INSTITUTIONAL DETERMINANTS OF JAPANESE OUTWARD FDI IN THE MANUFACTURING INDUSTRY

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Raphaël Chiappini

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Groupe de REcherche en Droit, Economie, GestionUMR CNRS 7321

Institutional determinants of Japanese outward FDI in themanufacturing industry

Raphael Chiappini1

Abstract

This paper explores the relationship between six indicators of governance andoutward foreign direct investment (FDI) in the Japanese manufacturing industry.We estimate a gravity model of FDI for 30 host countries covering the period 2005-2011, employing Heckman’s two-step sample selection correction in order to tacklethe issue of zero-value observations. The results indicate that Japanese overseasinvestments are driven by host market size, yen real exchange rate, macroeconomicstability, resource endowments and policy variables. In particular, we find thatconfidence societal rules, control of corruption, government effectiveness, politicalstability and private sector policies are important factors driving FDI.

Keywords: Outward foreign direct investment (FDI), institutions, gravity model,Heckman sample selection model.

JEL Classification: C34, F21, F23

1. Introduction

Over the last two decades, increased trade and financial liberalization has led to anotable growth in international mergers and acquisitions (M&A) resulting in expandedforeign direct investment (FDI) flows and creation of more multinational enterprises(MNEs). The process of globalization has drastically transformed firms’ production pat-terns by facilitating the international fragmentation of production. This internationalorganization of production involves splitting the production chain into different stageslocated across the world.Japanese firms are heavily involved in this new international division of labour, andJapanese overseas investments have two striking features. First, as depicted in Figure1, the end of the 1980s saw a surge in Japanese outward FDI flows, and between 1993and 2008, Japanese FDI increased by some 18.4% on average. The most recent financialcrisis has been characterized by a strong fall in Japanese FDI outflows (-33% on averagebetween 2008 and 2010), although they have shown some recovery since 2010. In 2012,the stock of outward Japanese FDI stood at $1,054.9 billion, more than five time thestock in 1990. Second, Japanese outflows of FDI exceed FDI inflows, although this gap

1GREDEG-CNRS, University of Nice Sophia-Antipolis, 250 rue Albert Einstein, 06560 Valbonne,phone: (+33) 4 93 95 42 55, E-mail: raphael.chiappini@gredeg.cnrs.fr

1

Figure 1: FDI inflows, outflows, inward stock and outward stock in Japan (billion $U.S.)

has been declining slowly over time: in 2012, Japanese stock of outward FDI was 5 timesmore than its stock of inward FDI, and in 1990 it was 20 times more. This characteristichas resulted in a strong increase in the development of Japanese productive capacitiesabroad.

One of the most important outcome of this boom in capital flows is the developmentof a huge body of international economics studies that investigate, theoretically andempirically, the fundamental factors determining overseas investment behaviour. Earlytheoretical analyses were based on neoclassical models of trade, but the introduction ofthe theory of MNEs has led to study on what determines production location. In par-ticular, Hymer (1976) and Kindleberger (1969) show that firms decide to enter a foreignmarket only if they dispose from ownership advantages, such as a new technology or adifferentiated product, that will allow them to compete with local firms. Buckley andCasson (1976) extended this theory and suggest that firms choose a production locationthat minimizes their overall costs. Thus, overseas investments can be an alternativemode to exporting or purchasing of a licence to serve foreign markets. Both these ap-proaches has depicted in the eclectic paradigm developed by Dunning (1977, 1979) whichshows that companies can adopt three different strategies (exporting, licensing, investingabroad) to serve external markets. Their choice among these strategies will depend onthree types of advantages: ownership-specific advantages; locational advantages in theforeign market such as proximity to consumers or lower production and transportation

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costs; and internalization advantages such as a better control over quality and use oftechnologies. All these advantages must be present for the firm to invest abroad.Relying on MNE theories, such as the eclectic paradigm, new trade theory developeda theoretical framework for overseas investments, that includes technology and countrycharacteristics. Following the work of Markusen (1984) and assuming monopolistic com-petition, Brainard (1997) developed the proximity-concentration model. The results ofhis model show that the firm’s choice to produce abroad rather than in the home coun-try depends on transportation costs, tariff levels, relative factor endowments and hostcountry size. However, this kind of analysis refers only to horizontal FDI, i.e. the relo-cation of the production in a foreign country. Helpman and Krugman (1985), buildingon Helpman (1984), developed a different theory which assumes that firms can also splitthe production of a single good across different countries around the world, i.e. verticalFDI. Their results emphasize the role of factor endowments and human capital stock inthe development of vertical FDI.Another strand in the literature, in particular Carr et al. (2001), combines the motiva-tions for horizontal and vertical FDI, and proposes the knowledge-capital model. Thistype of analysis provides evidence that horizontal FDI is determined by similarities inrelation to market size, factor endowments and transportation costs, while vertical FDIis determined mainly by differences in relative factor endowments. Finally, Faeth (2009)claims that empirical work on the determinants of FDI need to build on more than onetheory since there is a combination of factors that influence FDI.

The empirical literature tries to examine and confirm the results of the theoreticalmodels. Thus, factors such as market size (Brainard, 1997; Eaton and Tamura, 1994),skills endowment (Yeaple, 2003), exchange rates (Blonigen, 1997; Buch and Kleinert,2008), resource endowments (Buckley et al., 2007) and taxes (Wei, 2000) have beenthe subject of numerous empirical analyses. Among the various explanatory factorsproposed, institutional and political determinants require a specific focus. Indeed, inthe context of liberalization of trade and capital flows, attracting FDI is a concern forpolicy-makers, especially in emerging countries, since FDI is considered to facilitate tech-nological spillovers. The empirical literature shows that institutional quality is a driverof overseas investments. Wei (2000) provides evidence that corruption in the host coun-try negatively affects inward FDI. Benassy-Quere et al. (2007) support this, pointingout that both corruption and also bureaucracy, information, banking sector and legalinstitutions are important considerations in FDI decisions.

The purpose of this study is to examine the determinants of Japanese outward FDI,using sectoral-level data that includes zero-value observations, to highlight the key roleplayed by institutional characteristics, e.g. levels of corruption, government effective-ness, political stability and regulatory quality. We use the six indicators for governancedeveloped by Kauffman et al. (2010) in the Worldwide Governance Indicators Project(WGIP). Thus, our data differ from those used in previous studies, along several di-mensions. First, our data refer to the years 2005 to 2011, and cover ten Japanese

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manufacturing industries.2 Second, we use bilateral Japanese outward FDI stock for30 host countries.3 Third, we tackle the issue of zero-value observations using a To-bit model and Heckman’s two-step sample selection model. Fourth, we disaggregatethe results on the basis of the income classification proposed by the International Mone-tary Fund (IMF), and obtain two different country categories: developed and developing.

The remainder of the paper is structured as follows. Section 2 reviews the relevanttheoretical and empirical literature on the sources of FDI. Section 3 discusses the choiceof methodology used to analyse bilateral FDI stocks and describes the data. Section 4presents the empirical results. Section 5 offers some concluding remarks.

2. Literature review

As already mentioned, the literature on the determinants of firms’ overseas invest-ments is vast and includes both theoretical models and empirical investigations.4 Thepresent analysis applies an extended gravity model to bilateral FDI stocks, and selectionof the determinants of FDI is guided by previous theoretical and empirical works. Asindicated in the introduction, host market size, transportation costs and factor endow-ments determine FDI (Brainard, 1997; Helpman and Krugman, 1985). The inclusionof real exchange rate, host country taxes, host country inflation, host country tradeopenness, host country resource endowments and institutional quality is justified by theempirical literature. This section discusses the role of these driving forces in explainingoverseas investment, and develops corresponding hypotheses.

