DPRIETI Discussion Paper Series 21-E-080

Invoice Currency Choice under Financial Constraints and Bargaining:
Evidence from Japanese SMEs

GOTO, Mizuki
Hitotsubashi University

HAYAKAWA, Kazunobu
IDE-JETROKOIBUCHI, Satoshi
Chuo University

YOSHIMI, Taiyo
Chuo University

The Research Institute of Economy, Trade and Industry

https://www.rieti.go.jp/en/

RIETI Discussion Paper Series 21-E-080
October 2021

Invoice Currency Choice under Financial Constraints and Bargaining:
Evidence from Japanese SMEs
Mizuki Goto (Hitotsubashi University), Kazunobu Hayakawa (IDE-JETRO),
Satoshi Koibuchi (Chuo University), and Taiyo Yoshimi (Chuo University)
Abstract
Recent empirical studies explore factors behind the currency invoicing pattern in exports of listed firms byusing questionnaire surveys; however, there is insufficient evidence regarding small and medium-sizedenterprises (SMEs). We conducted a questionnaire survey for 2,100 unlisted manufacturers engaged inexports during the 2010s and received responses from 300 firms. By constructing a database with invoicecurrency choice and trade partner by export destination, we empirically examine the determinants of invoicecurrency choice in export using the probit model estimation. We confirm that the major determinants ofcurrency invoicing in existing research effectively work as determinants of currency invoicing by SMEs.
After controlling for various determinants, we found that financial constraints play an important role intheir invoice currency choice. The firms with deteriorated capital ratios and rapid sales growth depend moreon the producer’s currency invoicing. The results are confirmed through a robustness test using detailed
financial data, showing that the firms with lower capital ratios, lower liquidity positions, and greater
investment opportunities tend to use the producer’s currency invoicing. These novel findings are consistent
with the predictions from the theoretical research on the bargaining model of currency invoicing andcorporate risk management for hedging.
Keywords: Invoice currency; Japanese exports; SMEs; Financial constraint; R&D;
JEL classification: F23, F31, F33, G32
The RIETI Discussion Paper Series aims at widely disseminating research results in the form of professional
papers, with the goal of stimulating lively discussion. The views expressed in the papers are solely those of
the author(s), and neither represent those of the organization(s) to which the author(s) belong(s) nor the
Research Institute of Economy, Trade and Industry.

 This study is conducted as a part of the Project “Exchange Rate and International Currency” undertaken

at the Research Institute of Economy, Trade and Industry (RIETI). The authors are grateful for helpfulcomments and suggestions from Eiji Ogawa and other project members. This work was supported by a
Chuo University Joint Research Grant, the JSPS KAKENHI Grants (20K01753 to Koibuchi and 20H01518
to Yoshimi), and Endeavour United Holdings Foundation scholarship (to Goto).

Corresponding author: 742-1 Higashinakano, Hachioji, Tokyo, Japan 1920393; Email:koibuchi@tamacc.chuo-u.ac.jp
11. Introduction
While the recent empirical studies explore factors behind the currency invoicing pattern in exportsof listed firms by using the questionnaire survey, there is insufficient evidence regarding small
and medium-sized enterprises (SMEs). However, the currency risk exposure at the firm level isconsidered more important for unlisted firms and SMEs than listed firms because the SMEs
generally have weak financial conditions and have poor skills for managing currency risk.
The theoretical model, such as Goldberg and Tille (2013), discusses that the choice of invoicecurrency reflects the result of negotiation between exporter and importer. In this sense, exploring
the driving forces behind the invoice currency choice by the SMEs is also crucial because many
SMEs are supposed in a weak position in the negotiation of pricing and invoice currency with
importers. If the SMEs only have weak bargaining power, we expect the SMEs are forced tochoose the importer’s currency and shoulder the burden of currency risk exposure.
However, Ito et al. (2013, 2018), which are the recent empirical studies based on the questionnaire
surveys on Japanese listed manufactures, found that smaller listed firms more tend to choose
exporter’s currency as an invoice currency while larger listed firms choose importer’s currency
or US dollar as an invoice currency. They conclude that this invoice currency pattern is attributed
to the pricing-to-the market (PTM) behavior to stabilize the price in terms of local currency in thedestination market. Based on the observation of size effect in currency invoicing pattern forJapanese listed manufacturers, we predict that the Japanese unlisted firms tend to choose theJapanese yen as an invoice currency because they are generally much smaller than listed firms.
More importantly, SMEs typically face severer financial constraints than large firms because of
the existence of information asymmetry and bankruptcy costs. Froot et al. (1993) explain that if
external sources of finance are more costly to corporations than internally generated funds, there
will typically be a benefit of hedging by inducing more concave profit function of firms. However,hedging tools available to unlisted SMEs are relatively limited comparing to listed firms. Under
these circumstances, we predict that the SMEs facing financial constraints have an incentive tochoose producer’s currency invoicing to insulate their cash flows from exchange rate fluctuations.
To confirm these theoretical and empirical predictions, we conducted the questionnaire survey in2019 for 2,100 Japanese firms that are unlisted manufacturers experiencing exports, continuing
exports, or started export during the 2010s, and received a response from 300 firms. Using theresults of the questionnaire survey, we construct the database and conduct an empirical study to2

test the impacts of various determinants on the invoice currency choice by the Japanesemanufacturing SMEs.
This paper proceeds as follows. Section 2 reviews the related literature. Section 3 describes thequestionnaire survey. Section 4 describes the empirical methodology. Section 5 provides the main
results of the empirical examination. Section 6 reports the results of the robustness test. Section 7
concludes the paper.

2. Related literature
2-1. Theoretical models of invoice currency choice
Goldberg and Tille (2008) explore the driving forces of currency invoicing in international tradewith a simple model and a country-level dataset. They develop the theoretical model in which a
profit-maximizing exporter having the decreasing return to scale production function faces thedownward sloping demand curve in the destination country. The model shows the following three
forces: first, an exporter has an incentive to follow the invoicing strategy of its competitors, whichis called a “coalescing” motive. This is because fluctuations of its price relative to its competitors
due to exchange rate fluctuations translate into sizable movement in output, leading to fluctuations
in marginal cost and a higher marginal cost on average due to the convexity of a total cost function.
Second, the exporter wants to invoice in a currency that delivers a hedging benefit by limiting thedeviations between marginal cost and marginal revenue. Third, the exporter prefers a currency
with low transaction costs.
We derive several implications on a firm-level invoice currency choice from Goldberg and Tille(2008). First, exporters facing competition from local firms in the destination country who set
prices fully in their currency tend to choose the local currency according to the “coalescing”
motive. This tendency is more significant if firms export to a large country where many local
firms compete and goods are more homogenous. By contrast, the firms with highly differentiatedgoods are likely to choose producer’s currency. Second, the motivation to pursue hedging benefits
is intensified in the presence of imported inputs. The firms that import some inputs from thedestination country and pay for them in the currency of the destination country tend to choose thesame currency in export to that destination for hedging revenue and cost. Third, a currency suchas the US dollar is more likely to be used as a vehicle currency because of its low transaction cost,which is mainly driven by the liquidity in the foreign exchange rate market.

3

While Goldberg and Tille (2008) construct the model in which the exporter unilaterally chooses
the invoice currency, Goldberg and Tille (2013) build the bargaining model so that an exporter
negotiates with importers on the pricing and the choice of invoice currency, in which thebargaining power plays the role. Their model shows the counterintuitive results. While one may
expect that the higher bargaining power of risk-averse importers would lead them to reduce theirexchange rate exposure through higher local currency invoicing, their analysis shows that the
opposite is the case. This is because the higher bargaining power of importers results in morefavorable pricing to them, which in turn reduces the marginal impact of exchange rate risk on theimporter's payoff and leads them to accept more exchange rate exposure. They also show thedifferent patterns so that higher bargaining power of importers would raise the extent of local
currency invoicing in the case that the price is unilaterally preset by the exporter, and the importer
exercises her bargaining power only over the choice of invoice currency. If we focus on thebargaining power of the exporter-side, the higher bargaining power of the exporter will result inambiguous consequences. However, if we suppose the situation so that the price is preset by theexporters who are risk-averse, we predict that the higher bargaining power of exporters leads tothe producer’s currency invoicing.
This paper focuses on the invoice currency choice of exporting SMEs. We predict that small
exporters are forced to choose the importer’s currency and shoulder the burden of currency risk
exposure when the bargaining power of these small exporters is expected to be weak. We also
predict that even if the exporter is small in terms of sales or assets but holds the productdifferentiability and product competitiveness, this exporter has some extent of bargaining powerin the negotiation on the pricing and the choice of invoice currency and thus tends to choose theproducer’s currency invoicing.
In contrast to our prediction, Ito et al. (2013, 2018) point out that smaller listed firms more tend
to choose exporter’s currency (Japanese yen) as an invoice currency while larger listed firms
choose importer’s currency or US dollar as an invoice currency. They state that this contradictoryobservation in invoice currency choice is attributed to the PTM behavior by large Japanese
exporters who want to stabilize the price in terms of local currency in the destination market. They
also found that the tendency to choose the local currency or US dollar as an invoice currency ismore prevalent in intra-firm trade than inter-firm trade.
Based on the existence of size effect in currency invoicing patterns for Japanese manufacturers,
we predict that the Japanese unlisted firms tend to choose the Japanese yen as an invoice currency
in their export because they are generally much smaller than listed firms. We also predict the intra4

