.000)(0.000)(0.000)(0.000)year >

year >

year >

year >

year >First year

2008

2009

2010

2011

2012

Number of observations

737,500

608,822

485,791

356,350

234,705

R-squared

0.181

0.182

0.185

0.187

0.184

(I)

(II)

(III)

(IV)

(V)

0.0720***0.0740***0.078***

0.093***

0.098***(0.003)(0.003)(0.004)(0.004)(0.005)−0.033***−0.032***−0.031***−0.028***−0.024***(0.004)(0.004)(0.004)(0.005)(0.006)

year >

year >

year >

year >

year >First year

2008

2009

2010

2011

2012

Number of observations

710,241

586,373

468,040

343,588

226,502

R-squared−0.042−0.038−0.036−0.025−0.016

Kleibergen–Paap rk LM statistic

1,116

1,021

866

683

488

Cragg–Donald Wald F statistic

1,241

1,127

947

732

511

Centered R-squared−0.0417−0.0382−0.0362−0.0255−0.0163First
ln Value(B) IVFirst
ln ValueNotes: The firm-export destination-HS eight-digit level observations in “>X” of row “First year” arerestricted only to those that appear after year X in our dataset. The dependent variable is a dummy
variable that takes the value one if the invoicing currency is PC and zero otherwise. ***, **, and *represent statistical significance at the 1%, 5%, and 10% levels, respectively. Parentheses contain theheteroscedasticity-consistent SE. Standard errors are clustered at country–product pairs. We employ the
OLS method in panel (A). In panel (B), we estimate using the IV method by using as an instrument theimport country’s total imports of a concerned product from the world except for Thailand. All
specifications include import country–year and HS six-digit code–year fixed effects.

47

Table B10. Export Experience: Multinomial Logit Results(A) Full sampleFirst
ln Value
Capita

(I)

(II)

(III)

PCP

LCP

VCP

0.081***

0.011***−0.092***(0.002)(0.002)(0.002)−0.006***−0.002***0.008***(0.000)(0.000)(0.000)−0.045***0.164***−0.119***(0.001)(0.002)(0.002)

Number of observations

868,415(B) Forward rate = YESFirstln Value
Capita

(I)

(II)

(III)

PCP

LCP

VCP

0.055***

0.023***−0.079***(0.002)(0.003)(0.004)−0.007***−0.003***0.009***(0.000)(0.000)(0.000)0.004**

0.194***−0.199***(0.002)(0.005)(0.004)

Number of observations

425,979(C) Forward rate = NOFirst
ln Value
Capita

(I)

(II)

(III)

PCP

LCP

VCP

0.102***

0.000−0.101***(0.003)(0.000)(0.003)−0.005***−0.001***0.006***(0.000)(0.000)(0.000)−0.041***0.003***

0.038***(0.001)(0.000)(0.001)

Number of observations

442,436

48

(D) Higher Turnover =YES

(I)

(II)

(III)

PCP

LCP

VCP

0.0538***0.0198***−0.0737***(0.00222)(0.00298)(0.00338)−0.00664*** −0.00239***0.00902***(0.000204)(0.000318)(0.000345)−0.0175***0.225***−0.208***(0.00130)(0.00429)(0.00339)First
ln Value
CapitaNumber of observations

496,920(E) Higher Turnover =NO

(I)

(II)

(III)

PCP

LCP

VCP

0.110***−0.000493−0.109***(0.00293)(0.000509)(0.00295)−0.00546*** −0.000536***0.00599***(0.000322)(4.77e−05)(0.000325)−0.0417***0.00308***

0.0387***(0.00108)(0.000126)(0.00109)First
ln Value
CapitaNumber of observations

371,495

Notes: This table reports the estimation results (marginal effects) of the multinomial logit model on the
choice of invoicing currency. The categorical-dependent variables consider all pricing strategies,
including PCP (the default option), LCP, and VCP. In all specifications, we introduce year fixed effects
and dummy variables on Section of the HS tariff classification. ***, **, and * represent statisticalsignificance

at

the

1%,

5%,

and

10%

levels,respectively.

Parentheses

containtheheteroscedasticity-consistent standard error. Standard errors are clustered at country–product pairs. We
used all samples in panel (A). Panels (B) and (C) narrow down the sample to destination countries
where the forward exchange rates between THB and the local currency of that country are available
and unavailable, respectively. Panels (D) and (E) narrow down the sample to destination countries
where the turnover of the local currency of that country in the forex market is larger and smaller than
THB, respectively.