Market size

As discussed earlier, new trade theory shows that horizontal FDI is driven mainlyby host market size (Brainard, 1997). This corresponds to one of the three primarymotivations for overseas investments developed in the eclectic paradigm proposed byDunning (1977, 1979) that of market-seeking. Furthermore, in the empirical literature,host country GDP, used to proxy for host market size, is acknowledged to have a sig-nificant positive influence on FDI (Eaton and Tamura, 1994; Wei, 2000; Filippaios etal., 2003; Benassy-Quere et al., 2007; Kleinert and Toubal, 2010). Indeed, countrieswith bigger markets provide increased opportunities for firms to generate more profitsby exploiting the economies of scale linked to high demand.

Hypothesis 1 : Japanese overseas investments are positively affected by host countryGDP.

2Food and beverages, Textiles and apparel, Chemical products, Glass and ceramics, Lumber andpulp, Primary metals, General machinery, Electric machinery, Transportation equipment and Precisionmachinery.

3The list of host countries is available in Table A.1.4For a detailed review of the determinants of FDI, see Chakrabarti (2001), Blonigen (2005) and Faeth

(2009).

4

Real exchange rate

The impact of real exchange rate on FDI is mostly examined in the theoretical liter-ature using the two prominent models developed respectively by Froot and Stein (1991)and Blonigen (1997). Froot and Stein (1991) assume imperfect capital markets, andprovide evidence that appreciation of the home currency (host currency depreciation)will have a positive effect on FDI inflows. Indeed, the hypothesis of imperfect capitalmarkets supposes that the firm’s internal cost of capital is less expensive than borrowingfrom external sources. Thus, host country depreciation increases home country firms’wealth and decreases host country firms’ wealth because home firms benefit from greaterlow-cost funds. Froot and Stein (1991) prove their theory empirically using data on USFDI. Blonigen (1997) proposes a completely different theory focused on acquisition offoreign assets. Blonigen (1997) assumes that firms’ production and sales occur only intheir own country. Therefore, if the firm wants to buy assets in a foreign market, theyhave to be paid for in the foreign currency, while its profits are denominated in thehome currency. In this case, the exchange rate will affect overseas investment. Indeed,a depreciation in the foreign currency will raise the reservation bid from the domesticfirm which will result in increased in FDI in the foreign country.The positive effect of an appreciation of the home currency on domestic outward FDI isillustrated in most empirical studies (Blonigen, 1997; Kiyota and Urata, 2004).

Hypothesis 2 : Yen appreciation has a positive impact on Japanese overseas investments.

Taxes

The effect of taxes on FDI, i.e. whether outward FDI is influenced by corporateand income taxes, has been the subject of numerous empirical studies since the pio-neering work of Hartman (1994). The basic hypothesis is that higher taxes in the hostcountry discourage inward FDI in this country. This assumption has been proved inthe empirical literature. Hartman (1994), Grubert and Mutti (1991), Billington (1999)and Di Giovanni (2005) provide evidence that host country corporate and income taxessignificantly affect FDI flows. In particular, Devereux and Griffith (1998) show that theaverage effective tax rates has a strong effect on US investments in the European Union(EU) countries.

Hypothesis 3 : Higher tax rates in the host country decreases Japanese overseas invest-ments.

Inflation

In the economic literature, the inflation rate is seen as an indicator of both thequality of macroeconomic policy-making and macroeconomic instability (Buckley et al.,2007). Indeed, a volatile and unpredictable inflation rate in the host country createsuncertainty concerning MNE profits in this market. Buckley et al. (2007) also support

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the idea that an increasing inflation rate results in a devaluation of the local currencyand, thus, a decrease in the real value of local currency earnings for firms from the in-vesting country. However, their results for Chinese outward FDI tend to contradict thishypothesis because they find a positive relationship between FDI and inflation.

Hypothesis 4 : A higher rate of inflation in the host country discourages Japanese overseasinvestments.

Trade openness

In most of the existing empirical studies, trade openness has a positive and signif-icant impact on FDI outflows. Chakrabarti (2001) argues that a country’s degree ofopenness to international trade is a relevant determinant of the FDI decision becausemost investment projects concern the tradable sector. Furthermore, vertical FDI andvertical specialization imply intra-firm trade and, therefore, is attracted by a relativelyopen economy. Trade openness is also supposed to attract export-platform FDI, whichis investment in a host country aimed at serving another country with exports of finalgoods (Ekholm et al., 2007). There is strong support for this positive relationship in theliterature (Kravis and Lipsey, 1982; Wheeler and Moody, 1992).

Hypothesis 5 : Greater trade openness in the host country increases Japanese overseasinvestments.

Natural resources endowments

One of the three primary motivations for overseas investments according to the eclec-tic paradigm is resource-seeking (Dunning, 1977, 1979). Home country firms invest inthe foreign country in order to exploit local natural resources as inputs to their produc-tion processes. Buckley et al. (2007) show that the share of mineral ores and metals inthe host country’s merchandise exports has a significant and positive effect on Chineseoutward FDI.

Hypothesis 6 : Higher natural resource endowments in the host country increases Japaneseoverseas investments.

Quality of institutions

As discussed in the introduction, institutional quality is important for attractingFDI. The basic idea is that poor quality legal institutions increase the likelihood thatthe profits and assets of the investing firms will be expropriated, which depresses in-ward FDI. Furthermore, if poor institutions lead to poor infrastructures and increasethe cost of doing business, investing firms will expect lower profits, which will reducetheir investments in the targeted market (Blonigen, 2005). Wheeler and Moody (1992)provide an early study of the relationship between institutions and FDI. However, they

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find no evidence of a significant positive relationship between good quality of institutionsand location of US foreign affiliates. The study by Wei (2000) on bilateral investmentby 12 source countries to 45 host countries, clearly contradicts this result. Using threemeasures of corruption, Wei (2000) shows that a rise in the level of corruption in thehost country reduces inward FDI. Golberman and Shapiro (2002), relying on the sixindicators of governance proposed by the WGIP, point out that quality of institutionspositively influences both inward and outward FDI. Busse and Hefeker (2007), usingmeasures of political risk provided by the Political Risk Services (PRS) Group, indi-cate that government stability, law and order, corruption, democratic accountability ofgovernment and quality of bureaucracy have a significant and positive impact on for-eign investments inflows for a sample of 83 developing countries between 1984 and 2003.Benassy-Quere et al. (2007), relying on a gravity model for bilateral FDI stocks fromthe OECD member countries between 1985 and 2000, find that institutional distanceis an important factor explaining inward FDI. In contrast, Blonigen and Piger (2011)use a Bayesian approach to investigate the appropriate set of covariates to include ina regression model determining FDI activity. They find no robust evidence that hostcountry policy variables such as infrastructure or political institutions, influence FDI.Finally, Mathur and Singh (2013) using data from Transparency International (TI) fora panel of 29 countries over the time period 1980-2000, show that the perception ofcorruption plays a key role in firms’ decisions about the location of their production.

Hypothesis 7 : Higher quality of host country institutions increases Japanese overseasinvestments.