firm trades are less prevalent for SMEs than listed firms because many SMEs do not establish
their foreign subsidiaries in foreign countries, which diminishes the motivation for SMEs tochoose the local currency invoicing.
2-2. Corporate risk management under financial constraints
In the field of corporate finance, the theory of corporate risk management is another theoreticalfoundation for considering exchange rate risk management at a firm level. SMEs typically facetighter financial constraints than large listed firms because of the existence of information
asymmetry and bankruptcy costs. Froot et al. (1993) state that if external sources of finance are
more costly to corporations than internally generated funds, there will typically be a benefit ofhedging by inducing more concave profit function of firms. The empirical research by Géczy etal. (1997) reports that S&P firms with greater growth opportunities and tighter financial
constraints are more likely to use currency derivatives. This evidence suggests that firms mightuse derivatives to reduce cash flow variation that might otherwise preclude firms from investingin valuable growth opportunities. Firms with extensive foreign exchange-rate exposure andeconomies of scale in hedging activities are also more likely to use currency derivatives.
SMEs examined in this paper are supposed not only they face tighter financial constraints but also
limited availability to various hedging instruments mainly due to the lack of economies of scale,information asymmetry, and creditworthiness. In this sense, the theoretical predictions on invoicecurrency choice of Japanese SMEs can differ from invoice currency choice of large Japaneselisted firms.

3. Questionnaire Survey
To confirm the theoretical and empirical predictions discussed in the previous section, we sent thequestionnaire items to 2,100 firms in December 2019 requesting their responses by the end ofJanuary 2020. All firms are unlisted manufactures whose head office is located in Japan and are
categorized into three groups of sample firms. The first sample group consists of “Continuously
exporting” firms (1,050 firms) that are randomly chosen from all firms having the continuous
experience of exports, either direct or indirect exports, throughout the 2010s.1 The second groupis “Starting export in the early 2010s” firms (509 firms) that are all firms that started exports from2010 to 2014. The third group is “Starting export in the late 2010s” firms (541 firms) that are all
The direct export means a case that an exporter directly exports their goods to a customer located inforeign country. The indirect export means a case that an exporter export goods via a trade firm.
5
1

firms that started exports for the first time from 2015-2019. The number of responded firms(response rate) is “Continuously exporting” 164 firms (15.6%), “Starting export in the early 2010s”
63 firms (11.6%), and “Starting export in the late 2010s” 73 firms (14.3%), respectively.2 The
firm size in terms of sales is reported in Table 3-1.
Table 3-1. Descriptive statistics of responded firms
Sales (unit millon yen)N
Stating Export in Early 2010s Firms
Stating Export in Late 2010s Firms
Continuously Exporting Firms
Total
Number of destinations

73
63
164
300
274

Mean
Median Std. dev. Max
Min
4,919
1,800
8,674 56,788
113
5,687
1,838 14,300 108,452
140
7,304
2,759 11,766 81,000
280
6,384
2,422 11,708 108,452
113
3.4
3.0
2.4
12.0
1.0

In the questionnaire survey, we first ask the status of the main currency and trade partner in exports
by destination as of the fiscal year 2018, which is called “current export” data. The main currency
is defined as an invoice currency most frequently used in export to the trade partner in thedestination. Second, we also ask the same question to the import side for the same destination if
the respondent firm imports from the destination country in exports, which is called “current
import” data. Finally, we ask the retrospective information as of the year when the firm started its
export to that destination, which is called “start year export” data.
Trade partners include the following six types: (1) own subsidiaries (sales subsidiaries or plant),(2) related companies (with capital relationship), (3) local agency (with no capital relationship),(4) via Japanese trading companies (Shosha), (5) direct export to local customer, and (6) others.
Trade partners (1) and (2) correspond to the intra-firm trades, while the extent of capital tie differs
in the two trade partners. Trade partners (3) through (6) correspond to the inter-firm trades, which
trade with third parties without any capital relationship.
The last line of Table 3-1 reports the number of export destinations that the respondent firms
answered. 274 out of 300 respondent firms answer the information for at least one destination, in
which an average number of destinations is 3.4 countries and median 3.0 countries.3

4. Status of Invoice Currency Choice and Basic Statistics of Determinants
In the appendix, Table A-1 reports the number of responded firms by prefecture.
In the appendix, Table A-2 reports the number of destination countries that the respondent firms
answered.
623

Invoice currency choice by trade partner, by destination
Table 4-1 summarizes the status of choice of main currency by trade partner in export to three
categories of destinations: first, exports to the US; second, exports to advanced economies
including Canada, UK, Eurozone, Australia, and New Zealand; third, export to Asian countries
including China, Hong Kong, Korea, Taiwan, ASEAN countries, and India.
Table 4-1. Main currency by trade partner in exports
A. Export to the US by trade partner
Japanese
US dollar
Yen
1. Own subsidiary6
16
Percent to total
27.3
72.7
2. Related firm (with capital tie)
5
7
Percent to total
41.7
58.3
3. Local agency (wihout capital tie)
99Percent to total
50.0
50.0
4. via Japanese trading companies
29
5Percent to total
85.3
14.7
5. direct export to local customer
13
24
Percent to total
35.1
64.9
6. others
20Percent to total
100.0
0.0
Number of observations (NOB)
64
61
Percent to total
51.2
48.8

Local
currency
0
-0.0
-0
-0.0
-0
-0.0
-0
-0.0
-0
-0.0
-0
-0.0
-0
-0.0
--

Euro

B. Export to advanced economies except the US by trade partner
Japanese
Local
US dollar Euro
Export to advanced economies except theYen
US by trade partnercurrency
1. Own subsidiary3003Percent to total
50.0
0.0
0.0
50.0
2. Related firm (with capital tie)2302
Percent to total
28.6
42.9
0.0
28.6
3. Local agency (wihout capital tie)
23
5
4
10
Percent to total
54.8
11.9
9.5
23.8
4. via Japanese trading companies
36
701Percent to total
81.8
15.9
0.0
2.3
5. direct export to local customer
37
13
4
6
Percent to total
61.7
21.7
6.7
10.0
6. others
4000Percent to total
100.0
0.0
0.0
0.0
Number of observations (NOB)
105
28
8
22
Percent to total
64.4
17.2
4.9
13.5

7

Total
22
100.0
12
100.0
18
100.0
34
100.0
37
100.0
2
100.0
125
100.0

Total
6
100.0
7
100.0
42
100.0
44
100.0
60
100.0
4
100.0
163
100.0

Percent
to NOB
17.6
9.6
14.4
27.2
29.6
1.6
100.0

Percent
to NOB
3.7
4.3
25.8
27.0
36.8
2.5
100.0

Table 4-1. Main currency by trade partner in exports (continued)
C. Export to Asian Countries by trade partner
Japanese
US dollar
Yen
1. Own subsidiary
58
17
Percent to total
63.7
18.7
2. Related firm (with capital tie)
24
8
Percent to total
72.7
24.2
3. Local agency (wihout capital tie)
98
19
Percent to total
83.1
16.1
4. via Japanese trading companies
128
10
Percent to total
92.8
7.2
5. direct export to local customer
112
41
Percent to total
71.3
26.1
6. others
18
0Percent to total
100.0
0.0
Number of observations (NOB)
438
95
Percent to total
78.9
17.1

Local
currency
0
16
0.0
17.6
0
1
0.0
3.0
0
1
0.0
0.8
0
0
0.0
0.0
0
4
0.0
2.5
0
0
0.0
0.0
0
22
0.0
4.0