49

Table B11. Export Experience: Interaction with Year Dummy Variables
(I)

(II)

0.083***

0.096***(0.005)(0.005)−0.038***−0.039***(0.007)(0.007)−0.051***−0.049***(0.008)(0.008)−0.075***−0.066***(0.006)(0.007)−0.021***−0.017**(0.007)(0.007)0.023***

0.027***(0.007)(0.007)−0.022***−0.018**(0.007)(0.007)−0.004***−0.031***(0.000)(0.003)

MethodOLS

IV

Number of observations

894,997

861,442

R-squared

0.179−0.035FirstFirst*Year2009
First*Year2010
First*Year2011
First*Year2012
First*Year2013
First*Year2014
ln Value

Kleibergen–Paap rk LM statistic

1,245

Cragg–Donald Wald F statistic

1,403

Centered R-squared−0.0354

Notes: The firm-export destination-HS eight-digit level observations are restricted only to those thatappear after 2007 in. our dataset. Furthermore, we include only those in the first year of appearance inthe dataset. The dependent variable is a dummy variable that takes the value one if the invoicing
currency is THB and zero otherwise. ***, **, and * represent statistical significance at the 1%, 5%, and
10% levels, respectively. Parentheses contain the heteroscedasticity-consistent SE. All specifications
include import country–year and HS six-digit code–year fixed effects. We employ the OLS method incolumn (I). In column (II), we estimate using the IV method by using as an instrument the importcountry’s total imports of a concerned product from the world except for Thailand.

50

Table B12. Export Experience: Estimation by SITC Sectors
(I)

(II)

(III)

(IV)

(V)

(VI)

0.034**

0.038***

0.051**

0.037***

0.080***

0.019***(0.015)(0.011)(0.020)(0.006)(0.004)(0.004)−0.004*** −0.006***−0.002−0.003*** −0.003*** −0.003***(0.001)(0.001)(0.001)(0.000)(0.000)(0.000)

SITC

0&1

2&3

4

5

6

7

Number of observations

15,667

24,61010,30098,609218,361

234,413

R-squared

0.286

0.277

0.293

0.229

0.199

0.201

First
ln ValueNotes: The firm-export destination-HS eight-digit level observations are restricted only to those thatappear after 2007 in our dataset. Furthermore, we include only those in the first year of appearance inthe dataset. The dependent variable is a dummy variable that takes the value one if the invoicing
currency is THB and zero otherwise. ***, **, and * represent statistical significance at the 1%, 5%, and
10% levels, respectively. Parentheses contain the heteroscedasticity-consistent SE. All specifications
include import country–year and HS six-digit code–year fixed effects. We estimate using the OLS
method.

51

Table B13. Export Experience: Production Stages and Income Levels of Importers(A) Production stage
Parts and(B) Income level

Finished

Low

High

0.047***

0.067***

0.061***

0.055***(0.003)(0.003)(0.003)(0.003)−0.003***−0.005***−0.003***−0.004***(0.0002)(0.0002)(0.0003)(0.0002)

Number of observations

409,792

485,122402,414487,269

R-squared

0.200

0.166

0.258

0.122

componentsFirst
ln ValueNotes: The firm-export destination-HS eight-digit level observations are restricted only to those thatappear after 2007 in. our dataset. Furthermore, we include only those in the first year of appearance inthe dataset. The dependent variable is a dummy variable that takes the value one if the invoicing
currency is THB and zero otherwise. ***, **, and * represent statistical significance at the 1%, 5%, and
10% levels, respectively. Parentheses contain the heteroscedasticity-consistent SE. All specifications
include import country–year and HS six-digit code–year fixed effects. We estimate using the OLS
method. Finished products are defined as items categorized into 112, 122, 41, 51, 52, 61, 62, or 63 in the
BEC classification, while the rest are parts. In differentiating between high- and low-income countries,
we follow the World Bank classification. Specifically, high-income countries include ABW, AND, ARE,
ATG, AUS, AUT, BEL, BHR, BHS, BMU, BRB, BRN, CAN, CHE, CYM, CYP, CZE, DEU, DNK, ESP, EST,
FIN, FRA, FRO, GBR, GNQ, GRC, GRL, HKG, HUN, IRL, ISL, ISR, ITA, JPN, KOR, KWT, LUX, MAC,
MLT, MNP, NCL, NLD, NOR, NZL, OMN, PRI, PRT, PYF, QAT, SAU, SGP, SMR, SVK, SVN, SWE,
TTO, TWN, and USA.

52