Note that since we use a gravity model for FDI, distance and regional trade agree-ments are included as explanatory variables. Note also that Barrel and Pain (1998) andBlonigen and Piger (2011) provide evidence that regional trade agreements increasesM&A.

3. Empirical model, methodology and data

3.1. The gravity model for FDI

The gravity model is an important device employed by the trade literature to explainbilateral trade flows. It is also widely used in the empirical literature to study bilat-eral FDI flows and foreign affiliates sales. According to Blonigen et al. (2007, p.1309)this model has become ’the most widely used empirical specification of FDI’ (Blonigenet al., 2007, p. 1309). For example, Eaton and Tamura (1994) apply an augmentedgravity equation to evaluate the influence of factor endowments on US and JapaneseFDI positions, using annual data for the period 1985-1990. Wei (2000) also relies ona gravity equation to assess the impact of corruption on outward FDI. This kind ofanalysis focuses on a baseline equation which relates the logarithm of bilateral overseasinvestments to the log GDPs of the origin and destination countries and the logarithm ofthe distance between them. However, until recently, transposition of the gravity model

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to study overseas investments was not supported by the theory.Head and Ries (2008) try to provide some theoretical foundations for the use of a gravityequation to explain bilateral FDI stocks. They develop a model of FDI with heteroge-neous investors who want to control existing foreign assets. Their model yields an equa-tion very similar to the gravity model used for trade. In a more recent paper, Kleinertand Toubal (2010) provide theoretical underpinnings for the gravity equation applied tothe analysis of sales of foreign affiliates. They refer to three different theoretical modelsof sales of foreign affiliates of multinational firms, to derive a gravity equation. Using aneconometric analysis, they show that horizontal models of multinational firms, such asthe proximity-concentration model (Brainard, 1997), yield better results than verticalmodels. Kleinert and Toubal (2010) rely on an horizontal model where firms can servethe foreign market j either by exporting or by producing abroad. They show that ag-gregate sales of foreign affiliates from firm i in j can be expressed as:

nipijxij = nip1−σii τ

(1−σ)(1−ε)ij (1 − µ)YjP

σ−1j (1)

where ni represents the number of firms, pij the good price of firm i, xij , country j ’sconsumption of variety from country i, τij , the distance costs, Yj , the market size ofcountry j and Pj , the price index in country j.

According to Kleinert and Toubal (2010), nip1−σii can be viewed as the home coun-

try’s market capacity, denoted si, (1−µ)YjPσ−1j as country j ’s market capacity, denoted

mj and nipijxij as bilateral foreign affiliates’ production, denoted ASij . Finally, Kleinertand Toubal (2010) express distance costs (τij) as an increasing function of the geograph-ical distance between country i and j (τij = τDη1

ij ). Therefore, they rewrite equation(1) as:

ASij = si(τDη1ij )(1−σ)(1−ε) (2)

with τ representing the unit distance costs and η1 > 0.Equation (2) can be log-linearized to yield the gravity equation:

Ln(ASij) = α1 + ζ1ln(si) − β1ln(Dij) + ξ1ln(mj) (3)

where α1 = (1−σ)(1−ε)ln(τ) and β1 = (σ−1)(1−ε)η1. Kleinert and Toubal (2010)assume that home and host country market capacity can be proxied by home and hostGDP.We rely on equation (3) and define an augmented gravity equation applied to bilateralFDI stocks. Our gravity model of FDI can be expressed as follows (in log-linear form):

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Ln(FDIijkt) = α1 + γ1ln(GDPjt) + γ2ln(GDPCjt) + γ3ln(RERijt) + γ4Taxjt (4)

+ γ5Infljt + γ6Openjt + γ7Orejt + γ8Institutionjt + γ9ln(DISTij)

+ γ10RTAijt + βt + βk

where FDIijkt is the Japanese stock of FDI in country j, in sector k, in t, GDPjt, isthe GDP of country j in t, GDPCjt, is the per capita GDP of country j in t, RERijt isthe real exchange rate between the yen and currency of country j in t, Taxjt is the totaltax rate (in % of commercial profits) of country j in t, Infljt is the inflation rate (basedon consumer prices) of country j in t, Orejt captures the natural resources of countryj in t and is expressed as the percentage of ores and metal in country j ’s merchandiseexports, Institutionjt captures the quality of institutions of country j in t and is basedon the six measures proposed by Kauffman et al. (2010), DISTij , is the bilateral distancebetween Japan and country j, RTAijt is a dummy variable that equals 1 if country jhas a free trade agreement with Japan in t. βk and βt capture industry and time fixedeffects, respectively. Note that traditional dummies used to capture common languageor contiguity are not included in the equation because Japan is an island and has nocommon language with other countries in our sample.

3.2. Methodology issues: Zero-value observations

The most frequent approach developed in the empirical literature is to estimate thelog-linearized model (equation (4)) using the Ordinary Least Squares (OLS) estimator.Therefore, all zero-value observations are simply dropped from the estimation creatinga selection bias. Indeed, the exclusion of a sub-sample of the data can affect the sig-nificance of the test results, and leads to biased conclusions. Moreover, the higher thenumber of zero-value observations in the sample, the greater will be the selection biasand the higher the likelihood of obtaining biased results.Our sample captures bilateral Japanese FDI stock at industry level where zero-valueobservations are frequent. Indeed, our dataset contains 34% of zero-value observationsfor bilateral Japanese FDI stock.5 Thus, dropping zero FDI stocks would result in se-lection bias which could lead to wrong and biased interpretations of the determinants ofJapanese overseas investments.Estimation of the gravity model of trade entails the same problem which, until recently,was largely ignored in the empirical literature. In the case of randomly missing data,ignoring zeros reduces only the sample size, but does not create any bias. However, if thezero-value observations are not randomly distributed, as is the case of trade and invest-ment flows, their elimination leads to sample selection bias (Westerlund and Wilhelms-son, 2011) and biased OLS estimation parameters. Therefore, truncating the sample bydropping zero-value observations is not appropriate and results in loss of information.Various methods have been implemented in the empirical literature to overcome this

5The repartition of zero-value observations is available in Table A.2.

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problem. Some studies, such as Benassy-Quere et al. (2007) for FDI stocks, simply re-place the zeros with 1 or a small positive number. This seems to deal with the zero-valueobservations problem, but represents an ad hoc method that can lead to biased coeffi-cients if the equation is estimated using OLS (Gomez-Herrera, 2013). Other empiricalanalyses estimate the gravity equation using a Tobit model with a left censoring limit atzero. Eaton and Tamura (1994) and Wei (2000) apply a Tobit model to the estimationof a gravity equation for FDI. This kind of analysis reflects a situation where some obser-vations are unobservable and are recorded as zero, which is the case for FDI.6. However,this method is not appropriate for explaining why some trade or investment flows aremissing (Linders and De Groot, 2006). An alternative method suggested by Santos Silvaand Tenreyro (2006) is to estimate the gravity model directly from its non-linear form us-ing the Poisson Pseudo Maximum Likelihood (PPML). Also, Santos Silva and Tenreyro(2006) and Westerlund and Wilhelmsson (2011) show that this method provides robustresults in the presence of heteroskedasticity. However, the method is not appropriate ifthe probability of a positive value of trade or FDI between two countries is correlatedwith unobserved characteristics of that country pair. In this case, a Heckman’s two-stepsample selection correction is better. In this kind of model, zero-observations result fromthe firm’s decision not to export or not to invest to a certain market.In a recent paper, Gomez-Herrera (2013) discusses all the different approaches used todeal with the issue of zero trade flows. Relying on a dataset covering 80% of worldtrade, she shows that the Heckman sample selection model performs better than theother methods. Therefore, in the present study, we use Heckman’s two-step sample se-lection model to study the determinants of the Japanese bilateral FDI stock at industrylevel.