Euro

Total
91
100.0
33
100.0
118
100.0
138
100.0
157
100.0
18
100.0
555
100.0

Percent
to NOB
16.4
5.9
21.3
24.9
28.3
3.2
100.0

Table 4-1 shows the following characteristics: first, the shares of Japanese yen invoicing are 51
percent in exports to the US, 64 percent in exports to the advanced economies, and 79 percent inexports to Asian countries, respectively. In this sense, the Japanese SMEs tend to choose theproducer’s currency as a currency most frequently used in exports to all destinations.
Second, Japanese SMEs export their products mainly through inter-firm trades including exports
to the local agency, exports via Japanese trading companies, or direct exports to local customers,
while the percentage of intra-firm exports, such as exports to own subsidiaries and related firms,
to the total number of observations is relatively small. In terms of invoice currency choice, thepercentage of Japanese yen as the main currency in inter-firm exports is higher than intra-firm
exports. Especially, Japanese yen invoicing occupies most of the exports via Japanese trading
companies.
Third, the percentage of choosing the local currency as the main currency in export is higher inintra-firm trades, while US dollar invoicing is most prevalent in exports to the related firms in theadvanced economies and Asian countries.
Last, the share of Japanese yen invoicing in exports to Asian countries is generally larger thanexports to the US and advanced economies. The percentage of the US dollar invoicing in exports
to Asian countries is 17 percent, which is almost comparable to that in exports to the advanced
economies excluding the US.
8Basic statistics of firm-level determinants
Table 4-2 reports the summary statistics of firm-level proxy variables regarding the determinants
for invoice currency choice in exports.
Table 4-2. Summary statistics of country-level and firm-level variables
Variables
Currency volatility (weekly, 1 year)
Firm age (log)
Sales (log) (FY2018)
Profit per employee (FY2017 & 2018)
World top (global market)
Top 3 largest within domestic industry
Number of patent (log)
Sales growth (FY2009-2018)
Capital ratio (FY2018)
Usage of forward contract

Obs
26
300
300
288
300
300
300
294
300
300

Mean
Std. dev. Min
Max
0.01
0.00
0.02
0.01
3.89
0.43
2.40
4.69
7.86
1.32
4.73
11.59
1.39
3.10
-13.29
24.66
0.03
0.17
0.00
1.00
0.06
0.24
0.00
1.00
1.31
1.50
0.00
7.08
0.55
1.08
-0.66
7.98
0.32
0.29
0.00
0.95
0.15
0.36
0.00
1.00

The currency volatility is calculated as the standard deviation of the weekly bilateral nominal
exchange rate between the currency of the destination country and the Japanese yen during the
fiscal year 2018 that is the fiscal year immediately before the questionnaire survey. The firm age
is the logarithm of one plus the number of years from the establishment year of the firm that areincluded in the Teikoku Data Bank (TDB) database. The sales (log) are the log of unconsolidated
sales of the firm. Profit per employee is the two-year average of after-tax profit as of fiscal years
2017 and 2018 divided by the number of employees as of the fiscal year 2018. World top (global
market) is the dummy variable that takes one if the Nikkei news articles reported the firm’s
products were in the top share in the global market and zero otherwise. The top 3 largest within
the domestic industry is a dummy variable that takes one if the firm is within the top 3 largest
firms by sales among Japanese firms in the industry extracted from the TDB database that reports
the firm’s ranking within the Japanese firms in each industry. The number of patents is thelogarithm of one plus the number of patents from 2000 through 2019 compiled in the J-Plat Pat
database, which provides the database of all patents that Japanese firms take. Sales growth iscalculated as the growth rate between the sales of the firms as of the fiscal year 2009 and the sales
as of the fiscal year 2018. Capital ratio as of the fiscal year 2018 is provided by the TDB database.
The usage of a forward contract is a dummy variable that takes one if the firms answer they use
forward contract and zero otherwise. Finally, the longevity of export experience is defined as thelogarithm of one plus the number of years from the export start year of the destination that thefirm answers.

9Panels A to C of Table 4-3 report the results of the univariate analysis that assesses the influenceof each variable on the choice of invoice currency. The table presents summary statistics for proxyvariables and the results of tests of differences between the mean of these variables for users andnon-users of each currency.
Table 4-3. The results of the t-test between different sample groups
A. Japanese yen invoicing

Variables
Currency volatility (weekly, 1 year)
Firm age (log)
Sales (log) (FY2018)
Profit per employee (FY2017 & 2018)
World top (global market)
Top 3 largest within domestic industry
Number of patent (log)
Sales growth (FY2009-2018)
Capital ratio (FY2018)
Import currency (Japanese yen)
Import currency (US dollar)
Import currency (Local currency)
Usage of forward contract
Longevity of export experince (log)Main currency
Main currency(Japanese yen)=1(Japanese yen)=0
tObs Mean
Std. dev. Obs Mean
Std. dev. statistic p-value
617
0.01
0.00 255
0.01
0.00
0.95
0.341
658
3.94
0.41 272
3.89
0.48
1.66
0.097 *
658
7.89
1.40 272
8.18
1.21
-2.91
0.004 ***
626
1.55
3.38 257
1.59
2.83
-0.17
0.868
658
0.05
0.22 272
0.05
0.21
0.05
0.957
658
0.07
0.25 272
0.04
0.19
1.71
0.087 *
658
1.54
1.61 272
1.69
1.42
-1.36
0.175
653
0.62
1.05 268
0.45
0.79
2.36
0.018 **
658
0.32
0.29 272
0.35
0.28
-1.55
0.122
658
0.15
0.35 270
0.04
0.20
4.60
0.000 ***
658
0.05
0.23 270
0.21
0.41
-7.26
0.000 ***
658
0.03
0.18 270
0.10
0.30
-4.09
0.000 ***
658
0.12
0.33 272
0.29
0.45
-6.43
0.000 ***
495
2.52
0.79 200
2.54
0.79
-0.40
0.691

B. Local currency invoicing

Variables
Currency volatility (weekly, 1 year)
Firm age (log)
Sales (log) (FY2018)
Profit per employee (FY2017 & 2018)
World top (global market)
Top 3 largest within domestic industry
Number of patents (log)
Sales growth (FY2009-2018)
Capital ratio (FY2018)
Import currency (Japanese yen)
Import currency (US dollar)
Import currency (Local currency)
Usage of forward contract
Longevity of export experince (log)Main currency
Main currency(local currency)=1(local currency)=0
tObs Mean
Std. dev. Obs Mean
Std. dev. statistic p-value
111
0.01
0.00 761
0.01
0.00
-6.33
0.000 ***
112
3.87
0.50 818
3.93
0.42
-1.29
0.197
112
8.31
1.26 818
7.93
1.36
2.75
0.006 ***
108
2.06
3.27 775
1.49
3.22
1.72
0.086 *
112
0.06
0.24 818
0.05
0.21
0.74
0.459
112
0.04
0.19 818
0.06
0.24
-1.04
0.301
112
1.85
1.58 818
1.55
1.55
1.97
0.049 **
111
0.62
0.90 810
0.56
1.00
0.53
0.595
112
0.37
0.28 818
0.33
0.29
1.63
0.104
110
0.05
0.21 818
0.12
0.33
-2.45
0.015 **
110
0.17
0.38 818
0.09
0.29
2.76
0.006 ***
110
0.23
0.42 818
0.03
0.16
9.41
0.000 ***
112
0.32
0.47 818
0.15
0.36
4.60
0.000 ***
87
2.72
0.66 608
2.50
0.80
2.44
0.015 **

10

Table 4-3. The results of the t-test between different sample groups (continued)
C. US dollar (Vehicle currency) invoicing

Variables
Currency volatility (weekly, 1 year)
Firm age (log)
Sales (log) (FY2018)
Profit per employee (FY2017 & 2018)
World top (global market)
Top 3 largest within domestic industry
Number of patents (log)
Sales growth (FY2009-2018)
Capital ratio (FY2018)
Import currency (Japanese yen)
Import currency (US dollar)
Import currency (Local currency)
Usage of forward contract
Longevity of export experince (log)Main currency
Main currency(US dollar)=1(US dollar)=0
tObs Mean
Std. dev. Obs Mean
Std. dev. statistic p-value143
0.01
0.00 601
0.01
0.00
-2.08
0.038 **
157
3.91
0.46 645
3.93
0.42
-0.54
0.590
157
8.12
1.26 645
7.94
1.37
1.52 0.130146
1.25
2.46 616
1.59
3.22
-1.19
0.236
157
0.04
0.19 645
0.05
0.21
-0.53
0.599
157
0.04
0.19 645
0.07
0.25
-1.33
0.182
157
1.53
1.30 645
1.59
1.60
-0.40
0.687
154
0.32
0.66 641
0.62
1.04
-3.40
0.001 ***
157
0.34
0.28 645
0.33
0.29
0.45 0.655
157
0.06
0.23 644
0.14
0.35
-2.82
0.005 ***
157
0.23
0.42 644
0.06
0.23
6.90 0.000 ***157
0.01
0.08 644
0.04
0.20
-2.18
0.030 **
157
0.26
0.44 645
0.14
0.35
3.55 0.000 ***108
2.45
0.86 481
2.49
0.78
-0.51
0.610

Note: The asterisks, ***, **, and *, attached to p-values show that the difference of the mean ofthe variables within two subsample groups is statistically significant at 1 percent, 5 percent, and10 percent level, respectively.
In terms of the influence of the proxy variables of a firm’s financial characteristics such as sales
and profitability, user firms of Japanese yen invoicing are statistically different from non-users
with respect to the firm size measured by the log of sales. User firms of local currency invoicing
also have a significantly larger amount of sales than do non-users of local currency invoicing.
These results are consistent with the observation in the existing empirical research such as Ito etal. (2018), in which larger size Japanese listed firms have a tendency to choose the local currency
invoicing not Japanese yen invoicing. On the other hand, profit per employee for users of local
currency is statistically larger than that of non-users, while no such statistically significant
difference is observed for users and non-users of Japanese yen and US dollar invoicing.
The variables including the firm age, the world top, top 3 largest within the industry, and thenumber of patents represent the extent of firm’s competitiveness in the global market and industry
and firm’s product differentiability, by which we suppose the firms have higher bargaining powerin the negotiation with importers. The univariate tests suggest that users of Japanese yen invoicing
are statistically different from non-users only with respect to the firm age and the top 3 largest
within the industry, but its statistical significance level is only 10 percent, while no statisticallysignificant differences with respect to the world top and the number of patents. In terms of local
currency invoicing, the number of patents held by users of local currency invoicing is larger than11

that of non-users, which contradicts our prediction that the firms with higher productdifferentiability decrease the local currency invoicing due to higher bargaining power forexporters. There is no statistically significant difference between users and non-users of US dollar
invoicing in terms of these variables. In summary, although we observe some influence of proxyvariables regarding the extent of exporter’s bargaining power on the invoice currency choice,pieces of evidence are ambiguous by the univariate test.
The sales growth and the capital ratio constitute the proxy variables related to the financial
constraints of the firm. The sales growth has a statistically significant positive difference between
users and non-users of Japanese yen invoicing at a 5 percent significance level, while the capitalratio has no statistically significant difference. The sales growth also has a statistically significant
negative difference between users and non-users of US dollar invoicing, while the capital ratio
has no statistically significant difference. These results show that the impacts of variables related
to firm’s financial constraints on the invoice currency choice are ambiguous by the univariate test
due to the insignificant influence of the capital ratio.
Import currency is a dummy variable related to the choice of invoice currency on the import sideof the destination. These dummy variables represent the operational hedging strategy employedby the firm. The choice of invoice currency on the import-side has statistically significant impacts
on the choice of invoice currency on the export-side in the same destination.
Dummy variable for the use of forward contract by the firm has a statistically significant negative
difference on the Japanese yen invoicing at 1 percent significance level while the statisticallysignificant positive impact on the local currency invoicing at 5 percent level and no impact on the
US dollar invoicing. This is consistent with the view that the firm that can utilize the financial
hedge would not necessarily depend on the choice of producer’s currency (Japanese yen)invoicing for managing the exchange rate risk exposure.
Lastly, the longevity of export experience is calculated as the logarithm of one plus the numberof years after the firm started the exports to the destination. This variable represents the degree ofexperience for handling the currency risk management of destination currency. This variable isstatistically significant positive only on the choice of the local currency invoicing while nostatistically significant impacts on the Japanese yen and US dollar invoicing.

125. Empirical methodology
We employ the following probit model to examine the determinants of invoice currency decisions.
The sample is firm-destination-trade partner data, which is about the firm i’s export to thedestination country j through the trade channel (trade partner) k.

Prob(Currijk =1)=α0+ β Currency volatiliy1j+ γ1Own subsidiaryijk+ γ2Related firmjjk+ γ3Local agencyijk+ γ4Trade companyijk+ θ1 Firm agei + θ2 Salesi+ θ3Profitabilityi+ λ1 World topi + λ 2Domestic top3i + λ 3 Patenti+ µ1Sales growthi + µ2Capital ratioi+ ρ1Forwardi+ σ1Import currencyij+ σ2Experienceij+ δ Industry dummies +εijk
1i

The left-hand side variable, Curr is a binary variable that takes one if a concerned currency is themain currency in export to the trade partner in the destination country and zero otherwise. This
Curr variable can be Japanese yen, the local currency, and the US dollar, interchangeably,
depending on the purpose of analysis.
On the right-hand side, the Own subsidiary through Trade company are dummy variables for eachtrade partner in the destination country. For instance, if it is the case of firm i’s export to a local
agency without any capital tie in China, the dummy variable Local agency takes one while othertrade partner dummies such as Own subsidiary, Related firm, and Trade partner, take zero.
Among the six types of trade partners described in the previous section, we take the direct export
to local customers and others as a benchmark, thus drop the dummies for these two types of trade
partners from the regression model. Other explanatory variables included on the right-hand side
are the same variables defined in the previous section except for Industry dummies, which aredummy variables for the type of manufacturing industry that the firm belongs. Table 5-1 presents
the correlation coefficients between the selected explanatory variables.

13

Table 5-1. Correlation coefficients for explanatory variables
firm_agesales
profitsales_g cap_ratio patent dometop3 worldtop im_jpy
im_usdim_lc
0.291(0.000)profit-0.1380.235(0.000) (0.000)
sales_g-0.1650.094
0.235(0.000) (0.004) (0.000)
cap_ratio 0.118
0.353
0.276
-0.018(0.000) (0.000) (0.000) (0.591)
patent
0.106
0.577
0.211
0.094
0.320
(0.001) (0.000) (0.000) (0.004) (0.000)
dometop3 0.170
0.350
0.031
0.081
0.100
0.193
(0.000) (0.000) (0.353) (0.014) (0.002) (0.000)
worldtop -0.201
0.229
0.235
0.193
0.124
0.328
0.074
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.023)
im_jpy-0.027
0.121
0.052
0.120
0.096
0.116
0.071
0.076
(0.405) (0.000) (0.121) (0.000) (0.003) (0.000) (0.030) (0.021)
im_usd-0.0220.084-0.017-0.032-0.0740.057-0.004-0.025-0.120(0.495) (0.010) (0.613) (0.330) (0.024) (0.083) (0.904) (0.455) (0.000)
im_lc-0.021
0.099
0.078
0.016
0.020
0.119
-0.0570.017-0.0830.236(0.530) (0.002) (0.020) (0.629) (0.551) (0.000) (0.083) (0.615) (0.011) (0.000)
forward-0.030
0.252
0.070
0.087
0.017
0.315
0.049
0.152
-0.0020.109
0.052(0.363) (0.000) (0.036) (0.008) (0.597) (0.000) (0.132) (0.000) (0.952) (0.000) (0.111)
Note: Variables are as follows: "sales" is Sales (log); "profit" is Profit per employee (FY2017 and 2018); "sales_g" is Sales growth(FY2009-2018); "cap_ratio" is capital ratio (FY2018); "patent" is Number of patents (log). "dometop3" is Top 3 largest within thedomestic industry; "worldtop" is Worldtop (global market); "im_jpy" is Import currency (Japanese yen); "im_usd" is Importcurrency (US dollar); "im_lc" is Import currency (local currency); "forward" is Usage of forward contract.
sales