The procedure proposed by Heckman (1979) assumes that the gravity model is splitinto two parts: an outcome equation which evaluates the relationship between FDIstocks and a set of explanatory variables, and a selection equation which describes therelationship between the probability of positive FDI stock and a set of explanatoryvariables. The first step in the procedure evaluates the probability that Japan invests in aforeign country, using a probit specification. This estimation is then used to calculate theinverse Mill’s ratio (IMR) which captures the probability of selection variables omittedfrom the original equation. In the second step, the expected values of FDI stocks,conditional on Japan investing in the foreign country, are estimated using OLS.

3.3. Data

The panel dataset used in this study covers the period 2005 to 2011 for a sample of 30of Japan’s trading partners, classified as ’Developed’ or ’Developing’ countries accordingto the IMF’s World Economic Outlook Report 2012.7 The variable to be explained isthe bilateral stock of outward FDI from Japan to these 30 host countries in 10 manu-

6In our dataset, missing bilateral FDI values at sectoral level are reported as zeros.7See Table A.1 for the complete list of countries.

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facturing sectors. Information concerning this variable expressed in current yen is fromthe Bank of Japan’s database. Japanese bilateral stock of outward FDI at sectoral level,is converted into constant dollars using Consumer Price Index (CPI) and the bilateralyen-dollar exchange rate. We choose to use stocks rather than flows for several reasons.First, according to Devereux and Griffith (2002), stocks are a better measure than flowsof capital ownership because they account for FDI financed through local capital mar-kets. Second, stocks are less volatile than flows. Indeed, the bilateral FDI flows databaseintroduces the problem of confidentiality because, at sectoral level, FDI flows are oftenthe result of one or two takeovers.GDP and per capita GDP data expressed in constant US dollar are taken from the WorldBank’s World Development Indicators (WDI). Yen and foreign currency exchange ratesare from the IMF’s International Financial Statistics (IFS). They are expressed in realterms using the Japanese and foreign country’s CPIs. The total tax rate of Japan’s trad-ing partners expressed as a percentage of commercial profits is taken from the WorldBank WDI database. The inflation rate is calculated using the foreign country’s CPIand comes from the WDI database. The variable capturing countries’ trade opennessis calculated as the ratio of imports and exports of goods and services, to GDP. Thisratio is the measure of trade openness most commonly used in the empirical literatureand is usually described as trade share. This variable comes from the United NationsConference on Trade and Development (UNCTAD) database. Bilateral distance is com-puted using the distance in kilometres between Japan and its trading partners’ capitalcity. This variable is taken from the CEPII’s GeoDist database. Information on regionaltrade agreements is from the World Trade Organization (WTO). The list of countriesinvolved in a regional agreement with Japan is displayed in Table A.1. The variablemeasuring resource endowments is calculated as the percentage of ores and metals in thehost country’s merchandise exports, based on information from the WDI database.

In our study, we examine the link between political institutions and FDI in depth.To evaluate the quality of the institutions related to Japan’s trading partners, we relyon the Worldwide Governance Indicators (WGI) defined and calculated by Kaufmannet al. (2010). They construct six indicators reflecting six dimensions of governance:

• the rule of law, which captures agents’ confidence in the rules of society, especiallythe quality of contract and property rights enforcement, the police and courts;

• the control over corruption, which evaluates agents’ perceptions of how publicpower is exercised for private gain;

• the effectiveness of government, which captures agents’ perceptions of the qualityof public services and policy implementation, and the credibility of government;

• political stability and absence of violence/terrorism, which evaluates agents’ per-ceptions of the likelihood that the government will be destabilized or overthrown;

• regulatory quality, which captures agents’ perception of the ability of the govern-ment to formulate and implement sound policies and regulations for private sector

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development;

• voice and accountability, which evaluates agents’ perception of degree of freedomof expression, citizens’ right to vote, freedom of association, and free media.

The values of all these indicators range approximately between -2.5 and 2.5, withhigher values corresponding to better institutions. Note that these six indicators are notindependent (Kaufmann et al., 2010) which is why we treat them in different gravityequations. Table A.3 presents summary statistics of the explanatory variables, TableA.4 summarizes the variables and sources used in our study.

4. Empirical results

4.1. Results for the whole sample

Table 1 presents the regression results of equation (4) and the test statistics for thesix indicators of governance using the OLS estimator, a Tobit model and Heckman’stwo-step sample selection model. For the Heckman procedure, we present both the out-come and the selection equation. In the first stage of the Heckman process, assumingthat the error term follows a standard normal distribution, we estimated a probit model,then the inverse Mills’ ratio (IMR) is generated from the predicted values of the model.In the second stage, the regression of equation (4) including the IMR as an additionalexplanatory variable, is performed using the OLS estimator. Note that all specificationsinclude industry and time fixed effects.

It is important to notice that the IMR is highly significant in all six estimations,indicating that the sample selection bias problem has been corrected for. It shows thatignoring zero-value observations for Japanese outward FDI stock produces biased esti-mates. Thus, we can say that, on the one hand, the OLS estimator and the outcomeequation for the Heckman’s two-step procedure give similar results, with the exceptionof the sign of the coefficient of natural resource endowments in the host country. On theother hand, the Tobit model and the selection equation from the Heckman’s model giveidentical signs for the explanatory variables in all the specifications.

First, we find that all standard gravity variables are significant at 1% and have theexpected sign in all the specifications. We find that the size of the host market, proxiedby its GDP, strongly increases Japanese overseas investments in manufacturing, whichconfirms our first hypothesis. We find also that the distance between Japan and theforeign country significantly affects Japanese outward FDI. However, per capita GDPin the destination country has a significant negative impact on Japanese outward FDIstock in the manufacturing industry. This suggests that Japanese firms’ investment fallsas the development of the host country increases.

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Tab

le1:

Res

ult

sfo

rth

ew

hol

esa

mp

le(1)

(2)

(3)

Variable

OLS

Heckmantw

o-step

OLS

Heckmantw

o-step

OLS

Heckmantw

o-step

Ln(F

DI)

Tobit

Ln(F

DI)

Selection

Ln(F

DI)

Tobit

Ln(F

DI)

Selection

Ln(F

DI)

Tobit

Ln(F

DI)

Selection

Ln(GDPj)

0.702***

0.421***

1.147***

0.710***

0.720***

0.427***

1.108***

0.728***

0.708***

0.422***

1.152***

0.716***

(0.066)

(0.023)

(0.138)

(0.047)

(0.067)

(0.023)

(0.136)

(0.048)

(0.066)

(0.024)

(0.138)

(0.047)

Ln(GDPC

j)

-0.665***

-0.340***

-1.074***

-0.809***

-0.706***

-0.355***

-1.049***

-0.873***

-0.617***

-0.368***

-1.056***

-0.866***

(0.150)

(0.034)

(0.195)

(0.098)

(0.151)

(0.035)

(0.193)

(0.101)

(0.155)

(0.035)

(0.207)

(0.101)

Ln(RER

ij)