Using the above variables, we employ the probit model estimation to test the determinants of themain invoice currency of Japanese SMEs. Table 5-2 reports basic results for impact determinantsof Japanese yen invoicing.
First, the export to own subsidiaries has statistically significant negative coefficients in thespecification (1) through (4). After controlling for other factors, the probability that the Japanese
yen becomes the main invoice currency is 14 percent lower in this type of export than in the directexport to local customers and others, the benchmark export channel in all specifications. On theother hand, the export to related firms, another channel of the intra-firm trades, takes a negative
coefficient but is not statistically significant at a conventional significance level. The absolutevalue of the estimated coefficients is almost halved comparing to those of own subsidiaries. These
results show that the intra-firm trades tend to hinder the use of producer’s currency (Japanese yen)invoicing, though the extent of intra-firm relationship plays an important role. In own subsidiaries,
its capital is owned by the parent firms in Japan more than 50 percent while the capital of therelated firm is owned only 20 percent through 49 percent by parent firms and local firms indestination country sometimes participate in Japanese related firms as major shareholders.
Second, the inter-firm trades via Japanese trading companies tend to be invoiced in Japanese yen.
The estimated coefficients of export via Japanese trading companies take statistically significant
14positive coefficients at a 5 percent level in all specifications (1) through (5). The coefficients showthat in export though Japanese trade companies, the Japanese SMEs use Japanese yen as the main
invoice currency more by about 21 percent compared to the benchmark trade channel. These
results are quite reasonable because the export via Japanese trading companies is viewed as thetrade between Japanese firms.
Third, among the basic firm’s characteristics, on the one hand, the log of sales takes a negative
coefficient whose significance level is 10 percent, indicating that firms with larger sales tend to
invoice in foreign currencies. On the other hand, the profit per employee takes insignificant
coefficients in any specifications.
Forth, the firm age, which is the number of years since its establishment, takes a statisticallysignificant positive coefficient at a 1 percent significance level, which means that elder firms use
Japanese yen as the main invoice currency in exports. Moreover, firms with a top share in the
global niche market or the top 3 largest with domestic industry use Japanese yen invoicing morewhile the number of patents has no statistically significant impacts on Japanese yen invoicing.
These results suggest that the variables regarding product competitiveness and differentiability
linked to the bargaining power are crucial for Japanese yen invoicing.
Fifth, firms with higher sales growth, which is a proxy for a firm’s growth opportunity, tend to
invoiced in Japanese yen. Its statistical significance level is 1 percent. The capital ratio, thevariables related to the soundness of financial conditions, have negative coefficients that are
statistically significant at 10 percent level in (1) through (4) and 5 percent in (5). As stated above,the sales growth and the capital ratio combinedly constitute the measure of financial constraints
faced by the firm. These results suggest that firms having higher growth opportunities and moredeteriorated financial conditions tend to choose the Japanese yen as an invoice currency in exports.
Sixth, the firms using forward contracts are less likely to use the Japanese yen as an invoicecurrency in exports. The use of financial hedging such as forward contracts and the use of
producer’s currency as invoice currency are substitutable in terms of exchange rate risk hedging.
We interpret these results that the firms using the forward contract less depend on the hedging byinvoice currency.
Seventh, the coefficients of import currency (Japanese yen) are positive with a 1 percent
significance level. This means that firms importing goods from the same destination for export
have a strong tendency to use a single type of currency between import and export sides. We
15interpret this result as indicating that Japanese SMEs implement the operational hedging, in which
the firms net out the currency risk on both sides of trade flows.
Last, the specification (5) in Table 5-2 includes the dummy variables representing the longevityof export experience. If firms accumulate the knowledge of the destination country, the numberof years since they started exporting to a specific destination can be used as a proxy for the degreeof experience. We observe no statistically significant impacts. This result means that the impactsof the longevity of export experience, after controlling for the impact of firm age, is very limited
in our examination.

16

Table 5-2. Impact of determinants on exporter’s currency (Japanese yen) invoicing: alldestinations

VARIABLES
Currency volatility (weekly, 1 year)
Ex. to own subsidiaries
Ex. to related firms
Ex. to local Agency
Ex. via Japanese trading companies
Firm age (log)
Sales (log) (FY2018)
Profit per employee (FY2017 & 2018)
World top (global market)(1)
Japaneseyen(2)Japaneseyen(3)Japaneseyen(4)Japaneseyen(5)Japaneseyen

5.355(6.415)-0.139**(0.0580)-0.0831(0.0738)
0.0568(0.0398)0.217***(0.0328)0.131***(0.0443)-0.0274(0.0167)0.00146(0.00712)
0.120**(0.0548)

5.291(6.407)-0.142**(0.0580)-0.0812(0.0734)
0.0543(0.0398)
0.210***(0.0330)
0.105**(0.0430)-0.0273*(0.0165)
0.00216(0.00700)

5.109(6.404)-0.141**(0.0581)-0.0825(0.0739)
0.0569(0.0398)
0.219***(0.0324)0.115***(0.0434)-0.0311*(0.0176)0.000301(0.00701)

5.437(6.400)-0.149**(0.0586)-0.0814(0.0732)
0.0532(0.0399)
0.208***(0.0333)
0.124***(0.0442)-0.0338**(0.0170)0.00217(0.00705)
0.116**(0.0564)
0.164***(0.0485)0.401(7.195)-0.0942(0.0627)-0.0554(0.0804)
0.0834*(0.0459)0.206***(0.0393)
0.0562(0.0516)-0.0366*(0.0195)
0.00310(0.00754)
0.119*(0.0657)
0.167***(0.0549)

0.0905***(0.0235)-0.121*(0.0684)-0.235***(0.0545)0.220***(0.0251)

Yes

0.0816***(0.0283)-0.190**(0.0810)-0.303***(0.0673)0.244***(0.0273)
0.0146(0.0257)
Yes

Top 3 largest (domestic industry)

0.165***(0.0472)

Number of patent (log)0.100***(0.0237)-0.127*(0.0685)-0.234***(0.0542)0.223***(0.0251)

0.0950***(0.0235)-0.117*(0.0681)-0.222***(0.0532)
0.223***(0.0248)0.0212(0.0138)
0.107***(0.0239)-0.135**(0.0683)-0.246***(0.0563)
0.223***(0.0251)

Industry dummies

Yes

Yes

Yes

Observations
Pseudo R squaredStandard errors in parentheses

806
0.223

Sales growth (FY2009-2018)
Capital ratio (FY2018)
Usage of forward contract
Import currency (Japanese yen)
Longevity of export experince (log)

806
806
806
0.225
0.222
0.228
*** p<0.01, ** p<0.05, * p<0.1

17

617
0.252

Table 5-3 and Table 5-4 report the results on the impacts of determinants on local currency
invoicing and the US dollar invoicing, respectively. The main results are summarized as follows.
First, the currency volatility has statistically significant negative coefficients in local currency
invoicing while statistically significant positive coefficients in the US dollar invoicing. Combinedwith the result in the Japanese yen invoicing in which there are no impacts of the currency
volatility on Japanese yen invoicing, we interpret that large bilateral currency volatility of local
currency vis-à-vis Japanese yen hinders the local currency invoicing and promotes the vehicle
currency invoicing such as the US dollar.
Second, among the intra-firm trades, the exports to own subsidiaries have a statistically significant
positive coefficient at 10 percent level only in the local currency invoicing while no statisticallysignificant impacts on the US dollar invoicing.
Third, firms with the world's top share in a global niche market and firms with a large number ofpatents are less likely to use the local currency invoicing, while firms with the top 3 largest within
the domestic industry are less likely to use the US dollar.
Forth, among the variables related to financial constraints, the firm growth has statisticallysignificant negative impacts on the local currency invoicing and the US dollar invoicing. The
capital ratio has positive coefficients in both local currencies invoicing and the US dollar
invoicing, but statistically significant coefficients are observed only in the US dollar invoicing.
Fifth, the variables relating to the use of forward contracts and operational hedging are statistically
positive impacts on the local currency invoicing and the US dollar invoicing.

18

Table 5-3. Impact of determinants on local currency invoicing: all destinations
(1)
local
currency(2)
localcurrency(3)
localcurrency(4)
localcurrency(5)
localcurrency-24.88***(4.315)
Ex. to own subsidiaries
0.0602*(0.0338)
Ex. to related firms
0.00766(0.0340)
Ex. to local Agency-0.0103(0.0204)
Ex. via Japanese trading companies-0.0652***(0.0175)
Firm age (log)-0.0306(0.0217)
Sales (log) (FY2018)-0.000172(0.00859)
Profit per employee (FY2017 & 2018)
0.00411(0.00308)
World top (global market)-0.0391*(0.0210)Top 3 largest (domestic industry)-25.01***(4.329)0.0571*(0.0332)
0.00691(0.0341)-0.0103(0.0205)-0.0677***(0.0173)-0.0202(0.0206)-0.00295(0.00840)
0.00354(0.00313)-24.18***(4.298)0.0652*(0.0347)
0.00861(0.0340)-0.00863(0.0204)-0.0670***(0.0170)-0.0251(0.0203)
0.00498(0.00893)
0.00422(0.00299)-24.89***(4.316)0.0604*(0.0339)0.00740(0.0339)-0.0103(0.0204)-0.0650***(0.0175)-0.0303(0.0218)
8.47e-05(0.00872)
0.00406(0.00309)-0.0390*(0.0211)-0.00774(0.0420)-27.29***(4.980)0.0175(0.0287)-0.00732(0.0312)0.00327(0.0264)-0.0577***(0.0207)-0.00789(0.0248)0.00581(0.00997)
0.00342(0.00348)-0.0433**(0.0217)-0.0102(0.0436)-0.0211**(0.0104)
0.0188(0.0325)0.0851**(0.0345)
0.262***(0.0809)

Yes-0.0183(0.0119)-0.00979(0.0385)0.103**(0.0451)0.227***(0.0853)0.00467(0.0134)Yes

VARIABLES
Currency volatility (weekly, 1 year)-0.00966(0.0403)

Number of patent (log)-0.0214**(0.0102)0.0190(0.0325)0.0848**(0.0344)
0.263***(0.0807)-0.0225**(0.0106)
0.0157(0.0325)
0.0790**(0.0332)
0.268***(0.0812)-0.0147**(0.00678)-0.0237**(0.0105)
0.0217(0.0318)0.101***(0.0379)
0.281***(0.0827)