-0.064**

-0.049***

-0.083*

-0.108***

-0.071**

-0.052***

-0.091*

-0.116***

-0.063*

-0.046***

-0.079

-0.108***

(0.032)

(0.009)

(0.050)

(0.031)

(0.031)

(0.009)

(0.049)

(0.031)

(0.033)

(0.009)

(0.051)

(0.031)

Taxj

-0.370

-0.643***

-0.608

-0.466

-0.547

-0.693***

-0.840

-0.505*

-0.661

-0.727***

-1.001

-0.604**

(0.728)

(0.140)

(0.643)

(0.297)

(0.706)

(0.139)

(0.631)

(0.296)

(0.712)

(0.139)

(0.635)

(0.299)

Infl j

-5.966*

-1.540**

-8.928**

-3.549*

-5.576*

-1.491**

-8.395**

-3.162

-7.353**

-1.620**

-10.429***

-3.452*

(3.197)

(0.699)

(3.751)

(1.984)

(3.216)

(0.691)

(3.730)

(1.926)

(3.064)

(0.686)

(3.703)

(1.901)

Open

j0.187**

0.154***

0.472***

0.492***

0.132

0.139***

0.368***

0.486***

0.173**

0.143***

0.444***

0.476***

(0.084)

(0.021)

(0.123)

(0.072)

(0.088)

(0.021)

(0.121)

(0.072)

(0.086)

(0.021)

(0.122)

(0.072)

Ore j

-2.166**

0.534**

0.014

2.881***

-2.662**

0.401*

-0.834

2.678***

-2.182**

0.388

-0.198

2.588***

(1.064)

(0.239)

(1.155)

(0.532)

(1.128)

(0.244)

(1.160)

(0.541)

(1.115)

(0.245)

(1.148)

(0.548)

Institutionj

0.415***

0.132***

0.566***

0.173***

0.406***

0.128***

0.499***

0.197***

0.329**

0.171***

0.524***

0.240***

(0.131)

(0.029)

(0.146)

(0.075)

(0.121)

(0.028)

(0.129)

(0.062)

(0.161)

(0.035)

(0.171)

(0.079)

Ln(Dist ij)

-0.407***

-0.212***

-0.739***

-0.830***

-0.493***

-0.238***

-0.795***

-0.884***

-0.366***

-0.188***

-0.667***

-0.808***

(0.117)

(0.031)

(0.180)

(0.119)

(0.120)

(0.031)

(0.180)

(0.117)

(0.121)

(0.032)

(0.179)

(0.119)

RTA

ij0.763***

0.312***

1.311***

0.545***

0.754***

0.304***

1.205***

0.523***

0.602***

0.250***

1.074***

0.457***

(0.199)

(0.045)

(0.249)

(0.122)

(0.200)

(0.045)

(0.240)

(0.121)

(0.197)

(0.045)

(0.236)

(0.121)

Intercep

t2.599*

3.349***

9.095***

11.587***

3.789**

3.733***

9.956***

12.581***

1.945

3.428***

8.557***

11.944***

(1.497)

(0.360)

(2.055)

(1.316)

(1.665)

(0.400)

(2.156)

(1.380)

(1.476)

(0.355)

(2.058)

(1.333)

Mills

Ratio

1.709***

1.478***

1.705***

(0.427)

(0.419)

(0.424)

Adjusted

R2

0.236

0.237

0.234

LogPseudoLL

-1554.03

-1552.39

-1551.35

Rho

0.734

0.649

0.732

Observations

1370

2010

2010

1370

2010

2010

1370

2010

2010

Cen

soredObs.

640

640

640

640

640

640

Industry

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yea

rFE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

(1):

Ru

leo

fL

aw

.(2

):C

on

tro

lo

fco

rru

pti

on

.(3

):G

ove

rnm

ent

effec

tive

nes

s.*

,**

,**

*:

sign

ifica

nt

at

10

%,

5%

,1

%.

Sta

nd

ard

erro

rsin

bra

cket

s.

13

Tab

le1

(con

tinu

ed):

Res

ult

sfo

rth

ew

hol

esa

mp

le(4)

(5)

(6)

Variable

OLS

Heckmantw

o-step

OLS

Heckmantw

o-step

OLS

Heckmantw

o-step

Ln(F

DI)

Tobit

Ln(F

DI)

Selection

Ln(F

DI)

Tobit

Ln(F

DI)

Selection

Ln(F

DI)

Tobit

Ln(F

DI)

Selection

Ln(GDPj)

0.639***

0.422***

1.127***

0.735***

0.694***

0.416***

1.116***

0.713***

0.704***

0.425***

1.157***

0.714***

(0.066)

(0.023)

(0.137)

(0.049)

(0.066)

(0.023)

(0.139)

(0.047)

(0.068)

(0.024)

(0.147)

(0.047)

Ln(GDPC

j)

-0.294

-0.258***

-0.608***

-0.888***

-0.788***

-0.380***

-1.282***

-0.946***

-0.393***

-0.280***

-0.806***

-0.750***

(0.196)

(0.038)

(0.210)

(0.117)

(0.161)

(0.039)

(0.230)

(0.106)

(0.145)

(0.031)

(0.195)

(0.092)

Ln(RER

ij)

-0.077**

-0.054***

-0.100**

-0.123***

-0.064**

-0.047***

-0.075

-0.089***

-0.077**

-0.059***

-0.124**

-0.120***

(0.034)

(0.009)

(0.050)

(0.032)

(0.032)

(0.009)

(0.050)

(0.032)

(0.035)

(0.009)

(0.054)

(0.031)

Taxj

-0.681

-0.723***

-1.020

-0.624**

-0.409

-0.673***

-0.644

-0.555*

-0.762

-0.775***

-1.263*

-0.730**

(0.762)

(0.141)

(0.643)

(0.300)

(0.727)

(0.141)

(0.638)

(0.299)

(0.707)

(0.142)

(0.653)

(0.332)

Infl j

-10.386***

-2.772***

-14.236***

-3.401*

-5.374*

-1.231*

-7.176*

-1.761

-9.444***

-2.363***

-11.870***

-4.224**

(3.145)

(0.723)

(3.899)

(1.997)

(3.225)

(0.724)

(3.755)

(3.044)

(3.064)

(0.684)

(3.740)

(2.007)

Open

j0.225**

0.158***

0.509***

0.465***

0.134

0.137***

0.378***

0.480***

0.191**

0.163***

0.499***

0.503***

(0.096)

(0.022)

(0.127)

(0.072)

(0.084)

(0.021)

(0.121)

(0.073)

(0.084)

(0.021)

(0.131)

(0.072)

Ore j

-1.404**

0.710***

0.831

2.659***

-2.643**

0.326

-1.009

2.668***

-1.638

0.633**

0.354

2.847***

(1.119)

(0.413)

(1.193)

(0.554)

(1.072)

(0.255)

(1.137)

(0.562)

(1.073)

(0.248)

(1.149)

(0.554)

Institutionj

-0.172

0.001

-0.161

0.247**

0.640***

0.207***

0.968***

0.421***

-0.029

0.032

0.121

0.091

(0.181)

(0.042)

(0.193)

(0.103)

(0.171)

(0.046)

(0.228)

(0.105)

(0.088)

(0.022)

(0.114)

(0.060)

Ln(Dist ij)

-0.404***

-0.222***

-0.729***

-0.922***

-0.440***

-0.217***

-0.755***

-0.788***

-0.407***

-0.238***

-0.819***

-0.887***

(0.117)