Industry dummies

Yes

Yes

Yes

Observations
Pseudo R squaredStandard errors in parentheses

797
0.278

Sales growth (FY2009-2018)
Capital ratio (FY2018)
Usage of forward contract
Import currency (local currency)
Longevity of export experince (log)

797
797
797
0.275
0.283
0.278
*** p<0.01, ** p<0.05, * p<0.1

19

611
0.284

Table 5-4. Impact of determinants on vehicle currency (US dollar) invoicing: all destinations
except the US

VARIABLES
Currency volatility (weekly, 1 year)
Ex. to own subsidiaries
Ex. to related firms
Ex. to local Agency
Ex. via Japanese trading companies
Firm age (log)
Sales (log) (FY2018)
Profit per employee (FY2017 & 2018)
World top (global market)(1)
US
dollar(2)
USdollar(3)
USdollar(4)
USdollar(5)
USdollar

10.53*(5.418)-0.0307(0.0368)
0.0327(0.0607)-0.0662**(0.0304)-0.119***(0.0278)-0.0387(0.0380)
0.00225(0.0139)-0.00564(0.00690)-0.0564(0.0537)

10.11*(5.318)-0.0258(0.0366)
0.0330(0.0592)-0.0646**(0.0295)-0.111***(0.0276)-0.0268(0.0358)
0.00535(0.0135)-0.00549(0.00660)

10.64**(5.404)-0.0285(0.0370)0.0349(0.0610)-0.0661**(0.0303)-0.121***(0.0276)-0.0293(0.0369)
0.00588(0.0146)-0.00416(0.00680)

10.02*(5.298)-0.0213(0.0376)
0.0326(0.0590)-0.0631**(0.0296)-0.110***(0.0277)-0.0360(0.0370)0.00813(0.0138)-0.00592(0.00668)-0.0583(0.0512)-0.130***(0.0212)14.61**(5.916)-0.00959(0.0443)0.0423(0.0682)-0.102***(0.0302)-0.107***(0.0313)0.00283(0.0426)
0.00936(0.0154)-0.00504(0.00703)-0.0561(0.0608)-0.129***(0.0235)-0.0677***(0.0212)0.0909(0.0563)0.147***(0.0521)0.248***(0.0696)

Yes-0.0634***(0.0240)0.162**(0.0639)0.193***(0.0679)0.243***(0.0799)-0.0251(0.0203)
Yes

Top 3 largest (domestic industry)-0.130***(0.0216)

Number of patent (log)-0.0781***(0.0214)0.0929(0.0575)
0.141***(0.0514)0.247***(0.0692)-0.0706***(0.0211)0.0881(0.0563)0.139***(0.0506)
0.253***(0.0696)-0.0136(0.0117)-0.0813***(0.0213)
0.0981*(0.0574)0.151***(0.0534)
0.255***(0.0694)

Industry dummies

Yes

Yes

Yes

Observations
Pseudo R squaredStandard errors in parentheses

678
0.181

Sales growth (FY2009-2018)
Capital ratio (FY2018)
Usage of forward contract
Import currency (US dollar)
Longevity of export experince (log)

678
678
678
0.192
0.182
0.193
*** p<0.01, ** p<0.05, * p<0.1

20

514
0.231

6. Robustness tests
In the previous section, we found some evidence that the soundness of the financial condition offirms plays an important role. For testing the robustness of results, more detailed financial data
are available to many sample firms provided by the TDB, which covers almost 60 percent ofsample firms. Using the detailed financial data, we construct the new explanatory variables related
to the soundness of financial conditions for sample firms.
Table 6-1. Sample statistics of detailed financial variables
Obs
Sales (log) (FY2018)
ROA (FY2018)
Tangible fixed assets growth (FY2013-2018)
Capital ratio (FY2018)
Cash-assets ratio (FY2018)
Temporal liquidity (FY2018)

Mean
Std. dev. Min
Max
195
14.85
1.33
11.64
18.50
195
0.03
0.06
-0.38
0.43
169
0.12
0.45
-1.39
2.11
195
0.38
0.31
-1.52
0.95
195
0.16
0.14
0.00
0.82
195
0.22
0.41
0.00
3.80

The first variable is the log of sales as of the fiscal year 2018, which is calculated by using detailed
sample data. The second variable is the Return on Assets (ROA), which is calculated as after-tax
profit divided by total assets as of the fiscal year. The third variable is the growth rate of intangible
fixed assets from the fiscal year 2013 through 2018, a proxy variable to capture the investment
opportunity of the sample firm during the second half of the 2010s. The last three variables are
related to the soundness of firms. The capital ratio is calculated as capital divided by total assets.
This ratio can take a negative value when the capital is reported as a negative value. The cashassets ratio (the temporal liquidity) is calculated as the sum of cash and deposit divided by total
assets (sales).
Table 6-2 provides the results of the t-test between different sample groups for financial variables.
Similar to the results in Table 4-3, the size of sales has a negative difference between users andnon-users of Japanese yen invoicing and a positive difference for the local currency invoicing andthe US dollar invoicing. The growth rate of tangible fixed assets, a proxy for the firm’s investment
opportunity and demand for external finance, is higher for users of Japanese yen than non-users
while lower for users of US dollar. The growth rate does not have a statistically significant
influence on the local currency invoicing. The cash-assets ratio and temporal liquidity, proxies
for the availability of internal funds, promote the US dollar invoicing and hinder the Japanese yen
invoicing while both of these variables have no statistically significant influence on the local

21

currency invoicing. Last, we observe no statistical influence of the capital ratio on the invoicecurrency choice by the univariate test.
Table 6-2. The results of the t-test between different sample groups
A. Japanese yen invoicing
Main currency
Main currency(Japanese yen)=1(Japanese yen)=0
tVariables
Obs MeanStd. dev. Obs Mean
Std. dev. statistic p-value
Sales (log) (FY2018)403 14.86
1.41 192
15.13
1.22 -2.31
0.021 **ROA (FY2018)
403
0.03
0.06 192
0.03
0.05 -0.23
0.820
Tangible fixed assets growth (FY2013-2018)359
0.13
0.41 172
0.01
0.43
3.17
0.002 ***
Capital ratio (FY2018)403
0.39
0.31 192
0.43
0.27 -1.60
0.110
Cash-assets ratio (FY2018)403
0.15
0.13 192
0.18
0.15 -2.17
0.031 **
Temporal liquidity (FY2018)403
0.19
0.22 192
0.29
0.58 -2.90
0.004 ***
B. Local currency invoicing
Main currency
Main currency(local currency)=1(local currency)=0tVariables
Obs MeanStd. dev. Obs Mean
Std. dev. statistic p-value
Sales (log) (FY2018)76 15.24
1.26 519
14.90
1.37
2.01
0.045 **ROA (FY2018)76
0.04
0.05 519
0.03
0.06
1.12
0.265
Tangible fixed assets growth (FY2013-2018)65
0.05
0.49 466
0.09
0.41 -0.73
0.463
Capital ratio (FY2018)76
0.43
0.30 519
0.40
0.30
0.78
0.435
Cash-assets ratio (FY2018)76
0.16
0.14 519
0.16
0.14 -0.34
0.732
Temporal liquidity (FY2018)76
0.23
0.45 519
0.22
0.37
0.24
0.814
C. US dollar (Vehicle currency) invoicing
Main currency
Main currency(US dollar)=1(US dollar)=0tVariables
Obs MeanStd. dev. Obs Mean
Std. dev. statistic p-value
Sales (log) (FY2018)
110 15.15
1.25 409
14.88
1.37
1.82
0.070 *ROA (FY2018)
110
0.03
0.06 409
0.03
0.06 -0.84
0.402
Tangible fixed assets growth (FY2013-2018)
102 -0.02
0.39 364
0.12
0.42 -3.06
0.002 ***
Capital ratio (FY2018)110
0.42
0.25 409
0.40
0.30
0.74
0.458
Cash-assets ratio (FY2018)110
0.19
0.15 409
0.16
0.13
2.17
0.031 **
Temporal liquidity (FY2018)
110
0.32
0.66 409
0.20
0.22
3.25
0.001 ***

22

For further analysis, we conduct the probit model estimation by the same regression model
employed in section 5 using the new financial variables constructed from the financial data. Table
6-3 presents the correlation coefficients between the selected explanatory variables.
Table 6-3. Correlation coefficients for explanatory variables: detailed financial data
sales_2018
roa_2018
tangible_g_2013 cap_ratio_2018 cash_asset_2018
0.195(0.000)tangible_g_2013
0.103-0.031(0.017)(0.469)cap_ratio_2018
0.255
0.435
-0.024(0.000)(0.000)(0.583)
cash_asset_2018-0.2600.001-0.1520.316(0.000)(0.976)(0.000)(0.000)
temp_liq_2018-0.211-0.169-0.0730.275
0.744(0.000)(0.000)(0.091)(0.000)(0.000)Note: Variables are as follows: "roa_2018" is ROA (FY2018); "sales_g_2018" is Sales (log) (FY2018);"cap_ratio_2018" is Capital ratio (FY2018); "cash_2018" is Cash-asset ratio (FY2018); "tangible_g_2013" isTabgible fixed asset growth (FY2013-2018); "cash_asset_2018" is Cash-asset raio (FY2018); "temp_liq_2018"is Temporal liquidity (FY2018).

roa_2018

Table 6-4, 6-5, and 6-6 report the impacts of determinants on Japanese yen, US dollar, local
currency invoicing, respectively, using these financial data. For the limited coverage by thedetailed financial data provided by the TDB, the number of observations declines by almost 30
percent. For instance, the number of observations decreases from 806 in Table 5-2 (1) to 548 inTable 6-4 (1). The main results are summarized as follows.
First, the ROA, the after-tax profit divided by total assets, has statistically significant negative
impacts on US dollar invoicing. This means that less profitable firms are likely to choose thevehicle currency, i.e., the US dollar, invoicing.
Second, the growth rate of tangible fixed assets, which captures investment and growth
opportunities at the firm-level, has positive impacts on Japanese yen invoicing and negative
impacts on US dollar invoicing, respectively. Our interpretation is that firms with greater
investment opportunities try to minimize the risk from cash flow fluctuations by choosing theJapanese yen invoicing because these firms tend to take additional costs from tapping into externalfunding.
Third, after controlling for the impacts of the ROA and investment and growth opportunity, boththe capital ratio and cash position have statistically significant negative impacts on Japanese yen
23

invoicing (Table 6-4), and statistically significant positive impacts on US dollar invoicing (Table
6-6). We interpret these results as indicating that deteriorated financial condition and resulting
higher cost of the capital provide an incentive for Japanese exporting SMEs to stabilize their cash
flows by choosing the exporter’s currency (Japanese yen) invoicing.
Last, similar to the results through Table 5-2 to 5-4, the use of the forward contract hinders theJapanese yen invoicing and promotes the US dollar invoicing. These results are consistent withthe view that financial hedging is substitutable with Japanese yen invoicing as far as sample SMEs
can utilize financial hedging.

24

Table 6-4. Impact of determinants on Exporter’s currency (Japanese yen) invoicing: alldestinations using the detailed financial data

VARIABLES
Currency volatility (weekly, 1 year)
Ex. to own subsidiaries
Ex. to related firms
Ex. to local Agency
Ex. via Japanese trading companies
Top 3 largest (domestic industry)
Number of patent (log)
Sales (log) (FY2018)
ROA (FY2018)
Tangible fixed assets growth(FY2013-2018)
Capital ratio (FY2018)(1)
Japaneseyen(2)Japaneseyen(3)Japaneseyen(4)Japaneseyen(5)Japaneseyen(6)Japaneseyen

3.836(8.464)-0.198***(0.0761)-0.0845(0.0890)0.0628(0.0530)
0.236***(0.0447)0.230***(0.0531)0.0103(0.0187)-0.00204(0.0217)0.474(0.416)4.488(8.503)-0.206***(0.0764)-0.0800(0.0890)0.0590(0.0536)0.241***(0.0445)0.232***(0.0519)0.0114(0.0188)-0.0267(0.0217)0.155(0.391)

4.299(8.529)-0.204***(0.0771)-0.0899(0.0895)
0.0473(0.0547)0.229***(0.0459)
0.233***(0.0530)
0.0172(0.0191)-0.0275(0.0218)-0.0308(0.415)5.499(9.317)-0.177**(0.0816)-0.0448(0.0966)
0.0679(0.0600)
0.228***(0.0512)0.233***(0.0636)0.0370*(0.0214)-0.0179(0.0251)0.590(0.614)0.148**(0.0654)-0.183*(0.107)6.352(9.345)-0.188**(0.0820)-0.0574(0.0990)0.0654(0.0603)
0.237***(0.0507)0.226***(0.0659)
0.0359*(0.0214)-0.0384(0.0249)
0.507(0.579)0.125*(0.0661)6.286(9.334)-0.180**(0.0822)-0.0649(0.0990)0.0576(0.0606)
0.227***(0.0517)0.231***(0.0642)0.0384*(0.0214)-0.0378(0.0249)
0.317(0.574)0.144**(0.0657)-0.183**(0.0862)

Cash-assets ratio (FY2018)-0.540***(0.161)

Temporal liquidity (FY2018)
Usage of forward contract
Industry dummies
Observations
Pseudo R squaredStandard errors in parentheses-0.269***(0.0750)
Yes
548
0.163

-0.238***(0.0736)
Yes-0.585***(0.188)-0.237***(0.0904)-0.252***(0.0734)
Yes-0.235***(0.0827)
Yes

548
548
469
0.173
0.172
0.150
*** p<0.01, ** p<0.05, * p<0.1

25-0.198**(0.0800)
Yes-0.250**(0.106)-0.211***(0.0798)
Yes

469
0.161

469
0.156

Table 6-5. Impact of determinants on local currency invoicing: all destinations using the detailed
financial data
(1)
Local
currency(2)
Local
currency(3)
Local
currency(4)
Local
currency(5)
Local
currency(6)
Local
currency-31.05***(5.725)
Ex. to own subsidiaries
0.0744(0.0475)
Ex. to related firms
0.0256(0.0494)
Ex. to local Agency-0.0298(0.0252)
Ex. via Japanese trading companies -0.0936***(0.0237)
Top 3 largest (domestic industry)-0.0351(0.0381)
Number of patent (log)-0.0133(0.00963)
Sales (log) (FY2018)
0.00551(0.0114)
ROA (FY2018)
0.287(0.234)
Tangible fixed assets growth(FY2013-2018)
Capital ratio (FY2018)-0.0231(0.0424)
Cash-assets ratio (FY2018)-31.07***(5.739)0.0751(0.0477)0.0257(0.0498)-0.0312(0.0249)-0.0948***(0.0236)-0.0346(0.0390)-0.0137(0.00967)
0.00410(0.0114)0.243(0.219)-31.06***(5.738)0.0750(0.0477)0.0254(0.0497)-0.0310(0.0249)-0.0946***(0.0236)-0.0349(0.0389)-0.0144(0.00990)
0.00493(0.0115)0.259(0.226)-33.87***(6.681)0.0558(0.0498)
0.0198(0.0565)-0.0453(0.0287)-0.105***(0.0256)-0.0115(0.0620)-0.0255**(0.0123)
0.00677(0.0145)0.450(0.353)0.00680(0.0344)-0.0456(0.0577)-33.72***(6.689)0.0551(0.0498)
0.0169(0.0559)-0.0484*(0.0283)-0.106***(0.0257)-0.00866(0.0651)-0.0267**(0.0124)0.00410(0.0144)
0.352(0.330)0.00839(0.0351)-33.69***(6.691)
0.0564(0.0501)
0.0167(0.0557)-0.0483*(0.0282)-0.106***(0.0256)-0.00935(0.0646)-0.0262**(0.0124)0.00348(0.0144)
0.352(0.325)0.00803(0.0347)

VARIABLES
Currency volatility (weekly, 1 year)-0.00430(0.0809)

Temporal liquidity (FY2018)
Usage of forward contract
Industry dummies
Observations
Pseudo R squaredStandard errors in parentheses

0.0562(0.0452)
Yes
539
0.213

-0.0128(0.105)0.0433(0.0505)Yes

0.0547(0.0512)Yes-0.0172(0.0440)0.0524(0.0510)
Yes

539
539
428
0.212
0.213
0.203
*** p<0.01, ** p<0.05, * p<0.1

428
0.202

428
0.202

0.0613(0.0455)
Yes

26

0.00774(0.0260)
0.0632(0.0462)
Yes

Table 6-6. Impact of determinants on vehicle currency (US dollar) invoicing: all destinations
except the US using the detailed financial data
VARIABLES
Currency volatility (weekly, 1 year)
Ex. to own subsidiaries
Ex. to related firms
Ex. to local Agency
Ex. via Japanese trading companies
Top 3 largest (domestic industry)
Number of patent (log)
Sales (log) (FY2018)
ROA (FY2018)
Tangible fixed assets growth(FY2013-2018)
Capital ratio (FY2018)(1)
US dollar(2)
US dollar(3)
US dollar(4)
US dollar(5)
US dollar(6)
US dollar