(0.031)

(0.180)

(0.119)

(0.118)

(0.032)

(0.179)

(0.119)

(0.127)

(0.032)

(0.203)

(0.117)

RTA

ij0.591***

0.277***

1.072***

0.531***

0.691***

0.277***

1.176***

0.470***

0.626***

0.285***

1.167***

0.504***

(0.192)

(0.046)

(0.243)

(0.121)

(0.197)

(0.045)

(0.241)

(0.121)

(0.196)

(0.045)

(0.253)

(0.120)

Intercep

t-0.514

2.798***

5.389**

13.168***

3.989**

3.781***

11.250***

12.422***

0.462

3.100***

7.785***

11.683***

(2.130)

(0.456)

(2.352)

(1.548)

(1.618)

(0.401)

(2.328)

(1.355)

(1.627)

(0.385)

(2.342)

(1.346)

Mills

Ratio

1.668***

1.645***

1.654***

(0.419)

(0.439)

(0.443)

Adjusted

R2

0.231

0.237

0.231

PseudoLogLL

-1562.71

-1552.74

-1561.79

Rho

0.717

0.711

0.712

Observations

1370

2010

2010

1370

2010

2010

1370

2010

2010

Cen

soredObs.

640

640

640

640

640

640

Industry

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yea

rFE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

(4):

Po

liti

cal

sta

bili

ty.

(5):

Reg

ula

tory

qua

lity

.(6

):V

oic

ea

nd

acc

ou

nta

bili

ty.

*,*

*,*

**

:si

gnifi

can

ta

t1

0%

,5

%,

1%

.S

tan

da

rder

rors

inbr

ack

ets.

14

This result is consistent with the idea that Japanese firms invest largely in East Asiancountries, such as China or India, which have large market size, but still exhibit weakvalues of per capita GDP, reflecting low level of economic development.

Our results also provide evidence that policy variables are important determinantsof the location of investments by Japanese manufacturing firms. The results of the Tobitmodels and the selection equation from the Heckman’s procedure provide evidence thata higher total tax rate in the destination market discourages investments from firms inJapan. The coefficient generated by these two methods ranges between -0.45 and -0.75,confirming that a policy of low corporate and income taxes is attractive for JapaneseFDI. This conclusion is line with previous empirical studies (Hartman, 1994; Billington,1999; Di Giovanni, 2005) and confirms our third hypothesis. Our fourth hypothesis, thatmacroeconomic instability reflected by an increasing inflation rate affects FDI, is alsosupported by our results. The coefficient associated with the inflation rate is significantat 1% level in the OLS estimation and at only 5% and 10% in the Heckman selectionequations and the Tobit specifications. Also, the magnitude of the coefficient estimatedwith these two models is smaller than the coefficients generated by OLS, but is stillamong the highest of the explanatory variables. This supports the idea that macroe-conomic stability is a vector of investment attractiveness. Our results provide evidencethat trade openness is a significant determinant of Japanese overseas investments. In-deed, we find that the coefficient associated with this variable is significant and positivein all the specifications, except in the OLS when regulatory quality and control of cor-ruption are used as indicators. However, this problem disappears if we correct for theselection bias entailed by the zero-value observations. Thus, we can say that Japanesefirms invest in more open economies.

The results for natural resources endowments differ widely according to the estimatorused. Indeed, contrary to our expectations, the coefficient is negative if the gravity equa-tion is estimated using OLS. However, the sign changes if we include zero FDI stocksand control for selection bias. We find a significant and positive impact of existenceof ores and metals in the host country on Japanese overseas investments, in all Heck-man’s selection equations. Therefore, natural resources are an important motivationfor Japanese firms’ investments. This conclusion confirms our sixth hypothesis and theresource-seeking motive developed in Dunning (1977, 1979), but contradicts with theresults in Buckley et al. (2007) for China.

The coefficient of the real exchange rate is significant and negative, which goes againstour second hypothesis. Indeed, a yen appreciation, reflected in an increase in the bilateralreal exchange rate, entails a fall in Japanese overseas investments which is contrary tothe capital market friction (Froot and Stein, 1991) and goods market friction (Blonigen,1997) hypotheses. There are two main explanations for this phenomenon. First, almostall the currencies in our sample (and especially the US dollar) are stronger than the yen.Blonigen’s (1997) model assumes that a weak dollar will attract inward FDI. On the

15

contrary, the relative weakness of the yen against the dollar affects Japanese outwardFDI. Second, as suggested by De Santis et al. (2004), a yen appreciation induces loss ofcompetitiveness for Japanese manufacturing firms, which could reduce outward FDI ifthey rely on vertical FDI and if FDI and exports are complements.

Our results also support the idea developed by Barrel and Pain (1998) that beingpart of regional trade agreements matters for FDI. In fact, we find that the dummy vari-able capturing regional trade agreements is highly significant in all the specifications.As a consequence, the development of free trade agreements with foreign countries, suchas Singapore since 2002 and Malaysia and Mexico since 2005, has prompted Japanesefirms to invest in these countries.

Regarding the main focus of this paper, our study provides evidence that there is astrong positive relationship between the quality of the institutions in destination coun-tries, and Japanese overseas investments. The selection equations from the Heckman’smodel and the Tobit specifications show that an increase in the control of corruption,confidence of agents in the rules of society, government effectiveness, political stabilityand ability of government to make sound policies in the host country, is a strong pro-moter of investment by Japanese manufacturing firms. However, the variable for voiceand accountability in the destination country is not significant at the 10% level, regard-less of which model is used to estimate the gravity equation. Among the institutionalvariables, it is regulatory quality that has the highest coefficient according to both theTobit and Heckman models. This supports the idea that Japanese manufacturing firmsare sensitive to the ability of the host government to protect their interests. As a conse-quence, the development of good institutions and political stability in foreign countriesattracts Japanese FDI.

One of the limitations of the Heckman two-step procedure is that the parametersgenerated cannot be interpreted as elasticities (or semi-elasticities) on account of theinclusion in the model of the IMR. This issue can be overcome through calculation ofmarginal effects, which can be conditional or unconditional depending on the assump-tions made about the nature of the zero-value observations. If zero FDI stocks are ’true’zeros, we rely on conditional marginal effects, otherwise, if zero FDI stock is due tomissing or misreported data, we rely on unconditional marginal effects. Since in ourdataset we have treated missing observations as zeros, we compute the unconditionalmarginal effects for the Tobit and Heckman specifications in Table 2.

First, we note that both models (Tobit and Heckman) give very similar marginaleffects, with the exceptions of the results for taxes and resource endowments. In thecase of taxes, the Tobit model gives a higher semi-elasticity but a lower one for resourceendowments.

16

Tab

le2:

Un

con

dit

ion

alm

argi

nal

effec

tsu

sin

gT

obit

and

Hec

km

anse

lect

ion

mod

els

(wh

ole

sam

ple

)(1)

(2)

(3)

(4)

(5)

(6)

Variable

Tobit

Heckman

Tobit

Heckman

Tobit

Heckman

Tobit

Heckman

Tobit

Heckman

Tobit

Heckman

Ln(GDPj)

0.261***

0.219***

0.265***

0.223***

0.262***

0.220***

0.261***

0.226***

0.257***

0.220***

0.263***

0.221***

(0.011)

(0.014)

(0.011)

(0.014)

(0.011)

(0.015)

(0.011)

(0.015)

(0.011)

(0.014)

(0.011)

(0.015)

Ln(GDPC

j)

-0.211***

-0.249***

-0.224***

-0.268***

-0.228***

-0.266***

-0.160***

-0.272***

-0.235***

-0.292***

-0.173***

-0.232***

(0.023)

(0.030)

(0.024)

(0.031)

(0.024)

(0.031)

(0.027)

(0.035)

(0.026)

(0.032)

(0.022)

(0.028)

Ln(RER

ij)

-0.031***

-0.033***

-0.032***

-0.036***

-0.029***

-0.033***

-0.033***

-0.038***

-0.029***

-0.027***

-0.036***

-0.037***

(0.007)

(0.010)

(0.007)

(0.009)

(0.007)

(0.009)

(0.007)

(0.010)

(0.007)

(0.010)

(0.007)

(0.010)

Taxj

-0.399***

-0.144

-0.430***

-0.155*

-0.451***

-0.186**

-0.448***

-0.191**

-0.416***

-0.171***

-0.480***

-0.225**

(0.085)

(0.092)

(0.085)

(0.091)

(0.085)

(0.092)

(0.086)

(0.092)

(0.085)

(0.092)

(0.088)

(0.103)

Infl j

-0.955*

-1.093*

-0.925*

-0.970*

-1.005**

-1.061*

-1.718***

-1.044*

-0.761

-0.542

-1.463***

-1.304**

(0.519)

(0.612)

(0.515)

(0.592)

(0.506)

(0.585)

(0.534)

(0.614)

(0.532)

(0.627)

(0.523)

(0.620)

Open

j0.095***

0.152***

0.087***

0.149***

0.089***

0.146***

0.098***

0.142***

0.084***

0.148***

0.101***

0.155***

(0.015)

(0.022)

(0.015)

(0.022)

(0.015)

(0.022)

(0.015)

(0.022)

(0.015)

(0.022)

(0.015)

(0.022)

Ore j

0.331**

0.887***

0.249*

0.821***

0.240

0.796***

0.440***

0.816***

0.202

0.699***

0.392***

0.879***

(0.143)

(0.165)

(0.147)

(0.167)

(0.147)

(0.169)

(0.149)

(0.171)

(0.150)

(0.173)

(0.146)

(0.171)

Institutionj

0.082***

0.053**

0.079***

0.060***

0.106***

0.074***

0.001

0.076**

0.128***

0.130***

0.020

0.028

(0.020)

(0.023)

(0.017)

(0.019)

(0.022)

(0.024)

(0.027)

(0.031)

(0.029)

(0.032)

(0.015)

(0.018)

Ln(Dist ij)

-0.132***

-0.256***

-0.148***

-0.271***

-0.116***

-0.248***

-0.138***

-0.283***

-0.134***

-0.243***

-0.147***

-0.274***

(0.024)

(0.036)

(0.024)

(0.035)

(0.024)

(0.036)

(0.024)

(0.036)

(0.024)

(0.036)

(0.025)

(0.035)

RTA

ij0.211***

0.147***

0.205***

0.142***

0.166***

0.126***

0.185***

0.144***

0.185***

0.130***

0.191***

0.138***

(0.034)

(0.029)

(0.034)

(0.029)

(0.033)

(0.030)

(0.034)

(0.029)

(0.033)

(0.030)

(0.034)

(0.029)

(1):

Ru

leo

fL

aw

.(2

):C

on

tro

lo

fco

rru

pti

on

.(3

):G

ove

rnm

ent

effec

tive

nes

s.(4

):P

oli

tica

lst

abi

lity

.(5

):R

egu

lato

ryqu

ali

ty.

(6):

Vo

ice

an

da

cco

un

tabi

lity

.*

,**

,**

*:

sign

ifica

nt

at

10

%,

5%

,1

%.

Sta

nd

ard

erro

rsin

bra

cket

s.

17

However, estimation of the derived elasticities confirms that Japanese overseas in-vestments are heavily influenced by host market size, level of host country taxes, inflationand trade openness, the natural resource endowments of the host country, distance awayin kilometres, the level of the bilateral real exchange rate of the yen, and the foreigncountry institutions. For instance, we find that the value of the Japanese FDI stockincreases by more than 2% for a 10% increase in host market GDP. We find also thatthe highest marginal effect for institution variables is for regulatory quality: in fact, anincrease of one unit in the indicator entails an increase of 0.13% of Japanese FDI.If we compare the marginal effects generated by the Heckman two-step procedure withelasticities estimated using OLS, which ignores zero-value observations, we find that theOLS estimator tends to over-value all the coefficients. This applies especially to theinstitutional variables where values range between 0.33 and 0.64 for the significant onescompared to 0.13 and 0.42 using Heckman’s procedure.

4.2. Disaggregation of results: Developed and Developing countries

We use the developed country and developing country classifications proposed bythe IMF, resulting in our sample of 30 host countries spilt into 17 developed countriesand 13 developing economies. Blonigen and Wang (2004) show that the combinationof developed and developing countries within the sample could lead to biased results.Some variables, such as host country’s macroeconomic stability, or host country’s marketsize, could have opposite effects depending on the destination of the foreign investment.Therefore, we estimate equation (4) for the two categories of countries independently.We use the variable capturing political stability as a proxy for institutional quality. Table3 presents the results of the Heckman two-step estimator along with the unconditionalmarginal effects.

Similar to the previous estimations, we find that the IMR is significant at the 1%level, confirming the importance of correcting for the selection bias introduced by zero-value observations. Our results show also that the determinants of Japanese overseasinvestments are different depending on the host country category.First, our estimates show that host country market size is significant and positive forboth estimations, with higher marginal effects for developed countries. The market-seeking motivation is an important factor explaining a firm’s decision to invest abroadregardless of the country targeted. We find also that the marginal effect of host country’sper capita GDP is highly significant and negative.

Our findings provide evidence that an appreciation of the yen affects only Japaneseoverseas investments in developed countries. The effect seems to be positive, but non-significant when we consider yen appreciation against weaker currencies such as these ofthe developing countries in our sample. This is in line with Blonigen (1997). The highertaxes in developed countries work also to strongly decreases Japanese outward FDI, witha marginal effect of -0.55. This confirms that a policy of low taxes in developed coun-tries attracts Japanese FDI. The results are the reverse for developing countries since, in

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Table 3: Results from the Heckman’s two-step selection correction and unconditional marginaleffects for the developed and the developing countries of the sample

Variable Developed countries Developing countries

Selection Marginal effects Selection Marginal effects

Ln (GDPj) 0.861*** 0.273*** 0.518*** 0.119***(0.072) (0.022) (0.184) (0.040)

Ln (GDPCj) -1.644*** -0.522*** -0.804*** -0.186***(0.419) (0.133) (0.220) (0.055)

Ln (RERij) -0.372*** -0.118*** 0.043 0.010(0.126) (0.040) (0.051) (0.011)

Taxj -1.736*** -0.551*** 2.492*** 0.575***(0.503) (0.158) (0.755) (0.168)

Inflj 16.366** 5.199** -7.206** -1.663**(6.434) (2.036) (2.996) (0.684)

Openj 0.614*** 0.195*** 1.641*** 0.379***(0.162) (0.051) (0.415) (0.094)

Orej 3.386*** 1.076*** 0.613 0.141(1.057) (0.335) (1.471) (0.338)