13.49**(6.842)0.0750(0.0648)0.0770(0.0838)-0.0645(0.0399)-0.133***(0.0365)-0.168***(0.0232)0.0108(0.0156)-0.00443(0.0182)-0.828**(0.339)12.87*(6.939)0.0794(0.0656)
0.0730(0.0832)-0.0598(0.0415)-0.137***(0.0370)-0.166***(0.0242)0.0107(0.0158)
0.0169(0.0183)-0.469(0.311)13.08*(6.992)0.0700(0.0656)
0.0831(0.0854)-0.0501(0.0433)-0.127***(0.0383)-0.170***(0.0244)0.00624(0.0162)0.0191(0.0186)-0.365(0.330)10.45(7.869)0.0854(0.0753)
0.0567(0.0906)-0.0566(0.0494)-0.125***(0.0435)-0.182***(0.0244)-0.00520(0.0186)
0.0155(0.0218)-1.053**(0.527)-0.158***(0.0591)
0.219**(0.0972)9.810(7.963)0.0977(0.0771)
0.0732(0.0961)-0.0487(0.0512)-0.129***(0.0440)-0.181***(0.0256)-0.00264(0.0185)0.0332(0.0219)-0.832*(0.500)-0.139**(0.0606)

10.06(7.970)0.0834(0.0761)
0.0766(0.0966)-0.0415(0.0522)-0.122***(0.0448)-0.184***(0.0250)-0.00534(0.0187)
0.0354(0.0221)-0.707(0.496)-0.157***(0.0596)0.208***(0.0787)

Cash-assets ratio (FY2018)

0.428***(0.134)

Temporal liquidity (FY2018)
Usage of forward contract
Industry dummies
Observations
Pseudo R squaredStandard errors in parentheses

0.269***(0.0813)
Yes
468
0.169

0.203***(0.0744)Yes

0.424***(0.162)0.184**(0.0739)0.225***(0.0756)
Yes

0.251***(0.0909)
Yes

468
468
402
0.175
0.175
0.166
*** p<0.01, ** p<0.05, * p<0.1

270.179**(0.0824)
Yes

0.187**(0.0864)0.200**(0.0837)
Yes

402
0.170

402
0.169

7. Conclusion
We conducted the questionnaire survey for 2,100 firms that are unlisted manufacturers
experiencing exports, firms continuing exports throughout the 2010s, and firms that started
exports in the first half or the second half of 2010s with responses from 300 firms. By constructing
the database with the current status of invoice currency pattern and trade partner by export
destination as well as the same information on current imports and retrospective information asof year when the firms started exports, we empirically examined the determinants of invoicecurrency in export by the probit model estimation.
We confirm that many determinants on currency invoicing discussed in existing research on listed
manufacturers also function for exporting Japanese SMEs. As the country-level determinants, thecurrency volatility of the bilateral nominal exchange rate of local currency vis-à-vis Japanese yenhinders the local currency invoicing and promotes the vehicle currency (US dollar) invoicing. As
the trade channel (trade partner) determinants, the intra-firm trade, especially exports to own
subsidiaries, promotes the local currency invoicing and hinders the producer’s currency (Japaneseyen) invoicing. The trades through the Japanese trade companies, which is a view as thetransaction between Japanese firms, are mostly occupied by the Japanese yen invoicing. As thedeterminants of firm size characteristics, the firms with large amounts of sales less tend to use
producer’s currency invoicing. As the determinants of a firm’s product competitiveness andproduct differentiability relating to bargaining power in the negotiation with importers, firms witha top share in the global niche market and with the top 3 largest shares in the domestic industry
tend to use producer’s currency (Japanese yen) while the firms with a large number of patents areless likely to use the vehicle currency (US dollar) invoicing. We also found that the firm longevity
promotes the producer’s currency (Japanese yen) invoicing probably because the bargaining
power of an exporter in the negotiation with importers is improved by the credibility of the firm
which stems from its long history.
In addition to those determinants that have been discussed in the previous studies, we newly foundthat the variables closely relating to the financial constraints play an important role in thedetermination of invoice currency by Japanese SMEs. The firms with deteriorated capital ratio
combined with rapid sales growth depend more on producer’s currency (Japanese yen) invoicing,presumably to avoid the burden of exchange rate risk from foreign currency invoicing. These
results are confirmed even after controlling for the impacts of usage of other firm-level hedging
instruments such as forward contract and operational hedging through import-side invoicecurrency choice. We also conducted the robustness test for partial samples by using more detailed
28

financial data. In the robustness test, we confirm a higher likelihood of producer’s currency(Japanese yen) invoicing for firms with lower capital ratio, lower liquidity position and greater
investment opportunity.
These results for the firm-level financial constraints are consistent with the predictions from thetheoretical researches on the bargaining model of currency invoicing and corporate risk
management for hedging, and the recent empirical research on firm-level currency invoicing
patterns using the questionnaire survey.

29

References
Friberg, Richard, and Frederik Wilander (2008) “The currency determination of exports: A
questionnaire study”, Journal of International Economics, 75(1), pp.54-69.
Froot, Kenneth A., David S. Sharfstein, and Jeremy C. Stein (1993) “Risk management:
coordinating corporate investment and financing policies” Journal of Finance, Volume48(5),pp.1629-1658.
Geczy, C., B. A. Minton. And C. Schrand (1997) “Why firms use currency derivatives” The
Journal of Finance, Vol.52, Issue4, September 1997, pp.1323-1354
Goldberg, Linda S. and Cedric Tille (2008) “Vehicle currency use in international trade” Journalof International Economics, Vol. 76, pp. 177-192.
Goldberg, Linda S. and Cedric Tille (2013) “A bargaining theory of trade invoicing and pricing”
NBER Working Paper 18985, April 2013.
Goldberg, Linda S. and Cederic Tille (2016) “Micro, macro, and strategic forces in international
trade invoicing: Synthesis and novel patterns,” Journal of International Economics, Vol. 102,
pp. 173-178.
Ito, Takatoshi., Satoshi Koibuchi, Kiyotaka Sato and Junko Shimizu (2012) “The choice of an
invoicing currency by globally operating firms: A firm-level analysis of Japanese exporters,”
International Journal of Finance and Economics, Vol.17, Issue4, October 2012, pp.305-320.
Ito, Takatoshi., Satoshi Koibuchi, Kiyotaka Sato and Junko Shimizu (2018), Managing Currency
Risk, Edward Elgar.
Stulz, R. (1984) “Optimal hedging policies”, Journal of Financial and Quantitative Analysis 19,
127–140.

30

Appendix: Data description
Table A-1. Number of responded firms by prefecture

Prefecture
Total
1.Hokkaido

Number ofPercent to
responded
Prefecture
total
firms
300
100.0 24.Mie
5
1.7 25.Shiga

Number ofPercent to
responded
total
firms
5
1.7
1
0.3

2.Aomori3

1.0 26.Kyoto

18

6.0

3.Iwate2

0.7 27.Osaka

37

12.3

4.Miyagi

7

2.3 28.Hyogo

15

5.0

5.Akita1

0.3 29.Nara

6

2.0

6.Yamagata

2

0.7 30.Wakayama

1

0.3

7.Fukushima1

0.3 31.Tottori0

0.0

8.Ibaraki5

1.7 32.Shimane

00.0

9.Tochigi

3

1.0 33.Oakayama

7

2.3

10.Gunma2

0.7 34.Hiroshima

12

4.0

11.Saitama

16

5.3 35.Yamaguchi

2

0.7

12.Chiba

5

1.7 36.Tokushima3

1.0

13.Tokyo39

13.0 37.Kagawa

3

1.0

14.Kanagawa

13

4.3 38.Ehime

2

0.7

15.Niigata

8

2.7 39.Kochi

1

0.3

16.Yamanashi

7

2.3 40.Fukuoka

4

1.3

17.Nagano4

1.3 41.Saga2

0.7

18.Toyama0

0.0 42.Nagasaki

1

0.3

19.Ishikawa

2

0.7 43.Kumamoto

0

0.0

20.Fukui13

4.3 44.Oita

4

1.3

21.Gifu4

1.3 45.Miyazaki

1

0.3

22.Shizuoka

11

3.7 46.Kagoshima

4

1.3

23.Aichi17

5.7 47.Okinawa

1

0.3

31

Table A-2. Number of destinations in sample
Destination country (region)
Observations Percent to total
US
126
13.9
Mexico33
3.6
Canada18
2.0
Other countries in Americas
16
1.8
Eurozone
77
8.5
UK
35
3.9
other countries in Europe
23
2.5
Mid-East Asian and Aflican countries
12
1.3
Australia
10
1.1
New Zealand
2
0.2
China
184
20.2
HongKong
10
1.1
Thailand
121
13.3
Korea
61
6.7
Taiwan55
6.1
Vietnam31
3.4
Singapore13
1.4
Indonesia
21
2.3
Malaysia
14
1.5
Other Country in South East Asia
16
1.8
India
18
2.0
Other countries in South Asia
13
1.4
Total
909
100.0

32