Political Stability 0.495*** 0.157*** 0.774*** 0.179***(0.191) (0.061) (0.268) (0.060)

Ln (Distij) -2.432*** -0.452*** -0.470 -0.108*(0.492) (0.157) (0.298) (0.066)

RTAij -0.558** -0.200** 0.670*** 0.143***(0.232) (0.089) (0.193) (0.040)

Intercept 23.487*** 8.424***(4.152) (2.891)

IMR 1.750*** -1.099***(0.603) (0.418)

Rho 0.701 -0.652Observations 1190 820Censored obs 403 237Industry FE Yes YesYear FE Yes Yes

*,**,***: significant at 10 %, 5 %, 1 %.Standard errors in brackets.

these countries taxes are low, but there are other determinants of Japanese investmentin these locations.Our results indicate also that trade openness is an important variable for attractingJapanese FDI: the effect is highly significant and positive in both estimates. However,the marginal effect for developing countries is higher (0.38), reflecting the key role of

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openness for the development of inward FDI in these countries. In contrast, distanceis significant and negative only for Japanese overseas investments in other developedcountries. Most of the developing countries in our sample are located in east Asia whichis nearer to Japan than Europe or the US.The dummy variable capturing free trade agreements is positive only in the estimationthat includes developing countries. This is because out of the seven free trade agree-ments considered in our analysis, five relate to developing countries. This supports ourassumption that trade openness in developing countries attracts Japanese FDI.Our fourth hypothesis is supported only for FDI towards developing countries. Indeed,we find a strong significant negative impact of host country’s inflation rate on JapaneseFDI in these countries, characterized by a marginal effect of -1.66. This is evidence thatmacroeconomic stability in developing economies is a major driver of Japanese overseasinvestments. In contrast, we find a significant positive relationship between inflation andFDI towards developed countries. This would seem to contradict our hypothesis. How-ever, in the developed countries analysed here, inflation rates are limited and controlledby sound central bank monetary policy. As a result, Japanese firms do not equate a risein inflation with a rise in macroeconomic instability in these economies.Finally, we find that political stability is significant and positive in both cases reflectingthe importance of institutional quality in the firm’s decision about production location.However, similar to trade openness, the marginal effect is higher for developing countries.

5. Conclusions

In this paper, we extend understanding and knowledge of the major determinantsof Japanese overseas investments in the manufacturing industry. We estimate an aug-mented gravity model for FDI, derived from the theoretical underpinnings provided byKleinert and Toubal (2010), using data on bilateral stock of outward Japanese FDI, for30 host countries and 10 manufacturing industries. In our estimations, we also tacklethe issue of zero FDI stocks, relying on a Tobit model and Heckman’s sample selectionprocedure.Our results demonstrate that the main attractors of Japanese FDI in the manufacturingindustries are host market size, tax policy, distance, inflation, trade openness, naturalresource endowments and quality of institutions. In particular, we find that confidencein the rules of society, control of corruption, government effectiveness, political stabilityand the private sector policies are important factors driving Japanese FDI in the manu-facturing industry, while agents’ perception of degree of freedom of expression, citizens’right to vote and free media seem to have no significant effect on FDI.Disaggregating our sample into two categories of countries highlights two features. First,Japanese FDI towards other developed countries is attracted mainly by size of the mar-ket, a policy of low taxation, the bilateral real exchange rate of the yen, distance betweenthe countries and political stability. Second, we find that market size, trade opennessand political and macroeconomic stability are key determinants of Japanese investmentsin developing economies.

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Appendix

Table A.1: List of host countries and Regional Trade agreements (RTA)

Name Category RTA Name Category RTAAustralia Developed No Netherlands Developed NoBelgium Developed No New Zealand Developed NoBrazil Developing No Philippines Developing Yes (2006)Canada Developed No Russia Developing NoChina Developing No Saudi Arabia Developing NoFrance Developed No India Developing NoGermany Developed No Singapore Developed Yes (2002)Hong Kong Developed No South Africa Developing NoIndonesia Developing Yes (2005) Spain Developed NoIran Developing No Sweden Developed NoItaly Developed No Switzerland Developed Yes (2009)South Korea Developed No Thailand Developing Yes (2007)Luxembourg Developed No United Arab Emirates Developing NoMalaysia Developing Yes (2005) United Kingdom Developed NoMexico Developing Yes (2005) United States Developed No

Table A.2: Statistics on zero-value observations

Sectors FDI

Number Share

Food and beverages 68 32.4%Textile and apparel 118 56.2%Chemicals products 49 23.3%Glass and ceramics 115 54.8%Lumber and pulp 108 51.4%Primary metals 52 24.8%General machinery 45 21.4%Electric machinery 56 26.7%Transportation equipment 39 18.6%Precision machinery 66 31.4%

Total 716 34.1%

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Table A.3: Descriptive statistics

Variable Obs. Mean Std. Dev. Min. Max.

Ln (GDPj) 2100 6.029 1.214 3.185 9.371Ln (GDPCj) 2100 9.874 0.864 7.700 11.213Ln (RERij) 2100 -2.946 2.543 -5.505 4.478Taxj 2100 0.439 0.163 0.141 0.812Inflj 2070 0.040 0.036 -0.008 0.255Openj 2100 1.073 0.948 0.219 4.456Orej 2030 0.058 0.070 0.001 0.368Institution1 2100 0.767 0.970 -0.983 1.952Institution2 2100 0.819 1.114 -1.118 2.432Institution3 2100 0.955 0.858 -0.621 2.408Institution4 2100 0.199 0.883 -1.703 1.516Institution5 2100 0.839 0.886 -1.726 1.997Institution6 2100 0.468 1.031 -1.843 1.675Ln (Distij) 2100 8.886 0.572 7.053 9.828RTAij 2100 0.200 0.400 0 1

The variables Institution correspond to rule of law (1), control of corruption (2), govern-ment effectiveness (3), political stability (4), regulatory quality (5) and voice and account-ability (6).

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Table A.4: Definition of variables data sources

Variable Description Source

FDI Bilateral foreign direct investment stockfrom Japan to host country in current yen(transformed into constant U.S. dollars)

Bank of Japan

GDP Gross Domestic Product in constant U.S.dollars

World Development Indicators

GDPC Gross domestic product per capita in con-stant U.S. dollars

World Development Indicators

RER Real bilateral exchange rate between theyen and host country currency

International Financial Statistics

Tax Total tax rate (% of commercial profits)of host country

World Development Indicators

Infl Inflation rate (Consumer Price Index) ofhost country

World Development Indicators

Open Trade share of host country (ratio betweenexports and imports of goods and servicesto GDP)

UNCTAD

Ore Ores and metal exports (% of merchandiseexports)

World Development Indicators

Institution1 Rule of law, -2.5 to 2.5 scale Worldwide Governance Indicators ProjectInstitution2 Control of corruption, -2.5 to 2.5 scale Worldwide Governance Indicators ProjectInstitution3 Government effectiveness, -2.5 to 2.5 scale Worldwide Governance Indicators ProjectInstitution4 Political stability, -2.5 to 2.5 scale Worldwide Governance Indicators ProjectInstitution5 Regulatory quality, -2.5 to 2.5 scale Worldwide Governance Indicators ProjectInstitution6 Voice and accountability, -2.5 to 2.5 scale Worldwide Governance Indicators ProjectDIST Bilateral distance between Japan and host

countryCEPII

RTA Regional trade agreement, dummy vari-able

World Trade Organisation

26

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